system

Abstract

Provide a system. 【Solution means】 An information acquisition device that collects information in real time from an external data supply institution, A position data acquisition device that acquires position information by using the GPS function of a terminal to identify the current geographical position of a user, An evacuation route generation device that calculates an optimal evacuation route for a user based on the acquired position data and real-time external information, A notification creation device that generates and notifies individualized alarms and information according to the position information and settings of a user, An education information providing device that provides interactive education content for enhancing the disaster prevention awareness of a user in normal times, A multilingual support device that provides notifications and education information in multiple languages based on the language settings of a user, A device that automatically generates an optimized evacuation route within a staying area based on the information acquired in real time, A device that performs language conversion based on the settings of a user, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 areas where natural disasters occur frequently, it is difficult for people to obtain appropriate and timely evacuation information, which may lead to delays and errors in evacuation actions. Therefore, there is a need for a comprehensive disaster response system that can provide appropriate evacuation information in real time during disasters and further meet the needs of various users. In particular, it is necessary to provide information fairly to all people regardless of language, age, or physical condition.

Means for Solving the Problems

[0005] This invention collects disaster-related information in real time using data acquisition means and identifies the user's precise location using location information acquisition means. Based on this, evacuation route generation means calculates the optimal evacuation route for the user, and notification generation means provides prompt and appropriate notifications. Furthermore, educational content provision means provides knowledge about disaster prevention during normal times, and multilingual support means provides information in multiple languages, enabling comprehensive and individualized support. In this way, the invention provides a system that supports all users in taking prompt and appropriate action during disasters.

[0006] "Data acquisition means" refers to a device or process equipped with the function of collecting weather data and disaster information from external sources.

[0007] "Location information acquisition means" refers to a device or process that identifies the user's current location using GPS or other location information technologies.

[0008] "Evacuation route generation means" refers to a device or process that calculates the optimal evacuation route based on the user's location information and the disaster situation.

[0009] A "notification generation means" is a device or process that creates and transmits evacuation information or warnings to users at an appropriate time.

[0010] "Educational content delivery means" refers to a device or process that provides users with knowledge about disaster prevention in the form of quizzes or simulation games.

[0011] "Multilingual support means" refers to a device or process that translates information into multiple languages ​​and provides it in a language suitable for each user. [Brief explanation of the drawing]

[0012] [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]

[0013] 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.

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

[0015] In the following embodiments, a labeled 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.

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

[0017] In the following embodiments, a labeled 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.

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

[0019] 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."

[0020] [First Embodiment]

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

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0027] 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.

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

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that provides rapid and appropriate information and evacuation support during disasters, and combines data acquisition means, location information acquisition means, evacuation route generation means, notification generation means, educational content provision means, and multilingual support means.

[0034] Data acquisition method

[0035] The server collects data in real time using APIs from the Japan Meteorological Agency and disaster information providers. The server stores the acquired data in a database and prepares it for analysis. This allows for constant monitoring of the latest situation.

[0036] Location information acquisition method

[0037] When a user launches the application, the device uses its GPS function to obtain its current location. The device then sends the obtained location information to the server in an encrypted state. This allows the server to generate customized evacuation instructions for each user.

[0038] Generation of evacuation routes

[0039] The server calculates the optimal evacuation route based on the user's location information and collected disaster data, taking into account the situation which changes over time. The server also incorporates the risk of secondary disasters and traffic information into its analysis to determine a route that allows for safe and rapid evacuation.

[0040] Creating and sending notifications

[0041] The server, when it determines that evacuation is necessary, uses a notification generation mechanism to alert the user. The notification is personalized based on the user's location and language settings and is delivered as a push notification to the device. Users can quickly review the received notification and take immediate action.

[0042] Provision of educational content

[0043] During normal operation, the server provides users with quizzes and simulation games designed to raise disaster preparedness awareness. These contents are displayed interactively on the device, allowing users to consciously learn about disaster prevention.

[0044] Implementation Examples of Multilingual Support

[0045] The server delivers all notifications and educational content in the appropriate language based on the user's language settings. This ensures that all users can understand and respond to information, regardless of language differences.

[0046] As a concrete example, if a foreign user residing in Japan experiences an earthquake in a particular area, their device immediately transmits its location information to a server. The server then sends multilingual evacuation instructions to the device, taking into account the latest information, allowing the user to take appropriate action to ensure their safety. In this way, everyone, including users with diverse backgrounds, can receive appropriate assistance. This system makes it possible to improve the disaster response capabilities of society as a whole.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server regularly collects the latest weather data and earthquake information from APIs of the Japan Meteorological Agency and disaster information providers. This ensures that the server is always prepared to maintain the most up-to-date disaster risk information.

[0050] Step 2:

[0051] When an application is launched, the device uses its built-in GPS function to obtain the user's current location. This location information is then encrypted and sent to the server.

[0052] Step 3:

[0053] The server checks disaster information for the surrounding area based on the location information sent by the user and immediately determines the need for evacuation. After this, an evacuation route generation system calculates an appropriate evacuation route.

[0054] Step 4:

[0055] The server considers disaster information, traffic conditions, and the risk of secondary disasters to plan safe evacuation routes for users.

[0056] Step 5:

[0057] The server uses a notification generation mechanism to create personalized messages based on the evacuation route information it has formulated, and then translates them based on the user's language settings.

[0058] Step 6:

[0059] An evacuation notification is pushed from the server to the device. The device displays this notification on its screen so that the user can confirm it.

[0060] Step 7:

[0061] Users will take swift action based on the evacuation information they receive and evacuate according to safe routes.

[0062] Step 8:

[0063] During normal operation, the server provides users with quizzes and simulation games to improve their disaster preparedness knowledge. The content is available in multiple languages, allowing users to access it in their chosen language.

[0064] Step 9:

[0065] Users can acquire disaster preparedness knowledge on a daily basis by utilizing educational content provided on their devices. They can then apply this learned knowledge to respond appropriately in emergencies.

[0066] (Example 1)

[0067] 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."

[0068] During natural disasters and emergencies, information tends to become chaotic, and many people have difficulty obtaining accurate and timely information. Furthermore, differences in language and culture can lead to situations where appropriate assistance does not reach everyone. These issues hinder the implementation of efficient and effective evacuation plans.

[0069] 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.

[0070] In this invention, the server includes information acquisition means for collecting data from external information sources, location identification means for acquiring the current location using a location detection device, and route generation means for calculating the optimal escape route based on the acquired information and location. This enables real-time rapid information provision and support for the execution of evacuation plans.

[0071] "Information acquisition means" refers to a function or device for collecting necessary data from external information sources.

[0072] "Location determination means" refers to a function or device that uses a location detection device to determine the user's current location.

[0073] "Route generation means" refers to a function or device for calculating the optimal evacuation route based on acquired information and location information.

[0074] "Notification creation means" refers to a function or device for creating individualized alarms and notifications and distributing them appropriately to users.

[0075] "Educational information provision means" refers to a function or device for providing users with learning materials and content to raise disaster prevention awareness.

[0076] "Multilingual processing means" refers to a function or device that provides information in various languages ​​according to the user's language settings.

[0077] This invention is a system that provides rapid and appropriate information and evacuation support during disasters. It supports the safe evacuation of users by combining the following various means.

[0078] The server uses external information sources, such as APIs for information provision services from public institutions, to collect disaster information in real time. The collected data is stored in a database and used for analysis. This data includes information on weather, earthquakes, tsunamis, etc., and is updated as the situation develops.

[0079] As a means of determining location, the device uses its GPS function to obtain the current location when the user launches the application. The obtained location information is encrypted and securely transmitted to the server. Based on this information, the server can provide customized evacuation instructions for each user.

[0080] The route generation system uses the user's location information and collected disaster information to calculate the optimal evacuation route. This takes into account traffic information and risk data for secondary disasters. As a result, the user can obtain the route that allows for the safest and fastest evacuation.

[0081] As a notification generation method, if the server determines that evacuation is necessary, a personalized alert is generated. The notification is appropriately translated based on the user's language settings and delivered to the device as a push notification. Users can immediately check the notification and take swift action.

[0082] Using educational information delivery methods, the server provides content to raise disaster preparedness awareness. During normal times, it generates quizzes and simulation games and sends them to the user's device. Through this content, users can learn about disaster preparedness while having fun.

[0083] Through multilingual processing, the server provides information in various languages ​​according to the user's language settings. This makes it possible for all users to obtain accurate information, overcoming language barriers.

[0084] For example, if a foreign user is in a certain area when an earthquake occurs, the device can immediately send its location information to the server, which can then issue optimized, multilingual evacuation instructions that take the latest information into account.

[0085] An example of a prompt message could be an input such as, "I want to develop a system that generates multilingual evacuation instructions for foreign users during a disaster."

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The server collects data in real time from external sources. Specifically, it sends requests to weather information APIs to obtain weather and disaster-related data. The API endpoint is used as input, and the obtained dataset is output. This dataset contains information such as weather conditions and earthquake occurrences, and is stored in a database to prepare it for analysis.

[0089] Step 2:

[0090] The device uses GPS functionality to obtain the user's current location when they launch an application. Specifically, a location services service is called, and the current latitude and longitude are obtained as input. The output is encrypted location data, which is sent to the server. This ensures that the user's current location is securely transmitted to the server.

[0091] Step 3:

[0092] The server calculates the optimal evacuation route based on the user's location information and disaster data collected in real time. This process uses encrypted location information and collected disaster data as input. Data processing includes traffic condition and disaster risk analysis, generating safe and rapid evacuation route data as output. This route data enables appropriate guidance for the user.

[0093] Step 4:

[0094] If the server determines that evacuation is necessary, it uses a notification generation system to create and deliver an alert to the user. The inputs are optimal evacuation route data and the user's language settings. The output is a personalized, multilingual alert message. It is sent as a push notification to the device, prompting the user to immediately check the notification and take swift action.

[0095] Step 5:

[0096] The server provides educational content to raise disaster preparedness awareness. Input consists of learning materials such as quizzes and simulation games, and output is generated based on the user's language settings. Specifically, a generative AI model generates quiz questions and sends them to the terminal. The terminal displays these interactively, and the user enters answers to receive feedback.

[0097] Step 6:

[0098] The server is a multilingual support system that translates all information into various languages ​​according to the user's language settings. Inputs include notifications and educational content. Outputs are translated information tailored to the user's language. This allows users to receive information in a language they understand and use it to guide their actions.

[0099] (Application Example 1)

[0100] 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."

[0101] The challenge is to provide evacuation information quickly and appropriately during disasters, ensuring that users who speak diverse languages ​​can understand it equally and take safe evacuation actions. Furthermore, there is a need to provide educational content that raises disaster preparedness awareness even during normal times.

[0102] 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.

[0103] In this invention, the server includes an information acquisition device, a location data acquisition device, and an evacuation route generation device. This makes it possible to provide an individualized and optimal evacuation route based on information acquired in real time. Furthermore, by providing information in multiple languages ​​based on the user's language settings, all users can quickly understand the information and take safe evacuation actions.

[0104] An "information acquisition device" is a device that collects information in real time from external data supply organizations and plays the role of providing necessary data to other devices based on this information.

[0105] A "location data acquisition device" is a device that uses the GPS function of a terminal to acquire location information in order to determine the user's current geographical location.

[0106] An "evacuation route generation device" is a device that calculates the optimal evacuation route for a user based on acquired location data and real-time external information.

[0107] A "notification generation device" is a device that generates and sends personalized alarms and information based on the user's location information and settings.

[0108] An "educational information provision device" is a device that provides interactive educational content to raise users' disaster preparedness awareness during normal times.

[0109] A "multilingual device" is a device that provides notifications and educational information in multiple languages ​​based on the user's language settings.

[0110] A "device that automatically generates optimized evacuation routes within a dwelling area based on real-time acquired information" is a device that dynamically provides rapid and safe evacuation routes based on the latest disaster information and real-time location information.

[0111] A "language conversion device" is a device that converts all information within a system into the appropriate language according to the language set by the user.

[0112] This system is configured to provide rapid and appropriate evacuation support during disasters. Its main components include servers, terminals, and users.

[0113] The server collects weather and disaster information in real time through information acquisition devices and stores it in a database. This involves using APIs provided by external data providers and performing data analysis using Python. This data is stored in MongoDB and prepared for use when needed.

[0114] The device uses a location data acquisition device to obtain the user's current location via GPS when the application is launched. This information is sent to the server in an encrypted state. Flask is used for backend processing.

[0115] The server utilizes an evacuation route generation device to generate the optimal evacuation route for the user based on acquired location and disaster information. This process also takes real-time traffic information and terrain data into consideration to calculate the best route. Machine learning libraries (e.g., scikit-learn) are used for these calculations.

[0116] Furthermore, the notification generation device generates personalized alerts based on the user's language settings and location information, and delivers them via a push notification service. A front-end application using JavaScript® displays these notifications.

[0117] Furthermore, the educational information device provides interactive educational content to help users raise their disaster preparedness awareness during normal times. This content is displayed in the user's preferred language using a multilingual device.

[0118] A concrete example is a scenario where users within a smart city receive optimal evacuation information during a typhoon. This system ensures that information is personalized in a timely manner and accurately understood in each language, enabling people from diverse backgrounds to take safe actions.

[0119] An example of a prompt for a generative AI model is: "Based on the latest information about the typhoon, please generate evacuation routes within the city. Also, please tell me how to provide notifications in multiple languages."

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The server collects disaster information from external data sources using an information acquisition device. This process involves analyzing the data obtained via an API using Python and saving it to MongoDB. The input is disaster data obtained from an external API, and the output is organized disaster information stored in the database.

[0123] Step 2:

[0124] The terminal obtains the user's current location using a location data acquisition device. Here, location information is obtained using the smartphone's GPS function, encrypted, and sent to the server. The input is the user's GPS data, and the output is the user's location information sent to the server.

[0125] Step 3:

[0126] The server uses an evacuation route generation device to calculate the optimal evacuation route based on acquired user location information and collected disaster data. Real-time traffic conditions and topographic information are also taken into consideration. Machine learning is used to perform data calculations and output safe and rapid routes.

[0127] Step 4:

[0128] The server uses a notification generator to produce personalized alerts based on the user's language settings. This process creates appropriate evacuation instructions for the user based on the generated evacuation route information and sends them to the device via a push notification service. The inputs are the user's configuration information and the generated evacuation route, while the output is the notification delivered to the user's device.

[0129] Step 5:

[0130] The terminal uses an educational information provider to display disaster prevention content to the user. Here, interactive quizzes and simulations are provided using JavaScript. The input is educational content provided by the server, and the output is educational information that the user can view on their terminal.

[0131] Step 6:

[0132] The server uses a multilingual device to translate and provide all information in the appropriate language based on the user's language settings. The input is the user's language settings and all the information to be provided; the output is the information provided in the appropriate language.

[0133] 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.

[0134] This invention combines a system for providing information and evacuation support during disasters with an emotion engine that analyzes user emotions. By combining data acquisition means, location information acquisition means, evacuation route generation means, notification generation means, educational content provision means, emotion engine, and multilingual support means, more personalized evacuation support can be provided.

[0135] Embodiment of emotion analysis

[0136] The device captures the user's facial expressions and voice through its built-in camera and microphone while the user is using the application. The server uses this input data for an emotion engine to analyze and determine the user's emotional state. Based on this analysis, the server adjusts the support method according to the user's emotional state.

[0137] Adjusting evacuation orders based on emotions

[0138] If a user is experiencing significant stress, the server will adjust the evacuation route instructions to be more concise and reassuring. For example, it aims to reduce user anxiety by including gentle language and encouraging messages in the notifications.

[0139] Personalized notifications

[0140] The server personalizes the content and timing of notifications based on the analyzed emotional state. If it determines that the user is feeling anxious, it prioritizes sending notifications that provide detailed information about the distance to shelters and the current safety situation.

[0141] Optimizing disaster prevention education content

[0142] The server learns from user feedback regarding their emotions obtained from the terminal and adjusts the order and difficulty level of educational content accordingly. This allows for the effective delivery of disaster prevention knowledge while reducing emotional burden.

[0143] Examples of embodiments

[0144] For example, a user using an evacuation drill app might panic upon receiving an earthquake early warning. In this case, the device analyzes the user's facial expressions through the camera, and the server uses this data to determine that the user is feeling fear. Based on this determination, which is influenced by the emotion engine, the server sends a notification containing a reassuring message such as, "Please evacuate calmly." Similarly, in disaster preparedness quizzes, the difficulty level can be gradually increased, starting with easy questions to allow users to build confidence as they learn. In this way, incorporating an emotion engine makes it possible to provide a more personalized and user-friendly disaster response system.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The device activates its built-in camera and microphone while the user is using an application, collecting the user's facial expressions and voice. This prepares it for capturing emotional data in real time.

[0148] Step 2:

[0149] The device preprocesses the collected audio and video data and converts it into a format that can be analyzed by the emotion engine. It then sends this data to the server.

[0150] Step 3:

[0151] The server receives data sent from the terminal and analyzes it using an emotion engine. It identifies the user's emotional state (e.g., reassurance, fear, anxiety) from their facial expressions and tone of voice.

[0152] Step 4:

[0153] Based on the analyzed emotional state, the server considers evacuation routes and information provision tailored to each user's situation. In particular, if the user is experiencing stress, calming messages and simplified information will be prioritized.

[0154] Step 5:

[0155] The server generates personalized notification content and translates it into the user's language. The notification includes reassuring language and recommendations for appropriate action.

[0156] Step 6:

[0157] The server pushes the generated notification to the device. The device displays the notification on the screen so that the user can understand it immediately.

[0158] Step 7:

[0159] By reading the received notifications, users can receive appropriate evacuation guidance that takes their emotional state into consideration, enabling them to take swift and accurate action.

[0160] Step 8:

[0161] The server continuously learns from user emotional data and adjusts the order and content of disaster prevention education materials accordingly. This optimizes the user's learning experience and reduces emotional burden.

[0162] Step 9:

[0163] Users can acquire disaster preparedness knowledge without stress using tailored educational content, improving their ability to respond in emergencies.

[0164] (Example 2)

[0165] 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".

[0166] In disaster situations, rapid and accurate information provision is essential for evacuation support systems. However, conventional systems provide uniform information without considering users' feelings, making them insufficient in alleviating user anxiety. Furthermore, they sometimes fail to adequately support diverse languages, posing a particular challenge in multilingual countries and for foreign users.

[0167] 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.

[0168] In this invention, the server includes emotion analysis means, data collection means, and multilingual support means. This enables personalized evacuation assistance and information provision based on the user's emotional state.

[0169] "Data collection means" refers to a device or method for obtaining information from users in real time.

[0170] "Location information acquisition means" refers to a device or method for accurately measuring and tracking a user's current location.

[0171] "Evacuation route creation means" refers to a device or method for generating the optimal evacuation route for a user in an emergency.

[0172] "Notification generation means" refers to a device or method for generating messages or instructions to notify a user of necessary information.

[0173] "Educational information provision means" refers to a device or method that presents content or teaching materials for providing users with knowledge about disaster prevention.

[0174] "Emotional analysis means" refers to a device or method that analyzes a user's emotional state based on information such as the user's facial expressions and voice.

[0175] "Multilingual support means" refers to a device or method for providing information to users in various languages.

[0176] This invention is a comprehensive system that combines user emotion analysis to effectively provide information and evacuation support during disasters. The system mainly consists of a server and terminals, which perform their functions through bidirectional communication.

[0177] The server receives facial and voice data transmitted from the user's device and analyzes this data using a generative AI model as an emotion analysis tool. This analysis includes an emotion engine that determines the user's emotional state in real time and optimizes the support method based on that information. The data is usually transmitted to the server via a secure network. The server also has multilingual support capabilities and can provide information in various languages. This allows the server to provide appropriate information according to the user's language settings.

[0178] The device uses its built-in camera and microphone to acquire data related to the user's emotional state. For example, the camera captures the user's facial expressions, and the microphone captures their voice tone. This data is sent from the device to a server where emotion analysis is performed.

[0179] Users can receive notifications and educational information provided by the system. These notifications include personalized messages based on the user's emotional state, such as "Your evacuation route is safe. Please remain calm." This is expected to reduce user anxiety during disasters.

[0180] As a concrete example, using an evacuation drill application, the system simulates a scenario where a user panics the moment they receive an earthquake early warning. Even in this case, the system immediately analyzes their emotions and delivers a notification in appropriate language that provides reassurance. This kind of real-time response is made possible by utilizing generative AI models.

[0181] An example of a prompt message is, "Use the emotion engine to analyze the user's stress level and generate and send an appropriate evacuation route recommendation message." Such prompts allow the system to achieve its objectives efficiently and effectively.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] The device uses its built-in camera and microphone to capture the user's facial expressions and voice data. The input is the user's real-time visual and audio data. Specifically, the device tracks the user's face with the camera and records their voice tone with the microphone. The output is the captured raw audio and visual data.

[0185] Step 2:

[0186] The terminal sends captured facial and audio data to the server. The input here is the user data previously captured. Specifically, the terminal formats and encrypts the data appropriately and sends it to the server using a secure network protocol. The output is the encrypted customer data received by the server.

[0187] Step 3:

[0188] The server analyzes the received data using a generative AI model. The input consists of facial expression data and voice data sent from the terminal. Specifically, the server uses the generative AI model to classify the user's emotional state from this data. The output is the user's emotional state data as a result of the analysis.

[0189] Step 4:

[0190] The server generates an appropriate notification message to send to the user based on the analyzed emotional state of the user. The input is the emotional analysis result data on the server. Specifically, the server selects a message template corresponding to the emotional state and constructs the notification content based on the generated emotion. The output is the generated personalized message.

[0191] Step 5:

[0192] The server sends the generated notification to the terminal. The input is a personalized notification message. The server sends this to a specific user terminal. Specifically, the message is sent via a network protocol, received by the terminal, and displayed to the user. The output is the notification displayed on the terminal by the user.

[0193] Step 6:

[0194] The device recaptures the emotional changes of the user after they receive a notification. The input is the user's response or state change. The new data captured by the device as backup is sent to the server as feedback. Specifically, the device restarts its camera and microphone to acquire new data. The output is the feedback data sent to the server.

[0195] (Application Example 2)

[0196] 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".

[0197] In recent years, natural disasters have become more frequent, and many people need support to evacuate quickly and effectively. However, the psychological state of evacuees fluctuates depending on the situation, and it is necessary to provide appropriate support that is tailored to each individual's emotions. In particular, providing personalized information to users with different language and cultural backgrounds is a challenging task. Against this backdrop, there is a need for technology that can flexibly respond to changes in emotions while enabling individualized evacuation support in multiple languages.

[0198] 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.

[0199] In this invention, the server includes means for acquiring data, means for acquiring location information, means for generating evacuation routes, means for determining the user's emotional state using an emotion analysis engine, and means for providing personalized evacuation instructions and information that provides a sense of security. This enables personalized evacuation support based on the user's emotional state.

[0200] "Data acquisition means" refers to a device or function for collecting information on the user's emotional state, location, and external environment.

[0201] "Location information acquisition means" refers to a device or function that identifies the user's current location and uses that information to provide an evacuation route.

[0202] "Evacuation route generation means" refers to a device or function that calculates and presents the optimal evacuation route based on the user's location information and emotional state.

[0203] "Notification generation means" refers to a device or function for creating and sending personalized evacuation instructions or reassuring messages to users.

[0204] "Educational content delivery means" refers to a device or function that provides users with disaster prevention knowledge and adjusts the learning experience according to their emotional state.

[0205] "Multilingual support means" refers to a device or function that can provide information and content in multiple languages ​​based on the user's language settings.

[0206] An "emotion analysis engine" is a program or system that analyzes a user's facial expressions and voice data to determine their emotional state.

[0207] "Means of providing personalized evacuation instructions and reassuring information" refers to a device or function that provides the most appropriate evacuation information and psychological reassurance based on the user's emotional state and location information.

[0208] This invention is a system that uses an emotion analysis engine to determine a user's emotional state and provide personalized information in order to provide information and evacuation support during disasters. Its specific form is shown below.

[0209] The server captures the user's facial expressions and voice through data acquisition methods. This uses the camera and microphone built into the smartphone or mobile device. The acquired data is sent to the server's emotion analysis engine. This engine uses cloud services such as Microsoft® and Google® to analyze the patterns of facial expressions and voice to determine the user's emotional state.

[0210] The user's location information is obtained using the GPS function of their smartphone. This allows the server to accurately determine the user's current location and generate the optimal evacuation route. The evacuation route generation method uses map information services such as the Google Maps API to generate the best route according to the user's situation.

[0211] Based on the results of sentiment analysis and location information, the server sends evacuation instructions or reassuring messages to the user via a notification generation system, tailored to their emotions. Messages are delivered at the appropriate time using tools such as Firebase Cloud Messaging.

[0212] In addition, the server uses multilingual support to provide notifications and disaster prevention education content tailored to the user's language settings. For example, displayed text and audio guides are provided in the user's native language, facilitating understanding in multiple languages.

[0213] As a concrete example, when a foreign tourist is in a situation requiring evacuation due to an earthquake, the server detects the user's fear based on camera footage. This triggers a notification in English or the user's native language, containing gentle language and specific evacuation instructions. This notification includes a message such as, "This is a safe evacuation route," along with a map showing the evacuation route.

[0214] An example of a prompt for a generative AI model would be: "If the user's emotional state indicates fear, generate a notification message that includes evacuation instructions. The notification should use gentle language and show evacuation routes."

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The device uses a camera and microphone to capture the user's facial expressions and voice. The input consists of video footage captured by the camera and audio data recorded by the microphone, and this data is sent to the server.

[0218] Step 2:

[0219] The server inputs the acquired video and audio data into an emotion analysis engine to analyze the user's emotional state. As part of the data processing, the video and audio data are analyzed using an algorithm to determine the user's emotions. The results of the emotion analysis are then output.

[0220] Step 3:

[0221] The server uses GPS functionality to obtain location information from the device. The input is GPS data from the device, and the output is the coordinate information of the current location.

[0222] Step 4:

[0223] The server generates the optimal evacuation route using an evacuation route generation method based on the emotion analysis results and location information. The input is the emotional state and current location coordinates, and the output is evacuation route data obtained using the Google Maps API.

[0224] Step 5:

[0225] The server uses a notification generation mechanism to create an optimal evacuation order for the user. The input consists of the evacuation route and emotional state, and the output is a text message written in friendly language. This message includes prompts generated using an AI model.

[0226] Step 6:

[0227] The server translates generated messages according to the user's language settings through multilingual support mechanisms. Input is a text message, and a notification translated into the appropriate language is output.

[0228] Step 7:

[0229] The device uses Firebase Cloud Messaging to display personalized notifications sent from the server to the user. The input is translated notification data, which is output as a screen display or audio guidance.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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".

[0246] This invention is a system that provides rapid and appropriate information and evacuation support during disasters, and combines data acquisition means, location information acquisition means, evacuation route generation means, notification generation means, educational content provision means, and multilingual support means.

[0247] Data acquisition method

[0248] The server collects data in real time using APIs from the Japan Meteorological Agency and disaster information providers. The server stores the acquired data in a database and prepares it for analysis. This allows for constant monitoring of the latest situation.

[0249] Location information acquisition method

[0250] When a user launches the application, the device uses its GPS function to obtain its current location. The device then sends the obtained location information to the server in an encrypted state. This allows the server to generate customized evacuation instructions for each user.

[0251] Generation of evacuation routes

[0252] The server calculates the optimal evacuation route based on the user's location information and collected disaster data, taking into account the situation which changes over time. The server also incorporates the risk of secondary disasters and traffic information into its analysis to determine a route that allows for safe and rapid evacuation.

[0253] Creating and sending notifications

[0254] The server, when it determines that evacuation is necessary, uses a notification generation mechanism to alert the user. The notification is personalized based on the user's location and language settings and is delivered as a push notification to the device. Users can quickly review the received notification and take immediate action.

[0255] Provision of educational content

[0256] During normal operation, the server provides users with quizzes and simulation games designed to raise disaster preparedness awareness. These contents are displayed interactively on the device, allowing users to consciously learn about disaster prevention.

[0257] Implementation Examples of Multilingual Support

[0258] The server delivers all notifications and educational content in the appropriate language based on the user's language settings. This ensures that all users can understand and respond to information, regardless of language differences.

[0259] As a concrete example, if a foreign user residing in Japan experiences an earthquake in a particular area, their device immediately transmits its location information to a server. The server then sends multilingual evacuation instructions to the device, taking into account the latest information, allowing the user to take appropriate action to ensure their safety. In this way, everyone, including users with diverse backgrounds, can receive appropriate assistance. This system makes it possible to improve the disaster response capabilities of society as a whole.

[0260] The following describes the processing flow.

[0261] Step 1:

[0262] The server regularly collects the latest weather data and earthquake information from APIs of the Japan Meteorological Agency and disaster information providers. This ensures that the server is always prepared to maintain the most up-to-date disaster risk information.

[0263] Step 2:

[0264] When an application is launched, the device uses its built-in GPS function to obtain the user's current location. This location information is then encrypted and sent to the server.

[0265] Step 3:

[0266] The server checks disaster information for the surrounding area based on the location information sent by the user and immediately determines the need for evacuation. After this, an evacuation route generation system calculates an appropriate evacuation route.

[0267] Step 4:

[0268] The server considers disaster information, traffic conditions, and the risk of secondary disasters to plan safe evacuation routes for users.

[0269] Step 5:

[0270] The server uses a notification generation mechanism to create personalized messages based on the evacuation route information it has formulated, and then translates them based on the user's language settings.

[0271] Step 6:

[0272] An evacuation notification is pushed from the server to the device. The device displays this notification on its screen so that the user can confirm it.

[0273] Step 7:

[0274] Users will take swift action based on the evacuation information they receive and evacuate according to safe routes.

[0275] Step 8:

[0276] During normal operation, the server provides users with quizzes and simulation games to improve their disaster preparedness knowledge. The content is available in multiple languages, allowing users to access it in their chosen language.

[0277] Step 9:

[0278] Users can acquire disaster preparedness knowledge on a daily basis by utilizing educational content provided on their devices. They can then apply this learned knowledge to respond appropriately in emergencies.

[0279] (Example 1)

[0280] 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."

[0281] During natural disasters and emergencies, information tends to become chaotic, and many people have difficulty obtaining accurate and timely information. Furthermore, differences in language and culture can lead to situations where appropriate assistance does not reach everyone. These issues hinder the implementation of efficient and effective evacuation plans.

[0282] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 1 is realized by the following means respectively.

[0283] In this invention, the server includes an information acquisition means for collecting data from an external information source, a position identification means for acquiring the current position using a position detection device, and a route generation means for calculating an optimal escape route based on the acquired information and position. Thereby, it becomes possible to provide information quickly in real time and support the execution of an evacuation plan.

[0284] The "information acquisition means" is a function or device for collecting necessary data from an external information source.

[0285] The "position identification means" is a function or device for identifying the current position of the user using a position detection device.

[0286] The "route generation means" is a function or device for calculating an optimal evacuation route based on the acquired information and position information.

[0287] The "notification creation means" is a function or device for creating individualized warnings and notifications and appropriately distributing them to the user.

[0288] The "education information providing means" is a function or device for providing the user with learning materials and contents for enhancing disaster prevention awareness.

[0289] The "multilingual processing means" is a function or device for providing information in various languages according to the language settings of the user.

[0290] This invention is a system for providing information quickly and appropriately and supporting evacuation during a disaster. By combining the following various means, it supports the safe evacuation of users.

[0291] The server uses external information sources, such as APIs for information provision services from public institutions, to collect disaster information in real time. The collected data is stored in a database and used for analysis. This data includes information on weather, earthquakes, tsunamis, etc., and is updated as the situation develops.

[0292] As a means of determining location, the device uses its GPS function to obtain the current location when the user launches the application. The obtained location information is encrypted and securely transmitted to the server. Based on this information, the server can provide customized evacuation instructions for each user.

[0293] The route generation system uses the user's location information and collected disaster information to calculate the optimal evacuation route. This takes into account traffic information and risk data for secondary disasters. As a result, the user can obtain the route that allows for the safest and fastest evacuation.

[0294] As a notification generation method, if the server determines that evacuation is necessary, a personalized alert is generated. The notification is appropriately translated based on the user's language settings and delivered to the device as a push notification. Users can immediately check the notification and take swift action.

[0295] Using educational information delivery methods, the server provides content to raise disaster preparedness awareness. During normal times, it generates quizzes and simulation games and sends them to the user's device. Through this content, users can learn about disaster preparedness while having fun.

[0296] Through multilingual processing, the server provides information in various languages ​​according to the user's language settings. This makes it possible for all users to obtain accurate information, overcoming language barriers.

[0297] For example, if a foreign user is in a certain area when an earthquake occurs, the device can immediately send its location information to the server, which can then issue optimized, multilingual evacuation instructions that take the latest information into account.

[0298] An example of a prompt message could be an input such as, "I want to develop a system that generates multilingual evacuation instructions for foreign users during a disaster."

[0299] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0300] Step 1:

[0301] The server collects data in real time from external sources. Specifically, it sends requests to weather information APIs to obtain weather and disaster-related data. The API endpoint is used as input, and the obtained dataset is output. This dataset contains information such as weather conditions and earthquake occurrences, and is stored in a database to prepare it for analysis.

[0302] Step 2:

[0303] The device uses GPS functionality to obtain the user's current location when they launch an application. Specifically, a location services service is called, and the current latitude and longitude are obtained as input. The output is encrypted location data, which is sent to the server. This ensures that the user's current location is securely transmitted to the server.

[0304] Step 3:

[0305] The server calculates the optimal evacuation route based on the user's location information and disaster data collected in real time. This process uses encrypted location information and collected disaster data as input. Data processing includes traffic condition and disaster risk analysis, generating safe and rapid evacuation route data as output. This route data enables appropriate guidance for the user.

[0306] Step 4:

[0307] When the server determines that evacuation is necessary, it creates and distributes an alert to the user using the notification generation means. The input is the optimal evacuation route data and the user's language settings. As output, an individualized and multilingual alert message is generated. It is sent as a push notification to the terminal, prompting the user to immediately check the notification content and act quickly.

[0308] Step 5:

[0309] The server provides educational content to enhance disaster prevention awareness. The input is learning materials such as quizzes and simulation games, and the output is based on the user's language settings. As a specific operation, the generation AI model generates quiz questions and sends them to the terminal. The terminal displays this interactively, and the user inputs answers and receives feedback.

[0310] Step 6:

[0311] The server uses multilingual support means to translate all information into various languages according to the user's language settings. The input is the notification content and educational content. The output is the translated information in line with the user's language. This enables the user to receive information in a language they can understand and apply it to their actions.

[0312] (Application Example 1)

[0313] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0314] During a disaster, it is an issue to provide evacuation information quickly and appropriately so that users who speak various languages can equally understand it and take safe evacuation actions. Also, it is required to provide educational content to improve disaster prevention awareness even in normal times.

[0315] 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.

[0316] In this invention, the server includes an information acquisition device, a location data acquisition device, and an evacuation route generation device. This makes it possible to provide an individualized and optimal evacuation route based on information acquired in real time. Furthermore, by providing information in multiple languages ​​based on the user's language settings, all users can quickly understand the information and take safe evacuation actions.

[0317] An "information acquisition device" is a device that collects information in real time from external data supply organizations and plays the role of providing necessary data to other devices based on this information.

[0318] A "location data acquisition device" is a device that uses the GPS function of a terminal to acquire location information in order to determine the user's current geographical location.

[0319] An "evacuation route generation device" is a device that calculates the optimal evacuation route for a user based on acquired location data and real-time external information.

[0320] A "notification generation device" is a device that generates and sends personalized alarms and information based on the user's location information and settings.

[0321] An "educational information provision device" is a device that provides interactive educational content to raise users' disaster preparedness awareness during normal times.

[0322] A "multilingual device" is a device that provides notifications and educational information in multiple languages ​​based on the user's language settings.

[0323] A "device that automatically generates optimized evacuation routes within a dwelling area based on real-time acquired information" is a device that dynamically provides rapid and safe evacuation routes based on the latest disaster information and real-time location information.

[0324] A "language conversion device" is a device that converts all information within a system into the appropriate language according to the language set by the user.

[0325] This system is configured to provide rapid and appropriate evacuation support during disasters. Its main components include servers, terminals, and users.

[0326] The server collects weather and disaster information in real time through information acquisition devices and stores it in a database. This involves using APIs provided by external data providers and performing data analysis using Python. This data is stored in MongoDB and prepared for use when needed.

[0327] The device uses a location data acquisition device to obtain the user's current location via GPS when the application is launched. This information is sent to the server in an encrypted state. Flask is used for backend processing.

[0328] The server utilizes an evacuation route generation device to generate the optimal evacuation route for the user based on acquired location and disaster information. This process also takes real-time traffic information and terrain data into consideration to calculate the best route. Machine learning libraries (e.g., scikit-learn) are used for these calculations.

[0329] Furthermore, the notification generator creates personalized alerts based on the user's language settings and location information, and delivers them via a push notification service. A JavaScript-based frontend application displays these notifications.

[0330] Furthermore, the educational information device provides interactive educational content to help users raise their disaster preparedness awareness during normal times. This content is displayed in the user's preferred language using a multilingual device.

[0331] A concrete example is a scenario where users within a smart city receive optimal evacuation information during a typhoon. This system ensures that information is personalized in a timely manner and accurately understood in each language, enabling people from diverse backgrounds to take safe actions.

[0332] An example of a prompt for a generative AI model is: "Based on the latest information about the typhoon, please generate evacuation routes within the city. Also, please tell me how to provide notifications in multiple languages."

[0333] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0334] Step 1:

[0335] The server collects disaster information from external data sources using an information acquisition device. This process involves analyzing the data obtained via an API using Python and saving it to MongoDB. The input is disaster data obtained from an external API, and the output is organized disaster information stored in the database.

[0336] Step 2:

[0337] The terminal obtains the user's current location using a location data acquisition device. Here, location information is obtained using the smartphone's GPS function, encrypted, and sent to the server. The input is the user's GPS data, and the output is the user's location information sent to the server.

[0338] Step 3:

[0339] The server uses an evacuation route generation device to calculate the optimal evacuation route based on acquired user location information and collected disaster data. Real-time traffic conditions and topographic information are also taken into consideration. Machine learning is used to perform data calculations and output safe and rapid routes.

[0340] Step 4:

[0341] The server uses a notification generator to produce personalized alerts based on the user's language settings. This process creates appropriate evacuation instructions for the user based on the generated evacuation route information and sends them to the device via a push notification service. The inputs are the user's configuration information and the generated evacuation route, while the output is the notification delivered to the user's device.

[0342] Step 5:

[0343] The terminal uses an educational information provider to display disaster prevention content to the user. Here, interactive quizzes and simulations are provided using JavaScript. The input is educational content provided by the server, and the output is educational information that the user can view on their terminal.

[0344] Step 6:

[0345] The server uses a multilingual device to translate and provide all information in the appropriate language based on the user's language settings. The input is the user's language settings and all the information to be provided; the output is the information provided in the appropriate language.

[0346] 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.

[0347] This invention combines a system for providing information and evacuation support during disasters with an emotion engine that analyzes user emotions. By combining data acquisition means, location information acquisition means, evacuation route generation means, notification generation means, educational content provision means, emotion engine, and multilingual support means, more personalized evacuation support can be provided.

[0348] Embodiment of emotion analysis

[0349] The device captures the user's facial expressions and voice through its built-in camera and microphone while the user is using the application. The server uses this input data for an emotion engine to analyze and determine the user's emotional state. Based on this analysis, the server adjusts the support method according to the user's emotional state.

[0350] Adjusting evacuation orders based on emotions

[0351] If a user is experiencing significant stress, the server will adjust the evacuation route instructions to be more concise and reassuring. For example, it aims to reduce user anxiety by including gentle language and encouraging messages in the notifications.

[0352] Personalized notifications

[0353] The server personalizes the content and timing of notifications based on the analyzed emotional state. If it determines that the user is feeling anxious, it prioritizes sending notifications that provide detailed information about the distance to shelters and the current safety situation.

[0354] Optimizing disaster prevention education content

[0355] The server learns from user feedback regarding their emotions obtained from the terminal and adjusts the order and difficulty level of educational content accordingly. This allows for the effective delivery of disaster prevention knowledge while reducing emotional burden.

[0356] Examples of embodiments

[0357] For example, a user using an evacuation drill app might panic upon receiving an earthquake early warning. In this case, the device analyzes the user's facial expressions through the camera, and the server uses this data to determine that the user is feeling fear. Based on this determination, which is influenced by the emotion engine, the server sends a notification containing a reassuring message such as, "Please evacuate calmly." Similarly, in disaster preparedness quizzes, the difficulty level can be gradually increased, starting with easy questions to allow users to build confidence as they learn. In this way, incorporating an emotion engine makes it possible to provide a more personalized and user-friendly disaster response system.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] The device activates its built-in camera and microphone while the user is using an application, collecting the user's facial expressions and voice. This prepares it for capturing emotional data in real time.

[0361] Step 2:

[0362] The device preprocesses the collected audio and video data and converts it into a format that can be analyzed by the emotion engine. It then sends this data to the server.

[0363] Step 3:

[0364] The server receives data sent from the terminal and analyzes it using an emotion engine. It identifies the user's emotional state (e.g., reassurance, fear, anxiety) from their facial expressions and tone of voice.

[0365] Step 4:

[0366] Based on the analyzed emotional state, the server considers evacuation routes and information provision tailored to each user's situation. In particular, if the user is experiencing stress, calming messages and simplified information will be prioritized.

[0367] Step 5:

[0368] The server generates personalized notification content and translates it into the user's language. The notification includes reassuring language and recommendations for appropriate action.

[0369] Step 6:

[0370] The server pushes the generated notification to the device. The device displays the notification on the screen so that the user can understand it immediately.

[0371] Step 7:

[0372] By reading the received notifications, users can receive appropriate evacuation guidance that takes their emotional state into consideration, enabling them to take swift and accurate action.

[0373] Step 8:

[0374] The server continuously learns from user emotional data and adjusts the order and content of disaster prevention education materials accordingly. This optimizes the user's learning experience and reduces emotional burden.

[0375] Step 9:

[0376] Users can acquire disaster preparedness knowledge without stress using tailored educational content, improving their ability to respond in emergencies.

[0377] (Example 2)

[0378] 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".

[0379] In disaster situations, rapid and accurate information provision is essential for evacuation support systems. However, conventional systems provide uniform information without considering users' feelings, making them insufficient in alleviating user anxiety. Furthermore, they sometimes fail to adequately support diverse languages, posing a particular challenge in multilingual countries and for foreign users.

[0380] 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.

[0381] In this invention, the server includes emotion analysis means, data collection means, and multilingual support means. This enables personalized evacuation assistance and information provision based on the user's emotional state.

[0382] "Data collection means" refers to a device or method for obtaining information from users in real time.

[0383] "Location information acquisition means" refers to a device or method for accurately measuring and tracking a user's current location.

[0384] "Evacuation route creation means" refers to a device or method for generating the optimal evacuation route for a user in an emergency.

[0385] "Notification generation means" refers to a device or method for generating messages or instructions to notify a user of necessary information.

[0386] "Educational information provision means" refers to a device or method that presents content or teaching materials for providing users with knowledge about disaster prevention.

[0387] "Emotional analysis means" refers to a device or method that analyzes a user's emotional state based on information such as the user's facial expressions and voice.

[0388] "Multilingual support means" refers to a device or method for providing information to users in various languages.

[0389] This invention is a comprehensive system that combines user emotion analysis to effectively provide information and evacuation support during disasters. The system mainly consists of a server and terminals, which perform their functions through bidirectional communication.

[0390] The server receives facial and voice data transmitted from the user's device and analyzes this data using a generative AI model as an emotion analysis tool. This analysis includes an emotion engine that determines the user's emotional state in real time and optimizes the support method based on that information. The data is usually transmitted to the server via a secure network. The server also has multilingual support capabilities and can provide information in various languages. This allows the server to provide appropriate information according to the user's language settings.

[0391] The device uses its built-in camera and microphone to acquire data related to the user's emotional state. For example, the camera captures the user's facial expressions, and the microphone captures their voice tone. This data is sent from the device to a server where emotion analysis is performed.

[0392] Users can receive notifications and educational information provided by the system. These notifications include personalized messages based on the user's emotional state, such as "Your evacuation route is safe. Please remain calm." This is expected to reduce user anxiety during disasters.

[0393] As a concrete example, using an evacuation drill application, the system simulates a scenario where a user panics the moment they receive an earthquake early warning. Even in this case, the system immediately analyzes their emotions and delivers a notification in appropriate language that provides reassurance. This kind of real-time response is made possible by utilizing generative AI models.

[0394] An example of a prompt message is, "Use the emotion engine to analyze the user's stress level and generate and send an appropriate evacuation route recommendation message." Such prompts allow the system to achieve its objectives efficiently and effectively.

[0395] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0396] Step 1:

[0397] The device uses its built-in camera and microphone to capture the user's facial expressions and voice data. The input is the user's real-time visual and audio data. Specifically, the device tracks the user's face with the camera and records their voice tone with the microphone. The output is the captured raw audio and visual data.

[0398] Step 2:

[0399] The terminal sends captured facial and audio data to the server. The input here is the user data previously captured. Specifically, the terminal formats and encrypts the data appropriately and sends it to the server using a secure network protocol. The output is the encrypted customer data received by the server.

[0400] Step 3:

[0401] The server analyzes the received data using a generative AI model. The input consists of facial expression data and voice data sent from the terminal. Specifically, the server uses the generative AI model to classify the user's emotional state from this data. The output is the user's emotional state data as a result of the analysis.

[0402] Step 4:

[0403] The server generates an appropriate notification message to send to the user based on the analyzed emotional state of the user. The input is the emotional analysis result data on the server. Specifically, the server selects a message template corresponding to the emotional state and constructs the notification content based on the generated emotion. The output is the generated personalized message.

[0404] Step 5:

[0405] The server sends the generated notification to the terminal. The input is a personalized notification message. The server sends this to a specific user terminal. Specifically, the message is sent via a network protocol, received by the terminal, and displayed to the user. The output is the notification displayed on the terminal by the user.

[0406] Step 6:

[0407] The device recaptures the emotional changes of the user after they receive a notification. The input is the user's response or state change. The new data captured by the device as backup is sent to the server as feedback. Specifically, the device restarts its camera and microphone to acquire new data. The output is the feedback data sent to the server.

[0408] (Application Example 2)

[0409] 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."

[0410] In recent years, natural disasters have become more frequent, and many people need support to evacuate quickly and effectively. However, the psychological state of evacuees fluctuates depending on the situation, and it is necessary to provide appropriate support that is tailored to each individual's emotions. In particular, providing personalized information to users with different language and cultural backgrounds is a challenging task. Against this backdrop, there is a need for technology that can flexibly respond to changes in emotions while enabling individualized evacuation support in multiple languages.

[0411] 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.

[0412] In this invention, the server includes means for acquiring data, means for acquiring location information, means for generating evacuation routes, means for determining the user's emotional state using an emotion analysis engine, and means for providing personalized evacuation instructions and information that provides a sense of security. This enables personalized evacuation support based on the user's emotional state.

[0413] "Data acquisition means" refers to a device or function for collecting information on the user's emotional state, location, and external environment.

[0414] "Location information acquisition means" refers to a device or function that identifies the user's current location and uses that information to provide an evacuation route.

[0415] "Evacuation route generation means" refers to a device or function that calculates and presents the optimal evacuation route based on the user's location information and emotional state.

[0416] "Notification generation means" refers to a device or function for creating and sending personalized evacuation instructions or reassuring messages to users.

[0417] "Educational content delivery means" refers to a device or function that provides users with disaster prevention knowledge and adjusts the learning experience according to their emotional state.

[0418] "Multilingual support means" refers to a device or function that can provide information and content in multiple languages ​​based on the user's language settings.

[0419] An "emotion analysis engine" is a program or system that analyzes a user's facial expressions and voice data to determine their emotional state.

[0420] "Means of providing personalized evacuation instructions and reassuring information" refers to a device or function that provides the most appropriate evacuation information and psychological reassurance based on the user's emotional state and location information.

[0421] This invention is a system that uses an emotion analysis engine to determine a user's emotional state and provide personalized information in order to provide information and evacuation support during disasters. Its specific form is shown below.

[0422] The server captures the user's facial expressions and voice through data acquisition methods. This uses the camera and microphone built into the smartphone or mobile device. The acquired data is sent to the server's emotion analysis engine. This engine uses cloud services from companies such as Microsoft and Google to analyze the patterns of facial expressions and voice to determine the user's emotional state.

[0423] The user's location information is obtained using the GPS function of their smartphone. This allows the server to accurately determine the user's current location and generate the optimal evacuation route. The evacuation route generation method uses map information services such as the Google Maps API to generate the best route according to the user's situation.

[0424] Based on the results of sentiment analysis and location information, the server sends evacuation instructions or reassuring messages to the user via a notification generation system, tailored to their emotions. Messages are delivered at the appropriate time using tools such as Firebase Cloud Messaging.

[0425] In addition, the server uses multilingual support to provide notifications and disaster prevention education content tailored to the user's language settings. For example, displayed text and audio guides are provided in the user's native language, facilitating understanding in multiple languages.

[0426] As a concrete example, when a foreign tourist is in a situation requiring evacuation due to an earthquake, the server detects the user's fear based on camera footage. This triggers a notification in English or the user's native language, containing gentle language and specific evacuation instructions. This notification includes a message such as, "This is a safe evacuation route," along with a map showing the evacuation route.

[0427] An example of a prompt for a generative AI model would be: "If the user's emotional state indicates fear, generate a notification message that includes evacuation instructions. The notification should use gentle language and show evacuation routes."

[0428] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0429] Step 1:

[0430] The device uses a camera and microphone to capture the user's facial expressions and voice. The input consists of video footage captured by the camera and audio data recorded by the microphone, and this data is sent to the server.

[0431] Step 2:

[0432] The server inputs the acquired video and audio data into an emotion analysis engine to analyze the user's emotional state. As part of the data processing, the video and audio data are analyzed using an algorithm to determine the user's emotions. The results of the emotion analysis are then output.

[0433] Step 3:

[0434] The server uses GPS functionality to obtain location information from the device. The input is GPS data from the device, and the output is the coordinate information of the current location.

[0435] Step 4:

[0436] The server generates the optimal evacuation route using an evacuation route generation method based on the emotion analysis results and location information. The input is the emotional state and current location coordinates, and the output is evacuation route data obtained using the Google Maps API.

[0437] Step 5:

[0438] The server uses a notification generation mechanism to create an optimal evacuation order for the user. The input consists of the evacuation route and emotional state, and the output is a text message written in friendly language. This message includes prompts generated using an AI model.

[0439] Step 6:

[0440] The server translates generated messages according to the user's language settings through multilingual support mechanisms. Input is a text message, and a notification translated into the appropriate language is output.

[0441] Step 7:

[0442] The device uses Firebase Cloud Messaging to display personalized notifications sent from the server to the user. The input is translated notification data, which is output as a screen display or audio guidance.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] [Third Embodiment]

[0447] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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).

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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".

[0459] This invention is a system that provides rapid and appropriate information and evacuation support during disasters, and combines data acquisition means, location information acquisition means, evacuation route generation means, notification generation means, educational content provision means, and multilingual support means.

[0460] Data acquisition method

[0461] The server collects data in real time using APIs from the Japan Meteorological Agency and disaster information providers. The server stores the acquired data in a database and prepares it for analysis. This allows for constant monitoring of the latest situation.

[0462] Location information acquisition method

[0463] When a user launches the application, the device uses its GPS function to obtain its current location. The device then sends the obtained location information to the server in an encrypted state. This allows the server to generate customized evacuation instructions for each user.

[0464] Generation of evacuation routes

[0465] The server calculates the optimal evacuation route based on the user's location information and collected disaster data, taking into account the situation which changes over time. The server also incorporates the risk of secondary disasters and traffic information into its analysis to determine a route that allows for safe and rapid evacuation.

[0466] Creating and sending notifications

[0467] The server, when it determines that evacuation is necessary, uses a notification generation mechanism to alert the user. The notification is personalized based on the user's location and language settings and is delivered as a push notification to the device. Users can quickly review the received notification and take immediate action.

[0468] Provision of educational content

[0469] During normal operation, the server provides users with quizzes and simulation games designed to raise disaster preparedness awareness. These contents are displayed interactively on the device, allowing users to consciously learn about disaster prevention.

[0470] Implementation Examples of Multilingual Support

[0471] The server delivers all notifications and educational content in the appropriate language based on the user's language settings. This ensures that all users can understand and respond to information, regardless of language differences.

[0472] As a concrete example, if a foreign user residing in Japan experiences an earthquake in a particular area, their device immediately transmits its location information to a server. The server then sends multilingual evacuation instructions to the device, taking into account the latest information, allowing the user to take appropriate action to ensure their safety. In this way, everyone, including users with diverse backgrounds, can receive appropriate assistance. This system makes it possible to improve the disaster response capabilities of society as a whole.

[0473] The following describes the processing flow.

[0474] Step 1:

[0475] The server regularly collects the latest weather data and earthquake information from APIs of the Japan Meteorological Agency and disaster information providers. This ensures that the server is always prepared to maintain the most up-to-date disaster risk information.

[0476] Step 2:

[0477] When an application is launched, the device uses its built-in GPS function to obtain the user's current location. This location information is then encrypted and sent to the server.

[0478] Step 3:

[0479] The server checks disaster information for the surrounding area based on the location information sent by the user and immediately determines the need for evacuation. After this, an evacuation route generation system calculates an appropriate evacuation route.

[0480] Step 4:

[0481] The server considers disaster information, traffic conditions, and the risk of secondary disasters to plan safe evacuation routes for users.

[0482] Step 5:

[0483] The server uses a notification generation mechanism to create personalized messages based on the evacuation route information it has formulated, and then translates them based on the user's language settings.

[0484] Step 6:

[0485] An evacuation notification is pushed from the server to the device. The device displays this notification on its screen so that the user can confirm it.

[0486] Step 7:

[0487] Users will take swift action based on the evacuation information they receive and evacuate according to safe routes.

[0488] Step 8:

[0489] During normal operation, the server provides users with quizzes and simulation games to improve their disaster preparedness knowledge. The content is available in multiple languages, allowing users to access it in their chosen language.

[0490] Step 9:

[0491] Users can acquire disaster preparedness knowledge on a daily basis by utilizing educational content provided on their devices. They can then apply this learned knowledge to respond appropriately in emergencies.

[0492] (Example 1)

[0493] 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."

[0494] During natural disasters and emergencies, information tends to become chaotic, and many people have difficulty obtaining accurate and timely information. Furthermore, differences in language and culture can lead to situations where appropriate assistance does not reach everyone. These issues hinder the implementation of efficient and effective evacuation plans.

[0495] 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.

[0496] In this invention, the server includes information acquisition means for collecting data from external information sources, location identification means for acquiring the current location using a location detection device, and route generation means for calculating the optimal escape route based on the acquired information and location. This enables real-time rapid information provision and support for the execution of evacuation plans.

[0497] "Information acquisition means" refers to a function or device for collecting necessary data from external information sources.

[0498] "Location determination means" refers to a function or device that uses a location detection device to determine the user's current location.

[0499] "Route generation means" refers to a function or device for calculating the optimal evacuation route based on acquired information and location information.

[0500] "Notification creation means" refers to a function or device for creating individualized alarms and notifications and distributing them appropriately to users.

[0501] "Educational information provision means" refers to a function or device for providing users with learning materials and content to raise disaster prevention awareness.

[0502] "Multilingual processing means" refers to a function or device that provides information in various languages ​​according to the user's language settings.

[0503] This invention is a system that provides rapid and appropriate information and evacuation support during disasters. It supports the safe evacuation of users by combining the following various means.

[0504] The server uses external information sources, such as APIs for information provision services from public institutions, to collect disaster information in real time. The collected data is stored in a database and used for analysis. This data includes information on weather, earthquakes, tsunamis, etc., and is updated as the situation develops.

[0505] As a means of determining location, the device uses its GPS function to obtain the current location when the user launches the application. The obtained location information is encrypted and securely transmitted to the server. Based on this information, the server can provide customized evacuation instructions for each user.

[0506] The route generation system uses the user's location information and collected disaster information to calculate the optimal evacuation route. This takes into account traffic information and risk data for secondary disasters. As a result, the user can obtain the route that allows for the safest and fastest evacuation.

[0507] As a notification generation method, if the server determines that evacuation is necessary, a personalized alert is generated. The notification is appropriately translated based on the user's language settings and delivered to the device as a push notification. Users can immediately check the notification and take swift action.

[0508] Using educational information delivery methods, the server provides content to raise disaster preparedness awareness. During normal times, it generates quizzes and simulation games and sends them to the user's device. Through this content, users can learn about disaster preparedness while having fun.

[0509] Through multilingual processing, the server provides information in various languages ​​according to the user's language settings. This makes it possible for all users to obtain accurate information, overcoming language barriers.

[0510] For example, if a foreign user is in a certain area when an earthquake occurs, the device can immediately send its location information to the server, which can then issue optimized, multilingual evacuation instructions that take the latest information into account.

[0511] An example of a prompt message could be an input such as, "I want to develop a system that generates multilingual evacuation instructions for foreign users during a disaster."

[0512] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0513] Step 1:

[0514] The server collects data in real time from external sources. Specifically, it sends requests to weather information APIs to obtain weather and disaster-related data. The API endpoint is used as input, and the obtained dataset is output. This dataset contains information such as weather conditions and earthquake occurrences, and is stored in a database to prepare it for analysis.

[0515] Step 2:

[0516] The device uses GPS functionality to obtain the user's current location when they launch an application. Specifically, a location services service is called, and the current latitude and longitude are obtained as input. The output is encrypted location data, which is sent to the server. This ensures that the user's current location is securely transmitted to the server.

[0517] Step 3:

[0518] The server calculates the optimal evacuation route based on the user's location information and disaster data collected in real time. This process uses encrypted location information and collected disaster data as input. Data processing includes traffic condition and disaster risk analysis, generating safe and rapid evacuation route data as output. This route data enables appropriate guidance for the user.

[0519] Step 4:

[0520] If the server determines that evacuation is necessary, it uses a notification generation system to create and deliver an alert to the user. The inputs are optimal evacuation route data and the user's language settings. The output is a personalized, multilingual alert message. It is sent as a push notification to the device, prompting the user to immediately check the notification and take swift action.

[0521] Step 5:

[0522] The server provides educational content to raise disaster preparedness awareness. Input consists of learning materials such as quizzes and simulation games, and output is generated based on the user's language settings. Specifically, a generative AI model generates quiz questions and sends them to the terminal. The terminal displays these interactively, and the user enters answers to receive feedback.

[0523] Step 6:

[0524] The server is a multilingual support system that translates all information into various languages ​​according to the user's language settings. Inputs include notifications and educational content. Outputs are translated information tailored to the user's language. This allows users to receive information in a language they understand and use it to guide their actions.

[0525] (Application Example 1)

[0526] 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."

[0527] The challenge is to provide evacuation information quickly and appropriately during disasters, ensuring that users who speak diverse languages ​​can understand it equally and take safe evacuation actions. Furthermore, there is a need to provide educational content that raises disaster preparedness awareness even during normal times.

[0528] 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.

[0529] In this invention, the server includes an information acquisition device, a location data acquisition device, and an evacuation route generation device. This makes it possible to provide an individualized and optimal evacuation route based on information acquired in real time. Furthermore, by providing information in multiple languages ​​based on the user's language settings, all users can quickly understand the information and take safe evacuation actions.

[0530] An "information acquisition device" is a device that collects information in real time from external data supply organizations and plays the role of providing necessary data to other devices based on this information.

[0531] A "location data acquisition device" is a device that uses the GPS function of a terminal to acquire location information in order to determine the user's current geographical location.

[0532] An "evacuation route generation device" is a device that calculates the optimal evacuation route for a user based on acquired location data and real-time external information.

[0533] A "notification generation device" is a device that generates and sends personalized alarms and information based on the user's location information and settings.

[0534] An "educational information provision device" is a device that provides interactive educational content to raise users' disaster preparedness awareness during normal times.

[0535] A "multilingual device" is a device that provides notifications and educational information in multiple languages ​​based on the user's language settings.

[0536] A "device that automatically generates optimized evacuation routes within a dwelling area based on real-time acquired information" is a device that dynamically provides rapid and safe evacuation routes based on the latest disaster information and real-time location information.

[0537] A "language conversion device" is a device that converts all information within a system into the appropriate language according to the language set by the user.

[0538] This system is configured to provide rapid and appropriate evacuation support during disasters. Its main components include servers, terminals, and users.

[0539] The server collects weather and disaster information in real time through information acquisition devices and stores it in a database. This involves using APIs provided by external data providers and performing data analysis using Python. This data is stored in MongoDB and prepared for use when needed.

[0540] The device uses a location data acquisition device to obtain the user's current location via GPS when the application is launched. This information is sent to the server in an encrypted state. Flask is used for backend processing.

[0541] The server utilizes an evacuation route generation device to generate the optimal evacuation route for the user based on acquired location and disaster information. This process also takes real-time traffic information and terrain data into consideration to calculate the best route. Machine learning libraries (e.g., scikit-learn) are used for these calculations.

[0542] Furthermore, the notification generator creates personalized alerts based on the user's language settings and location information, and delivers them via a push notification service. A JavaScript-based frontend application displays these notifications.

[0543] Furthermore, the educational information device provides interactive educational content to help users raise their disaster preparedness awareness during normal times. This content is displayed in the user's preferred language using a multilingual device.

[0544] A concrete example is a scenario where users within a smart city receive optimal evacuation information during a typhoon. This system ensures that information is personalized in a timely manner and accurately understood in each language, enabling people from diverse backgrounds to take safe actions.

[0545] An example of a prompt for a generative AI model is: "Based on the latest information about the typhoon, please generate evacuation routes within the city. Also, please tell me how to provide notifications in multiple languages."

[0546] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0547] Step 1:

[0548] The server collects disaster information from external data sources using an information acquisition device. This process involves analyzing the data obtained via an API using Python and saving it to MongoDB. The input is disaster data obtained from an external API, and the output is organized disaster information stored in the database.

[0549] Step 2:

[0550] The terminal obtains the user's current location using a location data acquisition device. Here, location information is obtained using the smartphone's GPS function, encrypted, and sent to the server. The input is the user's GPS data, and the output is the user's location information sent to the server.

[0551] Step 3:

[0552] The server uses an evacuation route generation device to calculate the optimal evacuation route based on acquired user location information and collected disaster data. Real-time traffic conditions and topographic information are also taken into consideration. Machine learning is used to perform data calculations and output safe and rapid routes.

[0553] Step 4:

[0554] The server uses a notification generator to produce personalized alerts based on the user's language settings. This process creates appropriate evacuation instructions for the user based on the generated evacuation route information and sends them to the device via a push notification service. The inputs are the user's configuration information and the generated evacuation route, while the output is the notification delivered to the user's device.

[0555] Step 5:

[0556] The terminal uses an educational information provider to display disaster prevention content to the user. Here, interactive quizzes and simulations are provided using JavaScript. The input is educational content provided by the server, and the output is educational information that the user can view on their terminal.

[0557] Step 6:

[0558] The server uses a multilingual device to translate and provide all information in the appropriate language based on the user's language settings. The input is the user's language settings and all the information to be provided; the output is the information provided in the appropriate language.

[0559] 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.

[0560] This invention combines a system for providing information and evacuation support during disasters with an emotion engine that analyzes user emotions. By combining data acquisition means, location information acquisition means, evacuation route generation means, notification generation means, educational content provision means, emotion engine, and multilingual support means, more personalized evacuation support can be provided.

[0561] Embodiment of emotion analysis

[0562] The device captures the user's facial expressions and voice through its built-in camera and microphone while the user is using the application. The server uses this input data for an emotion engine to analyze and determine the user's emotional state. Based on this analysis, the server adjusts the support method according to the user's emotional state.

[0563] Adjusting evacuation orders based on emotions

[0564] If a user is experiencing significant stress, the server will adjust the evacuation route instructions to be more concise and reassuring. For example, it aims to reduce user anxiety by including gentle language and encouraging messages in the notifications.

[0565] Personalized notifications

[0566] The server personalizes the content and timing of notifications based on the analyzed emotional state. If it determines that the user is feeling anxious, it prioritizes sending notifications that provide detailed information about the distance to shelters and the current safety situation.

[0567] Optimizing disaster prevention education content

[0568] The server learns from user feedback regarding their emotions obtained from the terminal and adjusts the order and difficulty level of educational content accordingly. This allows for the effective delivery of disaster prevention knowledge while reducing emotional burden.

[0569] Examples of embodiments

[0570] For example, a user using an evacuation drill app might panic upon receiving an earthquake early warning. In this case, the device analyzes the user's facial expressions through the camera, and the server uses this data to determine that the user is feeling fear. Based on this determination, which is influenced by the emotion engine, the server sends a notification containing a reassuring message such as, "Please evacuate calmly." Similarly, in disaster preparedness quizzes, the difficulty level can be gradually increased, starting with easy questions to allow users to build confidence as they learn. In this way, incorporating an emotion engine makes it possible to provide a more personalized and user-friendly disaster response system.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] The device activates its built-in camera and microphone while the user is using an application, collecting the user's facial expressions and voice. This prepares it for capturing emotional data in real time.

[0574] Step 2:

[0575] The device preprocesses the collected audio and video data and converts it into a format that can be analyzed by the emotion engine. It then sends this data to the server.

[0576] Step 3:

[0577] The server receives data sent from the terminal and analyzes it using an emotion engine. It identifies the user's emotional state (e.g., reassurance, fear, anxiety) from their facial expressions and tone of voice.

[0578] Step 4:

[0579] Based on the analyzed emotional state, the server considers evacuation routes and information provision tailored to each user's situation. In particular, if the user is experiencing stress, calming messages and simplified information will be prioritized.

[0580] Step 5:

[0581] The server generates personalized notification content and translates it into the user's language. The notification includes reassuring language and recommendations for appropriate action.

[0582] Step 6:

[0583] The server pushes the generated notification to the device. The device displays the notification on the screen so that the user can understand it immediately.

[0584] Step 7:

[0585] By reading the received notifications, users can receive appropriate evacuation guidance that takes their emotional state into consideration, enabling them to take swift and accurate action.

[0586] Step 8:

[0587] The server continuously learns from user emotional data and adjusts the order and content of disaster prevention education materials accordingly. This optimizes the user's learning experience and reduces emotional burden.

[0588] Step 9:

[0589] Users can acquire disaster preparedness knowledge without stress using tailored educational content, improving their ability to respond in emergencies.

[0590] (Example 2)

[0591] 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."

[0592] In disaster situations, rapid and accurate information provision is essential for evacuation support systems. However, conventional systems provide uniform information without considering users' feelings, making them insufficient in alleviating user anxiety. Furthermore, they sometimes fail to adequately support diverse languages, posing a particular challenge in multilingual countries and for foreign users.

[0593] 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.

[0594] In this invention, the server includes emotion analysis means, data collection means, and multilingual support means. This enables personalized evacuation assistance and information provision based on the user's emotional state.

[0595] "Data collection means" refers to a device or method for obtaining information from users in real time.

[0596] "Location information acquisition means" refers to a device or method for accurately measuring and tracking a user's current location.

[0597] "Evacuation route creation means" refers to a device or method for generating the optimal evacuation route for a user in an emergency.

[0598] "Notification generation means" refers to a device or method for generating messages or instructions to notify a user of necessary information.

[0599] "Educational information provision means" refers to a device or method that presents content or teaching materials for providing users with knowledge about disaster prevention.

[0600] "Emotional analysis means" refers to a device or method that analyzes a user's emotional state based on information such as the user's facial expressions and voice.

[0601] "Multilingual support means" refers to a device or method for providing information to users in various languages.

[0602] This invention is a comprehensive system that combines user emotion analysis to effectively provide information and evacuation support during disasters. The system mainly consists of a server and terminals, which perform their functions through bidirectional communication.

[0603] The server receives facial and voice data transmitted from the user's device and analyzes this data using a generative AI model as an emotion analysis tool. This analysis includes an emotion engine that determines the user's emotional state in real time and optimizes the support method based on that information. The data is usually transmitted to the server via a secure network. The server also has multilingual support capabilities and can provide information in various languages. This allows the server to provide appropriate information according to the user's language settings.

[0604] The device uses its built-in camera and microphone to acquire data related to the user's emotional state. For example, the camera captures the user's facial expressions, and the microphone captures their voice tone. This data is sent from the device to a server where emotion analysis is performed.

[0605] Users can receive notifications and educational information provided by the system. These notifications include personalized messages based on the user's emotional state, such as "Your evacuation route is safe. Please remain calm." This is expected to reduce user anxiety during disasters.

[0606] As a concrete example, using an evacuation drill application, the system simulates a scenario where a user panics the moment they receive an earthquake early warning. Even in this case, the system immediately analyzes their emotions and delivers a notification in appropriate language that provides reassurance. This kind of real-time response is made possible by utilizing generative AI models.

[0607] An example of a prompt message is, "Use the emotion engine to analyze the user's stress level and generate and send an appropriate evacuation route recommendation message." Such prompts allow the system to achieve its objectives efficiently and effectively.

[0608] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0609] Step 1:

[0610] The device uses its built-in camera and microphone to capture the user's facial expressions and voice data. The input is the user's real-time visual and audio data. Specifically, the device tracks the user's face with the camera and records their voice tone with the microphone. The output is the captured raw audio and visual data.

[0611] Step 2:

[0612] The terminal sends captured facial and audio data to the server. The input here is the user data previously captured. Specifically, the terminal formats and encrypts the data appropriately and sends it to the server using a secure network protocol. The output is the encrypted customer data received by the server.

[0613] Step 3:

[0614] The server analyzes the received data using a generative AI model. The input consists of facial expression data and voice data sent from the terminal. Specifically, the server uses the generative AI model to classify the user's emotional state from this data. The output is the user's emotional state data as a result of the analysis.

[0615] Step 4:

[0616] The server generates an appropriate notification message to send to the user based on the analyzed emotional state of the user. The input is the emotional analysis result data on the server. Specifically, the server selects a message template corresponding to the emotional state and constructs the notification content based on the generated emotion. The output is the generated personalized message.

[0617] Step 5:

[0618] The server sends the generated notification to the terminal. The input is a personalized notification message. The server sends this to a specific user terminal. Specifically, the message is sent via a network protocol, received by the terminal, and displayed to the user. The output is the notification displayed on the terminal by the user.

[0619] Step 6:

[0620] The device recaptures the emotional changes of the user after they receive a notification. The input is the user's response or state change. The new data captured by the device as backup is sent to the server as feedback. Specifically, the device restarts its camera and microphone to acquire new data. The output is the feedback data sent to the server.

[0621] (Application Example 2)

[0622] 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."

[0623] In recent years, natural disasters have become more frequent, and many people need support to evacuate quickly and effectively. However, the psychological state of evacuees fluctuates depending on the situation, and it is necessary to provide appropriate support that is tailored to each individual's emotions. In particular, providing personalized information to users with different language and cultural backgrounds is a challenging task. Against this backdrop, there is a need for technology that can flexibly respond to changes in emotions while enabling individualized evacuation support in multiple languages.

[0624] 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.

[0625] In this invention, the server includes means for acquiring data, means for acquiring location information, means for generating evacuation routes, means for determining the user's emotional state using an emotion analysis engine, and means for providing personalized evacuation instructions and information that provides a sense of security. This enables personalized evacuation support based on the user's emotional state.

[0626] "Data acquisition means" refers to a device or function for collecting information on the user's emotional state, location, and external environment.

[0627] "Location information acquisition means" refers to a device or function that identifies the user's current location and uses that information to provide an evacuation route.

[0628] "Evacuation route generation means" refers to a device or function that calculates and presents the optimal evacuation route based on the user's location information and emotional state.

[0629] "Notification generation means" refers to a device or function for creating and sending personalized evacuation instructions or reassuring messages to users.

[0630] "Educational content delivery means" refers to a device or function that provides users with disaster prevention knowledge and adjusts the learning experience according to their emotional state.

[0631] "Multilingual support means" refers to a device or function that can provide information and content in multiple languages ​​based on the user's language settings.

[0632] An "emotion analysis engine" is a program or system that analyzes a user's facial expressions and voice data to determine their emotional state.

[0633] "Means of providing personalized evacuation instructions and reassuring information" refers to a device or function that provides the most appropriate evacuation information and psychological reassurance based on the user's emotional state and location information.

[0634] This invention is a system that uses an emotion analysis engine to determine a user's emotional state and provide personalized information in order to provide information and evacuation support during disasters. Its specific form is shown below.

[0635] The server captures the user's facial expressions and voice through data acquisition methods. This uses the camera and microphone built into the smartphone or mobile device. The acquired data is sent to the server's emotion analysis engine. This engine uses cloud services from companies such as Microsoft and Google to analyze the patterns of facial expressions and voice to determine the user's emotional state.

[0636] The user's location information is obtained using the GPS function of their smartphone. This allows the server to accurately determine the user's current location and generate the optimal evacuation route. The evacuation route generation method uses map information services such as the Google Maps API to generate the best route according to the user's situation.

[0637] Based on the results of sentiment analysis and location information, the server sends evacuation instructions or reassuring messages to the user via a notification generation system, tailored to their emotions. Messages are delivered at the appropriate time using tools such as Firebase Cloud Messaging.

[0638] In addition, the server uses multilingual support to provide notifications and disaster prevention education content tailored to the user's language settings. For example, displayed text and audio guides are provided in the user's native language, facilitating understanding in multiple languages.

[0639] As a concrete example, when a foreign tourist is in a situation requiring evacuation due to an earthquake, the server detects the user's fear based on camera footage. This triggers a notification in English or the user's native language, containing gentle language and specific evacuation instructions. This notification includes a message such as, "This is a safe evacuation route," along with a map showing the evacuation route.

[0640] An example of a prompt for a generative AI model would be: "If the user's emotional state indicates fear, generate a notification message that includes evacuation instructions. The notification should use gentle language and show evacuation routes."

[0641] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0642] Step 1:

[0643] The device uses a camera and microphone to capture the user's facial expressions and voice. The input consists of video footage captured by the camera and audio data recorded by the microphone, and this data is sent to the server.

[0644] Step 2:

[0645] The server inputs the acquired video and audio data into an emotion analysis engine to analyze the user's emotional state. As part of the data processing, the video and audio data are analyzed using an algorithm to determine the user's emotions. The results of the emotion analysis are then output.

[0646] Step 3:

[0647] The server uses GPS functionality to obtain location information from the device. The input is GPS data from the device, and the output is the coordinate information of the current location.

[0648] Step 4:

[0649] The server generates the optimal evacuation route using an evacuation route generation method based on the emotion analysis results and location information. The input is the emotional state and current location coordinates, and the output is evacuation route data obtained using the Google Maps API.

[0650] Step 5:

[0651] The server uses a notification generation mechanism to create an optimal evacuation order for the user. The input consists of the evacuation route and emotional state, and the output is a text message written in friendly language. This message includes prompts generated using an AI model.

[0652] Step 6:

[0653] The server translates generated messages according to the user's language settings through multilingual support mechanisms. Input is a text message, and a notification translated into the appropriate language is output.

[0654] Step 7:

[0655] The device uses Firebase Cloud Messaging to display personalized notifications sent from the server to the user. The input is translated notification data, which is output as a screen display or audio guidance.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] [Fourth Embodiment]

[0660] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0661] 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.

[0662] 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).

[0663] 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.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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".

[0673] This invention is a system that provides rapid and appropriate information and evacuation support during disasters, and combines data acquisition means, location information acquisition means, evacuation route generation means, notification generation means, educational content provision means, and multilingual support means.

[0674] Data acquisition method

[0675] The server collects data in real time using APIs from the Japan Meteorological Agency and disaster information providers. The server stores the acquired data in a database and prepares it for analysis. This allows for constant monitoring of the latest situation.

[0676] Location information acquisition method

[0677] When a user launches the application, the device uses its GPS function to obtain its current location. The device then sends the obtained location information to the server in an encrypted state. This allows the server to generate customized evacuation instructions for each user.

[0678] Generation of evacuation routes

[0679] The server calculates the optimal evacuation route based on the user's location information and collected disaster data, taking into account the situation which changes over time. The server also incorporates the risk of secondary disasters and traffic information into its analysis to determine a route that allows for safe and rapid evacuation.

[0680] Creating and sending notifications

[0681] The server, when it determines that evacuation is necessary, uses a notification generation mechanism to alert the user. The notification is personalized based on the user's location and language settings and is delivered as a push notification to the device. Users can quickly review the received notification and take immediate action.

[0682] Provision of educational content

[0683] During normal operation, the server provides users with quizzes and simulation games designed to raise disaster preparedness awareness. These contents are displayed interactively on the device, allowing users to consciously learn about disaster prevention.

[0684] Implementation Examples of Multilingual Support

[0685] The server delivers all notifications and educational content in the appropriate language based on the user's language settings. This ensures that all users can understand and respond to information, regardless of language differences.

[0686] As a concrete example, if a foreign user residing in Japan experiences an earthquake in a particular area, their device immediately transmits its location information to a server. The server then sends multilingual evacuation instructions to the device, taking into account the latest information, allowing the user to take appropriate action to ensure their safety. In this way, everyone, including users with diverse backgrounds, can receive appropriate assistance. This system makes it possible to improve the disaster response capabilities of society as a whole.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The server regularly collects the latest weather data and earthquake information from APIs of the Japan Meteorological Agency and disaster information providers. This ensures that the server is always prepared to maintain the most up-to-date disaster risk information.

[0690] Step 2:

[0691] When an application is launched, the device uses its built-in GPS function to obtain the user's current location. This location information is then encrypted and sent to the server.

[0692] Step 3:

[0693] The server checks disaster information for the surrounding area based on the location information sent by the user and immediately determines the need for evacuation. After this, an evacuation route generation system calculates an appropriate evacuation route.

[0694] Step 4:

[0695] The server considers disaster information, traffic conditions, and the risk of secondary disasters to plan safe evacuation routes for users.

[0696] Step 5:

[0697] The server uses a notification generation mechanism to create personalized messages based on the evacuation route information it has formulated, and then translates them based on the user's language settings.

[0698] Step 6:

[0699] An evacuation notification is pushed from the server to the device. The device displays this notification on its screen so that the user can confirm it.

[0700] Step 7:

[0701] Users will take swift action based on the evacuation information they receive and evacuate according to safe routes.

[0702] Step 8:

[0703] During normal operation, the server provides users with quizzes and simulation games to improve their disaster preparedness knowledge. The content is available in multiple languages, allowing users to access it in their chosen language.

[0704] Step 9:

[0705] Users can acquire disaster preparedness knowledge on a daily basis by utilizing educational content provided on their devices. They can then apply this learned knowledge to respond appropriately in emergencies.

[0706] (Example 1)

[0707] 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".

[0708] During natural disasters and emergencies, information tends to become chaotic, and many people have difficulty obtaining accurate and timely information. Furthermore, differences in language and culture can lead to situations where appropriate assistance does not reach everyone. These issues hinder the implementation of efficient and effective evacuation plans.

[0709] 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.

[0710] In this invention, the server includes information acquisition means for collecting data from external information sources, location identification means for acquiring the current location using a location detection device, and route generation means for calculating the optimal escape route based on the acquired information and location. This enables real-time rapid information provision and support for the execution of evacuation plans.

[0711] "Information acquisition means" refers to a function or device for collecting necessary data from external information sources.

[0712] "Location determination means" refers to a function or device that uses a location detection device to determine the user's current location.

[0713] "Route generation means" refers to a function or device for calculating the optimal evacuation route based on acquired information and location information.

[0714] "Notification creation means" refers to a function or device for creating individualized alarms and notifications and distributing them appropriately to users.

[0715] "Educational information provision means" refers to a function or device for providing users with learning materials and content to raise disaster prevention awareness.

[0716] "Multilingual processing means" refers to a function or device that provides information in various languages ​​according to the user's language settings.

[0717] This invention is a system that provides rapid and appropriate information and evacuation support during disasters. It supports the safe evacuation of users by combining the following various means.

[0718] The server uses external information sources, such as APIs for information provision services from public institutions, to collect disaster information in real time. The collected data is stored in a database and used for analysis. This data includes information on weather, earthquakes, tsunamis, etc., and is updated as the situation develops.

[0719] As a means of determining location, the device uses its GPS function to obtain the current location when the user launches the application. The obtained location information is encrypted and securely transmitted to the server. Based on this information, the server can provide customized evacuation instructions for each user.

[0720] The route generation system uses the user's location information and collected disaster information to calculate the optimal evacuation route. This takes into account traffic information and risk data for secondary disasters. As a result, the user can obtain the route that allows for the safest and fastest evacuation.

[0721] As a notification generation method, if the server determines that evacuation is necessary, a personalized alert is generated. The notification is appropriately translated based on the user's language settings and delivered to the device as a push notification. Users can immediately check the notification and take swift action.

[0722] Using educational information delivery methods, the server provides content to raise disaster preparedness awareness. During normal times, it generates quizzes and simulation games and sends them to the user's device. Through this content, users can learn about disaster preparedness while having fun.

[0723] Through multilingual processing, the server provides information in various languages ​​according to the user's language settings. This makes it possible for all users to obtain accurate information, overcoming language barriers.

[0724] For example, if a foreign user is in a certain area when an earthquake occurs, the device can immediately send its location information to the server, which can then issue optimized, multilingual evacuation instructions that take the latest information into account.

[0725] An example of a prompt message could be an input such as, "I want to develop a system that generates multilingual evacuation instructions for foreign users during a disaster."

[0726] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0727] Step 1:

[0728] The server collects data in real time from external sources. Specifically, it sends requests to weather information APIs to obtain weather and disaster-related data. The API endpoint is used as input, and the obtained dataset is output. This dataset contains information such as weather conditions and earthquake occurrences, and is stored in a database to prepare it for analysis.

[0729] Step 2:

[0730] The device uses GPS functionality to obtain the user's current location when they launch an application. Specifically, a location services service is called, and the current latitude and longitude are obtained as input. The output is encrypted location data, which is sent to the server. This ensures that the user's current location is securely transmitted to the server.

[0731] Step 3:

[0732] The server calculates the optimal evacuation route based on the user's location information and disaster data collected in real time. This process uses encrypted location information and collected disaster data as input. Data processing includes traffic condition and disaster risk analysis, generating safe and rapid evacuation route data as output. This route data enables appropriate guidance for the user.

[0733] Step 4:

[0734] If the server determines that evacuation is necessary, it uses a notification generation system to create and deliver an alert to the user. The inputs are optimal evacuation route data and the user's language settings. The output is a personalized, multilingual alert message. It is sent as a push notification to the device, prompting the user to immediately check the notification and take swift action.

[0735] Step 5:

[0736] The server provides educational content to raise disaster preparedness awareness. Input consists of learning materials such as quizzes and simulation games, and output is generated based on the user's language settings. Specifically, a generative AI model generates quiz questions and sends them to the terminal. The terminal displays these interactively, and the user enters answers to receive feedback.

[0737] Step 6:

[0738] The server is a multilingual support system that translates all information into various languages ​​according to the user's language settings. Inputs include notifications and educational content. Outputs are translated information tailored to the user's language. This allows users to receive information in a language they understand and use it to guide their actions.

[0739] (Application Example 1)

[0740] 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".

[0741] The challenge is to provide evacuation information quickly and appropriately during disasters, ensuring that users who speak diverse languages ​​can understand it equally and take safe evacuation actions. Furthermore, there is a need to provide educational content that raises disaster preparedness awareness even during normal times.

[0742] 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.

[0743] In this invention, the server includes an information acquisition device, a location data acquisition device, and an evacuation route generation device. This makes it possible to provide an individualized and optimal evacuation route based on information acquired in real time. Furthermore, by providing information in multiple languages ​​based on the user's language settings, all users can quickly understand the information and take safe evacuation actions.

[0744] An "information acquisition device" is a device that collects information in real time from external data supply organizations and plays the role of providing necessary data to other devices based on this information.

[0745] A "location data acquisition device" is a device that uses the GPS function of a terminal to acquire location information in order to determine the user's current geographical location.

[0746] An "evacuation route generation device" is a device that calculates the optimal evacuation route for a user based on acquired location data and real-time external information.

[0747] A "notification generation device" is a device that generates and sends personalized alarms and information based on the user's location information and settings.

[0748] An "educational information provision device" is a device that provides interactive educational content to raise users' disaster preparedness awareness during normal times.

[0749] A "multilingual device" is a device that provides notifications and educational information in multiple languages ​​based on the user's language settings.

[0750] A "device that automatically generates optimized evacuation routes within a dwelling area based on real-time acquired information" is a device that dynamically provides rapid and safe evacuation routes based on the latest disaster information and real-time location information.

[0751] A "language conversion device" is a device that converts all information within a system into the appropriate language according to the language set by the user.

[0752] This system is configured to provide rapid and appropriate evacuation support during disasters. Its main components include servers, terminals, and users.

[0753] The server collects weather and disaster information in real time through information acquisition devices and stores it in a database. This involves using APIs provided by external data providers and performing data analysis using Python. This data is stored in MongoDB and prepared for use when needed.

[0754] The device uses a location data acquisition device to obtain the user's current location via GPS when the application is launched. This information is sent to the server in an encrypted state. Flask is used for backend processing.

[0755] The server utilizes an evacuation route generation device to generate the optimal evacuation route for the user based on acquired location and disaster information. This process also takes real-time traffic information and terrain data into consideration to calculate the best route. Machine learning libraries (e.g., scikit-learn) are used for these calculations.

[0756] Furthermore, the notification generator creates personalized alerts based on the user's language settings and location information, and delivers them via a push notification service. A JavaScript-based frontend application displays these notifications.

[0757] Furthermore, the educational information device provides interactive educational content to help users raise their disaster preparedness awareness during normal times. This content is displayed in the user's preferred language using a multilingual device.

[0758] A concrete example is a scenario where users within a smart city receive optimal evacuation information during a typhoon. This system ensures that information is personalized in a timely manner and accurately understood in each language, enabling people from diverse backgrounds to take safe actions.

[0759] An example of a prompt for a generative AI model is: "Based on the latest information about the typhoon, please generate evacuation routes within the city. Also, please tell me how to provide notifications in multiple languages."

[0760] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0761] Step 1:

[0762] The server collects disaster information from external data sources using an information acquisition device. This process involves analyzing the data obtained via an API using Python and saving it to MongoDB. The input is disaster data obtained from an external API, and the output is organized disaster information stored in the database.

[0763] Step 2:

[0764] The terminal obtains the user's current location using a location data acquisition device. Here, location information is obtained using the smartphone's GPS function, encrypted, and sent to the server. The input is the user's GPS data, and the output is the user's location information sent to the server.

[0765] Step 3:

[0766] The server uses an evacuation route generation device to calculate the optimal evacuation route based on acquired user location information and collected disaster data. Real-time traffic conditions and topographic information are also taken into consideration. Machine learning is used to perform data calculations and output safe and rapid routes.

[0767] Step 4:

[0768] The server uses a notification generator to produce personalized alerts based on the user's language settings. This process creates appropriate evacuation instructions for the user based on the generated evacuation route information and sends them to the device via a push notification service. The inputs are the user's configuration information and the generated evacuation route, while the output is the notification delivered to the user's device.

[0769] Step 5:

[0770] The terminal uses an educational information provider to display disaster prevention content to the user. Here, interactive quizzes and simulations are provided using JavaScript. The input is educational content provided by the server, and the output is educational information that the user can view on their terminal.

[0771] Step 6:

[0772] The server uses a multilingual device to translate and provide all information in the appropriate language based on the user's language settings. The input is the user's language settings and all the information to be provided; the output is the information provided in the appropriate language.

[0773] 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.

[0774] This invention combines a system for providing information and evacuation support during disasters with an emotion engine that analyzes user emotions. By combining data acquisition means, location information acquisition means, evacuation route generation means, notification generation means, educational content provision means, emotion engine, and multilingual support means, more personalized evacuation support can be provided.

[0775] Embodiment of emotion analysis

[0776] The device captures the user's facial expressions and voice through its built-in camera and microphone while the user is using the application. The server uses this input data for an emotion engine to analyze and determine the user's emotional state. Based on this analysis, the server adjusts the support method according to the user's emotional state.

[0777] Adjusting evacuation orders based on emotions

[0778] If a user is experiencing significant stress, the server will adjust the evacuation route instructions to be more concise and reassuring. For example, it aims to reduce user anxiety by including gentle language and encouraging messages in the notifications.

[0779] Personalized notifications

[0780] The server personalizes the content and timing of notifications based on the analyzed emotional state. If it determines that the user is feeling anxious, it prioritizes sending notifications that provide detailed information about the distance to shelters and the current safety situation.

[0781] Optimizing disaster prevention education content

[0782] The server learns from user feedback regarding their emotions obtained from the terminal and adjusts the order and difficulty level of educational content accordingly. This allows for the effective delivery of disaster prevention knowledge while reducing emotional burden.

[0783] Examples of embodiments

[0784] For example, a user using an evacuation drill app might panic upon receiving an earthquake early warning. In this case, the device analyzes the user's facial expressions through the camera, and the server uses this data to determine that the user is feeling fear. Based on this determination, which is influenced by the emotion engine, the server sends a notification containing a reassuring message such as, "Please evacuate calmly." Similarly, in disaster preparedness quizzes, the difficulty level can be gradually increased, starting with easy questions to allow users to build confidence as they learn. In this way, incorporating an emotion engine makes it possible to provide a more personalized and user-friendly disaster response system.

[0785] The following describes the processing flow.

[0786] Step 1:

[0787] The device activates its built-in camera and microphone while the user is using an application, collecting the user's facial expressions and voice. This prepares it for capturing emotional data in real time.

[0788] Step 2:

[0789] The device preprocesses the collected audio and video data and converts it into a format that can be analyzed by the emotion engine. It then sends this data to the server.

[0790] Step 3:

[0791] The server receives data sent from the terminal and analyzes it using an emotion engine. It identifies the user's emotional state (e.g., reassurance, fear, anxiety) from their facial expressions and tone of voice.

[0792] Step 4:

[0793] Based on the analyzed emotional state, the server considers evacuation routes and information provision tailored to each user's situation. In particular, if the user is experiencing stress, calming messages and simplified information will be prioritized.

[0794] Step 5:

[0795] The server generates personalized notification content and translates it into the user's language. The notification includes reassuring language and recommendations for appropriate action.

[0796] Step 6:

[0797] The server pushes the generated notification to the device. The device displays the notification on the screen so that the user can understand it immediately.

[0798] Step 7:

[0799] By reading the received notifications, users can receive appropriate evacuation guidance that takes their emotional state into consideration, enabling them to take swift and accurate action.

[0800] Step 8:

[0801] The server continuously learns from user emotional data and adjusts the order and content of disaster prevention education materials accordingly. This optimizes the user's learning experience and reduces emotional burden.

[0802] Step 9:

[0803] Users can acquire disaster preparedness knowledge without stress using tailored educational content, improving their ability to respond in emergencies.

[0804] (Example 2)

[0805] 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".

[0806] In disaster situations, rapid and accurate information provision is essential for evacuation support systems. However, conventional systems provide uniform information without considering users' feelings, making them insufficient in alleviating user anxiety. Furthermore, they sometimes fail to adequately support diverse languages, posing a particular challenge in multilingual countries and for foreign users.

[0807] 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.

[0808] In this invention, the server includes emotion analysis means, data collection means, and multilingual support means. This enables personalized evacuation assistance and information provision based on the user's emotional state.

[0809] "Data collection means" refers to a device or method for obtaining information from users in real time.

[0810] "Location information acquisition means" refers to a device or method for accurately measuring and tracking a user's current location.

[0811] "Evacuation route creation means" refers to a device or method for generating the optimal evacuation route for a user in an emergency.

[0812] "Notification generation means" refers to a device or method for generating messages or instructions to notify a user of necessary information.

[0813] "Educational information provision means" refers to a device or method that presents content or teaching materials for providing users with knowledge about disaster prevention.

[0814] "Emotional analysis means" refers to a device or method that analyzes a user's emotional state based on information such as the user's facial expressions and voice.

[0815] "Multilingual support means" refers to a device or method for providing information to users in various languages.

[0816] This invention is a comprehensive system that combines user emotion analysis to effectively provide information and evacuation support during disasters. The system mainly consists of a server and terminals, which perform their functions through bidirectional communication.

[0817] The server receives facial and voice data transmitted from the user's device and analyzes this data using a generative AI model as an emotion analysis tool. This analysis includes an emotion engine that determines the user's emotional state in real time and optimizes the support method based on that information. The data is usually transmitted to the server via a secure network. The server also has multilingual support capabilities and can provide information in various languages. This allows the server to provide appropriate information according to the user's language settings.

[0818] The device uses its built-in camera and microphone to acquire data related to the user's emotional state. For example, the camera captures the user's facial expressions, and the microphone captures their voice tone. This data is sent from the device to a server where emotion analysis is performed.

[0819] Users can receive notifications and educational information provided by the system. These notifications include personalized messages based on the user's emotional state, such as "Your evacuation route is safe. Please remain calm." This is expected to reduce user anxiety during disasters.

[0820] As a concrete example, using an evacuation drill application, the system simulates a scenario where a user panics the moment they receive an earthquake early warning. Even in this case, the system immediately analyzes their emotions and delivers a notification in appropriate language that provides reassurance. This kind of real-time response is made possible by utilizing generative AI models.

[0821] An example of a prompt message is, "Use the emotion engine to analyze the user's stress level and generate and send an appropriate evacuation route recommendation message." Such prompts allow the system to achieve its objectives efficiently and effectively.

[0822] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0823] Step 1:

[0824] The device uses its built-in camera and microphone to capture the user's facial expressions and voice data. The input is the user's real-time visual and audio data. Specifically, the device tracks the user's face with the camera and records their voice tone with the microphone. The output is the captured raw audio and visual data.

[0825] Step 2:

[0826] The terminal sends captured facial and audio data to the server. The input here is the user data previously captured. Specifically, the terminal formats and encrypts the data appropriately and sends it to the server using a secure network protocol. The output is the encrypted customer data received by the server.

[0827] Step 3:

[0828] The server analyzes the received data using a generative AI model. The input consists of facial expression data and voice data sent from the terminal. Specifically, the server uses the generative AI model to classify the user's emotional state from this data. The output is the user's emotional state data as a result of the analysis.

[0829] Step 4:

[0830] The server generates an appropriate notification message to send to the user based on the analyzed emotional state of the user. The input is the emotional analysis result data on the server. Specifically, the server selects a message template corresponding to the emotional state and constructs the notification content based on the generated emotion. The output is the generated personalized message.

[0831] Step 5:

[0832] The server sends the generated notification to the terminal. The input is a personalized notification message. The server sends this to a specific user terminal. Specifically, the message is sent via a network protocol, received by the terminal, and displayed to the user. The output is the notification displayed on the terminal by the user.

[0833] Step 6:

[0834] The device recaptures the emotional changes of the user after they receive a notification. The input is the user's response or state change. The new data captured by the device as backup is sent to the server as feedback. Specifically, the device restarts its camera and microphone to acquire new data. The output is the feedback data sent to the server.

[0835] (Application Example 2)

[0836] 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".

[0837] In recent years, natural disasters have become more frequent, and many people need support to evacuate quickly and effectively. However, the psychological state of evacuees fluctuates depending on the situation, and it is necessary to provide appropriate support that is tailored to each individual's emotions. In particular, providing personalized information to users with different language and cultural backgrounds is a challenging task. Against this backdrop, there is a need for technology that can flexibly respond to changes in emotions while enabling individualized evacuation support in multiple languages.

[0838] 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.

[0839] In this invention, the server includes means for acquiring data, means for acquiring location information, means for generating evacuation routes, means for determining the user's emotional state using an emotion analysis engine, and means for providing personalized evacuation instructions and information that provides a sense of security. This enables personalized evacuation support based on the user's emotional state.

[0840] "Data acquisition means" refers to a device or function for collecting information on the user's emotional state, location, and external environment.

[0841] "Location information acquisition means" refers to a device or function that identifies the user's current location and uses that information to provide an evacuation route.

[0842] "Evacuation route generation means" refers to a device or function that calculates and presents the optimal evacuation route based on the user's location information and emotional state.

[0843] "Notification generation means" refers to a device or function for creating and sending personalized evacuation instructions or reassuring messages to users.

[0844] "Educational content delivery means" refers to a device or function that provides users with disaster prevention knowledge and adjusts the learning experience according to their emotional state.

[0845] "Multilingual support means" refers to a device or function that can provide information and content in multiple languages ​​based on the user's language settings.

[0846] An "emotion analysis engine" is a program or system that analyzes a user's facial expressions and voice data to determine their emotional state.

[0847] "Means of providing personalized evacuation instructions and reassuring information" refers to a device or function that provides the most appropriate evacuation information and psychological reassurance based on the user's emotional state and location information.

[0848] This invention is a system that uses an emotion analysis engine to determine a user's emotional state and provide personalized information in order to provide information and evacuation support during disasters. Its specific form is shown below.

[0849] The server captures the user's facial expressions and voice through data acquisition methods. This uses the camera and microphone built into the smartphone or mobile device. The acquired data is sent to the server's emotion analysis engine. This engine uses cloud services from companies such as Microsoft and Google to analyze the patterns of facial expressions and voice to determine the user's emotional state.

[0850] The user's location information is obtained using the GPS function of their smartphone. This allows the server to accurately determine the user's current location and generate the optimal evacuation route. The evacuation route generation method uses map information services such as the Google Maps API to generate the best route according to the user's situation.

[0851] Based on the results of sentiment analysis and location information, the server sends evacuation instructions or reassuring messages to the user via a notification generation system, tailored to their emotions. Messages are delivered at the appropriate time using tools such as Firebase Cloud Messaging.

[0852] In addition, the server uses multilingual support to provide notifications and disaster prevention education content tailored to the user's language settings. For example, displayed text and audio guides are provided in the user's native language, facilitating understanding in multiple languages.

[0853] As a concrete example, when a foreign tourist is in a situation requiring evacuation due to an earthquake, the server detects the user's fear based on camera footage. This triggers a notification in English or the user's native language, containing gentle language and specific evacuation instructions. This notification includes a message such as, "This is a safe evacuation route," along with a map showing the evacuation route.

[0854] An example of a prompt for a generative AI model would be: "If the user's emotional state indicates fear, generate a notification message that includes evacuation instructions. The notification should use gentle language and show evacuation routes."

[0855] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0856] Step 1:

[0857] The device uses a camera and microphone to capture the user's facial expressions and voice. The input consists of video footage captured by the camera and audio data recorded by the microphone, and this data is sent to the server.

[0858] Step 2:

[0859] The server inputs the acquired video and audio data into an emotion analysis engine to analyze the user's emotional state. As part of the data processing, the video and audio data are analyzed using an algorithm to determine the user's emotions. The results of the emotion analysis are then output.

[0860] Step 3:

[0861] The server uses GPS functionality to obtain location information from the device. The input is GPS data from the device, and the output is the coordinate information of the current location.

[0862] Step 4:

[0863] The server generates the optimal evacuation route using an evacuation route generation method based on the emotion analysis results and location information. The input is the emotional state and current location coordinates, and the output is evacuation route data obtained using the Google Maps API.

[0864] Step 5:

[0865] The server uses a notification generation mechanism to create an optimal evacuation order for the user. The input consists of the evacuation route and emotional state, and the output is a text message written in friendly language. This message includes prompts generated using an AI model.

[0866] Step 6:

[0867] The server translates generated messages according to the user's language settings through multilingual support mechanisms. Input is a text message, and a notification translated into the appropriate language is output.

[0868] Step 7:

[0869] The device uses Firebase Cloud Messaging to display personalized notifications sent from the server to the user. The input is translated notification data, which is output as a screen display or audio guidance.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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."

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] The following is further disclosed regarding the embodiments described above.

[0892] (Claim 1)

[0893] Data acquisition method,

[0894] Location information acquisition method,

[0895] Evacuation route generation method,

[0896] Notification generation means,

[0897] Means of providing educational content,

[0898] Multilingual support methods,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, which rapidly generates evacuation routes based on external information collected in real time.

[0902] (Claim 3)

[0903] The system according to claim 1, which provides personalized notifications and educational content according to the user's location information and language settings.

[0904] "Example 1"

[0905] (Claim 1)

[0906] Information acquisition means for collecting data from external information sources,

[0907] A position determination means that acquires the current position using a position detection device,

[0908] A path generation means that calculates the optimal escape route based on acquired information and location,

[0909] A notification creation method for creating and distributing individualized alarm notifications,

[0910] An educational information provision method that provides learning materials to raise disaster prevention awareness among users,

[0911] A multilingual processing device that provides information in various languages ​​according to the language setting,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, which rapidly generates evacuation routes based on external event information collected in real time.

[0915] (Claim 3)

[0916] The system according to claim 1, which provides personalized alarms and learning materials according to the user's location information and language settings.

[0917] "Application Example 1"

[0918] (Claim 1)

[0919] Information acquisition device and

[0920] Location data acquisition device,

[0921] Evacuation route generation device,

[0922] Notification generation device,

[0923] Educational information provision device,

[0924] Multilingual support devices and

[0925] A device that automatically generates optimized evacuation routes within the occupied area based on information acquired in real time,

[0926] A device that performs language conversion based on user settings,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] The system according to claim 1, which rapidly determines an evacuation route based on time-series information collected from an external source.

[0930] (Claim 3)

[0931] The system according to claim 1, which provides personalized notifications and educational information based on the user's location data and language settings.

[0932] "Example 2 of combining an emotion engine"

[0933] (Claim 1)

[0934] Data collection means,

[0935] Location information acquisition method,

[0936] Means for creating evacuation routes,

[0937] Notification generation means,

[0938] Educational information provision methods,

[0939] Emotion analysis methods,

[0940] Multilingual support methods,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, which generates a rapid and personalized evacuation route based on external information acquired in real time and the user's emotional state.

[0944] (Claim 3)

[0945] The system according to claim 1, which provides personalized notifications and educational information according to the user's location, language settings, and emotional state.

[0946] "Application example 2 when combining with an emotional engine"

[0947] (Claim 1)

[0948] Data acquisition method,

[0949] Location information acquisition method,

[0950] Evacuation route generation method,

[0951] Notification generation means,

[0952] Means of providing educational content,

[0953] Multilingual support methods,

[0954] A means of determining a user's emotional state using an emotion analysis engine,

[0955] Means of providing individualized evacuation orders and reassuring information,

[0956] A system that includes this.

[0957] (Claim 2)

[0958] The system according to claim 1, which quickly adjusts and provides evacuation routes based on external information collected in real time and the emotional state of the user.

[0959] (Claim 3)

[0960] The system according to claim 1, which provides personalized notifications and educational content according to the user's location information and emotional state. [Explanation of symbols]

[0961] 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. An information acquisition device that collects information in real time from an external data supply organization, A location data acquisition device that uses the GPS function of a device to obtain location information in order to determine the user's current geographical location, An evacuation route generation device that calculates the optimal evacuation route for users based on acquired location data and real-time external information, A notification generation device that generates and sends personalized alarms and information based on the user's location information and settings, An educational information provision device that provides interactive educational content to raise users' disaster preparedness awareness during normal times, A multilingual device that provides notifications and educational information in multiple languages ​​based on the user's language settings, A device that automatically generates optimized evacuation routes within the occupied area based on information acquired in real time, A device that performs language conversion based on user settings, A system that includes this.

2. The system according to claim 1, which rapidly determines an evacuation route based on time-series information collected from an external source.

3. The system according to claim 1, which provides personalized notifications and educational information based on the user's location data and language settings.

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