system

Abstract

We provide the system. [Solution] A data storage means for receiving and storing profile information of an individual or group, A generation means that runs a generation model based on saved profile information to generate appropriate disaster prevention information, A data transmission method that sends generated disaster prevention information to a local device, making the information viewable even when the network is unavailable, A method for updating disaster prevention information in the background using a temporarily established communication state, 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 recent years, due to the frequent occurrence of natural disasters, there is a demand for providing rapid and accurate information during disasters. However, there is a problem that it is difficult to provide personalized disaster prevention information to individual users in real time. In addition, even when the network is interrupted, the means for accessing such information are limited, and it is necessary to improve the disaster response ability.

Means for Solving the Problems

[0005] This invention provides data storage means based on user profile information, and generates optimized disaster prevention information for each user by executing a generation model using the profile information collected thereby. Furthermore, it transmits this disaster prevention information to a local device, enabling information viewing independent of the network environment. In addition, it ensures that the information is always up-to-date by updating it in the background when communication is temporarily restored. This makes it possible to support accurate information provision and rapid decision-making even during disasters.

[0006] "Profile information" refers to a set of information provided by the user, such as personal information, family structure, address, contact information, and special needs.

[0007] A "data storage means" is a mechanism for securely storing and managing received profile information.

[0008] A "generative model" is an algorithm or program used to generate optimal disaster prevention information based on collected data.

[0009] "Disaster prevention information" refers to information about actions and preparations that users should take in the event of a natural disaster.

[0010] A "local device" is a terminal or device that allows users to access disaster prevention information without connecting to a network.

[0011] "Data transmission means" refers to functions and technologies for transferring generated information to the user's local device.

[0012] An "update mechanism" is a system that utilizes a temporarily established communication state to keep generated information up-to-date. [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

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

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

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[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 is a system that provides optimal disaster prevention information based on individual and group profile information. This system consists mainly of a server, terminals, and users, each playing a specific role.

[0035] The server first receives the user's personal and family information and stores it in a database. This information is securely stored using encryption technology. Next, the server runs a generative model and generates the disaster prevention information that should be provided based on it. The generative model also uses external information, including geographic and meteorological data, to perform a risk assessment. The generated disaster prevention information is automatically transmitted to the user's terminal via a data transmission means.

[0036] The device stores disaster prevention information received from the server locally, allowing users to access the information even when the network is down. Furthermore, the device analyzes the received information and controls notifications to the user according to their urgency. Notifications are delivered to the user via voice alerts and vibration. The user interface is designed to be user-friendly, enabling users to check information in real time.

[0037] Users set up their own profiles within the application and update them as needed. These profiles include information such as evacuation destinations and emergency contacts. Users use disaster prevention information received through their devices to create evacuation plans in advance and act according to those plans during a disaster. Users can also provide feedback on the program's effectiveness after a disaster occurs.

[0038] For example, if a typhoon is approaching a region, this system can quickly provide evacuation information and emergency contact methods based on the user's current location. The server acquires new weather data, uses a generative model to calculate the optimal evacuation destination, and provides that information to the terminal. The terminal appropriately notifies the user of this information, and ensures safety during disasters by allowing access to stored information even when the network is unavailable.

[0039] In this way, this system enables a rapid and accurate response to natural disasters by providing individually customized information in real time.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server receives personal profile information sent by the user. This information includes basic data such as address, family structure, and emergency contact information. The server encrypts this information using security technology and stores it in a database.

[0043] Step 2:

[0044] The server periodically runs a generative model to generate optimal disaster prevention information based on individual profile information. During this process, weather data and geographical information acquired from external sources are also used for analysis, and the information is created after assessing disaster risk.

[0045] Step 3:

[0046] Once the generated disaster prevention information is ready, the server sends the information to the terminal. This information is formatted based on the user's priority settings.

[0047] Step 4:

[0048] The terminal saves disaster prevention information received from the server to its local storage. This allows users to access the information even if the terminal cannot connect to the network.

[0049] Step 5:

[0050] The device determines the urgency of disaster information and immediately notifies the user of high-urgency information. Notifications are made using voice alerts and vibrations to attract the user's attention.

[0051] Step 6:

[0052] Users can check disaster prevention information through an application on their device and create an appropriate evacuation plan. Based on their registered profile, users will develop evacuation routes and emergency procedures.

[0053] Step 7:

[0054] When communication is temporarily restored, the server automatically updates disaster prevention information and sends the latest information to the terminal. This automation ensures that users always receive information based on the current situation.

[0055] Step 8:

[0056] After a disaster response, users provide feedback through the system. This feedback is collected on the server and used to improve the generative model and the system.

[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] In recent years, the frequency and scale of natural disasters have increased, making it increasingly important for individuals and groups to quickly obtain personalized disaster preparedness information. However, conventional systems have difficulty providing real-time information tailored to individual situations, and there are also challenges in utilizing information when networks are disrupted.

[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 information management means for acquiring and storing attribute information of individuals or groups; information generation means for executing an artificial intelligence model based on the stored attribute information and generating optimal disaster response information; and information transmission means for transmitting the generated disaster response information to client devices, making the information accessible even when the network is unavailable. This enables the provision of customized disaster response information tailored to individual circumstances in real time, and makes it possible to utilize the information even when the network is disrupted.

[0062] "Individual or group attribute information" refers to information that indicates attributes about a user or organization, and includes data such as age, address, family structure, and emergency contact information.

[0063] "Information management means" refers to elements that provide processes and technologies for acquiring, storing, and protecting individual pieces of information.

[0064] An "artificial intelligence model" is an algorithm that generates predictions or suggestions based on specific inputs, and it is a model that uses machine learning or deep learning techniques.

[0065] "Information generation means" refers to elements that provide processes and functions for creating necessary information based on input data.

[0066] "Information transmission means" refers to elements that use communication technologies and protocols to transmit generated information to the intended recipient.

[0067] An "information update mechanism" is an element that has the function of modifying or re-acquiring data in the background in order to keep existing information up to date.

[0068] A "user interface" is a visual or physical interface through which a user interacts with a system, and it includes design elements to facilitate operation.

[0069] "Disaster response information" refers to data that provides specific guidelines and procedures for preparing for, responding to, and recovering from emergencies such as natural disasters.

[0070] This invention is a system that provides users with individually customized disaster response information. The system consists of a server, terminals, and users, and each element plays a specific role to achieve effective information delivery.

[0071] The server executes programs and utilizes multiple hardware and software environments. The server first receives attribute information from users or groups. This is done using web applications and database management systems (e.g., MySQL®, PostgreSQL). The received information is stored using encryption technologies such as AES to ensure security. Next, the server uses generative AI models (e.g., GPT-3®, BERT) to obtain the latest geographic and weather information from external APIs (e.g., Google® Maps API, OpenWeatherMap API) and uses this information to perform a risk assessment. Based on this, it generates appropriate disaster response information. An example of a prompt message is, "Based on the user's current location and attributes, please suggest the optimal evacuation action."

[0072] The device receives disaster response information sent from the server and stores it in local storage. This allows for information to be viewed even when offline. The device also determines the urgency of the information and notifies the user through methods such as push notifications, voice alerts, and vibrations. The application on the device provides an intuitive user interface so that the user can easily access the information.

[0073] By using this system, users can receive personalized disaster preparedness information in real time based on their pre-configured profile information. Users set their evacuation destinations and emergency contacts within the application and create a plan based on this information. In the event of a disaster, they are required to act according to this plan and ensure their safety. Further improvements to the system will be made by providing feedback on the program's effectiveness.

[0074] In this way, the system, through the coordinated functioning of each element, enables a rapid and accurate response to natural disasters, thereby supporting the safety of users.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server receives personal or group attribute information from the user. Data entered by the user through the application (e.g., age, address, family structure, contact information, etc.) is sent to the server using a secure protocol (such as HTTPS), and this information is stored in a database using encryption technology such as AES. This process ensures that individual profile information is securely stored.

[0078] Step 2:

[0079] The server retrieves geographic and weather information from external APIs based on stored attribute information. Based on this, it generates a prompt message and provides it to a generation AI model (e.g., GPT-3). Specifically, it calls APIs (such as Google Maps API and OpenWeatherMap API) to obtain the user's current location and weather information, generating the prompt message "Please suggest the optimal evacuation action based on the user's current location and attributes," and inputting it into the generation AI model. The output is individually customized disaster response information.

[0080] Step 3:

[0081] The server transmits disaster response information obtained by the generated AI model to the terminal. A secure protocol is used for data transmission to ensure the information reaches the user's terminal. The transmitted information includes specific evacuation locations, transportation options, and communication methods.

[0082] Step 4:

[0083] The terminal receives disaster response information from the server, stores it locally, and begins analysis. Based on the stored data, the terminal determines the urgency level and notifies the user using push notifications, voice alerts, vibrations, etc. Furthermore, even if the network is unavailable, the locally stored information will be accessible to the user.

[0084] Step 5:

[0085] Based on disaster response information received via their device, users set up evacuation plans and update emergency contacts as needed. By opening the settings menu, creating a plan, entering necessary data, and saving it, this information is utilized by the system. In the event of a disaster, users are required to act quickly according to this plan.

[0086] (Application Example 1)

[0087] 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."

[0088] In the event of a disaster at a large-scale commercial facility, there is a need to provide individualized and optimized safety information to each visitor quickly, minimizing confusion and facilitating efficient evacuation. However, current disaster prevention systems struggle to provide information that fully utilizes visitor attributes and location information, and providing appropriate information, especially when communication networks are disrupted, remains a challenge.

[0089] 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.

[0090] In this invention, the server includes information holding means for receiving and storing attribute data of individuals or groups, generation means for executing a generation AI model based on the stored attribute data to generate optimal safety information, and transmission means for transmitting the generated safety information to a communication terminal, making the information viewable even when the communication network is unavailable. This makes it possible for each visitor in large commercial facilities to receive individually optimized evacuation information in real time, thereby supporting smooth evacuation actions.

[0091] "Attribute data" refers to information related to an individual or group, and in this invention, it includes visitor profiles and location information.

[0092] "Information retention means" refers to a device or software that has the function of storing and managing received attribute data.

[0093] A "generative AI model" refers to a model that uses artificial intelligence to analyze and predict based on accumulated data, and plays a role in creating optimal safety information.

[0094] "Generation means" refers to a device or software that processes data to create optimal safety information based on the data obtained.

[0095] A "communication terminal" refers to a device that a user uses to receive and display information, such as a smartphone or tablet.

[0096] "Transmission means" refers to a device or software that has the function of transmitting generated information to a communication terminal.

[0097] "Route guidance means" refers to a device or software that provides the optimal evacuation route based on the physical location information of a visitor.

[0098] The system implementing this invention is configured to provide optimal evacuation information to visitors in large-scale commercial facilities. It consists of a server, a communication terminal, and various sensors.

[0099] The server securely stores attribute data collected from visitors using information retention methods. This allows for the management of visitors' location information and profiles. Next, an AI model is used based on the stored data to generate security information in real time. The generated information is transmitted to each visitor's communication terminal via a transmission method.

[0100] The communication terminal immediately notifies visitors of received evacuation information. Information stored on the terminal can be accessed even when the communication network is unavailable. The terminal also includes a route guidance system that identifies the visitor's current location and suggests the optimal evacuation route. Voice guidance and visual information enable visitors to evacuate safely and quickly.

[0101] As a concrete example, if a fire breaks out in a shopping mall, the server will instantly calculate the optimal evacuation route based on the visitor's location and notify them. In this process, natural language processing is applied by a generative AI model, and a prompt such as "Generate an emergency alert within the shopping mall based on the profile and location information of user ID 12345" is used. As a result, the terminal screen will be provided with both a visual map and voice instructions, allowing visitors to follow the instructions and evacuate safely.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server receives attribute data from visitors and stores it in a database using an information retention system. The input consists of the visitor's profile information and location information, which is stored securely in an encrypted state. The output is the updated visitor information stored in the server's database.

[0105] Step 2:

[0106] The server runs a generative AI model based on stored attribute data to generate optimal safety information. The inputs are attribute data and dynamically acquired geographical and weather data. The prompt is "Generate an emergency alert within the shopping mall based on the profile and location information of user ID 12345." The output is personalized evacuation information tailored to each visitor.

[0107] Step 3:

[0108] The server transmits the generated safety information to each visitor's communication terminal using a transmission method. The input is optimized safety information, which is transmitted via wireless communication. The output is the evacuation information received by the communication terminal.

[0109] Step 4:

[0110] The terminal analyzes the received evacuation information and, based on the visitor's current location, uses route guidance to suggest the optimal evacuation route. Inputs are safety information received from the server and the terminal's location information. Outputs are a map displayed on the terminal's screen and voice instructions.

[0111] Step 5:

[0112] The user checks the evacuation route displayed on the terminal and evacuates safely according to the instructions. The input is the information and instructions displayed on the terminal, and the output is the user's actual evacuation actions.

[0113] Step 6:

[0114] The server updates safety information in the background, utilizing temporarily established communication states even while evacuations are in progress. The input is newly acquired geographical and meteorological data, and the generating AI model is run again to filter the necessary information. The output is the latest evacuation information and its retransmission.

[0115] 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.

[0116] This invention relates to a disaster prevention information provision system that takes into account not only individual or group profile information but also the user's emotional state. The system aims to provide personalized information based on the user's emotions by combining it with an emotion engine.

[0117] The server receives emotional data collected from the user's terminal, in addition to regular profile data, and stores this data in a database. This emotional data is analyzed by artificial intelligence based on the user's facial expressions, voice tone, and input speed. The server then uses this data to run a generative model, generating appropriate disaster prevention information tailored to the user's situation and emotions.

[0118] The terminal functions as a device that presents this generated disaster prevention information to the user. Not only does it passively receive information from the server, but it also incorporates an emotion engine, allowing it to monitor the user's real-time emotional changes and dynamically adjust the content and presentation of the information. For example, it can provide concise and direct instructions to anxious users, and detailed information and additional options to calm users.

[0119] Users configure their profiles using the system and allow emotional data to be collected on a daily basis. This permission allows the system to continuously receive emotional feedback and improve the accuracy of its models. In the event of a disaster, users can act according to the information provided on the device and send feedback through the application. For example, if an earthquake occurs and the emotion engine detects that the user is experiencing fear, the device will immediately display calming advice and emergency contact information.

[0120] In this way, by utilizing the emotion engine, it becomes possible to provide disaster prevention information that takes into account the user's psychological state, thereby supporting more effective disaster countermeasures.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The server receives personal profile information and emotional data transmitted from the user's terminal. Emotional data is generated by an emotion engine that analyzes the user's facial expressions and tone of voice. This data is encrypted using a secure protocol and safely stored in a database.

[0124] Step 2:

[0125] The server combines stored profile data and emotion data to run a generative model. This generative model considers the user's current emotional state and past trends to generate optimal disaster prevention information. For example, it adjusts the information to include more reassuring information for users who are highly anxious.

[0126] Step 3:

[0127] Once the generated disaster prevention information is ready, the server sends this information to the user's terminal using a data transmission method. Here, different notification modes are applied depending on the priority and urgency of the information.

[0128] Step 4:

[0129] The terminal saves disaster prevention information received from the server to its local storage. This saving process allows users to access necessary information even when the terminal is not connected to the network.

[0130] Step 5:

[0131] The device continuously monitors the user's real-time emotional state. The emotion engine adjusts how information is presented at the appropriate time when it detects the user's emotions. For example, if the user is feeling stressed, it simplifies the information presentation and clarifies instructions.

[0132] Step 6:

[0133] Users will check the disaster prevention information displayed on their devices and take the necessary actions. They will also check evacuation routes and contact information in advance based on the information provided, and prepare to ensure their safety.

[0134] Step 7:

[0135] When communication is temporarily restored, the server automatically updates disaster prevention information and sends the new information to the terminal. This process also takes into account the latest sentiment data to ensure that the information is as useful as possible to the user.

[0136] Step 8:

[0137] After disaster response is complete, users provide their experience and feedback on the system through the application. This feedback is forwarded to the server and used to improve the generative model and emotion engine.

[0138] (Example 2)

[0139] 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".

[0140] Conventional disaster prevention information systems struggled to provide flexible information tailored to individual emotional states and circumstances, making it difficult to respond to the specific needs of each user. This posed a problem, particularly in emergencies, where users had difficulty taking appropriate and swift action. Furthermore, in the event of communication disruptions, information could not be updated, making it impossible to continuously provide the latest disaster prevention information.

[0141] 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.

[0142] In this invention, the server includes recording means for receiving and storing attribute information and emotional data of individuals or groups; creation means for executing a generation AI model based on the stored attribute information and emotional data to generate appropriate disaster prevention information; and transfer means for transmitting the generated disaster prevention information to a user device, making the information viewable even without communication. This provides disaster prevention information customized according to the user's emotions, enabling the continuous supply of up-to-date information even in emergencies.

[0143] "Individual or group attribute information" refers to identifiable characteristics or information related to an individual or group, including age, gender, occupation, hobbies, and location.

[0144] "Emotional data" refers to data that indicates the user's psychological or emotional state, and includes facial expressions, tone of voice, and typing speed.

[0145] "Recording means" refers to an element that has the function of saving received information and making it accessible later.

[0146] A "generative AI model" refers to a mathematical model used to generate new data or information based on pre-learned patterns.

[0147] "Creation means" refers to an element that has the function of generating new information or data based on the input data.

[0148] "Transfer means" refers to an element that has the function of transmitting information to different devices or systems.

[0149] A "user device" refers to an electronic device used by a user to receive and display information, and includes smartphones, tablets, and computers.

[0150] "Communication environment" refers to the network connectivity necessary for sending and receiving information between devices.

[0151] This invention is a disaster prevention information provision system that realizes personalized information provision during disasters, and mainly consists of a server, a terminal, and a user. The server receives user attribute information and sentiment data and stores this data using recording means. Specifically, it organizes the data using a database management system to enable rapid access. The server also creates disaster prevention information using a generative AI model based on the stored data. The generative AI model uses general deep learning and natural language processing technologies to efficiently generate disaster prevention information based on the user's sentiment.

[0152] The terminal functions as a user device and has the ability to receive and display disaster prevention information transferred from the server. At the same time, the terminal is equipped with an emotion engine that collects real-time emotion data from the user through its camera and microphone. This allows the terminal to adjust how information is presented according to the situation, maintaining a state where the user can easily receive the information.

[0153] Users can take appropriate actions based on the information provided through their devices. The system's accuracy and usefulness can be further improved by users submitting feedback through the application about their actual actions and feelings during a disaster.

[0154] As a concrete example, if an earthquake occurs while the user is out, the device detects the user's anxiety and the server provides advice such as, "Take a deep breath to calm yourself. The nearest evacuation center is XX." An example of a prompt to be input to the generating AI model is, "The user is in a state of tension. Generate concise and reassuring disaster prevention information."

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The server receives and records user attribute information and emotional data. Inputs include facial expression data, voice tone, and input speed sent from the user's terminal, while output is the storage of this data in an organized format in a database. Specifically, the server uses a database management system to store the data and organize it for quick access in subsequent processes.

[0158] Step 2:

[0159] The server runs a generative AI model using stored attribute information and sentiment data. It uses user information and sentiment data retrieved from a database as input, and generates personalized disaster prevention information as output. Specifically, the AI ​​model analyzes the sentiment data and generates appropriate advice and notifications using natural language generation technology based on the prompt text.

[0160] Step 3:

[0161] The server transfers the generated disaster prevention information to the terminal. The input is the previously generated disaster prevention information, and the output is the specific disaster prevention measures information sent to the terminal. The server encodes the data using a communication protocol and securely transfers it to the terminal, making it viewable even offline.

[0162] Step 4:

[0163] The terminal displays received disaster prevention information to the user. The input is disaster prevention information received from the server, and the output is information that the user can view on the screen. Specifically, the terminal monitors the user's emotional state in real time and dynamically changes the amount and format of information displayed as needed. For example, it will present information in large, easy-to-read font to a user who is feeling anxious.

[0164] Step 5:

[0165] Users act based on the disaster prevention information provided and send feedback to the server via their device. The input is the information the user has confirmed and the actions they have taken, and the output is the content of the feedback. Here, the user uses the application's feedback function to tell the server whether the information was helpful and how they received it. The server uses this feedback to improve the system and aims to provide more accurate information.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0168] Conventional disaster information systems present uniform information without considering the user's psychological state, which can lead to the information not being used appropriately and potentially inducing panic. Furthermore, current technology makes it difficult to update information when communication networks are unavailable, hindering a rapid response during emergencies.

[0169] 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.

[0170] In this invention, the server includes emotion analysis means for analyzing the emotional state of individual users and providing adaptive disaster response information based on the identified emotions; information recording means for receiving and storing attribute information of individuals or groups; and information creation means for executing a generation model based on the stored attribute information and generating appropriate disaster response information. This enables the presentation of effective disaster response information tailored to the user's psychological state and allows for information confirmation and updating even when communication networks are unavailable.

[0171] "Individual or group attribute information" refers to data concerning the characteristics and traits associated with a specific individual or group.

[0172] "Information recording means" refers to a device or system for storing received attribute information.

[0173] A "generative model" is an algorithm or formula used to generate appropriate output based on input data.

[0174] "Information creation means" refers to a function or device for generating disaster response information by executing a generation model.

[0175] A "receiving device" is a terminal or device used by a user to receive disaster response information.

[0176] "Information transmission means" refers to a function or device for transmitting generated information to a receiving device.

[0177] A "communication network" is a network used for sending and receiving information.

[0178] "Information update means" refers to a function or device that keeps information up-to-date in the background.

[0179] "Emotional state" refers to information about an individual's psychological and emotional state.

[0180] "Emotional analysis means" refers to a function or device that identifies the user's emotional state and provides information adaptively based on that state.

[0181] This system consists of a server and terminals, and provides disaster prevention information using a generative AI model based on user attribute information and emotional state. The server receives attribute information of individuals or groups and stores it using information recording means. The main software used for analyzing attribute information includes OpenCV and PyTorch.

[0182] The server uses emotion analysis to understand the user's psychological state based on their facial expressions and voice data. This analysis generates disaster preparedness information tailored to the identified emotions and transmits it to the user's smart glasses or other receiving devices. AI technology is employed in the emotion analysis, and the generated information can be viewed even when network access is unavailable.

[0183] The device dynamically provides the user with visual and audio instructions based on the information it receives. For example, if the user is in a state of panic, the device will offer specific advice such as, "Take a deep breath. The shelter is the park in front of the station." To achieve this, the device uses its built-in microphone and camera to monitor the user's emotional state in real time and adjust the way information is displayed accordingly.

[0184] In this way, personalized responses using emotion analysis become possible, helping users take appropriate actions even in emergencies. Prompts such as, "If a user is in a state of panic during an earthquake, how can I encourage them to take deep breaths and provide information about the nearest evacuation center?" can be used to train the generative AI model.

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The server receives user attribute information from the database and stores it using an information recording device. At this stage, data such as user ID, past activity history, and registered emergency contact information are entered. This data is stored as is and used for subsequent processing.

[0188] Step 2:

[0189] The device acquires the user's facial expressions and voice data in real time and analyzes their psychological state using emotion analysis tools. The input is raw data acquired from the camera and microphone, and the output is emotion evaluation data such as "nervous" or "calm." OpenCV and PyTorch are used for this analysis to identify the user's current emotional state.

[0190] Step 3:

[0191] The server takes in analyzed emotional state and attribute information and runs a generative AI model to generate disaster preparedness information tailored to the user. The input is emotional evaluation data and user profile data, and the output is a disaster prevention message appropriate to the user's state. At this stage, the AI ​​model designs the optimal information according to the user's state.

[0192] Step 4:

[0193] The device receives generated disaster prevention information and presents it to the user visually and audibly. For example, if the device determines that the user is in a state of panic, it will display voice guidance such as "Take a deep breath" and evacuation routes. The input is disaster response information from a generating AI model, and the output is the actual information presented to the user.

[0194] Step 5:

[0195] The user acts based on the information presented. In an emergency, they follow the instructions given by the device and send feedback to the device again during their journey to a shelter. This feedback helps the system improve its accuracy and improves future information generation. The input is user feedback information, and the output is updated data that will be useful for future information provision.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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".

[0212] This invention is a system that provides optimal disaster prevention information based on individual and group profile information. This system consists mainly of a server, terminals, and users, each playing a specific role.

[0213] The server first receives the user's personal and family information and stores it in a database. This information is securely stored using encryption technology. Next, the server runs a generative model and generates the disaster prevention information that should be provided based on it. The generative model also uses external information, including geographic and meteorological data, to perform a risk assessment. The generated disaster prevention information is automatically transmitted to the user's terminal via a data transmission means.

[0214] The device stores disaster prevention information received from the server locally, allowing users to access the information even when the network is down. Furthermore, the device analyzes the received information and controls notifications to the user according to their urgency. Notifications are delivered to the user via voice alerts and vibration. The user interface is designed to be user-friendly, enabling users to check information in real time.

[0215] Users set up their own profiles within the application and update them as needed. These profiles include information such as evacuation destinations and emergency contacts. Users use disaster prevention information received through their devices to create evacuation plans in advance and act according to those plans during a disaster. Users can also provide feedback on the program's effectiveness after a disaster occurs.

[0216] For example, if a typhoon is approaching a region, this system can quickly provide evacuation information and emergency contact methods based on the user's current location. The server acquires new weather data, uses a generative model to calculate the optimal evacuation destination, and provides that information to the terminal. The terminal appropriately notifies the user of this information, and ensures safety during disasters by allowing access to stored information even when the network is unavailable.

[0217] In this way, this system enables a rapid and accurate response to natural disasters by providing individually customized information in real time.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The server receives personal profile information sent by the user. This information includes basic data such as address, family structure, and emergency contact information. The server encrypts this information using security technology and stores it in a database.

[0221] Step 2:

[0222] The server periodically runs a generative model to generate optimal disaster prevention information based on individual profile information. During this process, it also uses weather data and geographical information acquired from external sources for analysis, creating information after assessing disaster risk.

[0223] Step 3:

[0224] Once the generated disaster prevention information is ready, the server sends the information to the terminal. This information is formatted based on the user's priority settings.

[0225] Step 4:

[0226] The terminal saves disaster prevention information received from the server to its local storage. This allows users to access the information even if the terminal cannot connect to the network.

[0227] Step 5:

[0228] The device determines the urgency of disaster information and immediately notifies the user of high-urgency information. Notifications are made using voice alerts and vibrations to attract the user's attention.

[0229] Step 6:

[0230] Users can check disaster prevention information through an application on their device and create an appropriate evacuation plan. Based on their registered profile, users will develop evacuation routes and emergency procedures.

[0231] Step 7:

[0232] When communication is temporarily restored, the server automatically updates disaster prevention information and sends the latest information to the terminal. This automation ensures that users always receive information based on the current situation.

[0233] Step 8:

[0234] After a disaster response, users provide feedback through the system. This feedback is collected on the server and used to improve the generative model and the system.

[0235] (Example 1)

[0236] 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."

[0237] In recent years, the frequency and scale of natural disasters have increased, making it increasingly important for individuals and groups to quickly obtain personalized disaster preparedness information. However, conventional systems have difficulty providing real-time information tailored to individual situations, and there are also challenges in utilizing information when networks are disrupted.

[0238] 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.

[0239] In this invention, the server includes information management means for acquiring and storing attribute information of individuals or groups; information generation means for executing an artificial intelligence model based on the stored attribute information and generating optimal disaster response information; and information transmission means for transmitting the generated disaster response information to client devices, making the information accessible even when the network is unavailable. This enables the provision of customized disaster response information tailored to individual circumstances in real time, and makes it possible to utilize the information even when the network is disrupted.

[0240] "Individual or group attribute information" refers to information that indicates attributes about a user or organization, and includes data such as age, address, family structure, and emergency contact information.

[0241] "Information management means" refers to elements that provide processes and technologies for acquiring, storing, and protecting individual pieces of information.

[0242] An "artificial intelligence model" is an algorithm that generates predictions or suggestions based on specific inputs, and it is a model that uses machine learning or deep learning techniques.

[0243] "Information generation means" refers to elements that provide processes and functions for creating necessary information based on input data.

[0244] "Information transmission means" refers to elements that use communication technologies and protocols to transmit generated information to the intended recipient.

[0245] An "information update mechanism" is an element that has the function of modifying or re-acquiring data in the background in order to keep existing information up to date.

[0246] A "user interface" is a visual or physical interface through which a user interacts with a system, and it includes design elements to facilitate operation.

[0247] "Disaster response information" refers to data that provides specific guidelines and procedures for preparing for, responding to, and recovering from emergencies such as natural disasters.

[0248] This invention is a system that provides users with individually customized disaster response information. The system consists of a server, terminals, and users, and each element plays a specific role to achieve effective information delivery.

[0249] The server executes programs and utilizes multiple hardware and software environments. The server first receives attribute information from users or groups. This is done using web applications and database management systems (e.g., MySQL, PostgreSQL). The received information is stored using encryption technologies such as AES to ensure security. Next, the server uses generative AI models (e.g., GPT-3, BERT) to obtain the latest geographic and weather information from external APIs (e.g., Google Maps API, OpenWeatherMap API) and uses this information to perform a risk assessment. Based on this, it generates appropriate disaster response information. An example of a prompt message is, "Based on the user's current location and attributes, please suggest the optimal evacuation action."

[0250] The device receives disaster response information sent from the server and stores it in local storage. This allows for information to be viewed even when offline. The device also determines the urgency of the information and notifies the user through methods such as push notifications, voice alerts, and vibrations. The application on the device provides an intuitive user interface so that the user can easily access the information.

[0251] By using this system, users can receive personalized disaster preparedness information in real time based on their pre-configured profile information. Users set their evacuation destinations and emergency contacts within the application and create a plan based on this information. In the event of a disaster, they are required to act according to this plan and ensure their safety. Further improvements to the system will be made by providing feedback on the program's effectiveness.

[0252] In this way, the system enables a rapid and accurate response to natural disasters through the coordinated functioning of each element, thereby supporting the safety of users.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The server receives personal or group attribute information from the user. Data entered by the user through the application (e.g., age, address, family structure, contact information, etc.) is sent to the server using a secure protocol (such as HTTPS), and this information is stored in a database using encryption technology such as AES. This process ensures that individual profile information is securely stored.

[0256] Step 2:

[0257] The server retrieves geographic and weather information from external APIs based on stored attribute information. Based on this, it generates a prompt message and provides it to a generation AI model (e.g., GPT-3). Specifically, it calls APIs (such as Google Maps API and OpenWeatherMap API) to obtain the user's current location and weather information, generating the prompt message "Please suggest the optimal evacuation action based on the user's current location and attributes," and inputting it into the generation AI model. The output is individually customized disaster response information.

[0258] Step 3:

[0259] The server transmits disaster response information obtained by the generated AI model to the terminal. A secure protocol is used for data transmission to ensure the information reaches the user's terminal. The transmitted information includes specific evacuation locations, transportation options, and communication methods.

[0260] Step 4:

[0261] The terminal receives disaster response information from the server, stores it locally, and begins analysis. Based on the stored data, the terminal determines the urgency level and notifies the user using push notifications, voice alerts, vibrations, etc. Furthermore, even if the network is unavailable, the locally stored information will be accessible to the user.

[0262] Step 5:

[0263] Based on disaster response information received via their device, users set up evacuation plans and update emergency contacts as needed. By opening the settings menu, creating a plan, entering necessary data, and saving it, this information is utilized by the system. In the event of a disaster, users are required to act quickly according to this plan.

[0264] (Application Example 1)

[0265] 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."

[0266] In the event of a disaster at a large-scale commercial facility, there is a need to provide individualized and optimized safety information to each visitor quickly, minimizing confusion and facilitating efficient evacuation. However, current disaster prevention systems struggle to provide information that fully utilizes visitor attributes and location information, and providing appropriate information, especially when communication networks are disrupted, remains a challenge.

[0267] 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.

[0268] In this invention, the server includes information holding means for receiving and storing attribute data of individuals or groups, generation means for executing a generation AI model based on the stored attribute data to generate optimal safety information, and transmission means for transmitting the generated safety information to a communication terminal, making the information viewable even when the communication network is unavailable. This makes it possible for each visitor in large commercial facilities to receive individually optimized evacuation information in real time, thereby supporting smooth evacuation actions.

[0269] "Attribute data" refers to information related to an individual or group, and in this invention, it includes visitor profiles and location information.

[0270] "Information retention means" refers to a device or software that has the function of storing and managing received attribute data.

[0271] A "generative AI model" refers to a model that uses artificial intelligence to analyze and predict based on accumulated data, and plays a role in creating optimal safety information.

[0272] "Generation means" refers to a device or software that processes data to create optimal safety information based on the obtained data.

[0273] A "communication terminal" refers to a device that a user uses to receive and display information, such as a smartphone or tablet.

[0274] "Transmission means" refers to a device or software that has the function of transmitting generated information to a communication terminal.

[0275] "Route guidance means" refers to a device or software that provides the optimal evacuation route based on the physical location information of a visitor.

[0276] The system implementing this invention is configured to provide optimal evacuation information to visitors in large-scale commercial facilities. It consists of a server, a communication terminal, and various sensors.

[0277] The server securely stores attribute data collected from visitors using information retention methods. This allows for the management of visitors' location information and profiles. Next, an AI model is used based on the stored data to generate security information in real time. The generated information is transmitted to each visitor's communication terminal via a transmission method.

[0278] The communication terminal immediately notifies the visitor of the received evacuation information. The information stored in the terminal can be referenced even when the communication network is unavailable. In addition, the terminal has a route guidance means for identifying the current location of the visitor and presenting an optimal evacuation route. By voice guidance and visual information, the visitor can evacuate safely and quickly.

[0279] As a specific example, when a fire breaks out in a shopping mall, the server immediately calculates an optimal evacuation route based on the location of the visitor and issues a notification. At this time, natural language processing is applied by the generation AI model, and a prompt sentence such as "Please generate an emergency alert in the shopping mall based on the profile and location information of user ID 12345." is used. As a result, both visual maps and voice instructions are provided on the screen of the terminal, so that the visitor can evacuate safely following the instructions.

[0280] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0281] Step 1:

[0282] The server receives the attribute data from the visitor and stores it in the database using the information retention means. The input is the profile information and location information of the visitor, which is stored in a safely encrypted state. The output is the updated visitor information stored in the database within the server.

[0283] Step 2:

[0284] The server executes the generation AI model based on the stored attribute data to generate optimal safety information. The input is the attribute data and dynamically acquired geographical and meteorological data. The prompt sentence "Please generate an emergency alert in the shopping mall based on the profile and location information of user ID 12345." is applied. The output is individual evacuation information corresponding to the visitor.

[0285] Step 3:

[0286] The server transmits the generated safety information to the communication terminals of each visitor using the communication means. The input is the optimized safety information, which is transmitted through wireless communication. The output is the evacuation information received by the communication terminal.

[0287] Step 4:

[0288] The terminal analyzes the received evacuation information and presents the optimal evacuation route using the route guidance means based on the current position of the visitor. The input is the safety information received from the server and the position information of the terminal. The output is the map and voice instructions displayed on the screen of the terminal.

[0289] Step 5:

[0290] The user checks the evacuation route presented by the terminal and evacuates safely according to the instructions. The input is the information and instructions displayed on the terminal, and the output is the actual evacuation behavior of the user.

[0291] Step 6:

[0292] The server uses the temporarily established communication state even during the evacuation to update the safety information in the background. The input is the newly acquired geographical and meteorological data, and the generation AI model is executed again to review the necessary information. The output is the latest evacuation information and its retransmission.

[0293] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0294] The present invention is a disaster prevention information providing system that also considers the user's emotional state in addition to the profile information of individuals or groups. This system aims to provide personalized information based on the user's emotion by combining an emotion engine.

[0295] The server receives emotional data collected from the user's terminal, in addition to regular profile data, and stores this data in a database. This emotional data is analyzed by artificial intelligence based on the user's facial expressions, voice tone, and input speed. The server then uses this data to run a generative model, generating appropriate disaster prevention information tailored to the user's situation and emotions.

[0296] The terminal functions as a device that presents this generated disaster prevention information to the user. Not only does it passively receive information from the server, but it also incorporates an emotion engine, allowing it to monitor the user's real-time emotional changes and dynamically adjust the content and presentation of the information. For example, it can provide concise and direct instructions to anxious users, and detailed information and additional options to calm users.

[0297] Users configure their profiles using the system and allow emotional data to be collected on a daily basis. This permission allows the system to continuously receive emotional feedback and improve the accuracy of its models. In the event of a disaster, users can act according to the information provided on the device and send feedback through the application. For example, if an earthquake occurs and the emotion engine detects that the user is experiencing fear, the device will immediately display calming advice and emergency contact information.

[0298] In this way, by utilizing the emotion engine, it becomes possible to provide disaster prevention information that takes into account the user's psychological state, thereby supporting more effective disaster countermeasures.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The server receives the personal profile information and emotional data sent from the user terminal. The emotional data is generated by an emotion engine that analyzes the user's facial expressions and tone of voice. These data are encrypted using a secure protocol and safely stored in a database.

[0302] Step 2:

[0303] The server combines the stored profile data and emotional data and runs a generation model. This generation model takes into account the user's current emotional state and past trends and generates optimal disaster prevention information. For example, it is adjusted to include more information that gives a sense of security to users with strong anxiety.

[0304] Step 3:

[0305] When the generated disaster prevention information is ready, the server sends this information to the user's terminal using data transmission means. Here, different notification modes are applied according to the priority and urgency of the information.

[0306] Step 4:

[0307] The terminal stores the disaster prevention information received from the server in local storage. This storage process enables the user to check the necessary information even when the terminal is not connected to the network.

[0308] Step 5:

[0309] The terminal continuously monitors the user's real-time emotional state. When the user's emotion is confirmed, the emotion engine adjusts the way to present information at an appropriate timing. For example, when the user is feeling stressed, the information presentation is made concise and the instructions are made clear.

[0310] Step 6:

[0311] Users will check the disaster prevention information displayed on their devices and take the necessary actions. They will also check evacuation routes and contact information in advance based on the information provided, and prepare to ensure their safety.

[0312] Step 7:

[0313] When communication is temporarily restored, the server automatically updates disaster prevention information and sends the new information to the terminal. This process also takes into account the latest sentiment data to ensure that the information is as useful as possible to the user.

[0314] Step 8:

[0315] After disaster response is complete, users provide their experience and feedback on the system through the application. This feedback is sent to the server and used to improve the generative model and emotion engine.

[0316] (Example 2)

[0317] 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".

[0318] Conventional disaster prevention information systems struggled to provide flexible information tailored to individual emotional states and circumstances, making it difficult to respond to the specific needs of each user. This posed a problem, particularly in emergencies, where users had difficulty taking appropriate and swift action. Furthermore, in the event of communication disruptions, information could not be updated, making it impossible to continuously provide the latest disaster prevention information.

[0319] 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.

[0320] In this invention, the server includes recording means for receiving and storing attribute information and emotional data of individuals or groups; creation means for executing a generation AI model based on the stored attribute information and emotional data to generate appropriate disaster prevention information; and transfer means for transmitting the generated disaster prevention information to a user device, making the information viewable even without communication. This provides disaster prevention information customized according to the user's emotions, enabling the continuous supply of up-to-date information even in emergencies.

[0321] "Individual or group attribute information" refers to identifiable characteristics or information related to an individual or group, including age, gender, occupation, hobbies, and location.

[0322] "Emotional data" refers to data that indicates the user's psychological or emotional state, and includes things like facial expressions, tone of voice, and typing speed.

[0323] "Recording means" refers to an element that has the function of saving received information and making it accessible later.

[0324] A "generative AI model" refers to a mathematical model used to generate new data or information based on pre-learned patterns.

[0325] "Creation means" refers to an element that has the function of generating new information or data based on the input data.

[0326] "Transfer means" refers to an element that has the function of transmitting information to different devices or systems.

[0327] A "user device" refers to an electronic device used by a user to receive and display information, and includes smartphones, tablets, and computers.

[0328] "Communication environment" refers to the network connectivity necessary for sending and receiving information between devices.

[0329] This invention is a disaster prevention information provision system that realizes personalized information provision during disasters, and mainly consists of a server, a terminal, and a user. The server receives user attribute information and sentiment data and stores this data using recording means. Specifically, it organizes the data using a database management system to enable rapid access. The server also creates disaster prevention information using a generative AI model based on the stored data. The generative AI model uses general deep learning and natural language processing technologies to efficiently generate disaster prevention information based on the user's sentiment.

[0330] The terminal functions as a user device and has the ability to receive and display disaster prevention information transferred from the server. At the same time, the terminal is equipped with an emotion engine that collects real-time emotion data from the user through its camera and microphone. This allows the terminal to adjust how information is presented according to the situation, maintaining a state where the user can easily receive the information.

[0331] Users can take appropriate actions based on the information provided through their devices. The system's accuracy and usefulness can be further improved by users submitting feedback through the application about their actual actions and feelings during a disaster.

[0332] As a concrete example, if an earthquake occurs while the user is out, the device detects the user's anxiety and the server provides advice such as, "Take a deep breath to calm yourself. The nearest evacuation center is XX." An example of a prompt to be input to the generating AI model is, "The user is in a state of tension. Generate concise and reassuring disaster prevention information."

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] The server receives and records user attribute information and emotional data. Inputs include facial expression data, voice tone, and input speed sent from the user's terminal, while output is the storage of this data in an organized format in a database. Specifically, the server uses a database management system to store the data and organize it for quick access in subsequent processes.

[0336] Step 2:

[0337] The server runs a generative AI model using stored attribute information and sentiment data. It uses user information and sentiment data retrieved from a database as input, and generates personalized disaster prevention information as output. Specifically, the AI ​​model analyzes the sentiment data and generates appropriate advice and notifications using natural language generation technology based on the prompt text.

[0338] Step 3:

[0339] The server transfers the generated disaster prevention information to the terminal. The input is the previously generated disaster prevention information, and the output is the specific disaster prevention measures information sent to the terminal. The server encodes the data using a communication protocol and securely transfers it to the terminal, making it viewable even offline.

[0340] Step 4:

[0341] The terminal displays received disaster prevention information to the user. The input is disaster prevention information received from the server, and the output is information that the user can view on the screen. Specifically, the terminal monitors the user's emotional state in real time and dynamically changes the amount and format of information displayed as needed. For example, it will present information in large, easy-to-read font to a user who is feeling anxious.

[0342] Step 5:

[0343] Users act based on the disaster prevention information provided and send feedback to the server via their device. The input is the information the user has confirmed and the actions they have taken, and the output is the content of the feedback. Here, the user uses the application's feedback function to tell the server whether the information was helpful and how they received it. The server uses this feedback to improve the system and aims to provide more accurate information.

[0344] (Application Example 2)

[0345] 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."

[0346] Conventional disaster information systems present uniform information without considering the user's psychological state, potentially leading to misuse of the information and inducing panic. Furthermore, current technology makes it difficult to update information when communication networks are unavailable, hindering a rapid response during emergencies.

[0347] 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.

[0348] In this invention, the server includes emotion analysis means for analyzing the emotional state of individual users and providing adaptive disaster response information based on the identified emotions; information recording means for receiving and storing attribute information of individuals or groups; and information creation means for executing a generation model based on the stored attribute information and generating appropriate disaster response information. This enables the presentation of effective disaster response information tailored to the user's psychological state and allows for information confirmation and updating even when communication networks are unavailable.

[0349] "Individual or group attribute information" refers to data concerning the characteristics and traits associated with a specific individual or group.

[0350] "Information recording means" refers to a device or system for storing received attribute information.

[0351] A "generative model" is an algorithm or formula used to generate appropriate output based on input data.

[0352] "Information creation means" refers to a function or device for generating disaster response information by executing a generation model.

[0353] A "receiving device" is a terminal or device used by a user to receive disaster response information.

[0354] "Information transmission means" refers to a function or device for transmitting generated information to a receiving device.

[0355] A "communication network" is a network used for sending and receiving information.

[0356] "Information update means" refers to a function or device that keeps information up-to-date in the background.

[0357] "Emotional state" refers to information about an individual's psychological and emotional state.

[0358] "Emotional analysis means" refers to a function or device that identifies the user's emotional state and provides information adaptively based on that state.

[0359] This system consists of a server and terminals, and provides disaster prevention information using a generative AI model based on user attribute information and emotional state. The server receives attribute information of individuals or groups and stores it using information recording means. The main software used for analyzing attribute information includes OpenCV and PyTorch.

[0360] The server uses emotion analysis to understand the user's psychological state based on their facial expressions and voice data. This analysis generates disaster preparedness information tailored to the identified emotions and transmits it to the user's smart glasses or other receiving devices. AI technology is employed in the emotion analysis, and the generated information can be viewed even when network access is unavailable.

[0361] The device dynamically provides the user with visual and audio instructions based on the information it receives. For example, if the user is in a state of panic, the device will offer specific advice such as, "Take a deep breath. The shelter is the park in front of the station." To achieve this, the device uses its built-in microphone and camera to monitor the user's emotional state in real time and adjust the way information is displayed accordingly.

[0362] In this way, personalized responses using emotion analysis become possible, helping users take appropriate actions even in emergencies. Prompts such as, "If a user is in a state of panic during an earthquake, how can I encourage them to take deep breaths and provide information about the nearest evacuation center?" can be used to train the generative AI model.

[0363] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0364] Step 1:

[0365] The server receives user attribute information from the database and stores it using an information recording device. At this stage, data such as user ID, past activity history, and registered emergency contact information are entered. This data is stored as is and used for subsequent processing.

[0366] Step 2:

[0367] The device acquires the user's facial expressions and voice data in real time and analyzes their psychological state using emotion analysis tools. The input is raw data acquired from the camera and microphone, and the output is emotion evaluation data such as "nervous" or "calm." OpenCV and PyTorch are used for this analysis to identify the user's current emotional state.

[0368] Step 3:

[0369] The server takes in analyzed emotional state and attribute information and runs a generative AI model to generate disaster preparedness information tailored to the user. The input is emotional evaluation data and user profile data, and the output is a disaster prevention message appropriate to the user's state. At this stage, the AI ​​model designs the optimal information according to the user's state.

[0370] Step 4:

[0371] The device receives generated disaster prevention information and presents it to the user visually and audibly. For example, if the device determines that the user is in a state of panic, it will display voice guidance such as "Take a deep breath" and evacuation routes. The input is disaster response information from a generating AI model, and the output is the actual information presented to the user.

[0372] Step 5:

[0373] The user acts based on the information presented. In an emergency, they follow the instructions given by the device and send feedback to the device again during their journey to a shelter. This feedback helps the system improve its accuracy and improves future information generation. The input is user feedback information, and the output is updated data that will be useful for future information provision.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] [Third Embodiment]

[0378] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0379] 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.

[0380] 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).

[0381] 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.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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".

[0390] This invention is a system that provides optimal disaster prevention information based on individual and group profile information. This system consists mainly of a server, terminals, and users, each playing a specific role.

[0391] The server first receives the user's personal and family information and stores it in a database. This information is securely stored using encryption technology. Next, the server runs a generative model and generates the disaster prevention information that should be provided based on it. The generative model also uses external information, including geographic and meteorological data, to perform a risk assessment. The generated disaster prevention information is automatically transmitted to the user's terminal via a data transmission means.

[0392] The device stores disaster prevention information received from the server locally, allowing users to access the information even when the network is down. Furthermore, the device analyzes the received information and controls notifications to the user according to their urgency. Notifications are delivered to the user via voice alerts and vibration. The user interface is designed to be user-friendly, enabling users to check information in real time.

[0393] Users set up their own profiles within the application and update them as needed. These profiles include information such as evacuation destinations and emergency contacts. Users use disaster prevention information received through their devices to create evacuation plans in advance and act according to those plans during a disaster. Users can also provide feedback on the program's effectiveness after a disaster occurs.

[0394] For example, if a typhoon is approaching a region, this system can quickly provide evacuation information and emergency contact methods based on the user's current location. The server acquires new weather data, uses a generative model to calculate the optimal evacuation destination, and provides that information to the terminal. The terminal appropriately notifies the user of this information, and ensures safety during disasters by allowing access to stored information even when the network is unavailable.

[0395] In this way, this system enables a rapid and accurate response to natural disasters by providing individually customized information in real time.

[0396] The following describes the processing flow.

[0397] Step 1:

[0398] The server receives personal profile information sent by the user. This information includes basic data such as address, family structure, and emergency contact information. The server encrypts this information using security technology and stores it in a database.

[0399] Step 2:

[0400] The server periodically runs a generative model to generate optimal disaster prevention information based on individual profile information. During this process, it also uses weather data and geographical information acquired from external sources for analysis, creating information after assessing disaster risk.

[0401] Step 3:

[0402] Once the generated disaster prevention information is ready, the server sends the information to the terminal. This information is formatted based on the user's priority settings.

[0403] Step 4:

[0404] The terminal saves disaster prevention information received from the server to its local storage. This allows users to access the information even if the terminal cannot connect to the network.

[0405] Step 5:

[0406] The device determines the urgency of disaster information and immediately notifies the user of high-urgency information. Notifications are made using voice alerts and vibrations to attract the user's attention.

[0407] Step 6:

[0408] Users can check disaster prevention information through an application on their device and create an appropriate evacuation plan. Based on their registered profile, users will develop evacuation routes and emergency procedures.

[0409] Step 7:

[0410] When communication is temporarily restored, the server automatically updates disaster prevention information and sends the latest information to the terminal. This automation ensures that users always receive information based on the current situation.

[0411] Step 8:

[0412] After a disaster response, users provide feedback through the system. This feedback is collected on the server and used to improve the generative model and the system.

[0413] (Example 1)

[0414] 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."

[0415] In recent years, the frequency and scale of natural disasters have increased, making it increasingly important for individuals and groups to quickly obtain personalized disaster preparedness information. However, conventional systems have difficulty providing real-time information tailored to individual situations, and there are also challenges in utilizing information when networks are disrupted.

[0416] 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.

[0417] In this invention, the server includes information management means for acquiring and storing attribute information of individuals or groups; information generation means for executing an artificial intelligence model based on the stored attribute information and generating optimal disaster response information; and information transmission means for transmitting the generated disaster response information to client devices, making the information accessible even when the network is unavailable. This enables the provision of customized disaster response information tailored to individual circumstances in real time, and makes it possible to utilize the information even when the network is disrupted.

[0418] "Individual or group attribute information" refers to information that indicates attributes about a user or organization, and includes data such as age, address, family structure, and emergency contact information.

[0419] "Information management means" refers to elements that provide processes and technologies for acquiring, storing, and protecting individual pieces of information.

[0420] An "artificial intelligence model" is an algorithm that generates predictions or suggestions based on specific inputs, and it is a model that uses machine learning or deep learning techniques.

[0421] "Information generation means" refers to elements that provide processes and functions for creating necessary information based on input data.

[0422] "Information transmission means" refers to elements that use communication technologies and protocols to transmit generated information to the intended recipient.

[0423] An "information update mechanism" is an element that has the function of modifying or re-acquiring data in the background in order to keep existing information up to date.

[0424] A "user interface" is a visual or physical interface through which a user interacts with a system, and it includes design elements to facilitate operation.

[0425] "Disaster response information" refers to data that provides specific guidelines and procedures for preparing for, responding to, and recovering from emergencies such as natural disasters.

[0426] This invention is a system that provides users with individually customized disaster response information. The system consists of a server, terminals, and users, and each element plays a specific role to achieve effective information delivery.

[0427] The server executes programs and utilizes multiple hardware and software environments. The server first receives attribute information from users or groups. This is done using web applications and database management systems (e.g., MySQL, PostgreSQL). The received information is stored using encryption technologies such as AES to ensure security. Next, the server uses generative AI models (e.g., GPT-3, BERT) to obtain the latest geographic and weather information from external APIs (e.g., Google Maps API, OpenWeatherMap API) and uses this information to perform a risk assessment. Based on this, it generates appropriate disaster response information. An example of a prompt message is, "Based on the user's current location and attributes, please suggest the optimal evacuation action."

[0428] The device receives disaster response information sent from the server and stores it in local storage. This allows for information to be viewed even when offline. The device also determines the urgency of the information and notifies the user through methods such as push notifications, voice alerts, and vibrations. The application on the device provides an intuitive user interface so that the user can easily access the information.

[0429] By using this system, users can receive personalized disaster preparedness information in real time based on their pre-configured profile information. Users set their evacuation destinations and emergency contacts within the application and create a plan based on this information. In the event of a disaster, they are required to act according to this plan and ensure their safety. Further improvements to the system will be made by providing feedback on the program's effectiveness.

[0430] In this way, the system enables a rapid and accurate response to natural disasters through the coordinated functioning of each element, thereby supporting the safety of users.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The server receives personal or group attribute information from the user. Data entered by the user through the application (e.g., age, address, family structure, contact information, etc.) is sent to the server using a secure protocol (such as HTTPS), and this information is stored in a database using encryption technology such as AES. This process ensures that individual profile information is securely stored.

[0434] Step 2:

[0435] The server retrieves geographic and weather information from external APIs based on stored attribute information. Based on this, it generates a prompt message and provides it to a generation AI model (e.g., GPT-3). Specifically, it calls APIs (such as Google Maps API and OpenWeatherMap API) to obtain the user's current location and weather information, generating the prompt message "Please suggest the optimal evacuation action based on the user's current location and attributes," and inputting it into the generation AI model. The output is individually customized disaster response information.

[0436] Step 3:

[0437] The server transmits disaster response information obtained by the generated AI model to the terminal. A secure protocol is used for data transmission to ensure the information reaches the user's terminal. The transmitted information includes specific evacuation locations, transportation options, and communication methods.

[0438] Step 4:

[0439] The terminal receives disaster response information from the server, stores it locally, and begins analysis. Based on the stored data, the terminal determines the urgency level and notifies the user using push notifications, voice alerts, vibrations, etc. Furthermore, even if the network is unavailable, the locally stored information will be accessible to the user.

[0440] Step 5:

[0441] Based on disaster response information received via their device, users set up evacuation plans and update emergency contacts as needed. By opening the settings menu, creating a plan, entering necessary data, and saving it, this information is utilized by the system. In the event of a disaster, users are required to act quickly according to this plan.

[0442] (Application Example 1)

[0443] 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."

[0444] In the event of a disaster at a large-scale commercial facility, there is a need to provide individualized and optimized safety information to each visitor quickly, minimizing confusion and facilitating efficient evacuation. However, current disaster prevention systems struggle to provide information that fully utilizes visitor attributes and location information, and providing appropriate information, especially when communication networks are disrupted, remains a challenge.

[0445] 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.

[0446] In this invention, the server includes information holding means for receiving and storing attribute data of individuals or groups, generation means for executing a generation AI model based on the stored attribute data to generate optimal safety information, and transmission means for transmitting the generated safety information to a communication terminal, making the information viewable even when the communication network is unavailable. This makes it possible for each visitor in large commercial facilities to receive individually optimized evacuation information in real time, thereby supporting smooth evacuation actions.

[0447] "Attribute data" refers to information related to an individual or group, and in this invention, it includes visitor profiles and location information.

[0448] "Information retention means" refers to a device or software that has the function of storing and managing received attribute data.

[0449] A "generative AI model" refers to a model that uses artificial intelligence to analyze and predict based on accumulated data, and plays a role in creating optimal safety information.

[0450] "Generation means" refers to a device or software that processes data to create optimal safety information based on the obtained data.

[0451] A "communication terminal" refers to a device that a user uses to receive and display information, such as a smartphone or tablet.

[0452] "Transmission means" refers to a device or software that has the function of transmitting generated information to a communication terminal.

[0453] "Route guidance means" refers to a device or software that provides the optimal evacuation route based on the physical location information of a visitor.

[0454] The system implementing this invention is configured to provide optimal evacuation information to visitors in large-scale commercial facilities. It consists of a server, a communication terminal, and various sensors.

[0455] The server securely stores attribute data collected from visitors using information retention methods. This allows for the management of visitors' location information and profiles. Next, an AI model is used based on the stored data to generate security information in real time. The generated information is transmitted to each visitor's communication terminal via a transmission method.

[0456] The communication terminal immediately notifies visitors of received evacuation information. Information stored on the terminal can be accessed even when the communication network is unavailable. The terminal also includes a route guidance system that identifies the visitor's current location and suggests the optimal evacuation route. Voice guidance and visual information enable visitors to evacuate safely and quickly.

[0457] As a concrete example, if a fire breaks out in a shopping mall, the server will instantly calculate the optimal evacuation route based on the visitor's location and notify them. In this process, natural language processing is applied by a generative AI model, and a prompt such as "Generate an emergency alert within the shopping mall based on the profile and location information of user ID 12345" is used. As a result, the terminal screen will be provided with both a visual map and voice instructions, allowing visitors to follow the instructions and evacuate safely.

[0458] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0459] Step 1:

[0460] The server receives attribute data from visitors and stores it in a database using an information retention system. The input consists of the visitor's profile information and location information, which are stored securely in an encrypted state. The output is the updated visitor information stored in the server's database.

[0461] Step 2:

[0462] The server runs a generative AI model based on stored attribute data to generate optimal safety information. The inputs are attribute data and dynamically acquired geographical and weather data. The prompt is "Generate an emergency alert within the shopping mall based on the profile and location information of user ID 12345." The output is personalized evacuation information tailored to each visitor.

[0463] Step 3:

[0464] The server transmits the generated safety information to each visitor's communication terminal using a transmission method. The input is optimized safety information, which is transmitted via wireless communication. The output is the evacuation information received by the communication terminal.

[0465] Step 4:

[0466] The terminal analyzes the received evacuation information and, based on the visitor's current location, uses route guidance to suggest the optimal evacuation route. Inputs are safety information received from the server and the terminal's location information. Outputs are a map displayed on the terminal's screen and voice instructions.

[0467] Step 5:

[0468] The user checks the evacuation route displayed on the terminal and evacuates safely according to the instructions. The input is the information and instructions displayed on the terminal, and the output is the user's actual evacuation actions.

[0469] Step 6:

[0470] The server updates safety information in the background, utilizing temporarily established communication status even while evacuation is in progress. The input is newly acquired geographical and meteorological data, and the generating AI model is run again to filter the necessary information. The output is the latest evacuation information and its retransmission.

[0471] 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.

[0472] This invention relates to a disaster prevention information provision system that takes into account not only individual or group profile information but also the user's emotional state. The system aims to provide personalized information based on the user's emotions by combining it with an emotion engine.

[0473] The server receives emotional data collected from the user's terminal, in addition to regular profile data, and stores this data in a database. This emotional data is analyzed by artificial intelligence based on the user's facial expressions, voice tone, and input speed. The server then uses this data to run a generative model, generating appropriate disaster prevention information tailored to the user's situation and emotions.

[0474] The terminal functions as a device that presents this generated disaster prevention information to the user. Not only does it passively receive information from the server, but it also incorporates an emotion engine, allowing it to monitor the user's real-time emotional changes and dynamically adjust the content and presentation of the information. For example, it can provide concise and direct instructions to anxious users, and detailed information and additional options to calm users.

[0475] Users configure their profiles using the system and allow emotional data to be collected on a daily basis. This permission allows the system to continuously receive emotional feedback and improve the accuracy of its models. In the event of a disaster, users can act according to the information provided on the device and send feedback through the application. For example, if an earthquake occurs and the emotion engine detects that the user is experiencing fear, the device will immediately display calming advice and emergency contact information.

[0476] In this way, by utilizing the emotion engine, it becomes possible to provide disaster prevention information that takes into account the user's psychological state, thereby supporting more effective disaster countermeasures.

[0477] The following describes the processing flow.

[0478] Step 1:

[0479] The server receives personal profile information and emotional data transmitted from the user's terminal. Emotional data is generated by an emotion engine that analyzes the user's facial expressions and tone of voice. This data is encrypted using a secure protocol and securely stored in a database.

[0480] Step 2:

[0481] The server combines stored profile data and emotion data to run a generative model. This generative model considers the user's current emotional state and past trends to generate optimal disaster prevention information. For example, it adjusts the information to include more reassuring information for users who are highly anxious.

[0482] Step 3:

[0483] Once the generated disaster prevention information is ready, the server sends this information to the user's terminal using a data transmission method. Here, different notification modes are applied depending on the priority and urgency of the information.

[0484] Step 4:

[0485] The terminal saves disaster prevention information received from the server to its local storage. This saving process allows users to access necessary information even when the terminal is not connected to the network.

[0486] Step 5:

[0487] The device continuously monitors the user's real-time emotional state. The emotion engine adjusts how information is presented at the appropriate time when it detects the user's emotions. For example, if the user is stressed, it simplifies the information presentation and clarifies instructions.

[0488] Step 6:

[0489] Users will check the disaster prevention information displayed on their devices and take the necessary actions. They will also check evacuation routes and contact information in advance based on the information provided, and prepare to ensure their safety.

[0490] Step 7:

[0491] When communication is temporarily restored, the server automatically updates disaster prevention information and sends the new information to the terminal. This process also takes into account the latest sentiment data to ensure that the information is as useful as possible to the user.

[0492] Step 8:

[0493] After disaster response is complete, users provide their experience and feedback on the system through the application. This feedback is sent to the server and used to improve the generative model and emotion engine.

[0494] (Example 2)

[0495] 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."

[0496] Conventional disaster prevention information systems struggled to provide flexible information tailored to individual emotional states and circumstances, making it difficult to respond to the specific needs of each user. This posed a problem, particularly in emergencies, where users had difficulty taking appropriate and swift action. Furthermore, in the event of communication disruptions, information could not be updated, making it impossible to continuously provide the latest disaster prevention information.

[0497] 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.

[0498] In this invention, the server includes recording means for receiving and storing attribute information and emotional data of individuals or groups; creation means for executing a generation AI model based on the stored attribute information and emotional data to generate appropriate disaster prevention information; and transfer means for transmitting the generated disaster prevention information to a user device, making the information viewable even without communication. This provides disaster prevention information customized according to the user's emotions, enabling the continuous supply of up-to-date information even in emergencies.

[0499] "Individual or group attribute information" refers to identifiable characteristics or information related to an individual or group, including age, gender, occupation, hobbies, and location.

[0500] "Emotional data" refers to data that indicates the user's psychological or emotional state, and includes things like facial expressions, tone of voice, and typing speed.

[0501] "Recording means" refers to an element that has the function of saving received information and making it accessible later.

[0502] A "generative AI model" refers to a mathematical model used to generate new data or information based on pre-learned patterns.

[0503] "Creation means" refers to an element that has the function of generating new information or data based on the input data.

[0504] "Transfer means" refers to an element that has the function of transmitting information to different devices or systems.

[0505] A "user device" refers to an electronic device used by a user to receive and display information, and includes smartphones, tablets, and computers.

[0506] "Communication environment" refers to the network connectivity necessary for sending and receiving information between devices.

[0507] This invention is a disaster prevention information provision system that realizes personalized information provision during disasters, and mainly consists of a server, a terminal, and a user. The server receives user attribute information and sentiment data and stores this data using recording means. Specifically, it organizes the data using a database management system to enable rapid access. The server also creates disaster prevention information using a generative AI model based on the stored data. The generative AI model uses general deep learning and natural language processing technologies to efficiently generate disaster prevention information based on the user's sentiment.

[0508] The terminal functions as a user device and has the ability to receive and display disaster prevention information transferred from the server. At the same time, the terminal is equipped with an emotion engine that collects real-time emotion data from the user through its camera and microphone. This allows the terminal to adjust how information is presented according to the situation, maintaining a state where the user can easily receive the information.

[0509] Users can take appropriate actions based on the information provided through their devices. The system's accuracy and usefulness can be further improved by users submitting feedback through the application about their actual actions and feelings during a disaster.

[0510] As a concrete example, if an earthquake occurs while the user is out, the device detects the user's anxiety and the server provides advice such as, "Take a deep breath to calm yourself. The nearest evacuation center is XX." An example of a prompt to be input to the generating AI model is, "The user is in a state of tension. Generate concise and reassuring disaster prevention information."

[0511] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0512] Step 1:

[0513] The server receives and records user attribute information and emotional data. Inputs include facial expression data, voice tone, and input speed sent from the user's terminal, while output is the storage of this data in an organized format in a database. Specifically, the server uses a database management system to store the data and organize it for quick access in subsequent processes.

[0514] Step 2:

[0515] The server runs a generative AI model using stored attribute information and sentiment data. It uses user information and sentiment data retrieved from a database as input, and generates personalized disaster prevention information as output. Specifically, the AI ​​model analyzes the sentiment data and generates appropriate advice and notifications using natural language generation technology based on the prompt text.

[0516] Step 3:

[0517] The server transfers the generated disaster prevention information to the terminal. The input is the previously generated disaster prevention information, and the output is the specific disaster prevention measures information sent to the terminal. The server encodes the data using a communication protocol and securely transfers it to the terminal, making it viewable even offline.

[0518] Step 4:

[0519] The terminal displays received disaster prevention information to the user. The input is disaster prevention information received from the server, and the output is information that the user can view on the screen. Specifically, the terminal monitors the user's emotional state in real time and dynamically changes the amount and format of information displayed as needed. For example, it will present information in large, easy-to-read font to a user who is feeling anxious.

[0520] Step 5:

[0521] Users act based on the disaster prevention information provided and send feedback to the server via their device. The input is the information the user has confirmed and the actions they have taken, and the output is the content of the feedback. Here, the user uses the application's feedback function to tell the server whether the information was helpful and how they received it. The server uses this feedback to improve the system and aims to provide more accurate information.

[0522] (Application Example 2)

[0523] 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."

[0524] Conventional disaster information systems present uniform information without considering the user's psychological state, potentially leading to misuse of the information and inducing panic. Furthermore, current technology makes it difficult to update information when communication networks are unavailable, hindering a rapid response during emergencies.

[0525] 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.

[0526] In this invention, the server includes emotion analysis means for analyzing the emotional state of individual users and providing adaptive disaster response information based on the identified emotions; information recording means for receiving and storing attribute information of individuals or groups; and information creation means for executing a generation model based on the stored attribute information and generating appropriate disaster response information. This enables the presentation of effective disaster response information tailored to the user's psychological state and allows for information confirmation and updating even when communication networks are unavailable.

[0527] "Individual or group attribute information" refers to data concerning the characteristics and traits associated with a specific individual or group.

[0528] "Information recording means" refers to a device or system for storing received attribute information.

[0529] A "generative model" is an algorithm or formula used to generate appropriate output based on input data.

[0530] "Information creation means" refers to a function or device for generating disaster response information by executing a generation model.

[0531] A "receiving device" is a terminal or device used by a user to receive disaster response information.

[0532] "Information transmission means" refers to a function or device for transmitting generated information to a receiving device.

[0533] A "communication network" is a network used for sending and receiving information.

[0534] "Information update means" refers to a function or device that keeps information up-to-date in the background.

[0535] "Emotional state" refers to information about an individual's psychological and emotional state.

[0536] "Emotional analysis means" refers to a function or device that identifies the user's emotional state and provides information adaptively based on that state.

[0537] This system consists of a server and terminals, and provides disaster prevention information using a generative AI model based on user attribute information and emotional state. The server receives attribute information of individuals or groups and stores it using information recording means. The main software used for analyzing attribute information includes OpenCV and PyTorch.

[0538] The server uses emotion analysis to understand the user's psychological state based on their facial expressions and voice data. This analysis generates disaster preparedness information tailored to the identified emotions and transmits it to the user's smart glasses or other receiving devices. AI technology is employed in the emotion analysis, and the generated information can be viewed even when network access is unavailable.

[0539] The device dynamically provides the user with visual and audio instructions based on the information it receives. For example, if the user is in a state of panic, the device will offer specific advice such as, "Take a deep breath. The shelter is the park in front of the station." To achieve this, the device uses its built-in microphone and camera to monitor the user's emotional state in real time and adjust the way information is displayed accordingly.

[0540] In this way, personalized responses using emotion analysis become possible, helping users take appropriate actions even in emergencies. Prompts such as, "If a user is in a state of panic during an earthquake, how can I encourage them to take deep breaths and provide information about the nearest evacuation center?" can be used to train the generative AI model.

[0541] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0542] Step 1:

[0543] The server receives user attribute information from the database and stores it using an information recording device. At this stage, data such as user ID, past activity history, and registered emergency contact information are entered. This data is stored as is and used for subsequent processing.

[0544] Step 2:

[0545] The device acquires the user's facial expressions and voice data in real time and analyzes their psychological state using emotion analysis tools. The input is raw data acquired from the camera and microphone, and the output is emotion evaluation data such as "nervous" or "calm." OpenCV and PyTorch are used for this analysis to identify the user's current emotional state.

[0546] Step 3:

[0547] The server takes in analyzed emotional state and attribute information and runs a generative AI model to generate disaster preparedness information tailored to the user. The input is emotional evaluation data and user profile data, and the output is a disaster prevention message appropriate to the user's state. At this stage, the AI ​​model designs the optimal information according to the user's state.

[0548] Step 4:

[0549] The device receives generated disaster prevention information and presents it to the user visually and audibly. For example, if the device determines that the user is in a state of panic, it will display voice guidance such as "Take a deep breath" and evacuation routes. The input is disaster response information from a generating AI model, and the output is the actual information presented to the user.

[0550] Step 5:

[0551] The user acts based on the information presented. In an emergency, they follow the instructions given by the device and send feedback to the device again during their journey to a shelter. This feedback helps the system improve its accuracy and improves future information generation. The input is user feedback information, and the output is updated data that will be useful for future information provision.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] [Fourth Embodiment]

[0556] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0557] 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.

[0558] 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).

[0559] 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.

[0560] 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.

[0561] 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).

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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".

[0569] This invention is a system that provides optimal disaster prevention information based on individual and group profile information. This system consists mainly of a server, terminals, and users, each playing a specific role.

[0570] The server first receives the user's personal and family information and stores it in a database. This information is securely stored using encryption technology. Next, the server runs a generative model and generates the disaster prevention information that should be provided based on it. The generative model also uses external information, including geographic and meteorological data, to perform a risk assessment. The generated disaster prevention information is automatically transmitted to the user's terminal via a data transmission means.

[0571] The device stores disaster prevention information received from the server locally, allowing users to access the information even when the network is down. Furthermore, the device analyzes the received information and controls notifications to the user according to their urgency. Notifications are delivered to the user via voice alerts and vibration. The user interface is designed to be user-friendly, enabling users to check information in real time.

[0572] Users set up their own profiles within the application and update them as needed. These profiles include information such as evacuation destinations and emergency contacts. Users use disaster prevention information received through their devices to create evacuation plans in advance and act according to those plans during a disaster. Users can also provide feedback on the program's effectiveness after a disaster occurs.

[0573] For example, if a typhoon is approaching a region, this system can quickly provide evacuation information and emergency contact methods based on the user's current location. The server acquires new weather data, uses a generative model to calculate the optimal evacuation destination, and provides that information to the terminal. The terminal appropriately notifies the user of this information, and ensures safety during disasters by allowing access to stored information even when the network is unavailable.

[0574] In this way, this system enables a rapid and accurate response to natural disasters by providing individually customized information in real time.

[0575] The following describes the processing flow.

[0576] Step 1:

[0577] The server receives personal profile information sent by the user. This information includes basic data such as address, family structure, and emergency contact information. The server encrypts this information using security technology and stores it in a database.

[0578] Step 2:

[0579] The server periodically runs a generative model to generate optimal disaster prevention information based on individual profile information. During this process, it also uses weather data and geographical information acquired from external sources for analysis, creating information after assessing disaster risk.

[0580] Step 3:

[0581] Once the generated disaster prevention information is ready, the server sends the information to the terminal. This information is formatted based on the user's priority settings.

[0582] Step 4:

[0583] The terminal saves disaster prevention information received from the server to its local storage. This allows users to access the information even if the terminal cannot connect to the network.

[0584] Step 5:

[0585] The device determines the urgency of disaster information and immediately notifies the user of high-urgency information. Notifications are made using voice alerts and vibrations to attract the user's attention.

[0586] Step 6:

[0587] Users can check disaster prevention information through an application on their device and create an appropriate evacuation plan. Based on their registered profile, users will develop evacuation routes and emergency procedures.

[0588] Step 7:

[0589] When communication is temporarily restored, the server automatically updates disaster prevention information and sends the latest information to the terminal. This automation ensures that users always receive information based on the current situation.

[0590] Step 8:

[0591] After a disaster response, users provide feedback through the system. This feedback is collected on the server and used to improve the generative model and the system.

[0592] (Example 1)

[0593] 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".

[0594] In recent years, the frequency and scale of natural disasters have increased, making it increasingly important for individuals and groups to quickly obtain personalized disaster preparedness information. However, conventional systems have difficulty providing real-time information tailored to individual situations, and there are also challenges in utilizing information when networks are disrupted.

[0595] 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.

[0596] In this invention, the server includes information management means for acquiring and storing attribute information of individuals or groups; information generation means for executing an artificial intelligence model based on the stored attribute information and generating optimal disaster response information; and information transmission means for transmitting the generated disaster response information to client devices, making the information accessible even when the network is unavailable. This enables the provision of customized disaster response information tailored to individual circumstances in real time, and makes it possible to utilize the information even when the network is disrupted.

[0597] "Individual or group attribute information" refers to information that indicates attributes about a user or organization, and includes data such as age, address, family structure, and emergency contact information.

[0598] "Information management means" refers to elements that provide processes and technologies for acquiring, storing, and protecting individual pieces of information.

[0599] An "artificial intelligence model" is an algorithm that generates predictions or suggestions based on specific inputs, and it is a model that uses machine learning or deep learning techniques.

[0600] "Information generation means" refers to elements that provide processes and functions for creating necessary information based on input data.

[0601] "Information transmission means" refers to elements that use communication technologies and protocols to transmit generated information to the intended recipient.

[0602] An "information update mechanism" is an element that has the function of modifying or re-acquiring data in the background in order to keep existing information up to date.

[0603] A "user interface" is a visual or physical interface through which a user interacts with a system, and it includes design elements to facilitate operation.

[0604] "Disaster response information" refers to data that provides specific guidelines and procedures for preparing for, responding to, and recovering from emergencies such as natural disasters.

[0605] This invention is a system that provides users with individually customized disaster response information. The system consists of a server, terminals, and users, and each element plays a specific role to achieve effective information delivery.

[0606] The server executes programs and utilizes multiple hardware and software environments. The server first receives attribute information from users or groups. This is done using web applications and database management systems (e.g., MySQL, PostgreSQL). The received information is stored using encryption technologies such as AES to ensure security. Next, the server uses generative AI models (e.g., GPT-3, BERT) to obtain the latest geographic and weather information from external APIs (e.g., Google Maps API, OpenWeatherMap API) and uses this information to perform a risk assessment. Based on this, it generates appropriate disaster response information. An example of a prompt message is, "Based on the user's current location and attributes, please suggest the optimal evacuation action."

[0607] The device receives disaster response information sent from the server and stores it in local storage. This allows for information to be viewed even when offline. The device also determines the urgency of the information and notifies the user through methods such as push notifications, voice alerts, and vibrations. The application on the device provides an intuitive user interface so that the user can easily access the information.

[0608] By using this system, users can receive personalized disaster preparedness information in real time based on their pre-configured profile information. Users set their evacuation destinations and emergency contacts within the application and create a plan based on this information. In the event of a disaster, they are required to act according to this plan and ensure their safety. Further improvements to the system will be made by providing feedback on the program's effectiveness.

[0609] In this way, the system enables a rapid and accurate response to natural disasters through the coordinated functioning of each element, thereby supporting the safety of users.

[0610] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0611] Step 1:

[0612] The server receives personal or group attribute information from the user. Data entered by the user through the application (e.g., age, address, family structure, contact information, etc.) is sent to the server using a secure protocol (such as HTTPS), and this information is stored in a database using encryption technology such as AES. This process ensures that individual profile information is securely stored.

[0613] Step 2:

[0614] The server retrieves geographic and weather information from external APIs based on stored attribute information. Based on this, it generates a prompt message and provides it to a generation AI model (e.g., GPT-3). Specifically, it calls APIs (such as Google Maps API and OpenWeatherMap API) to obtain the user's current location and weather information, generating the prompt message "Please suggest the optimal evacuation action based on the user's current location and attributes," and inputting it into the generation AI model. The output is individually customized disaster response information.

[0615] Step 3:

[0616] The server transmits disaster response information obtained by the generated AI model to the terminal. A secure protocol is used for data transmission to ensure the information reaches the user's terminal. The transmitted information includes specific evacuation locations, transportation options, and communication methods.

[0617] Step 4:

[0618] The terminal receives disaster response information from the server, stores it locally, and begins analysis. Based on the stored data, the terminal determines the urgency level and notifies the user using push notifications, voice alerts, vibrations, etc. Furthermore, even if the network is unavailable, the locally stored information will be accessible to the user.

[0619] Step 5:

[0620] Based on disaster response information received via their device, users set up evacuation plans and update emergency contacts as needed. By opening the settings menu, creating a plan, entering necessary data, and saving it, this information is utilized by the system. In the event of a disaster, users are required to act quickly according to this plan.

[0621] (Application Example 1)

[0622] 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".

[0623] In the event of a disaster at a large-scale commercial facility, there is a need to provide individualized and optimized safety information to each visitor quickly, minimizing confusion and facilitating efficient evacuation. However, current disaster prevention systems struggle to provide information that fully utilizes visitor attributes and location information, and providing appropriate information, especially when communication networks are disrupted, remains a challenge.

[0624] 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.

[0625] In this invention, the server includes information holding means for receiving and storing attribute data of individuals or groups, generation means for executing a generation AI model based on the stored attribute data to generate optimal safety information, and transmission means for transmitting the generated safety information to a communication terminal, making the information viewable even when the communication network is unavailable. This makes it possible for each visitor in large commercial facilities to receive individually optimized evacuation information in real time, thereby supporting smooth evacuation actions.

[0626] "Attribute data" refers to information related to an individual or group, and in this invention, it includes visitor profiles and location information.

[0627] "Information retention means" refers to a device or software that has the function of storing and managing received attribute data.

[0628] A "generative AI model" refers to a model that uses artificial intelligence to analyze and predict based on accumulated data, and plays a role in creating optimal safety information.

[0629] "Generation means" refers to a device or software that processes data to create optimal safety information based on the obtained data.

[0630] A "communication terminal" refers to a device that a user uses to receive and display information, such as a smartphone or tablet.

[0631] "Transmission means" refers to a device or software that has the function of transmitting generated information to a communication terminal.

[0632] "Route guidance means" refers to a device or software that provides the optimal evacuation route based on the physical location information of a visitor.

[0633] The system implementing this invention is configured to provide optimal evacuation information to visitors in large-scale commercial facilities. It consists of a server, a communication terminal, and various sensors.

[0634] The server securely stores attribute data collected from visitors using information retention methods. This allows for the management of visitors' location information and profiles. Next, an AI model is used based on the stored data to generate security information in real time. The generated information is transmitted to each visitor's communication terminal via a transmission method.

[0635] The communication terminal immediately notifies visitors of received evacuation information. Information stored on the terminal can be accessed even when the communication network is unavailable. The terminal also includes a route guidance system that identifies the visitor's current location and suggests the optimal evacuation route. Voice guidance and visual information enable visitors to evacuate safely and quickly.

[0636] As a concrete example, if a fire breaks out in a shopping mall, the server will instantly calculate the optimal evacuation route based on the visitor's location and notify them. In this process, natural language processing is applied by a generative AI model, and a prompt such as "Generate an emergency alert within the shopping mall based on the profile and location information of user ID 12345" is used. As a result, the terminal screen will be provided with both a visual map and voice instructions, allowing visitors to follow the instructions and evacuate safely.

[0637] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0638] Step 1:

[0639] The server receives attribute data from visitors and stores it in a database using an information retention system. The input consists of the visitor's profile information and location information, which are stored securely in an encrypted state. The output is the updated visitor information stored in the server's database.

[0640] Step 2:

[0641] The server runs a generative AI model based on stored attribute data to generate optimal safety information. The inputs are attribute data and dynamically acquired geographical and weather data. The prompt is "Generate an emergency alert within the shopping mall based on the profile and location information of user ID 12345." The output is personalized evacuation information tailored to each visitor.

[0642] Step 3:

[0643] The server transmits the generated safety information to each visitor's communication terminal using a transmission method. The input is optimized safety information, which is transmitted via wireless communication. The output is the evacuation information received by the communication terminal.

[0644] Step 4:

[0645] The terminal analyzes the received evacuation information and, based on the visitor's current location, uses route guidance to suggest the optimal evacuation route. Inputs are safety information received from the server and the terminal's location information. Outputs are a map displayed on the terminal's screen and voice instructions.

[0646] Step 5:

[0647] The user checks the evacuation route displayed on the terminal and evacuates safely according to the instructions. The input is the information and instructions displayed on the terminal, and the output is the user's actual evacuation actions.

[0648] Step 6:

[0649] The server updates safety information in the background, utilizing temporarily established communication states even while evacuations are in progress. The input is newly acquired geographical and meteorological data, and the generating AI model is run again to filter the necessary information. The output is the latest evacuation information and its retransmission.

[0650] 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.

[0651] This invention relates to a disaster prevention information provision system that takes into account not only individual or group profile information but also the user's emotional state. The system aims to provide personalized information based on the user's emotions by combining it with an emotion engine.

[0652] The server receives emotional data collected from the user's terminal, in addition to regular profile data, and stores this data in a database. This emotional data is analyzed by artificial intelligence based on the user's facial expressions, voice tone, and input speed. The server then uses this data to run a generative model, generating appropriate disaster prevention information tailored to the user's situation and emotions.

[0653] The terminal functions as a device that presents this generated disaster prevention information to the user. Not only does it passively receive information from the server, but it also incorporates an emotion engine, allowing it to monitor the user's real-time emotional changes and dynamically adjust the content and presentation of the information. For example, it can provide concise and direct instructions to anxious users, and detailed information and additional options to calm users.

[0654] Users configure their profiles using the system and allow emotional data to be collected on a daily basis. This permission allows the system to continuously receive emotional feedback and improve the accuracy of its models. In the event of a disaster, users can act according to the information provided on the device and send feedback through the application. For example, if an earthquake occurs and the emotion engine detects that the user is experiencing fear, the device will immediately display calming advice and emergency contact information.

[0655] In this way, by utilizing the emotion engine, it becomes possible to provide disaster prevention information that takes into account the user's psychological state, thereby supporting more effective disaster countermeasures.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] The server receives personal profile information and emotional data transmitted from the user's terminal. Emotional data is generated by an emotion engine that analyzes the user's facial expressions and tone of voice. This data is encrypted using a secure protocol and securely stored in a database.

[0659] Step 2:

[0660] The server combines stored profile data and emotion data to run a generative model. This generative model considers the user's current emotional state and past trends to generate optimal disaster prevention information. For example, it adjusts the information to include more reassuring information for users who are highly anxious.

[0661] Step 3:

[0662] Once the generated disaster prevention information is ready, the server sends this information to the user's terminal using a data transmission method. Here, different notification modes are applied depending on the priority and urgency of the information.

[0663] Step 4:

[0664] The terminal saves disaster prevention information received from the server to its local storage. This saving process allows users to access necessary information even when the terminal is not connected to the network.

[0665] Step 5:

[0666] The device continuously monitors the user's real-time emotional state. The emotion engine adjusts how information is presented at the appropriate time when it detects the user's emotions. For example, if the user is stressed, it simplifies the information presentation and clarifies instructions.

[0667] Step 6:

[0668] Users will check the disaster prevention information displayed on their devices and take the necessary actions. They will also check evacuation routes and contact information in advance based on the information provided, and prepare to ensure their safety.

[0669] Step 7:

[0670] When communication is temporarily restored, the server automatically updates disaster prevention information and sends the new information to the terminal. This process also takes into account the latest sentiment data to ensure that the information is as useful as possible to the user.

[0671] Step 8:

[0672] After disaster response is complete, users provide their experience and feedback on the system through the application. This feedback is sent to the server and used to improve the generative model and emotion engine.

[0673] (Example 2)

[0674] 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".

[0675] Conventional disaster prevention information systems struggled to provide flexible information tailored to individual emotional states and circumstances, making it difficult to respond to the specific needs of each user. This posed a problem, particularly in emergencies, where users had difficulty taking appropriate and swift action. Furthermore, in the event of communication disruptions, information could not be updated, making it impossible to continuously provide the latest disaster prevention information.

[0676] 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.

[0677] In this invention, the server includes recording means for receiving and storing attribute information and emotional data of individuals or groups; creation means for executing a generation AI model based on the stored attribute information and emotional data to generate appropriate disaster prevention information; and transfer means for transmitting the generated disaster prevention information to a user device, making the information viewable even without communication. This provides disaster prevention information customized according to the user's emotions, enabling the continuous supply of up-to-date information even in emergencies.

[0678] "Individual or group attribute information" refers to identifiable characteristics or information related to an individual or group, including age, gender, occupation, hobbies, and location.

[0679] "Emotional data" refers to data that indicates the user's psychological or emotional state, and includes things like facial expressions, tone of voice, and typing speed.

[0680] "Recording means" refers to an element that has the function of saving received information and making it accessible later.

[0681] A "generative AI model" refers to a mathematical model used to generate new data or information based on pre-learned patterns.

[0682] "Creation means" refers to an element that has the function of generating new information or data based on the input data.

[0683] "Transfer means" refers to an element that has the function of transmitting information to different devices or systems.

[0684] A "user device" refers to an electronic device used by a user to receive and display information, and includes smartphones, tablets, and computers.

[0685] "Communication environment" refers to the network connectivity necessary for sending and receiving information between devices.

[0686] This invention is a disaster prevention information provision system that realizes personalized information provision during disasters, and mainly consists of a server, a terminal, and a user. The server receives user attribute information and sentiment data and stores this data using recording means. Specifically, it organizes the data using a database management system to enable rapid access. The server also creates disaster prevention information using a generative AI model based on the stored data. The generative AI model uses general deep learning and natural language processing technologies to efficiently generate disaster prevention information based on the user's sentiment.

[0687] The terminal functions as a user device and has the ability to receive and display disaster prevention information transferred from the server. At the same time, the terminal is equipped with an emotion engine that collects real-time emotion data from the user through its camera and microphone. This allows the terminal to adjust how information is presented according to the situation, maintaining a state where the user can easily receive the information.

[0688] Users can take appropriate actions based on the information provided through their devices. The system's accuracy and usefulness can be further improved by users submitting feedback through the application about their actual actions and feelings during a disaster.

[0689] As a concrete example, if an earthquake occurs while the user is out, the device detects the user's anxiety and the server provides advice such as, "Take a deep breath to calm yourself. The nearest evacuation center is XX." An example of a prompt to be input to the generating AI model is, "The user is in a state of tension. Generate concise and reassuring disaster prevention information."

[0690] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0691] Step 1:

[0692] The server receives and records user attribute information and emotional data. Inputs include facial expression data, voice tone, and input speed sent from the user's terminal, while output is the storage of this data in an organized format in a database. Specifically, the server uses a database management system to store the data and organize it for quick access in subsequent processes.

[0693] Step 2:

[0694] The server runs a generative AI model using stored attribute information and sentiment data. It uses user information and sentiment data retrieved from a database as input, and generates personalized disaster prevention information as output. Specifically, the AI ​​model analyzes the sentiment data and generates appropriate advice and notifications using natural language generation technology based on the prompt text.

[0695] Step 3:

[0696] The server transfers the generated disaster prevention information to the terminal. The input is the previously generated disaster prevention information, and the output is the specific disaster prevention measures information sent to the terminal. The server encodes the data using a communication protocol and securely transfers it to the terminal, making it viewable even offline.

[0697] Step 4:

[0698] The terminal displays received disaster prevention information to the user. The input is disaster prevention information received from the server, and the output is information that the user can view on the screen. Specifically, the terminal monitors the user's emotional state in real time and dynamically changes the amount and format of information displayed as needed. For example, it will present information in large, easy-to-read font to a user who is feeling anxious.

[0699] Step 5:

[0700] Users act based on the disaster prevention information provided and send feedback to the server via their device. The input is the information the user has confirmed and the actions they have taken, and the output is the content of the feedback. Here, the user uses the application's feedback function to tell the server whether the information was helpful and how they received it. The server uses this feedback to improve the system and aims to provide more accurate information.

[0701] (Application Example 2)

[0702] 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".

[0703] Conventional disaster information systems present uniform information without considering the user's psychological state, potentially leading to misuse of the information and inducing panic. Furthermore, current technology makes it difficult to update information when communication networks are unavailable, hindering a rapid response during emergencies.

[0704] 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.

[0705] In this invention, the server includes emotion analysis means for analyzing the emotional state of individual users and providing adaptive disaster response information based on the identified emotions; information recording means for receiving and storing attribute information of individuals or groups; and information creation means for executing a generation model based on the stored attribute information and generating appropriate disaster response information. This enables the presentation of effective disaster response information tailored to the user's psychological state and allows for information confirmation and updating even when communication networks are unavailable.

[0706] "Individual or group attribute information" refers to data concerning the characteristics and traits associated with a specific individual or group.

[0707] "Information recording means" refers to a device or system for storing received attribute information.

[0708] A "generative model" is an algorithm or formula used to generate appropriate output based on input data.

[0709] "Information creation means" refers to a function or device for generating disaster response information by executing a generation model.

[0710] A "receiving device" is a terminal or device used by a user to receive disaster response information.

[0711] "Information transmission means" refers to a function or device for transmitting generated information to a receiving device.

[0712] A "communication network" is a network used for sending and receiving information.

[0713] "Information update means" refers to a function or device that keeps information up-to-date in the background.

[0714] "Emotional state" refers to information about an individual's psychological and emotional state.

[0715] "Emotional analysis means" refers to a function or device that identifies the user's emotional state and provides information adaptively based on that state.

[0716] This system consists of a server and terminals, and provides disaster prevention information using a generative AI model based on user attribute information and emotional state. The server receives attribute information of individuals or groups and stores it using information recording means. The main software used for analyzing attribute information includes OpenCV and PyTorch.

[0717] The server uses emotion analysis to understand the user's psychological state based on their facial expressions and voice data. This analysis generates disaster preparedness information tailored to the identified emotions and transmits it to the user's smart glasses or other receiving devices. AI technology is employed in the emotion analysis, and the generated information can be viewed even when network access is unavailable.

[0718] The device dynamically provides the user with visual and audio instructions based on the information it receives. For example, if the user is in a state of panic, the device will offer specific advice such as, "Take a deep breath. The shelter is the park in front of the station." To achieve this, the device uses its built-in microphone and camera to monitor the user's emotional state in real time and adjust the way information is displayed accordingly.

[0719] In this way, personalized responses using emotion analysis become possible, helping users take appropriate actions even in emergencies. Prompts such as, "If a user is in a state of panic during an earthquake, how can I encourage them to take deep breaths and provide information about the nearest evacuation center?" can be used to train the generative AI model.

[0720] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0721] Step 1:

[0722] The server receives user attribute information from the database and stores it using an information recording device. At this stage, data such as user ID, past activity history, and registered emergency contact information are entered. This data is stored as is and used for subsequent processing.

[0723] Step 2:

[0724] The device acquires the user's facial expressions and voice data in real time and analyzes their psychological state using emotion analysis tools. The input is raw data acquired from the camera and microphone, and the output is emotion evaluation data such as "nervous" or "calm." OpenCV and PyTorch are used for this analysis to identify the user's current emotional state.

[0725] Step 3:

[0726] The server takes in analyzed emotional state and attribute information and runs a generative AI model to generate disaster preparedness information tailored to the user. The input is emotional evaluation data and user profile data, and the output is a disaster prevention message appropriate to the user's state. At this stage, the AI ​​model designs the optimal information according to the user's state.

[0727] Step 4:

[0728] The device receives generated disaster prevention information and presents it to the user visually and audibly. For example, if the device determines that the user is in a state of panic, it will display voice guidance such as "Take a deep breath" and evacuation routes. The input is disaster response information from a generating AI model, and the output is the actual information presented to the user.

[0729] Step 5:

[0730] The user acts based on the information presented. In an emergency, they follow the instructions given by the device and send feedback to the device again during their journey to a shelter. This feedback helps the system improve its accuracy and improves future information generation. The input is user feedback information, and the output is updated data that will be useful for future information provision.

[0731] 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.

[0732] 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.

[0733] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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."

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] The following is further disclosed regarding the embodiments described above.

[0753] (Claim 1)

[0754] A data storage means for receiving and storing profile information of an individual or group,

[0755] A generation means that runs a generation model based on saved profile information to generate appropriate disaster prevention information,

[0756] A data transmission method that sends generated disaster prevention information to a local device, making the information viewable even when the network is unavailable,

[0757] A method for updating disaster prevention information in the background using a temporarily established communication state,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, characterized in that the data transmission means has a function to determine the degree of urgency based on predetermined conditions and control notifications.

[0761] (Claim 3)

[0762] The system according to claim 1, characterized in that the generation means acquires external geographic data and meteorological data, performs a risk assessment based on them, and improves the generated disaster prevention information.

[0763] "Example 1"

[0764] (Claim 1)

[0765] Information management means for acquiring and maintaining attribute information of individuals or groups,

[0766] An information generation means that runs an artificial intelligence model based on retained attribute information to generate optimal disaster response information,

[0767] An information transmission means that transmits generated disaster response information to client devices, making the information accessible even when the network is unavailable,

[0768] An information update method that updates disaster response information in the background using a temporarily established communication state,

[0769] A user configuration means that allows adjustment of attribute information through the user interface,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, characterized in that the information transmission means has a function to determine the degree of urgency based on predetermined criteria and control notifications.

[0773] (Claim 3)

[0774] The system according to claim 1, characterized in that the information generation means acquires external geographic information and meteorological information, performs a risk assessment based on it, and improves the generated disaster countermeasure information.

[0775] "Application Example 1"

[0776] (Claim 1)

[0777] Information storage means for receiving and storing attribute data of individuals or groups,

[0778] A generation means that runs a generation AI model based on stored attribute data to generate optimal safety information,

[0779] A means of transmission that sends generated security information to a communication terminal, making the information viewable even when the communication network is unavailable,

[0780] An update method that updates security information in the background using a temporarily established communication state,

[0781] A route guidance means that provides the shortest evacuation route based on the physical location of the visitor,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, characterized in that the transmission means has a function to determine the degree of urgency based on predetermined conditions and to control the notification.

[0785] (Claim 3)

[0786] The system according to claim 1, characterized in that the generation means acquires external geographic data and meteorological data, performs a risk assessment based on them, and improves the generated safety information.

[0787] "Example 2 of combining an emotion engine"

[0788] (Claim 1)

[0789] A recording means for receiving and storing attribute information and emotional data of individuals or groups,

[0790] A means for generating appropriate disaster prevention information by executing a generation AI model based on stored attribute information and emotion data,

[0791] A transfer method that transmits generated disaster prevention information to user devices, making the information viewable even without communication,

[0792] A revision method that uses a temporarily established communication environment to update disaster prevention information in the background,

[0793] An adjustment mechanism that dynamically adjusts the information content and presentation method according to the user's emotional state,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, characterized in that the forwarding means has a function to determine the urgency based on predetermined conditions and manage notifications.

[0797] (Claim 3)

[0798] The system according to claim 1, characterized in that the creation means acquires external location data and environmental data, performs a risk assessment based on them, and improves the generated disaster prevention information.

[0799] "Application example 2 when combining with an emotional engine"

[0800] (Claim 1)

[0801] Information recording means for receiving and storing attribute information of an individual or group,

[0802] An information creation means that executes a generation model based on stored attribute information to generate appropriate disaster countermeasure information,

[0803] An information transmission means that transmits generated disaster response information to receiving devices, making the information accessible even when the communication network is unavailable,

[0804] An information update method that updates disaster response information in the background using a temporarily established communication state,

[0805] An emotion analysis means that analyzes the emotional state of individual users and provides adaptive disaster response information based on the identified emotions,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, characterized in that the information transmission means has a function to determine priority based on a predetermined state and adjust notifications.

[0809] (Claim 3)

[0810] The system according to claim 1, characterized in that the information creation means acquires external location information and weather information, performs a risk assessment based on it, and improves the generated disaster countermeasure information. [Explanation of symbols]

[0811] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data storage means for receiving and storing profile information of an individual or group, A generation means that runs a generation model based on saved profile information to generate appropriate disaster prevention information, A data transmission method that sends generated disaster prevention information to a local device, making the information viewable even when the network is unavailable, A method for updating disaster prevention information in the background using a temporarily established communication state, A system that includes this.

2. The system according to claim 1, characterized in that the data transmission means has a function to determine the degree of urgency based on predetermined conditions and control notifications.

3. The system according to claim 1, characterized in that the generation means acquires external geographic data and meteorological data, performs a risk assessment based on them, and improves the generated disaster prevention information.

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