system

Abstract

We provide the system. [Solution] Means for obtaining the user's location information, A means of obtaining attribute information such as the user's language settings, Means for collecting disaster information in real time, A means for generating alert messages in an appropriate language based on the user's location and attribute information, A means of notifying the user of the generated alert message, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the event of a disaster, it is very important to provide prompt and accurate information. However, especially in information transmission to foreign language speakers and people with disabilities, the language barrier poses a major obstacle. As a result, there are problems in making appropriate evacuations and safety measures during disasters difficult. In addition, there is a problem that the effect of evacuation behavior is not fully exerted because individual alerts and guidance based on the current location and attribute information of individual users are not provided.

Means for Solving the Problems

[0005] This invention provides means for acquiring user location information and attribute information such as user language settings, and includes means for generating alert messages in an appropriate language based on the user's location and attribute information by collecting disaster information in real time. Furthermore, by constructing a system that includes means for notifying the user of the generated alert messages, it becomes possible to overcome language barriers and quickly provide appropriate disaster information and action guidelines to individual users. As a result, evacuation actions during disasters can be facilitated and user safety can be ensured.

[0006] A "user" is an individual or group that utilizes the system and is a recipient of the information and services provided by the system.

[0007] "Location information" refers to geographical data of the user's current location, and is obtained through GPS or IP address.

[0008] "Language settings" refer to the user's choice of language to understand information and serve as the basis for the system's provision of multilingual content.

[0009] "Attribute information" refers to specific data about a user, including individual characteristics such as language settings, age, and special needs.

[0010] "Disaster information" refers to real-time data on natural disasters and emergencies, provided in the form of warnings and forecasts, and used to encourage users to take safe actions.

[0011] An "alert message" is a message used to notify users of important information during a disaster, and is generated based on location information and attribute information.

[0012] "Notification" refers to a means of informing a user of information, and is the act of transmitting alert messages or additional information to the user through their device. [Brief explanation of the drawing]

[0013] [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]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0017] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a storage with a reference numeral 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.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a system that provides multilingual disaster information based on a user's location and attribute information, and the configuration for implementing this system is described below. Basically, it acquires, processes, and distributes information using a server, user terminals, and a communication network.

[0035] The server receives location and attribute information from the user's terminal. This information includes the user's current geographical coordinates, the user's selected language settings, and other attribute information. Based on this information, the server collects real-time publicly available disaster information from external information providers and records it in a database.

[0036] The user's device can transmit location and attribute information to the server through communication. The device also has the capability to receive multilingual alert messages sent from the server, notifying the user via pop-ups or alarms. This allows the user to take quick and appropriate action to ensure their own safety.

[0037] The server analyzes the collected disaster information and generates appropriate alert messages based on the user's current location and language settings. These messages are translated and created in multiple languages ​​by a generation AI, ensuring they are sent in a format easily understood by the user. For example, if a user is in a coastal area of ​​Japan and their language setting is English, the server will generate and send the message "Tsunami Alert: Immediate evacuation to safe area advised." to their device when a tsunami warning is issued.

[0038] Furthermore, the server updates information provided to users and offers additional safety guidance in response to changes in the disaster situation. It can also send necessary follow-up messages to users who are evacuating. This addresses the diverse needs of users during a disaster and supports safe and rapid evacuation.

[0039] Thus, the present invention provides a system that enables the provision of disaster information in multiple languages ​​tailored to the individual circumstances of each user, thereby addressing the problem of language barriers in particular and ensuring that all users can obtain accurate information.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device obtains the user's current location using GPS or other location-based technologies. This location information is used to determine if the user is in an area potentially affected by a disaster.

[0043] Step 2:

[0044] The device retrieves the user's language settings and individual attribute information from within the application and sends it to the server. This allows the server to recognize which language the user prefers to receive information in, enabling optimized message delivery.

[0045] Step 3:

[0046] The server collects disaster information in real time from weather information providers and disaster response organizations via public APIs. This information includes earthquake occurrences, tsunami warnings, and typhoon paths.

[0047] Step 4:

[0048] The server analyzes the collected disaster information and compares it with each user's location information. This analysis allows the server to determine whether a user is likely to be affected by the disaster.

[0049] Step 5:

[0050] The server assesses the need for warnings or evacuation instructions for users and uses AI to generate multilingual alert messages. These messages are translated based on the user's language settings and created in the most effective format.

[0051] Step 6:

[0052] The server prepares the generated alert message and quickly sends it to the relevant user's device. Often, notifications are configured to prompt users with high priority.

[0053] Step 7:

[0054] The device instantly notifies the user of any alert messages it receives. Notifications can be visual and audible, allowing the user to quickly grasp the information.

[0055] Step 8:

[0056] The server continuously monitors changes in disaster information and provides follow-up information to users as needed. This includes safety checks after evacuation is complete and further action instructions.

[0057] (Example 1)

[0058] 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."

[0059] During disasters, a lack of information and language barriers can prevent safety information from reaching users in a timely manner. In particular, there is a need to provide accurate and rapid information to users who speak different languages, but conventional systems struggle to achieve this efficiently.

[0060] 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.

[0061] In this invention, the server includes means for acquiring the user's spatial information, means for acquiring attribute information such as the user's language settings, and means for collecting disaster information in real time. This makes it possible to quickly provide disaster information in multiple languages ​​tailored to the individual circumstances of each user.

[0062] "User spatial information" refers to geographical coordinates and location data that indicate the user's current location.

[0063] "Attribute information" refers to information that indicates a user's personal settings, language choices, and other identifiable characteristics.

[0064] "Disaster information" refers to real-time information provided regarding natural disasters and dangerous events.

[0065] "External providers" refer to public institutions and private information services that provide disaster information and related data.

[0066] A "data set" refers to a collection of information organized for the purpose of accumulating disaster information and user information.

[0067] "Multilingual alert messages" refer to warning messages that are translated according to the user's language settings.

[0068] A "generative AI model" refers to a program that uses artificial intelligence to process data and assist in tasks such as language translation and message generation.

[0069] "Information equipment" refers to computers and servers used for processing and communicating disaster information.

[0070] A "terminal" refers to a device that a user directly operates and uses to communicate with information devices.

[0071] This invention provides a system for providing disaster information in multiple languages ​​using an information device and a terminal. The information device functions as a server, collecting disaster information in real time and using a generative AI model for analysis and multilingual translation. The terminal transmits the user's location and attribute information to the server, receives alert messages provided by the server, and notifies the user.

[0072] The server uses a high-precision GPS module to measure location information and stores the obtained spatial information in a database. It also periodically collects information from external providers of disaster information via APIs. Then, it uses a generative AI model to process the disaster information in real time and generates multilingual alert messages according to the user's language settings.

[0073] The device acquires attribute information such as location and language settings via the user interface and sends it to the server. Alert messages received from the server are notified to the user as pop-ups or audio alarms, prompting them to take immediate action.

[0074] For example, if a user is in a coastal area, the device will tell the server its current location. The server will assess the risk of disaster and, if necessary, generate a message such as "Tsunami Alert: Immediate evacuation to safe area advised," which is an English translation of any issued tsunami warning, and send it to the device.

[0075] A concrete example of a prompt message is: "The user's current location is 34.6937° N, 135.5023° E, and the language setting is Japanese. Please provide tsunami warning information." When this prompt is sent to the server, an appropriate message is generated and delivered to the user quickly.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user terminal obtains location information from GPS and the user's language setting from the terminal's settings. The input consists of the current location coordinates obtained from the GPS device and the user's selected language setting. This information is combined and sent to the server as a data packet. The output is a data packet containing the user's spatial information and attribute information, which is sent to the server.

[0079] Step 2:

[0080] The server receives location and attribute information transmitted from the user's terminal. The input is data packets from the user's terminal. Based on this, the server records the user's current location and language in the database. The output is the location and attribute information recorded for each user.

[0081] Step 3:

[0082] The server collects real-time disaster information through APIs from external disaster information providers. The input is disaster information obtained from external providers. This information is stored in a database for subsequent analysis. The output is the latest disaster information recorded in the server's database.

[0083] Step 4:

[0084] The server generates appropriate alert messages based on collected disaster information and user location information. Inputs include disaster information, user location information, and attribute information. A generative AI model analyzes the data and translates the alerts into each user's language, creating multilingual messages. The output is the alert message translated into the user's language.

[0085] Step 5:

[0086] The server sends the generated alert message to the user's terminal. The input is the generated multilingual alert message. The message is sent to the terminal as a push notification, ensuring it reaches the user quickly. The output is the alert message sent to the user's terminal.

[0087] Step 6:

[0088] The user terminal receives alert messages from the server and notifies the user via pop-ups and audio alarms. The input is the alert message sent from the server. The terminal displays the message content to alert the user. The output is the alert information the user receives visually and aurally.

[0089] Step 7:

[0090] The server continuously monitors changes in disaster information and generates follow-up messages for users as needed. The input is the latest disaster information, which is continuously acquired. Based on this, additional guidance is created in multiple languages ​​and sent to users. The output is follow-up messages containing ongoing instructions.

[0091] (Application Example 1)

[0092] 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."

[0093] The goal is to solve the problems that autonomous vehicles face when accurately and quickly recognizing disasters during operation and selecting safe and optimal routes. In particular, there is a need for real-time, multilingual support for disaster information and to ensure the safety and convenience of the vehicles.

[0094] 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.

[0095] In this invention, the server includes means for acquiring the user's location information, means for acquiring attribute information such as the user's language settings, and means for collecting disaster information in real time. This makes it possible to notify the autonomous vehicle of disaster information in the relevant language before it enters a dangerous area, and to also suggest an alternative route.

[0096] "User location information" refers to data that indicates the geographical coordinates where the user is currently located.

[0097] "User language settings" refer to settings that indicate the user's preferred language.

[0098] "Attribute information" refers to specific data about a user, including information such as language settings.

[0099] "Disaster information" refers to data that shows the situation regarding natural disasters and unexpected events.

[0100] "Means of real-time collection" refers to methods for instantly obtaining current situations and information.

[0101] An "alert message" is a notification sent to a user to warn or alert them.

[0102] An "autonomous vehicle" is a vehicle that can operate independently without requiring human intervention.

[0103] "Operational information" refers to data about the route and speed of a vehicle as it moves.

[0104] A "hazardous area" is an area where a disaster or other danger is occurring or is highly likely to occur.

[0105] An "alternative route" is a recommended alternative travel route to avoid dangerous areas.

[0106] The system for implementing this invention consists of a server, a user terminal, and an autonomous vehicle. The server receives the user's location and attribute information, and based on this information, collects disaster information in real time from external information providers. The server also generates multilingual alert messages using a generative AI model and sends them to the user terminal.

[0107] The user terminal has the function of sending location information and attribute information to the server and notifies the user of received alert messages. The autonomous vehicle constantly monitors operational information and adjusts its route based on the information from the server.

[0108] Specifically, the servers utilize cloud infrastructure (e.g., AWS®, Google® Cloud) and leverage generative AI models (e.g., machine translation APIs) to deliver instantly translated messages to users. The hardware involves acquiring real-time location information using GPS-equipped devices and sending and receiving data via a communication network.

[0109] For example, suppose a user is riding in an autonomous vehicle when heavy rain causes flooding. The server generates a message in the user's preferred language saying "Heavy Rain Alert: Switch to alternative route via Route 246" and sends it to the vehicle's system. An example of a prompt for the generating AI model would be "Translate 'Heavy Rain Alert: Switch to alternative route via Route 246' to user's preferred language."

[0110] Thus, the present invention provides a system that supports the safe operation of autonomous vehicles and helps users take quick and appropriate action in the event of a disaster.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server receives location and attribute information from the user's terminal. It takes the user's current geographical coordinates and attribute information, including language settings, as input and outputs it as user information stored in the database. Based on this information, the user can be identified.

[0114] Step 2:

[0115] The server obtains real-time disaster information from external information providers based on the user's location. The input is the user's geographical coordinates, which are used to collect relevant disaster information. The output provides real-time disaster information for the user's current location.

[0116] Step 3:

[0117] The server analyzes the collected disaster information and uses a generative AI model to generate alert messages appropriate to the user's language. The input consists of disaster information and the user's language settings, and the output is a multilingual alert message. This process involves the use of a machine translation API. Specifically, the prompt "Translate 'Disaster Details' to user's preferred language." is input to the generative AI model.

[0118] Step 4:

[0119] The server sends the generated alert message to the user's terminal. The input here is a multilingual alert message, and the output is the alert displayed on the user's terminal. The user's terminal notifies the user of this message and takes specific actions to inform the user, such as a pop-up or an audible alarm.

[0120] Step 5:

[0121] If the user terminal is connected to the autonomous vehicle, the server will provide further operational information and suggest alternative routes before entering a hazardous area. The inputs at this time are disaster information and the vehicle's current location, and the output is the recommended alternative route. The autonomous vehicle will then automatically decide whether to select the suggested route.

[0122] 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.

[0123] This invention provides a system that offers customized disaster information based on the user's emotional state, in addition to the user's location information, language settings, and other attribute information, during a disaster. To implement this, a method for operating a server, user terminal, communication network, and emotion recognition engine in combination is described.

[0124] The user terminal obtains the user's current location and registers the user's language settings and other attribute information. This information is sent to the server and recorded in the database as an individual user profile. Furthermore, an emotion recognition engine installed on the terminal determines the user's emotional state through voice analysis and input data, and sends the results to the server.

[0125] The server collaborates with external disaster information providers to collect disaster information in real time. Based on this information, it references the user's profile and uses generative AI to create alert messages in appropriate language. In this process, it utilizes emotional data obtained from the emotion engine to adjust the intensity of the information and the tone of the message that the user needs. For example, if the server determines that the user is in a high-stress state, it will generate a message that is calmer and includes more specific advice.

[0126] As a concrete example, consider a user who lives in a high-rise building in an urban area and prefers to speak Japanese. Suppose this user is perceived as "anxious," and an earthquake occurs in real time. The server aggregates this information and generates a specific and reassuring message, such as "Please stay calm. Proceed calmly towards the nearest emergency exit," and sends it to the user's device.

[0127] Through the above process, this system provides appropriate alerts and informational guidance tailored to each user's linguistic needs and emotional state when delivering disaster information. This creates an environment where users can deal with disasters more safely and with greater peace of mind.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The device uses GPS functionality to obtain the user's current location. This allows the system to identify the user's area and prepare to provide area-specific disaster information.

[0131] Step 2:

[0132] The device retrieves the user's language settings and other attribute information from the application settings and sends this information to the server. This allows the server to prepare messages in the user's appropriate language and format.

[0133] Step 3:

[0134] The emotion recognition engine on the device analyzes the user's emotions based on their voice or text input. This analysis result is sent to a server to understand the user's current emotional state.

[0135] Step 4:

[0136] The server stores disaster information obtained in real time from external disaster information providers in a database and matches it with the user's location information. This procedure identifies disasters that the user may be affected by.

[0137] Step 5:

[0138] The server uses a generation AI to generate the most appropriate alert message based on the user's location, language settings, and emotional state. The content of the generated message is adjusted according to the user's emotional state; for example, if the user is stressed, gentler language is used to encourage a calm response.

[0139] Step 6:

[0140] The server sends the generated alert message to the user's device. This message is delivered to the user in real time via push notifications, email, etc.

[0141] Step 7:

[0142] The device instantly displays received messages and notifies the user visually and audibly. This allows users to quickly receive disaster information.

[0143] Step 8:

[0144] The server continuously monitors the disaster situation and the user's emotional state, providing follow-up messages and further safety information as needed. This ensures user confidence and promotes optimal evacuation actions.

[0145] (Example 2)

[0146] 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".

[0147] During disasters, users need timely and appropriate disaster information, but providing customized information to individual users is difficult. Therefore, there is a need to receive information in a language that users can easily understand and to support responses that are appropriate to their psychological state at the time.

[0148] 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.

[0149] In this invention, the server includes means for acquiring user location data, means for acquiring user language setting information, and means for analyzing user voice and input data to determine emotional state. This makes it possible to provide appropriate and reassuring disaster information to individual users.

[0150] "Location data" refers to information that indicates the user's current geographical location.

[0151] "Language settings information" refers to the setting that allows users to specify their preferred language for displaying information.

[0152] "Disaster data" refers to various types of information about natural disasters that are collected in real time.

[0153] "Emotional state" refers to the emotional state of the user, analyzed based on their voice and input data.

[0154] A "generative model" is an artificial intelligence model that generates messages based on the user's profile and emotional state.

[0155] An "alert message" is a warning or guidance message sent to a user regarding a disaster.

[0156] "Notification means" refers to the methods or technologies used to inform the user of a generated message.

[0157] This system operates by combining servers, terminals, a communication network, and an emotion recognition engine to provide users with appropriate information during disasters. User terminals collect information such as the user's current location, language settings, and emotional state. Specifically, they use GPS installed in the terminal and language information from user settings. The terminals also incorporate an emotion recognition engine that analyzes the user's voice and text to determine their emotional state, thereby detecting the user's mental state.

[0158] Information collected by the device is sent to the server in real time. The server collaborates with external disaster information services to obtain necessary disaster data. Next, a generative AI model is used to create customized alert messages that are tailored to the user's profile and real-time emotional state. This process generates prompts for inputting the collected data into the generative AI model, using a format such as "User: City center, Japanese, anxious. Disaster: Earthquake. Please create an appropriate message."

[0159] As a concrete example, if a user who prefers Japanese and lives in a high-rise building in an urban area is detected as being anxious during an earthquake, the server will generate a message saying, "Please stay calm. Proceed calmly towards the nearest emergency exit," and send a push notification to their device. In this way, users can receive information optimized for their individual circumstances, enabling them to take safer actions.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The device collects the user's location data and attribute information. Specifically, it uses a GPS module to obtain location data and retrieves language information from the device's settings. This information is then sent to the server. The input consists of sensors and configuration information built into the device, while the output is the transmission of location data and attribute information to the server.

[0163] Step 2:

[0164] The device's emotion recognition engine determines the user's emotional state. It analyzes the user's voice input and text messages to identify emotions and sends that data to the server. The input is the user's voice or text, and the output is emotional state data. Specifically, it performs tone analysis and keyword analysis using speech recognition software.

[0165] Step 3:

[0166] The server retrieves disaster data from external disaster information services. The server communicates with these services using APIs to receive real-time earthquake and weather information. Input is data obtained from disaster information APIs, and output is detailed information about the disaster. The specific operation involves information collection through periodic data requests.

[0167] Step 4:

[0168] The server generates prompt text based on the user's profile and emotional state, and inputs it into the generative AI model. For example, it might create a prompt such as "User: Urban area, Japanese, Anxious. Disaster: Earthquake. Please create an appropriate message." Using this prompt, the generative AI model generates a customized alert message. The input is the user profile, emotional state, and disaster information, and the output is the generated alert message.

[0169] Step 5:

[0170] The server generates an alert message and sends it to the terminal, which then notifies the user. Typically, the HTTPS protocol is used to send the message to the terminal, which then notifies the user of this message via a pop-up notification or audio. The input is the message from the server, and the output is the alert message that is notified to the user. Specifically, the terminal receives the message and displays it on the screen.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0173] In recent years, it has become necessary to overcome the communication barriers faced by users with diverse languages ​​and cultures when receiving disaster information. However, general disaster information provision methods often provide uniform information without considering the user's emotional state or individual attributes, thus creating a barrier to users taking appropriate action. Furthermore, the insufficient provision of information tailored to emotional states may lead to delays in understanding and action, especially for users experiencing high levels of stress and anxiety. Therefore, it is necessary to develop a system that provides customized disaster information tailored to the user's emotional state in real time.

[0174] 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.

[0175] In this invention, the server includes a device for acquiring the user's location information, a device for acquiring characteristic information such as the user's language settings, a device for collecting disaster information in real time, and a device for adjusting the content and tone of notification messages generated using an emotion recognition engine that analyzes the user's emotional state. As a result, personalized evacuation guidance and safety information tailored to the user's emotional state are provided to the user in real time, enabling quick and appropriate decision-making and action during a disaster.

[0176] "User location information" refers to data indicating the user's current geographical location, which is obtained using GPS or other location-determining technologies.

[0177] "User language setting and other characteristic information" refers to data that includes individual user language preferences and personal attributes, and is used to deliver information in a way that is easy for users to understand.

[0178] A "device for collecting disaster information in real time" is a device that instantly collects the latest data related to disasters such as earthquakes and typhoons.

[0179] A "notification message" is a message generated to convey disaster information and warnings to the user, and its content is adjusted to suit the user's characteristics and emotions.

[0180] An "emotion recognition engine" is an engine comprised of technologies for analyzing a user's emotional state, using voice analysis and input data to identify the user's emotions.

[0181] A "network device" is a device used for data communication with the outside world, enabling information exchange with a server.

[0182] In order to implement this invention, it is necessary to construct a system that combines a user terminal, a server, a communication network, and an emotion recognition engine.

[0183] The user terminal is equipped with GPS to acquire the user's location information. The terminal also has the ability to record language settings and other characteristic information and send this information to the server. Utilizing the microphone function, the user's emotional state is analyzed through an emotion recognition engine. The emotion recognition engine is implemented using voice analysis technology, identifying emotions from the user's voice input and sending the results to the server.

[0184] The server acquires disaster information in real time from external disaster information providers. Then, using a generative AI model based on each user's profile information and emotional data, it generates notification messages tailored to the user. The content and tone of these messages are adjusted according to the user's emotional state. For example, if the system determines that the user is in a high-stress state, it will include specific advice in a calmer tone.

[0185] Notification messages generated from the server are delivered to user terminals via the network. This allows users to receive disaster information tailored to their individual circumstances and obtain concrete guidelines for calmly responding to the situation.

[0186] As a concrete example, consider a scenario where a user living in an urban area receives typhoon evacuation information. If the user is perceived as feeling anxious, the generated message will be something like, "Please rest assured, act calmly when moving to a safe place," to help them stay calm. An example of a prompt to the generating AI model would be, "The user is currently at location information. The currently confirmed emotional state is emotional. Based on the latest disaster information, generate a safe and calm message for the user."

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The device obtains the user's location information using GPS. This location information is output as coordinate data in digital format. The device obtains location information as input data and prepares to send it to the server.

[0190] Step 2:

[0191] The device retrieves the user's language settings and characteristics from its device settings. This information is used as input to create a dataset for transmission to the server. This dataset includes the user's preferred language and other individual characteristics.

[0192] Step 3:

[0193] The device uses a microphone to collect the user's voice and sends that voice data to an emotion recognition engine. The emotion recognition engine analyzes the voice data and performs a process to determine the user's emotional state. As output, the user's emotional state is identified and sent from the device to the server.

[0194] Step 4:

[0195] The server collects disaster information in real time from external disaster information providers. The input disaster information is processed immediately, its urgency is assessed, and its priority is determined for output.

[0196] Step 5:

[0197] The server uses a generative AI model to create prompt messages based on location information, language settings, characteristics, and emotional states received from the user. Using these prompt messages as input, it generates notification messages tailored to the user. The generated notification messages are tone-adjusted to ensure the user fully understands their content and can take appropriate action.

[0198] Step 6:

[0199] The server sends the generated notification message to the terminal via the communication network. Upon receiving the generated message as input data, the terminal prepares to display it and notifies the user visually or audibly.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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".

[0216] This invention relates to a system that provides multilingual disaster information based on a user's location and attribute information, and the configuration for implementing this system is described below. Basically, it acquires, processes, and distributes information using a server, user terminals, and a communication network.

[0217] The server receives location and attribute information from the user's terminal. This information includes the user's current geographical coordinates, the user's selected language settings, and other attribute information. Based on this information, the server collects real-time publicly available disaster information from external information providers and records it in a database.

[0218] The user's device can transmit location and attribute information to the server through communication. The device also has the capability to receive multilingual alert messages sent from the server, notifying the user via pop-ups or alarms. This allows the user to take quick and appropriate action to ensure their own safety.

[0219] The server analyzes the collected disaster information and generates appropriate alert messages based on the user's current location and language settings. These messages are translated and created in multiple languages ​​by a generation AI, ensuring they are sent in a format easily understood by the user. For example, if a user is in a coastal area of ​​Japan and their language setting is English, the server will generate and send the message "Tsunami Alert: Immediate evacuation to safe area advised." to their device when a tsunami warning is issued.

[0220] Furthermore, the server updates information provided to users and offers additional safety guidance in response to changes in the disaster situation. It can also send necessary follow-up messages to users who are evacuating. This addresses the diverse needs of users during a disaster and supports safe and rapid evacuation.

[0221] Thus, the present invention provides a system that enables the provision of disaster information in multiple languages ​​tailored to the individual circumstances of each user, thereby addressing the problem of language barriers in particular and ensuring that all users can obtain accurate information.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The device obtains the user's current location using GPS or other location-based technologies. This location information is used to determine if the user is in an area potentially affected by a disaster.

[0225] Step 2:

[0226] The device retrieves the user's language settings and individual attribute information from within the application and sends it to the server. This allows the server to recognize which language the user prefers to receive information in, enabling optimized message delivery.

[0227] Step 3:

[0228] The server collects disaster information in real time from weather information providers and disaster response organizations via public APIs. This information includes earthquake occurrences, tsunami warnings, and typhoon paths.

[0229] Step 4:

[0230] The server analyzes the collected disaster information and compares it with each user's location information. This analysis allows the server to determine whether a user is likely to be affected by the disaster.

[0231] Step 5:

[0232] The server assesses the need for warnings or evacuation instructions for users and uses AI to generate multilingual alert messages. These messages are translated based on the user's language settings and created in the most effective format.

[0233] Step 6:

[0234] The server prepares the generated alert message and quickly sends it to the relevant user's device. Often, notifications are configured to prompt users with high priority.

[0235] Step 7:

[0236] The device instantly notifies the user of any alert messages it receives. Notifications can be visual and audible, allowing the user to quickly grasp the information.

[0237] Step 8:

[0238] The server continuously monitors changes in disaster information and provides follow-up information to users as needed. This includes safety checks after evacuation is complete and further action instructions.

[0239] (Example 1)

[0240] 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."

[0241] During disasters, a lack of information and language barriers can prevent safety information from reaching users in a timely manner. In particular, there is a need to provide accurate and rapid information to users who speak different languages, but conventional systems struggle to achieve this efficiently.

[0242] 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.

[0243] In this invention, the server includes means for acquiring the user's spatial information, means for acquiring attribute information such as the user's language settings, and means for collecting disaster information in real time. This makes it possible to quickly provide disaster information in multiple languages ​​tailored to the individual circumstances of each user.

[0244] "User spatial information" refers to geographical coordinates and location data that indicate the user's current location.

[0245] "Attribute information" refers to information that indicates a user's personal settings, language choices, and other identifiable characteristics.

[0246] "Disaster information" refers to real-time information provided regarding natural disasters and dangerous events.

[0247] "External providers" refer to public institutions and private information services that provide disaster information and related data.

[0248] A "data set" refers to a collection of information organized for the purpose of accumulating disaster information and user information.

[0249] "Multilingual alert messages" refer to warning messages that are translated according to the user's language settings.

[0250] A "generative AI model" refers to a program that uses artificial intelligence to process data and assist in tasks such as language translation and message generation.

[0251] "Information equipment" refers to computers and servers used for processing and communicating disaster information.

[0252] A "terminal" refers to a device that a user directly operates and uses to communicate with information devices.

[0253] This invention provides a system for providing disaster information in multiple languages ​​using an information device and a terminal. The information device functions as a server, collecting disaster information in real time and using a generative AI model for analysis and multilingual translation. The terminal transmits the user's location and attribute information to the server, receives alert messages provided by the server, and notifies the user.

[0254] The server uses a high-precision GPS module to measure location information and stores the obtained spatial information in a database. It also periodically collects information from external providers of disaster information via APIs. Then, it uses a generative AI model to process the disaster information in real time and generates multilingual alert messages according to the user's language settings.

[0255] The device acquires attribute information such as location and language settings via the user interface and sends it to the server. Alert messages received from the server are notified to the user as pop-ups or audio alarms, prompting them to take immediate action.

[0256] For example, if a user is in a coastal area, the device will tell the server its current location. The server will assess the risk of disaster and, if necessary, generate a message such as "Tsunami Alert: Immediate evacuation to safe area advised," which is an English translation of any issued tsunami warning, and send it to the device.

[0257] A concrete example of a prompt message is: "The user's current location is 34.6937° N, 135.5023° E, and the language setting is Japanese. Please provide tsunami warning information." When this prompt is sent to the server, an appropriate message is generated and delivered to the user quickly.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The user terminal obtains location information from GPS and the user's language setting from the terminal's settings. The input consists of the current location coordinates obtained from the GPS device and the user's selected language setting. This information is combined and sent to the server as a data packet. The output is a data packet containing the user's spatial information and attribute information, which is sent to the server.

[0261] Step 2:

[0262] The server receives location and attribute information transmitted from the user's terminal. The input is data packets from the user's terminal. Based on this, the server records the user's current location and language in the database. The output is the location and attribute information recorded for each user.

[0263] Step 3:

[0264] The server collects real-time disaster information through APIs from external disaster information providers. The input is disaster information obtained from external providers. This information is stored in a database for subsequent analysis. The output is the latest disaster information recorded in the server's database.

[0265] Step 4:

[0266] The server generates appropriate alert messages based on collected disaster information and user location information. Inputs include disaster information, user location information, and attribute information. A generative AI model analyzes the data and translates the alerts into each user's language, creating multilingual messages. The output is the alert message translated into the user's language.

[0267] Step 5:

[0268] The server sends the generated alert message to the user's terminal. The input is the generated multilingual alert message. The message is sent to the terminal as a push notification, ensuring it reaches the user quickly. The output is the alert message sent to the user's terminal.

[0269] Step 6:

[0270] The user terminal receives alert messages from the server and notifies the user via pop-ups and audio alarms. The input is the alert message sent from the server. The terminal displays the message content to alert the user. The output is the alert information the user receives visually and aurally.

[0271] Step 7:

[0272] The server continuously monitors changes in disaster information and generates follow-up messages for users as needed. The input is the latest disaster information, which is continuously acquired. Based on this, additional guidance is created in multiple languages ​​and sent to users. The output is follow-up messages containing ongoing instructions.

[0273] (Application Example 1)

[0274] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] The goal is to solve the problems that autonomous vehicles face when accurately and quickly recognizing disasters during operation and selecting safe and optimal routes. In particular, there is a need for real-time, multilingual support for disaster information and to ensure the safety and convenience of the vehicles.

[0276] 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.

[0277] In this invention, the server includes means for acquiring the user's location information, means for acquiring attribute information such as the user's language settings, and means for collecting disaster information in real time. This makes it possible to notify the autonomous vehicle of disaster information in the relevant language before it enters a dangerous area, and to also suggest an alternative route.

[0278] "User location information" refers to data that indicates the geographical coordinates where the user is currently located.

[0279] "User language settings" refer to settings that indicate the user's preferred language.

[0280] "Attribute information" refers to specific data about a user, including information such as language settings.

[0281] "Disaster information" refers to data that shows the situation regarding natural disasters and unexpected events.

[0282] The "means for real-time collection" is a method for immediately obtaining the current situation and information.

[0283] An "alert message" is a notification sent to prompt a warning or attention to the user.

[0284] An "autonomous vehicle" is a vehicle that can operate independently without the need for human driving.

[0285] "Operation information" is data related to the route and speed when the vehicle is moving.

[0286] A "dangerous area" is an area where a disaster or other danger is occurring or is likely to occur.

[0287] An "alternative route" is another recommended moving route to avoid a dangerous area.

[0288] The system for implementing this invention consists of a server, a user terminal, and an autonomous vehicle. The server receives the user's location information and attribute information, and based on this information, collects disaster information in real time from external information providers. The server also uses a generative AI model to generate multi-language alert messages and sends them to the user terminal.

[0289] The user terminal has a function of sending location information and attribute information to the server, and notifies the user of the received alert message. The autonomous vehicle constantly monitors the operation information and adjusts the operation route based on the information from the server.

[0290] Specifically, the server uses cloud infrastructure (e.g., AWS, Google Cloud) and utilizes a generative AI model (e.g., machine translation API) to deliver the immediately translated message to the user. As hardware, it uses a GPS-equipped device to obtain real-time location information and transmits and receives data through a communication network.

[0291] For example, suppose a user is riding in an autonomous vehicle when heavy rain causes flooding. The server generates a message in the user's preferred language saying "Heavy Rain Alert: Switch to alternative route via Route 246" and sends it to the vehicle's system. An example of a prompt for the generating AI model would be "Translate 'Heavy Rain Alert: Switch to alternative route via Route 246' to user's preferred language."

[0292] Thus, the present invention provides a system that supports the safe operation of autonomous vehicles and helps users take quick and appropriate action in the event of a disaster.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The server receives location and attribute information from the user's terminal. It takes the user's current geographical coordinates and attribute information, including language settings, as input and outputs it as user information stored in the database. Based on this information, the user can be identified.

[0296] Step 2:

[0297] The server obtains real-time disaster information from external information providers based on the user's location. The input is the user's geographical coordinates, which are used to collect relevant disaster information. The output provides real-time disaster information for the user's current location.

[0298] Step 3:

[0299] The server analyzes the collected disaster information and uses a generative AI model to generate alert messages appropriate to the user's language. The input consists of disaster information and the user's language settings, and the output is a multilingual alert message. This process involves the use of a machine translation API. Specifically, the prompt "Translate 'Disaster Details' to user's preferred language." is input to the generative AI model.

[0300] Step 4:

[0301] The server sends the generated alert message to the user's terminal. The input here is a multilingual alert message, and the output is the alert displayed on the user's terminal. The user's terminal notifies the user of this message and takes specific actions to inform the user, such as a pop-up or an audible alarm.

[0302] Step 5:

[0303] If the user terminal is connected to the autonomous vehicle, the server will provide further operational information and suggest alternative routes before entering a hazardous area. The inputs at this time are disaster information and the vehicle's current location, and the output is the recommended alternative route. The autonomous vehicle will then automatically decide whether to select the suggested route.

[0304] 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.

[0305] This invention provides a system that offers customized disaster information based on the user's emotional state, in addition to the user's location information, language settings, and other attribute information, during a disaster. To implement this, a method for operating a server, user terminal, communication network, and emotion recognition engine in combination is described.

[0306] The user terminal acquires the user's current location and also registers the user's language settings and other attribute information. This information is sent to the server and recorded in the database as an individual user profile. Furthermore, an emotion recognition engine installed on the terminal determines the user's emotional state through voice analysis and input data, and sends the result to the server.

[0307] The server collaborates with external disaster information providers to collect disaster information in real time. Based on this information, the process of referring to the user's profile and creating an alert message in an appropriate language using a generative AI proceeds. In this process, the emotional data obtained from the emotion engine is utilized to adjust the intensity of the information required by the user and the tone of the message. For example, if the user is determined to be in a high-stress state, the server generates a message that includes more gentle and specific advice.

[0308] As a specific example, consider the case where a user lives in a high-rise building in the city and prefers Japanese. Suppose an earthquake occurs in real time when this user is recognized as "anxious". The server aggregates this information and generates a specific and reassuring message such as "Please stay calm. Act calmly towards the nearest emergency exit." and sends it to the terminal.

[0309] Through the above process, when the system provides disaster information, it realizes appropriate alerts and information guidance according to the individual language needs of users and their individual emotional states. This provides an environment in which users can cope with disasters more safely and with peace of mind.

[0310] The following describes the processing flow.

[0311] Step 1:

[0312] The terminal acquires the user's current location using the GPS function. This identifies which area the user is in and prepares to provide region-specific disaster information.

[0313] Step 2:

[0314] The device retrieves the user's language settings and other attribute information from the application settings and sends this information to the server. This allows the server to prepare messages in the user's appropriate language and format.

[0315] Step 3:

[0316] The emotion recognition engine on the device analyzes the user's emotions based on their voice or text input. This analysis result is sent to a server to understand the user's current emotional state.

[0317] Step 4:

[0318] The server stores disaster information obtained in real time from external disaster information providers in a database and matches it with the user's location information. This procedure identifies disasters that the user may be affected by.

[0319] Step 5:

[0320] The server uses a generation AI to generate the most appropriate alert message based on the user's location, language settings, and emotional state. The content of the generated message is adjusted according to the user's emotional state; for example, if the user is stressed, gentler language is used to encourage a calm response.

[0321] Step 6:

[0322] The server sends the generated alert message to the user's device. This message is delivered to the user in real time via push notifications, email, etc.

[0323] Step 7:

[0324] The device instantly displays received messages and notifies the user visually and audibly. This allows users to quickly receive disaster information.

[0325] Step 8:

[0326] The server continuously monitors the disaster situation and the user's emotional state, providing follow-up messages and further safety information as needed. This ensures user confidence and promotes optimal evacuation actions.

[0327] (Example 2)

[0328] 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".

[0329] During disasters, users need timely and appropriate disaster information, but providing customized information to individual users is difficult. Therefore, there is a need to receive information in a language that users can easily understand and to support responses that are appropriate to their psychological state at the time.

[0330] 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.

[0331] In this invention, the server includes means for acquiring user location data, means for acquiring user language setting information, and means for analyzing user voice and input data to determine emotional state. This makes it possible to provide appropriate and reassuring disaster information to individual users.

[0332] "Location data" refers to information that indicates the user's current geographical location.

[0333] "Language settings information" refers to the setting that allows users to specify their preferred language for displaying information.

[0334] "Disaster data" refers to various types of information about natural disasters that are collected in real time.

[0335] "Emotional state" refers to the emotional state of the user, analyzed based on their voice and input data.

[0336] A "generative model" is an artificial intelligence model that generates messages based on the user's profile and emotional state.

[0337] An "alert message" is a warning or guidance message sent to a user regarding a disaster.

[0338] "Notification means" refers to the methods or technologies used to inform the user of a generated message.

[0339] This system operates by combining servers, terminals, a communication network, and an emotion recognition engine to provide users with appropriate information during disasters. User terminals collect information such as the user's current location, language settings, and emotional state. Specifically, they use GPS installed in the terminal and language information from user settings. The terminals also incorporate an emotion recognition engine that analyzes the user's voice and text to determine their emotional state, thereby detecting the user's mental state.

[0340] Information collected by the device is sent to the server in real time. The server collaborates with external disaster information services to obtain necessary disaster data. Next, a generative AI model is used to create customized alert messages that are tailored to the user's profile and real-time emotional state. This process generates prompts for inputting the collected data into the generative AI model, using a format such as "User: City center, Japanese, anxious. Disaster: Earthquake. Please create an appropriate message."

[0341] As a concrete example, if a user who prefers Japanese and lives in a high-rise building in an urban area is detected as being anxious during an earthquake, the server will generate a message saying, "Please stay calm. Proceed calmly towards the nearest emergency exit," and send a push notification to their device. In this way, users can receive information optimized for their individual circumstances, enabling them to take safer actions.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] The device collects the user's location data and attribute information. Specifically, it uses a GPS module to obtain location data and retrieves language information from the device's settings. This information is then sent to the server. The input consists of sensors and configuration information built into the device, while the output is the transmission of location data and attribute information to the server.

[0345] Step 2:

[0346] The device's emotion recognition engine determines the user's emotional state. It analyzes the user's voice input and text messages to identify emotions and sends that data to the server. The input is the user's voice or text, and the output is emotional state data. Specifically, it performs tone analysis and keyword analysis using speech recognition software.

[0347] Step 3:

[0348] The server retrieves disaster data from external disaster information services. The server communicates with these services using APIs to receive real-time earthquake and weather information. Input is data obtained from disaster information APIs, and output is detailed information about the disaster. The specific operation involves information collection through periodic data requests.

[0349] Step 4:

[0350] The server generates prompt text based on the user's profile and emotional state, and inputs it into the generative AI model. For example, it might create a prompt such as "User: Urban area, Japanese, Anxious. Disaster: Earthquake. Please create an appropriate message." Using this prompt, the generative AI model generates a customized alert message. The input is the user profile, emotional state, and disaster information, and the output is the generated alert message.

[0351] Step 5:

[0352] The server generates an alert message and sends it to the terminal, which then notifies the user. Typically, the HTTPS protocol is used to send the message to the terminal, which then notifies the user of this message via a pop-up notification or audio. The input is the message from the server, and the output is the alert message that is notified to the user. Specifically, the terminal receives the message and displays it on the screen.

[0353] (Application Example 2)

[0354] 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."

[0355] In recent years, it has become necessary to overcome the communication barriers faced by users with diverse languages ​​and cultures when receiving disaster information. However, general disaster information provision methods often provide uniform information without considering the user's emotional state or individual attributes, thus creating a barrier to users taking appropriate action. Furthermore, the insufficient provision of information tailored to emotional states may lead to delays in understanding and action, especially for users experiencing high levels of stress and anxiety. Therefore, it is necessary to develop a system that provides customized disaster information tailored to the user's emotional state in real time.

[0356] 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.

[0357] In this invention, the server includes a device for acquiring the user's location information, a device for acquiring characteristic information such as the user's language settings, a device for collecting disaster information in real time, and a device for adjusting the content and tone of notification messages generated using an emotion recognition engine that analyzes the user's emotional state. As a result, personalized evacuation guidance and safety information tailored to the user's emotional state are provided to the user in real time, enabling quick and appropriate decision-making and action during a disaster.

[0358] "User location information" refers to data indicating the user's current geographical location, which is obtained using GPS or other location-determining technologies.

[0359] "User language setting and other characteristic information" refers to data that includes individual user language preferences and personal attributes, and is used to deliver information in a way that is easy for users to understand.

[0360] A "device for collecting disaster information in real time" is a device that instantly collects the latest data related to disasters such as earthquakes and typhoons.

[0361] A "notification message" is a message generated to convey disaster information and warnings to the user, and its content is adjusted to suit the user's characteristics and emotions.

[0362] An "emotion recognition engine" is an engine comprised of technologies for analyzing a user's emotional state, using voice analysis and input data to identify the user's emotions.

[0363] A "network device" is a device used for data communication with the outside world, enabling information exchange with a server.

[0364] In order to implement this invention, it is necessary to construct a system that combines a user terminal, a server, a communication network, and an emotion recognition engine.

[0365] The user terminal is equipped with GPS to acquire the user's location information. The terminal also has the ability to record language settings and other characteristic information and send this information to the server. Utilizing the microphone function, the user's emotional state is analyzed through an emotion recognition engine. The emotion recognition engine is implemented using voice analysis technology, identifying emotions from the user's voice input and sending the results to the server.

[0366] The server acquires disaster information in real time from external disaster information providers. Then, using a generative AI model based on each user's profile information and emotional data, it generates notification messages tailored to the user. The content and tone of these messages are adjusted according to the user's emotional state. For example, if the system determines that the user is in a high-stress state, it will include specific advice in a calmer tone.

[0367] Notification messages generated from the server are delivered to user terminals via the network. This allows users to receive disaster information tailored to their individual circumstances and obtain concrete guidelines for calmly responding to the situation.

[0368] As a concrete example, consider a scenario where a user living in an urban area receives typhoon evacuation information. If the user is perceived as feeling anxious, the generated message will be something like, "Please rest assured, act calmly when moving to a safe place," to help them stay calm. An example of a prompt to the generating AI model would be, "The user is currently at location information. The currently confirmed emotional state is emotional. Based on the latest disaster information, generate a safe and calm message for the user."

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The device obtains the user's location information using GPS. This location information is output as coordinate data in digital format. The device obtains location information as input data and prepares to send it to the server.

[0372] Step 2:

[0373] The device retrieves the user's language settings and characteristics from its device settings. This information is used as input to create a dataset for transmission to the server. This dataset includes the user's preferred language and other individual characteristics.

[0374] Step 3:

[0375] The device uses a microphone to collect the user's voice and sends that voice data to an emotion recognition engine. The emotion recognition engine analyzes the voice data and performs a process to determine the user's emotional state. As output, the user's emotional state is identified and sent from the device to the server.

[0376] Step 4:

[0377] The server collects disaster information in real time from external disaster information providers. The input disaster information is processed immediately, its urgency is assessed, and its priority is determined for output.

[0378] Step 5:

[0379] The server uses a generative AI model to create prompt messages based on location information, language settings, characteristics, and emotional states received from the user. Using these prompt messages as input, it generates notification messages tailored to the user. The generated notification messages are tone-adjusted to ensure the user fully understands their content and can take appropriate action.

[0380] Step 6:

[0381] The server sends the generated notification message to the terminal via the communication network. Upon receiving the generated message as input data, the terminal prepares to display it and notifies the user visually or audibly.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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".

[0398] This invention relates to a system that provides multilingual disaster information based on a user's location and attribute information, and the configuration for implementing this system is described below. Basically, it acquires, processes, and distributes information using a server, user terminals, and a communication network.

[0399] The server receives location and attribute information from the user's terminal. This information includes the user's current geographical coordinates, the user's selected language settings, and other attribute information. Based on this information, the server collects real-time publicly available disaster information from external information providers and records it in a database.

[0400] The user's device can transmit location and attribute information to the server through communication. The device also has the capability to receive multilingual alert messages sent from the server, notifying the user via pop-ups or alarms. This allows the user to take quick and appropriate action to ensure their own safety.

[0401] The server analyzes the collected disaster information and generates appropriate alert messages based on the user's current location and language settings. These messages are translated and created in multiple languages ​​by a generation AI, ensuring they are sent in a format easily understood by the user. For example, if a user is in a coastal area of ​​Japan and their language setting is English, the server will generate and send the message "Tsunami Alert: Immediate evacuation to safe area advised." to their device when a tsunami warning is issued.

[0402] Furthermore, the server updates information provided to users and offers additional safety guidance in response to changes in the disaster situation. It can also send necessary follow-up messages to users who are evacuating. This addresses the diverse needs of users during a disaster and supports safe and rapid evacuation.

[0403] Thus, the present invention provides a system that enables the provision of disaster information in multiple languages ​​tailored to the individual circumstances of each user, thereby addressing the problem of language barriers in particular and ensuring that all users can obtain accurate information.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The device obtains the user's current location using GPS or other location-based technologies. This location information is used to determine if the user is in an area potentially affected by a disaster.

[0407] Step 2:

[0408] The device retrieves the user's language settings and individual attribute information from within the application and sends it to the server. This allows the server to recognize which language the user prefers to receive information in, enabling optimized message delivery.

[0409] Step 3:

[0410] The server collects disaster information in real time from weather information providers and disaster response organizations via public APIs. This information includes earthquake occurrences, tsunami warnings, and typhoon paths.

[0411] Step 4:

[0412] The server analyzes the collected disaster information and compares it with each user's location information. This analysis allows the server to determine whether a user is likely to be affected by the disaster.

[0413] Step 5:

[0414] The server assesses the need for warnings or evacuation instructions for users and uses AI to generate multilingual alert messages. These messages are translated based on the user's language settings and created in the most effective format.

[0415] Step 6:

[0416] The server prepares the generated alert message and quickly sends it to the relevant user's device. Often, notifications are configured to prompt users with high priority.

[0417] Step 7:

[0418] The device instantly notifies the user of any alert messages it receives. Notifications can be visual and audible, allowing the user to quickly grasp the information.

[0419] Step 8:

[0420] The server continuously monitors changes in disaster information and provides follow-up information to users as needed. This includes safety checks after evacuation is complete and further action instructions.

[0421] (Example 1)

[0422] 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."

[0423] During disasters, a lack of information and language barriers can prevent safety information from reaching users in a timely manner. In particular, there is a need to provide accurate and rapid information to users who speak different languages, but conventional systems struggle to achieve this efficiently.

[0424] 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.

[0425] In this invention, the server includes means for acquiring the user's spatial information, means for acquiring attribute information such as the user's language settings, and means for collecting disaster information in real time. This makes it possible to quickly provide disaster information in multiple languages ​​tailored to the individual circumstances of each user.

[0426] "User spatial information" refers to geographical coordinates and location data that indicate the user's current location.

[0427] "Attribute information" refers to information that indicates a user's personal settings, language choices, and other identifiable characteristics.

[0428] "Disaster information" refers to real-time information provided regarding natural disasters and dangerous events.

[0429] "External providers" refer to public institutions and private information services that provide disaster information and related data.

[0430] A "data set" refers to a collection of information organized for the purpose of accumulating disaster information and user information.

[0431] "Multilingual alert messages" refer to warning messages that are translated according to the user's language settings.

[0432] A "generative AI model" refers to a program that uses artificial intelligence to process data and assist in tasks such as language translation and message generation.

[0433] "Information equipment" refers to computers and servers used for processing and communicating disaster information.

[0434] A "terminal" refers to a device that a user directly operates and uses to communicate with information devices.

[0435] This invention provides a system for providing disaster information in multiple languages ​​using an information device and a terminal. The information device functions as a server, collecting disaster information in real time and using a generative AI model for analysis and multilingual translation. The terminal transmits the user's location and attribute information to the server, receives alert messages provided by the server, and notifies the user.

[0436] The server uses a high-precision GPS module to measure location information and stores the obtained spatial information in a database. It also periodically collects information from external providers of disaster information via APIs. Then, it uses a generative AI model to process the disaster information in real time and generates multilingual alert messages according to the user's language settings.

[0437] The device acquires attribute information such as location and language settings via the user interface and sends it to the server. Alert messages received from the server are notified to the user as pop-ups or audio alarms, prompting them to take immediate action.

[0438] For example, if a user is in a coastal area, the device will tell the server its current location. The server will assess the risk of disaster and, if necessary, generate a message such as "Tsunami Alert: Immediate evacuation to safe area advised," which is an English translation of any issued tsunami warning, and send it to the device.

[0439] A concrete example of a prompt message is: "The user's current location is 34.6937° N, 135.5023° E, and the language setting is Japanese. Please provide tsunami warning information." When this prompt is sent to the server, an appropriate message is generated and delivered to the user quickly.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The user terminal obtains location information from GPS and the user's language setting from the terminal's settings. The input consists of the current location coordinates obtained from the GPS device and the user's selected language setting. This information is combined and sent to the server as a data packet. The output is a data packet containing the user's spatial information and attribute information, which is sent to the server.

[0443] Step 2:

[0444] The server receives location and attribute information transmitted from the user's terminal. The input is data packets from the user's terminal. Based on this, the server records the user's current location and language in the database. The output is the location and attribute information recorded for each user.

[0445] Step 3:

[0446] The server collects real-time disaster information through APIs from external disaster information providers. The input is disaster information obtained from external providers. This information is stored in a database for subsequent analysis. The output is the latest disaster information recorded in the server's database.

[0447] Step 4:

[0448] The server generates appropriate alert messages based on collected disaster information and user location information. Inputs include disaster information, user location information, and attribute information. A generative AI model analyzes the data and translates the alerts into each user's language, creating multilingual messages. The output is the alert message translated into the user's language.

[0449] Step 5:

[0450] The server sends the generated alert message to the user's terminal. The input is the generated multilingual alert message. The message is sent to the terminal as a push notification, ensuring it reaches the user quickly. The output is the alert message sent to the user's terminal.

[0451] Step 6:

[0452] The user terminal receives alert messages from the server and notifies the user via pop-ups and audio alarms. The input is the alert message sent from the server. The terminal displays the message content to alert the user. The output is the alert information the user receives visually and aurally.

[0453] Step 7:

[0454] The server continuously monitors changes in disaster information and generates follow-up messages for users as needed. The input is the latest disaster information, which is continuously acquired. Based on this, additional guidance is created in multiple languages ​​and sent to users. The output is follow-up messages containing ongoing instructions.

[0455] (Application Example 1)

[0456] 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."

[0457] The goal is to solve the problems that autonomous vehicles face when accurately and quickly recognizing disasters during operation and selecting safe and optimal routes. In particular, there is a need for real-time, multilingual support for disaster information and to ensure the safety and convenience of the vehicles.

[0458] 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.

[0459] In this invention, the server includes means for acquiring the user's location information, means for acquiring attribute information such as the user's language settings, and means for collecting disaster information in real time. This makes it possible to notify the autonomous vehicle of disaster information in the relevant language before it enters a dangerous area, and to also suggest an alternative route.

[0460] "User location information" refers to data that indicates the geographical coordinates where the user is currently located.

[0461] "User language settings" refer to settings that indicate the user's preferred language.

[0462] "Attribute information" refers to specific data about a user, including information such as language settings.

[0463] "Disaster information" refers to data that shows the situation regarding natural disasters and unexpected events.

[0464] "Means of real-time collection" refers to methods for instantly obtaining current situations and information.

[0465] An "alert message" is a notification sent to a user to warn or alert them.

[0466] An "autonomous vehicle" is a vehicle that can operate independently without requiring human intervention.

[0467] "Operational information" refers to data about the route and speed of a vehicle as it moves.

[0468] A "hazardous area" is an area where a disaster or other danger is occurring or is highly likely to occur.

[0469] An "alternative route" is a recommended alternative travel route to avoid dangerous areas.

[0470] The system for implementing this invention consists of a server, a user terminal, and an autonomous vehicle. The server receives the user's location and attribute information, and based on this information, collects disaster information in real time from external information providers. The server also generates multilingual alert messages using a generative AI model and sends them to the user terminal.

[0471] The user terminal has the function of sending location information and attribute information to the server and notifies the user of received alert messages. The autonomous vehicle constantly monitors operational information and adjusts its route based on the information from the server.

[0472] Specifically, the server uses cloud infrastructure (e.g., AWS, Google Cloud) and leverages generative AI models (e.g., machine translation APIs) to deliver instantly translated messages to users. The hardware involves acquiring real-time location information using GPS-equipped devices and sending and receiving data via a communication network.

[0473] For example, suppose a user is riding in an autonomous vehicle when heavy rain causes flooding. The server generates a message in the user's preferred language saying "Heavy Rain Alert: Switch to alternative route via Route 246" and sends it to the vehicle's system. An example of a prompt for the generating AI model would be "Translate 'Heavy Rain Alert: Switch to alternative route via Route 246' to user's preferred language."

[0474] Thus, the present invention provides a system that supports the safe operation of autonomous vehicles and helps users take quick and appropriate action in the event of a disaster.

[0475] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0476] Step 1:

[0477] The server receives location and attribute information from the user's terminal. It takes the user's current geographical coordinates and attribute information, including language settings, as input and outputs it as user information stored in the database. Based on this information, the user can be identified.

[0478] Step 2:

[0479] The server obtains real-time disaster information from external information providers based on the user's location. The input is the user's geographical coordinates, which are used to collect relevant disaster information. The output provides real-time disaster information for the user's current location.

[0480] Step 3:

[0481] The server analyzes the collected disaster information and uses a generative AI model to generate alert messages appropriate to the user's language. The input consists of disaster information and the user's language settings, and the output is a multilingual alert message. This process involves the use of a machine translation API. Specifically, the prompt "Translate 'Disaster Details' to user's preferred language." is input to the generative AI model.

[0482] Step 4:

[0483] The server sends the generated alert message to the user's terminal. The input here is a multilingual alert message, and the output is the alert displayed on the user's terminal. The user's terminal notifies the user of this message and takes specific actions to inform the user, such as a pop-up or an audible alarm.

[0484] Step 5:

[0485] If the user terminal is connected to the autonomous vehicle, the server will provide further operational information and suggest alternative routes before entering a hazardous area. The inputs at this time are disaster information and the vehicle's current location, and the output is the recommended alternative route. The autonomous vehicle will then automatically decide whether to select the suggested route.

[0486] 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.

[0487] This invention provides a system that offers customized disaster information based on the user's emotional state, in addition to the user's location information, language settings, and other attribute information, during a disaster. To implement this, a method for operating a server, user terminal, communication network, and emotion recognition engine in combination is described.

[0488] The user terminal obtains the user's current location and registers the user's language settings and other attribute information. This information is sent to the server and recorded in the database as an individual user profile. Furthermore, an emotion recognition engine installed on the terminal determines the user's emotional state through voice analysis and input data, and sends the results to the server.

[0489] The server collaborates with external disaster information providers to collect disaster information in real time. Based on this information, it references the user's profile and uses generative AI to create alert messages in appropriate language. In this process, it utilizes emotional data obtained from the emotion engine to adjust the intensity of the information and the tone of the message that the user needs. For example, if the server determines that the user is in a high-stress state, it will generate a message that is calmer and includes more specific advice.

[0490] As a concrete example, consider a user who lives in a high-rise building in an urban area and prefers to speak Japanese. Suppose this user is perceived as "anxious," and an earthquake occurs in real time. The server aggregates this information and generates a specific and reassuring message, such as "Please stay calm. Proceed calmly towards the nearest emergency exit," and sends it to the user's device.

[0491] Through the above process, this system provides appropriate alerts and informational guidance tailored to each user's linguistic needs and emotional state when delivering disaster information. This creates an environment where users can deal with disasters more safely and with greater peace of mind.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The device uses GPS functionality to obtain the user's current location. This allows the system to identify the user's area and prepare to provide area-specific disaster information.

[0495] Step 2:

[0496] The device retrieves the user's language settings and other attribute information from the application settings and sends this information to the server. This allows the server to prepare messages in the user's appropriate language and format.

[0497] Step 3:

[0498] The emotion recognition engine on the device analyzes the user's emotions based on their voice or text input. This analysis result is sent to a server to understand the user's current emotional state.

[0499] Step 4:

[0500] The server stores disaster information obtained in real time from external disaster information providers in a database and matches it with the user's location information. This procedure identifies disasters that the user may be affected by.

[0501] Step 5:

[0502] The server uses a generation AI to generate the most appropriate alert message based on the user's location, language settings, and emotional state. The content of the generated message is adjusted according to the user's emotional state; for example, if the user is stressed, gentler language is used to encourage a calm response.

[0503] Step 6:

[0504] The server sends the generated alert message to the user's device. This message is delivered to the user in real time via push notifications, email, etc.

[0505] Step 7:

[0506] The device instantly displays received messages and notifies the user visually and audibly. This allows users to quickly receive disaster information.

[0507] Step 8:

[0508] The server continuously monitors the disaster situation and the user's emotional state, providing follow-up messages and further safety information as needed. This ensures user confidence and promotes optimal evacuation actions.

[0509] (Example 2)

[0510] 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."

[0511] During disasters, users need timely and appropriate disaster information, but providing customized information to individual users is difficult. Therefore, there is a need to receive information in a language that users can easily understand and to support responses that are appropriate to their psychological state at the time.

[0512] 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.

[0513] In this invention, the server includes means for acquiring user location data, means for acquiring user language setting information, and means for analyzing user voice and input data to determine emotional state. This makes it possible to provide appropriate and reassuring disaster information to individual users.

[0514] "Location data" refers to information that indicates the user's current geographical location.

[0515] "Language settings information" refers to the setting that allows users to specify their preferred language for displaying information.

[0516] "Disaster data" refers to various types of information about natural disasters that are collected in real time.

[0517] "Emotional state" refers to the emotional state of the user, analyzed based on their voice and input data.

[0518] A "generative model" is an artificial intelligence model that generates messages based on the user's profile and emotional state.

[0519] An "alert message" is a warning or guidance message sent to a user regarding a disaster.

[0520] "Notification means" refers to the methods or technologies used to inform the user of a generated message.

[0521] This system operates by combining servers, terminals, a communication network, and an emotion recognition engine to provide users with appropriate information during disasters. User terminals collect information such as the user's current location, language settings, and emotional state. Specifically, they use GPS installed in the terminal and language information from user settings. The terminals also incorporate an emotion recognition engine that analyzes the user's voice and text to determine their emotional state, thereby detecting the user's mental state.

[0522] Information collected by the device is sent to the server in real time. The server collaborates with external disaster information services to obtain necessary disaster data. Next, a generative AI model is used to create customized alert messages that are tailored to the user's profile and real-time emotional state. This process generates prompts for inputting the collected data into the generative AI model, using a format such as "User: City center, Japanese, anxious. Disaster: Earthquake. Please create an appropriate message."

[0523] As a concrete example, if a user who prefers Japanese and lives in a high-rise building in an urban area is detected as being anxious during an earthquake, the server will generate a message saying, "Please stay calm. Proceed calmly towards the nearest emergency exit," and send a push notification to their device. In this way, users can receive information optimized for their individual circumstances, enabling them to take safer actions.

[0524] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0525] Step 1:

[0526] The device collects the user's location data and attribute information. Specifically, it uses a GPS module to obtain location data and retrieves language information from the device's settings. This information is then sent to the server. The input consists of sensors and configuration information built into the device, while the output is the transmission of location data and attribute information to the server.

[0527] Step 2:

[0528] The device's emotion recognition engine determines the user's emotional state. It analyzes the user's voice input and text messages to identify emotions and sends that data to the server. The input is the user's voice or text, and the output is emotional state data. Specifically, it performs tone analysis and keyword analysis using speech recognition software.

[0529] Step 3:

[0530] The server retrieves disaster data from external disaster information services. The server communicates with these services using APIs to receive real-time earthquake and weather information. Input is data obtained from disaster information APIs, and output is detailed information about the disaster. The specific operation involves information collection through periodic data requests.

[0531] Step 4:

[0532] The server generates prompt text based on the user's profile and emotional state, and inputs it into the generative AI model. For example, it might create a prompt such as "User: Urban area, Japanese, Anxious. Disaster: Earthquake. Please create an appropriate message." Using this prompt, the generative AI model generates a customized alert message. The input is the user profile, emotional state, and disaster information, and the output is the generated alert message.

[0533] Step 5:

[0534] The server generates an alert message and sends it to the terminal, which then notifies the user. Typically, the HTTPS protocol is used to send the message to the terminal, which then notifies the user of this message via a pop-up notification or audio. The input is the message from the server, and the output is the alert message that is notified to the user. Specifically, the terminal receives the message and displays it on the screen.

[0535] (Application Example 2)

[0536] 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."

[0537] In recent years, it has become necessary to overcome the communication barriers faced by users with diverse languages ​​and cultures when receiving disaster information. However, general disaster information provision methods often provide uniform information without considering the user's emotional state or individual attributes, thus creating a barrier to users taking appropriate action. Furthermore, the insufficient provision of information tailored to emotional states may lead to delays in understanding and action, especially for users experiencing high levels of stress and anxiety. Therefore, it is necessary to develop a system that provides customized disaster information tailored to the user's emotional state in real time.

[0538] 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.

[0539] In this invention, the server includes a device for acquiring the user's location information, a device for acquiring characteristic information such as the user's language settings, a device for collecting disaster information in real time, and a device for adjusting the content and tone of notification messages generated using an emotion recognition engine that analyzes the user's emotional state. As a result, personalized evacuation guidance and safety information tailored to the user's emotional state are provided to the user in real time, enabling quick and appropriate decision-making and action during a disaster.

[0540] "User location information" refers to data indicating the user's current geographical location, which is obtained using GPS or other location-determining technologies.

[0541] "User language setting and other characteristic information" refers to data that includes individual user language preferences and personal attributes, and is used to deliver information in a way that is easy for users to understand.

[0542] A "device for collecting disaster information in real time" is a device that instantly collects the latest data related to disasters such as earthquakes and typhoons.

[0543] A "notification message" is a message generated to convey disaster information and warnings to the user, and its content is adjusted to suit the user's characteristics and emotions.

[0544] An "emotion recognition engine" is an engine comprised of technologies for analyzing a user's emotional state, using voice analysis and input data to identify the user's emotions.

[0545] A "network device" is a device used for data communication with the outside world, enabling information exchange with a server.

[0546] In order to implement this invention, it is necessary to construct a system that combines a user terminal, a server, a communication network, and an emotion recognition engine.

[0547] The user terminal is equipped with GPS to acquire the user's location information. The terminal also has the ability to record language settings and other characteristic information and send this information to the server. Utilizing the microphone function, the user's emotional state is analyzed through an emotion recognition engine. The emotion recognition engine is implemented using voice analysis technology, identifying emotions from the user's voice input and sending the results to the server.

[0548] The server acquires disaster information in real time from external disaster information providers. Then, using a generative AI model based on each user's profile information and emotional data, it generates notification messages tailored to the user. The content and tone of these messages are adjusted according to the user's emotional state. For example, if the system determines that the user is in a high-stress state, it will include specific advice in a calmer tone.

[0549] Notification messages generated from the server are delivered to user terminals via the network. This allows users to receive disaster information tailored to their individual circumstances and obtain concrete guidelines for calmly responding to the situation.

[0550] As a concrete example, consider a scenario where a user living in an urban area receives typhoon evacuation information. If the user is perceived as feeling anxious, the generated message will be something like, "Please rest assured, act calmly when moving to a safe place," to help them stay calm. An example of a prompt to the generating AI model would be, "The user is currently at location information. The currently confirmed emotional state is emotional. Based on the latest disaster information, generate a safe and calm message for the user."

[0551] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0552] Step 1:

[0553] The device obtains the user's location information using GPS. This location information is output as coordinate data in digital format. The device obtains location information as input data and prepares to send it to the server.

[0554] Step 2:

[0555] The device retrieves the user's language settings and characteristics from its device settings. This information is used as input to create a dataset for transmission to the server. This dataset includes the user's preferred language and other individual characteristics.

[0556] Step 3:

[0557] The device uses a microphone to collect the user's voice and sends that voice data to an emotion recognition engine. The emotion recognition engine analyzes the voice data and performs a process to determine the user's emotional state. As output, the user's emotional state is identified and sent from the device to the server.

[0558] Step 4:

[0559] The server collects disaster information in real time from external disaster information providers. The input disaster information is processed immediately, its urgency is assessed, and its priority is determined for output.

[0560] Step 5:

[0561] The server uses a generative AI model to create prompt messages based on location information, language settings, characteristics, and emotional states received from the user. Using these prompt messages as input, it generates notification messages tailored to the user. The generated notification messages are tone-adjusted to ensure the user fully understands their content and can take appropriate action.

[0562] Step 6:

[0563] The server sends the generated notification message to the terminal via the communication network. Upon receiving the generated message as input data, the terminal prepares to display it and notifies the user visually or audibly.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] [Fourth Embodiment]

[0568] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0569] 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.

[0570] 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).

[0571] 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.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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".

[0581] This invention relates to a system that provides multilingual disaster information based on a user's location and attribute information, and the configuration for implementing this system is described below. Basically, it acquires, processes, and distributes information using a server, user terminals, and a communication network.

[0582] The server receives location and attribute information from the user's terminal. This information includes the user's current geographical coordinates, the user's selected language settings, and other attribute information. Based on this information, the server collects real-time publicly available disaster information from external information providers and records it in a database.

[0583] The user's device can transmit location and attribute information to the server through communication. The device also has the capability to receive multilingual alert messages sent from the server, notifying the user via pop-ups or alarms. This allows the user to take quick and appropriate action to ensure their own safety.

[0584] The server analyzes the collected disaster information and generates appropriate alert messages based on the user's current location and language settings. These messages are translated and created in multiple languages ​​by a generation AI, ensuring they are sent in a format easily understood by the user. For example, if a user is in a coastal area of ​​Japan and their language setting is English, the server will generate and send the message "Tsunami Alert: Immediate evacuation to safe area advised." to their device when a tsunami warning is issued.

[0585] Furthermore, the server updates information provided to users and offers additional safety guidance in response to changes in the disaster situation. It can also send necessary follow-up messages to users who are evacuating. This addresses the diverse needs of users during a disaster and supports safe and rapid evacuation.

[0586] Thus, the present invention provides a system that enables the provision of disaster information in multiple languages ​​tailored to the individual circumstances of each user, thereby addressing the problem of language barriers in particular and ensuring that all users can obtain accurate information.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The device obtains the user's current location using GPS or other location-based technologies. This location information is used to determine if the user is in an area potentially affected by a disaster.

[0590] Step 2:

[0591] The device retrieves the user's language settings and individual attribute information from within the application and sends it to the server. This allows the server to recognize which language the user prefers to receive information in, enabling optimized message delivery.

[0592] Step 3:

[0593] The server collects disaster information in real time from weather information providers and disaster response organizations via public APIs. This information includes earthquake occurrences, tsunami warnings, and typhoon paths.

[0594] Step 4:

[0595] The server analyzes the collected disaster information and compares it with each user's location information. This analysis allows the server to determine whether a user is likely to be affected by the disaster.

[0596] Step 5:

[0597] The server assesses the need for warnings or evacuation instructions for users and uses AI to generate multilingual alert messages. These messages are translated based on the user's language settings and created in the most effective format.

[0598] Step 6:

[0599] The server prepares the generated alert message and quickly sends it to the relevant user's device. Often, notifications are configured to prompt users with high priority.

[0600] Step 7:

[0601] The device instantly notifies the user of any alert messages it receives. Notifications can be visual and audible, allowing the user to quickly grasp the information.

[0602] Step 8:

[0603] The server continuously monitors changes in disaster information and provides follow-up information to users as needed. This includes safety checks after evacuation is complete and further action instructions.

[0604] (Example 1)

[0605] 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".

[0606] During disasters, a lack of information and language barriers can prevent safety information from reaching users in a timely manner. In particular, there is a need to provide accurate and rapid information to users who speak different languages, but conventional systems struggle to achieve this efficiently.

[0607] 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.

[0608] In this invention, the server includes means for acquiring the user's spatial information, means for acquiring attribute information such as the user's language settings, and means for collecting disaster information in real time. This makes it possible to quickly provide disaster information in multiple languages ​​tailored to the individual circumstances of each user.

[0609] "User spatial information" refers to geographical coordinates and location data that indicate the user's current location.

[0610] "Attribute information" refers to information that indicates a user's personal settings, language choices, and other identifiable characteristics.

[0611] "Disaster information" refers to real-time information provided regarding natural disasters and dangerous events.

[0612] "External providers" refer to public institutions and private information services that provide disaster information and related data.

[0613] A "data set" refers to a collection of information organized for the purpose of accumulating disaster information and user information.

[0614] "Multilingual alert messages" refer to warning messages that are translated according to the user's language settings.

[0615] A "generative AI model" refers to a program that uses artificial intelligence to process data and assist in tasks such as language translation and message generation.

[0616] "Information equipment" refers to computers and servers used for processing and communicating disaster information.

[0617] A "terminal" refers to a device that a user directly operates and uses to communicate with information devices.

[0618] This invention provides a system for providing disaster information in multiple languages ​​using an information device and a terminal. The information device functions as a server, collecting disaster information in real time and using a generative AI model for analysis and multilingual translation. The terminal transmits the user's location and attribute information to the server, receives alert messages provided by the server, and notifies the user.

[0619] The server uses a high-precision GPS module to measure location information and stores the obtained spatial information in a database. It also periodically collects information from external providers of disaster information via APIs. Then, it uses a generative AI model to process the disaster information in real time and generates multilingual alert messages according to the user's language settings.

[0620] The device acquires attribute information such as location and language settings via the user interface and sends it to the server. Alert messages received from the server are notified to the user as pop-ups or audio alarms, prompting them to take immediate action.

[0621] For example, if a user is in a coastal area, the device will tell the server its current location. The server will assess the risk of disaster and, if necessary, generate a message such as "Tsunami Alert: Immediate evacuation to safe area advised," which is an English translation of any issued tsunami warning, and send it to the device.

[0622] A concrete example of a prompt message is: "The user's current location is 34.6937° N, 135.5023° E, and the language setting is Japanese. Please provide tsunami warning information." When this prompt is sent to the server, an appropriate message is generated and delivered to the user quickly.

[0623] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0624] Step 1:

[0625] The user terminal obtains location information from GPS and the user's language setting from the terminal's settings. The input consists of the current location coordinates obtained from the GPS device and the user's selected language setting. This information is combined and sent to the server as a data packet. The output is a data packet containing the user's spatial information and attribute information, which is sent to the server.

[0626] Step 2:

[0627] The server receives location and attribute information transmitted from the user's terminal. The input is data packets from the user's terminal. Based on this, the server records the user's current location and language in the database. The output is the location and attribute information recorded for each user.

[0628] Step 3:

[0629] The server collects real-time disaster information through APIs from external disaster information providers. The input is disaster information obtained from external providers. This information is stored in a database for subsequent analysis. The output is the latest disaster information recorded in the server's database.

[0630] Step 4:

[0631] The server generates appropriate alert messages based on collected disaster information and user location information. Inputs include disaster information, user location information, and attribute information. A generative AI model analyzes the data and translates the alerts into each user's language, creating multilingual messages. The output is the alert message translated into the user's language.

[0632] Step 5:

[0633] The server sends the generated alert message to the user's terminal. The input is the generated multilingual alert message. The message is sent to the terminal as a push notification, ensuring it reaches the user quickly. The output is the alert message sent to the user's terminal.

[0634] Step 6:

[0635] The user terminal receives alert messages from the server and notifies the user via pop-ups and audio alarms. The input is the alert message sent from the server. The terminal displays the message content to alert the user. The output is the alert information the user receives visually and aurally.

[0636] Step 7:

[0637] The server continuously monitors changes in disaster information and generates follow-up messages for users as needed. The input is the latest disaster information, which is continuously acquired. Based on this, additional guidance is created in multiple languages ​​and sent to users. The output is follow-up messages containing ongoing instructions.

[0638] (Application Example 1)

[0639] 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".

[0640] The goal is to solve the problems that autonomous vehicles face when accurately and quickly recognizing disasters during operation and selecting safe and optimal routes. In particular, there is a need for real-time, multilingual support for disaster information and to ensure the safety and convenience of the vehicles.

[0641] 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.

[0642] In this invention, the server includes means for acquiring the user's location information, means for acquiring attribute information such as the user's language settings, and means for collecting disaster information in real time. This makes it possible to notify the autonomous vehicle of disaster information in the relevant language before it enters a dangerous area, and to also suggest an alternative route.

[0643] "User location information" refers to data that indicates the geographical coordinates where the user is currently located.

[0644] "User language settings" refer to settings that indicate the user's preferred language.

[0645] "Attribute information" refers to specific data about a user, including information such as language settings.

[0646] "Disaster information" refers to data that shows the situation regarding natural disasters and unexpected events.

[0647] "Means of real-time collection" refers to methods for instantly obtaining current situations and information.

[0648] An "alert message" is a notification sent to a user to warn or alert them.

[0649] An "autonomous vehicle" is a vehicle that can operate independently without requiring human intervention.

[0650] "Operational information" refers to data about the route and speed of a vehicle as it moves.

[0651] A "hazardous area" is an area where a disaster or other danger is occurring or is highly likely to occur.

[0652] An "alternative route" is a recommended alternative travel route to avoid dangerous areas.

[0653] The system for implementing this invention consists of a server, a user terminal, and an autonomous vehicle. The server receives the user's location and attribute information, and based on this information, collects disaster information in real time from external information providers. The server also generates multilingual alert messages using a generative AI model and sends them to the user terminal.

[0654] The user terminal has the function of sending location information and attribute information to the server and notifies the user of received alert messages. The autonomous vehicle constantly monitors operational information and adjusts its route based on the information from the server.

[0655] Specifically, the server uses cloud infrastructure (e.g., AWS, Google Cloud) and leverages generative AI models (e.g., machine translation APIs) to deliver instantly translated messages to users. The hardware involves acquiring real-time location information using GPS-equipped devices and sending and receiving data via a communication network.

[0656] For example, suppose a user is riding in an autonomous vehicle when heavy rain causes flooding. The server generates a message in the user's preferred language saying "Heavy Rain Alert: Switch to alternative route via Route 246" and sends it to the vehicle's system. An example of a prompt for the generating AI model would be "Translate 'Heavy Rain Alert: Switch to alternative route via Route 246' to user's preferred language."

[0657] Thus, the present invention provides a system that supports the safe operation of autonomous vehicles and helps users take quick and appropriate action in the event of a disaster.

[0658] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0659] Step 1:

[0660] The server receives location and attribute information from the user's terminal. It takes the user's current geographical coordinates and attribute information, including language settings, as input and outputs it as user information stored in the database. Based on this information, the user can be identified.

[0661] Step 2:

[0662] The server obtains real-time disaster information from external information providers based on the user's location. The input is the user's geographical coordinates, which are used to collect relevant disaster information. The output provides real-time disaster information for the user's current location.

[0663] Step 3:

[0664] The server analyzes the collected disaster information and uses a generative AI model to generate alert messages appropriate to the user's language. The input consists of disaster information and the user's language settings, and the output is a multilingual alert message. This process involves the use of a machine translation API. Specifically, the prompt "Translate 'Disaster Details' to user's preferred language." is input to the generative AI model.

[0665] Step 4:

[0666] The server sends the generated alert message to the user's terminal. The input here is a multilingual alert message, and the output is the alert displayed on the user's terminal. The user's terminal notifies the user of this message and takes specific actions to inform the user, such as a pop-up or an audible alarm.

[0667] Step 5:

[0668] If the user terminal is connected to the autonomous vehicle, the server will provide further operational information and suggest alternative routes before entering a hazardous area. The inputs at this time are disaster information and the vehicle's current location, and the output is the recommended alternative route. The autonomous vehicle will then automatically decide whether to select the suggested route.

[0669] 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.

[0670] This invention provides a system that offers customized disaster information based on the user's emotional state, in addition to the user's location information, language settings, and other attribute information, during a disaster. To implement this, a method for operating a server, user terminal, communication network, and emotion recognition engine in combination is described.

[0671] The user terminal obtains the user's current location and registers the user's language settings and other attribute information. This information is sent to the server and recorded in the database as an individual user profile. Furthermore, an emotion recognition engine installed on the terminal determines the user's emotional state through voice analysis and input data, and sends the results to the server.

[0672] The server collaborates with external disaster information providers to collect disaster information in real time. Based on this information, it references the user's profile and uses generative AI to create alert messages in appropriate language. In this process, it utilizes emotional data obtained from the emotion engine to adjust the intensity of the information and the tone of the message that the user needs. For example, if the server determines that the user is in a high-stress state, it will generate a message that is calmer and includes more specific advice.

[0673] As a concrete example, consider a user who lives in a high-rise building in an urban area and prefers to speak Japanese. Suppose this user is perceived as "anxious," and an earthquake occurs in real time. The server aggregates this information and generates a specific and reassuring message, such as "Please stay calm. Proceed calmly towards the nearest emergency exit," and sends it to the user's device.

[0674] Through the above process, this system provides appropriate alerts and informational guidance tailored to each user's linguistic needs and emotional state when delivering disaster information. This creates an environment where users can deal with disasters more safely and with greater peace of mind.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] The device uses GPS functionality to obtain the user's current location. This allows the system to identify the user's area and prepare to provide area-specific disaster information.

[0678] Step 2:

[0679] The device retrieves the user's language settings and other attribute information from the application settings and sends this information to the server. This allows the server to prepare messages in the user's appropriate language and format.

[0680] Step 3:

[0681] The emotion recognition engine on the device analyzes the user's emotions based on their voice or text input. This analysis result is sent to a server to understand the user's current emotional state.

[0682] Step 4:

[0683] The server stores disaster information obtained in real time from external disaster information providers in a database and matches it with the user's location information. This procedure identifies disasters that the user may be affected by.

[0684] Step 5:

[0685] The server uses a generation AI to generate the most appropriate alert message based on the user's location, language settings, and emotional state. The content of the generated message is adjusted according to the user's emotional state; for example, if the user is stressed, gentler language is used to encourage a calm response.

[0686] Step 6:

[0687] The server sends the generated alert message to the user's device. This message is delivered to the user in real time via push notifications, email, etc.

[0688] Step 7:

[0689] The device instantly displays received messages and notifies the user visually and audibly. This allows users to quickly receive disaster information.

[0690] Step 8:

[0691] The server continuously monitors the disaster situation and the user's emotional state, providing follow-up messages and further safety information as needed. This ensures user confidence and promotes optimal evacuation actions.

[0692] (Example 2)

[0693] 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".

[0694] During disasters, users need timely and appropriate disaster information, but providing customized information to individual users is difficult. Therefore, there is a need to receive information in a language that users can easily understand and to support responses that are appropriate to their psychological state at the time.

[0695] 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.

[0696] In this invention, the server includes means for acquiring user location data, means for acquiring user language setting information, and means for analyzing user voice and input data to determine emotional state. This makes it possible to provide appropriate and reassuring disaster information to individual users.

[0697] "Location data" refers to information that indicates the user's current geographical location.

[0698] "Language settings information" refers to the setting that allows users to specify their preferred language for displaying information.

[0699] "Disaster data" refers to various types of information about natural disasters that are collected in real time.

[0700] "Emotional state" refers to the emotional state of the user, analyzed based on their voice and input data.

[0701] A "generative model" is an artificial intelligence model that generates messages based on the user's profile and emotional state.

[0702] An "alert message" is a warning or guidance message sent to a user regarding a disaster.

[0703] "Notification means" refers to the methods or technologies used to inform the user of a generated message.

[0704] This system operates by combining servers, terminals, a communication network, and an emotion recognition engine to provide users with appropriate information during disasters. User terminals collect information such as the user's current location, language settings, and emotional state. Specifically, they use GPS installed in the terminal and language information from user settings. The terminals also incorporate an emotion recognition engine that analyzes the user's voice and text to determine their emotional state, thereby detecting the user's mental state.

[0705] Information collected by the device is sent to the server in real time. The server collaborates with external disaster information services to obtain necessary disaster data. Next, a generative AI model is used to create customized alert messages that are tailored to the user's profile and real-time emotional state. This process generates prompts for inputting the collected data into the generative AI model, using a format such as "User: City center, Japanese, anxious. Disaster: Earthquake. Please create an appropriate message."

[0706] As a concrete example, if a user who prefers Japanese and lives in a high-rise building in an urban area is detected as being anxious during an earthquake, the server will generate a message saying, "Please stay calm. Proceed calmly towards the nearest emergency exit," and send a push notification to their device. In this way, users can receive information optimized for their individual circumstances, enabling them to take safer actions.

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

[0708] Step 1:

[0709] The device collects the user's location data and attribute information. Specifically, it uses a GPS module to obtain location data and retrieves language information from the device's settings. This information is then sent to the server. The input consists of sensors and configuration information built into the device, while the output is the transmission of location data and attribute information to the server.

[0710] Step 2:

[0711] The device's emotion recognition engine determines the user's emotional state. It analyzes the user's voice input and text messages to identify emotions and sends that data to the server. The input is the user's voice or text, and the output is emotional state data. Specifically, it performs tone analysis and keyword analysis using speech recognition software.

[0712] Step 3:

[0713] The server retrieves disaster data from external disaster information services. The server communicates with these services using APIs to receive real-time earthquake and weather information. Input is data obtained from disaster information APIs, and output is detailed information about the disaster. The specific operation involves information collection through periodic data requests.

[0714] Step 4:

[0715] The server generates prompt text based on the user's profile and emotional state, and inputs it into the generative AI model. For example, it might create a prompt such as "User: Urban area, Japanese, Anxious. Disaster: Earthquake. Please create an appropriate message." Using this prompt, the generative AI model generates a customized alert message. The input is the user profile, emotional state, and disaster information, and the output is the generated alert message.

[0716] Step 5:

[0717] The server generates an alert message and sends it to the terminal, which then notifies the user. Typically, the HTTPS protocol is used to send the message to the terminal, which then notifies the user of this message via a pop-up notification or audio. The input is the message from the server, and the output is the alert message that is notified to the user. Specifically, the terminal receives the message and displays it on the screen.

[0718] (Application Example 2)

[0719] 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".

[0720] In recent years, it has become necessary to overcome the communication barriers faced by users with diverse languages ​​and cultures when receiving disaster information. However, general disaster information provision methods often provide uniform information without considering the user's emotional state or individual attributes, thus creating a barrier to users taking appropriate action. Furthermore, the insufficient provision of information tailored to emotional states may lead to delays in understanding and action, especially for users experiencing high levels of stress and anxiety. Therefore, it is necessary to develop a system that provides customized disaster information tailored to the user's emotional state in real time.

[0721] 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.

[0722] In this invention, the server includes a device for acquiring the user's location information, a device for acquiring characteristic information such as the user's language settings, a device for collecting disaster information in real time, and a device for adjusting the content and tone of notification messages generated using an emotion recognition engine that analyzes the user's emotional state. As a result, personalized evacuation guidance and safety information tailored to the user's emotional state are provided to the user in real time, enabling quick and appropriate decision-making and action during a disaster.

[0723] "User location information" refers to data indicating the user's current geographical location, which is obtained using GPS or other location-determining technologies.

[0724] "User language setting and other characteristic information" refers to data that includes individual user language preferences and personal attributes, and is used to deliver information in a way that is easy for users to understand.

[0725] A "device for collecting disaster information in real time" is a device that instantly collects the latest data related to disasters such as earthquakes and typhoons.

[0726] A "notification message" is a message generated to convey disaster information and warnings to the user, and its content is adjusted to suit the user's characteristics and emotions.

[0727] An "emotion recognition engine" is an engine comprised of technologies for analyzing a user's emotional state, using voice analysis and input data to identify the user's emotions.

[0728] A "network device" is a device used for data communication with the outside world, enabling information exchange with a server.

[0729] In order to implement this invention, it is necessary to construct a system that combines a user terminal, a server, a communication network, and an emotion recognition engine.

[0730] The user terminal is equipped with GPS to acquire the user's location information. The terminal also has the ability to record language settings and other characteristic information and send this information to the server. Utilizing the microphone function, the user's emotional state is analyzed through an emotion recognition engine. The emotion recognition engine is implemented using voice analysis technology, identifying emotions from the user's voice input and sending the results to the server.

[0731] The server acquires disaster information in real time from external disaster information providers. Then, using a generative AI model based on each user's profile information and emotional data, it generates notification messages tailored to the user. The content and tone of these messages are adjusted according to the user's emotional state. For example, if the system determines that the user is in a high-stress state, it will include specific advice in a calmer tone.

[0732] Notification messages generated from the server are delivered to user terminals via the network. This allows users to receive disaster information tailored to their individual circumstances and obtain concrete guidelines for calmly responding to the situation.

[0733] As a concrete example, consider a scenario where a user living in an urban area receives typhoon evacuation information. If the user is perceived as feeling anxious, the generated message will be something like, "Please rest assured, act calmly when moving to a safe place," to help them stay calm. An example of a prompt to the generating AI model would be, "The user is currently at location information. The currently confirmed emotional state is emotional. Based on the latest disaster information, generate a safe and calm message for the user."

[0734] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0735] Step 1:

[0736] The device obtains the user's location information using GPS. This location information is output as coordinate data in digital format. The device obtains location information as input data and prepares to send it to the server.

[0737] Step 2:

[0738] The device retrieves the user's language settings and characteristics from its device settings. This information is used as input to create a dataset for transmission to the server. This dataset includes the user's preferred language and other individual characteristics.

[0739] Step 3:

[0740] The device uses a microphone to collect the user's voice and sends that voice data to an emotion recognition engine. The emotion recognition engine analyzes the voice data and performs a process to determine the user's emotional state. As output, the user's emotional state is identified and sent from the device to the server.

[0741] Step 4:

[0742] The server collects disaster information in real time from external disaster information providers. The input disaster information is processed immediately, its urgency is assessed, and its priority is determined for output.

[0743] Step 5:

[0744] The server uses a generative AI model to create prompt messages based on location information, language settings, characteristics, and emotional states received from the user. Using these prompt messages as input, it generates notification messages tailored to the user. The generated notification messages are tone-adjusted to ensure the user fully understands their content and can take appropriate action.

[0745] Step 6:

[0746] The server sends the generated notification message to the terminal via the communication network. Upon receiving the generated message as input data, the terminal prepares to display it and notifies the user visually or audibly.

[0747] 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.

[0748] 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.

[0749] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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."

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] The following is further disclosed regarding the embodiments described above.

[0769] (Claim 1)

[0770] Means for obtaining the user's location information,

[0771] A means of obtaining attribute information such as the user's language settings,

[0772] Means for collecting disaster information in real time,

[0773] A means for generating alert messages in an appropriate language based on the user's location and attribute information,

[0774] A means of notifying the user of the generated alert message,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, comprising means for continuously providing users with additional safety information and evacuation guidelines.

[0778] (Claim 3)

[0779] The system according to claim 1, comprising a terminal that transmits location information and attribute information to a server and receives alerts and safety information from the server.

[0780] "Example 1"

[0781] (Claim 1)

[0782] Means for acquiring the user's spatial information,

[0783] A means of obtaining attribute information such as the user's language settings,

[0784] Means for collecting disaster information in real time,

[0785] A means of recording disaster information obtained from external providers into a data set,

[0786] A means for generating alert messages in multiple languages ​​based on the user's spatial information and attribute information,

[0787] A method for translating alert messages using a generative AI model,

[0788] A means of notifying the user of the generated alert message,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, comprising means for providing users with continuous safety instructions and evacuation guidelines.

[0792] (Claim 3)

[0793] The system according to claim 1, comprising a terminal that transmits spatial information and attribute information to an information device and receives alerts and safety information from the information device.

[0794] "Application Example 1"

[0795] (Claim 1)

[0796] Means for obtaining the user's location information,

[0797] A means of obtaining attribute information such as the user's language settings,

[0798] Means for collecting disaster information in real time,

[0799] A means for generating alert messages in an appropriate language based on the user's location and attribute information,

[0800] A means of notifying the user of the generated alert message,

[0801] A means of acquiring operational information for autonomous vehicles,

[0802] A means of suggesting an alternative route before an autonomous vehicle enters a dangerous area,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, comprising means for continuously providing users with additional safety information and evacuation guidelines.

[0806] (Claim 3)

[0807] The system according to claim 1, comprising a terminal that transmits location information and attribute information to a server and receives alerts and safety information from the server.

[0808] "Example 2 of combining an emotion engine"

[0809] (Claim 1)

[0810] A means of obtaining user location data,

[0811] A means of obtaining information about the user's language settings,

[0812] Means for collecting disaster data in real time,

[0813] A means of analyzing the user's voice and input data to determine their emotional state,

[0814] A means of creating alert messages using a generative model according to the emotional state,

[0815] A means of notifying the user of the generated alert message,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, comprising means for continuously providing users with additional safety information and evacuation guidelines.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising a terminal that transmits location data and emotional state to a server and receives alerts and safety information from the server.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] A device that acquires the user's location information,

[0824] A device that acquires characteristic information such as the user's language settings,

[0825] A device for collecting disaster information in real time,

[0826] A device that generates notification messages in an appropriate language based on the user's location and characteristics,

[0827] A device that uses an emotion recognition engine to analyze the user's emotional state and adjusts the content and tone of the generated notification messages,

[0828] A device that notifies the user of the generated notification message,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, comprising a device that continuously provides the user with additional safety information and evacuation guidelines, and a device that provides personalized evacuation guidance that responds to the user's emotions.

[0832] (Claim 3)

[0833] The system according to claim 1, comprising a terminal that transmits location information and characteristic information to a network device and receives notifications and security information from the network device. [Explanation of symbols]

[0834] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for obtaining the user's location information, A means of obtaining attribute information such as the user's language settings, Means for collecting disaster information in real time, A means for generating alert messages in an appropriate language based on the user's location and attribute information, A means of notifying the user of the generated alert message, A system that includes this.

2. The system according to claim 1, comprising means for continuously providing users with additional safety information and evacuation guidelines.

3. The system according to claim 1, comprising a terminal that transmits location information and attribute information to a server and receives alerts and safety information from the server.

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