system

Abstract

The system according to this embodiment aims to provide personalized evacuation instructions in real time during a disaster. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a navigation unit. The collection unit collects disaster information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides evacuation instructions based on the analysis results obtained by the analysis unit. The navigation unit conveys the evacuation instructions provided by the provision unit to the user.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide evacuation instructions optimized for an individual in real time when a disaster occurs.

[0005] The system according to the embodiment aims to provide evacuation instructions optimized for an individual in real time when a disaster occurs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a navigation unit. The collection unit collects disaster information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides evacuation instructions based on the analysis results obtained by the analysis unit. The navigation unit conveys the evacuation instructions provided by the provision unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized evacuation instructions in real time during a disaster. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An embodiment of the present invention provides a disaster response system that is an AI chatbot that organizes the situation when an earthquake occurs and provides personalized action guidelines. This disaster response system uses a multimodal AI that integrates real-time disaster information, evacuation shelter information, and safety confirmation functions. This AI comprehensively processes voice, images, and text and provides optimal evacuation instructions tailored to the individual's situation via AR display and voice. This overcomes impaired judgment and language barriers during panic and guides appropriate actions through real-time situation analysis. For example, the disaster response system provides real-time disaster information, evacuation shelter information, and safety confirmation functions when an earthquake occurs. The disaster response system comprehensively processes voice, images, and text and provides optimal evacuation instructions tailored to the individual's situation via AR display and voice. This overcomes impaired judgment and language barriers during panic and guides appropriate actions through real-time situation analysis. Next, the disaster response system provides individually optimized instructions according to the user's health condition and physical abilities. For example, a user who has difficulty moving physically is instructed to move to a nearby evacuation shelter or safe place. Furthermore, the disaster response system uses AI to analyze the damage situation in the surrounding area and provides information to local governments and rescue teams in real time. This enables rapid rescue operations. In addition, the disaster response system features visual evacuation route display using AR technology, easy operation in emergencies using voice recognition, and real-time multilingual translation functionality. This allows users to intuitively understand evacuation routes and act quickly. Through this system, the aim is to minimize human casualties during disasters and realize a society where everyone can live with peace of mind. For example, in the event of an earthquake, the disaster response system will provide evacuation instructions in real time, supporting users in evacuating safely. The disaster response system also uses AI to monitor congestion and safety at evacuation centers and suggests appropriate evacuation destinations, enabling efficient operation of evacuation centers. In this way, the disaster response system can solve problems such as information overload and insufficiency, impaired judgment due to panic, and delays in appropriate action due to language barriers during earthquakes.

[0029] The disaster response system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a navigation unit. The collection unit collects disaster information. The collection unit can collect disaster information such as earthquake information, flood information, and fire information. The collection unit can collect disaster information in various ways, such as by using sensors or by collecting from social media. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze disaster information using, for example, data mining or machine learning algorithms. Based on the analysis results of the disaster information, the analysis unit generates evacuation instructions to be provided to the user. The provision unit provides evacuation instructions based on the analysis results obtained by the analysis unit. The provision unit can provide evacuation instructions to the user by setting, for example, the notification method and the format of the information to be provided. The navigation unit conveys the evacuation instructions provided by the provision unit to the user. The navigation unit can convey evacuation instructions to the user by methods such as voice guidance or map display. As a result, the disaster response system according to this embodiment can provide the user with optimal evacuation instructions by integrating the collection, analysis, provision, and navigation of disaster information.

[0030] The data collection unit collects disaster information. For example, it can collect disaster information such as earthquake information, flood information, and fire information. Specifically, for earthquake information, it uses seismometers and acceleration sensors to detect the occurrence of earthquakes and identify their intensity and epicenter. For flood information, it uses river water level sensors and rain gauges to monitor water level rises and rainfall to assess flood risk. For fire information, it uses smoke sensors and temperature sensors to detect the occurrence of fires and identify their location and scale. The data collection unit collects data from these sensors in real time and transmits it to a central database. The data collection unit also collects information from social media. For example, it collects posts related to disasters from platforms and extracts useful information using text mining techniques. This allows the data collection unit to collect not only official sensor information but also real-time information from the general public. Furthermore, the data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provision departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze disaster information using data mining and machine learning algorithms. Specifically, it uses data mining techniques to extract patterns and trends from collected data and predict the probability of disaster occurrence and the scope of impact. Machine learning algorithms learn from past disaster data and evaluate disaster risk based on new data. For example, it analyzes data on earthquake epicenters and seismic intensity to predict the probability of aftershocks. It also analyzes flood water level data and rainfall data to predict the speed of flood progression and the scope of impact. For fire information, it analyzes data from smoke sensors and temperature sensors to evaluate the speed of fire spread and the need for evacuation. Based on these analysis results, the analysis unit generates evacuation orders to be provided to users. Furthermore, the analysis unit can continuously revise its analysis results based on data that is updated in real time, enabling it to respond to the latest situation. For example, if new earthquake, flood, or fire information is collected, the analysis unit immediately incorporates the new data and updates the analysis results. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The service provider provides evacuation orders based on the analysis results obtained by the analysis unit. The service provider can, for example, set the notification method and the format of the information provided to deliver evacuation orders to users. Specifically, it can send push notifications to users' devices, providing information on the necessity of evacuation and evacuation destinations. Possible notification methods include smartphone app notifications, SMS, email, and voice calls. The service provider selects the most appropriate notification method based on the user's location and situation to ensure reliable information transmission. The format of the information provided can also be flexibly configured. For example, it can combine text notifications with maps, images, and voice guidance to provide information in an easy-to-understand manner for users. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information provided. For example, it can revise evacuation routes and improve the content of instructions based on feedback from users who have received evacuation orders. This allows the service provider to provide users with quick and appropriate evacuation orders, minimizing the risk of disaster. Additionally, the service provider can reliably transmit information using multiple communication methods. For example, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the service provider to quickly and reliably provide users with action instructions, minimizing the risk of disaster.

[0033] The navigation unit conveys evacuation instructions provided by the service provider to the user. The navigation unit can convey evacuation instructions to the user through methods such as voice guidance and map display. Specifically, it displays the optimal evacuation route based on the user's current location and evacuation destination via a smartphone app, and provides warnings through voice guidance and vibration notifications. The navigation unit calculates and displays the optimal evacuation route based on real-time updated map information. For example, it suggests detour routes to avoid areas affected by floods or fires, helping users evacuate safely. The navigation unit also monitors the user's movement and can modify the evacuation route as needed. For example, if a user deviates from the designated evacuation route or if new disaster information is collected, the navigation unit immediately calculates a new route and notifies the user. Furthermore, the navigation unit can consider congestion when multiple users evacuate simultaneously and provide the optimal evacuation route. This allows the navigation unit to provide users with quick and appropriate evacuation instructions and support safe evacuation. Additionally, the navigation unit can collect user feedback and continuously improve the accuracy and effectiveness of its navigation functions. For example, based on feedback from users who have received evacuation orders, evacuation routes and guidance content can be revised. This allows the navigation system to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0034] The analysis unit includes an individual optimization unit that provides individually optimized instructions tailored to the user's health condition and physical abilities. The individual optimization unit, for example, evaluates the user's health condition, such as heart rate, blood pressure, and medical history, and provides optimal evacuation instructions. The individual optimization unit can also evaluate the user's physical abilities, such as walking speed, endurance, and muscle strength, and provide optimal evacuation instructions. This allows the analysis unit to provide optimal evacuation instructions tailored to the user's health condition and physical abilities. Some or all of the above-described processes in the individual optimization unit may be performed using AI, for example, or without AI. For example, the individual optimization unit can input the user's health data into AI to generate optimal evacuation instructions.

[0035] The analysis unit includes an information provision unit that analyzes the damage situation in the surrounding area and provides information to local governments and rescue teams in real time. The information provision unit evaluates the damage situation in the surrounding area, such as the extent of damage to buildings and the passability of roads, and provides information to local governments and rescue teams. The information provision unit can analyze the damage situation in real time and support rapid rescue operations. As a result, the analysis unit can analyze the damage situation in the surrounding area in real time and support rapid rescue operations. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI. For example, the information provision unit can input damage data into AI and provide information in real time.

[0036] The navigation unit displays evacuation routes visually using AR technology. The navigation unit displays evacuation routes visually, for example, using a smartphone app or a dedicated device. The navigation unit can display evacuation routes using AR technology so that users can intuitively understand them. In this way, the navigation unit can enable users to intuitively understand evacuation routes by visually displaying them. Some or all of the above-described processes in the visual display of evacuation routes using AR technology may be performed using, for example, a generative AI, or not using a generative AI. For example, the navigation unit can input evacuation route data into a generative AI and generate a visual display of the evacuation route.

[0037] The navigation unit performs easy operation in emergencies using voice recognition. The navigation unit achieves easy operation in emergencies by using, for example, deep learning-based voice recognition technology and keyword spotting technology. The navigation unit can support emergency operations using voice recognition technology so that users can easily operate it by voice. This allows the navigation unit to quickly receive evacuation instructions even in emergencies through easy operation using voice recognition. Some or all of the above-described processes in easy operation in emergencies using voice recognition may be performed using, for example, a generative AI, or not using a generative AI. For example, the navigation unit can input voice data into a generative AI to achieve easy operation using voice recognition.

[0038] The navigation unit is equipped with a multilingual real-time translation function. The navigation unit achieves multilingual real-time translation using, for example, machine translation or translation memory technology. The navigation unit can provide a multilingual real-time translation function so that users can receive evacuation instructions in different languages. In this way, the navigation unit can overcome language barriers and provide appropriate evacuation instructions through multilingual support. Some or all of the above-described processing in the multilingual real-time translation function may be performed using, for example, a generative AI, or without a generative AI. For example, the navigation unit can input translation data into a generative AI to achieve multilingual real-time translation.

[0039] The data collection unit analyzes past disaster data and selects the optimal information collection method. For example, the data collection unit analyzes past earthquake data to identify the most reliable information sources. The data collection unit can analyze information collection patterns during past disasters and select efficient collection methods. The data collection unit can also determine the optimal timing for information collection in a specific area based on past disaster data. This enables the data collection unit to efficiently collect information by utilizing past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past disaster data into AI to select the optimal information collection method.

[0040] The data collection unit filters disaster information based on the user's current location. For example, the data collection unit prioritizes collecting the most relevant disaster information based on the user's current location. The data collection unit can collect information on nearby evacuation shelters based on the user's location. The data collection unit can also provide disaster information limited to a specific area, taking the user's location into consideration. This allows the data collection unit to provide highly relevant information based on the user's location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location information into AI and perform location-based information filtering.

[0041] The data collection unit analyzes users' social media activity and collects relevant information when collecting disaster information. For example, the data collection unit analyzes users' social media posts and collects relevant disaster information. The data collection unit can analyze posts from users' followers and friends to collect reliable information. The data collection unit can also analyze trends on social media to collect the latest disaster information. This allows the data collection unit to utilize social media to collect the latest disaster information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into AI and collect relevant disaster information.

[0042] The data collection unit selects the optimal data collection method when collecting disaster information, taking into account the user's device information. For example, if the user is using a smartphone, the data collection unit utilizes mobile data to collect information. If the user is using a tablet, the data collection unit can collect information optimized for a large screen. If the user is using a smartwatch, the data collection unit can also collect concise and highly visible information. This enables the data collection unit to collect information optimized for the user's device. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's device information into AI to select the optimal data collection method.

[0043] The analysis unit optimizes the analysis algorithm by referring to past disaster data during the analysis. For example, the analysis unit selects the optimal analysis algorithm by referring to past earthquake data. The analysis unit can optimize the algorithm based on the analysis results from past disasters. The analysis unit can also determine the optimal method of analysis for a specific region from past disaster data. This allows the analysis unit to optimize the analysis algorithm by utilizing past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past disaster data into AI and select the optimal analysis algorithm.

[0044] The analysis unit performs its analysis while considering the user's current health status and physical abilities. For example, the analysis unit adjusts the content of evacuation instructions considering the user's health status. The analysis unit can propose the optimal evacuation route considering the user's physical abilities. The analysis unit can also analyze appropriate evacuation methods based on the user's health data. This enables the analysis unit to perform analysis according to the user's health status and physical abilities. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health data into AI and perform analysis based on health status and physical abilities.

[0045] The analysis unit performs analysis while considering the user's geographical location information. For example, the analysis unit analyzes the optimal evacuation route based on the user's current location. The analysis unit can provide information on nearby evacuation shelters while considering the user's location information. The analysis unit can also perform analysis limited to a specific region based on the user's geographical location information. This allows the analysis unit to provide highly relevant analysis results based on the user's location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's location information into AI and perform analysis based on the location information.

[0046] The analysis unit improves the accuracy of its analysis by analyzing the user's social media activity during the analysis process. For example, the analysis unit analyzes the user's social media posts and incorporates relevant information into the analysis. The analysis unit can also analyze posts from the user's followers and friends and use reliable information in the analysis. The analysis unit can also analyze trends on social media and incorporate the latest information into the analysis. In this way, the analysis unit can improve the accuracy of its analysis by utilizing social media. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input social media data into AI to improve the accuracy of its analysis.

[0047] The information provider adjusts the level of detail of the instructions based on the severity of the disaster when providing them. For example, in the case of a major disaster, the information provider may provide detailed evacuation instructions. In the case of a minor disaster, the information provider may provide concise evacuation instructions. The information provider may also provide evacuation instructions with an appropriate level of detail depending on the severity of the disaster. This allows the information provider to provide evacuation instructions with an appropriate level of detail depending on the severity of the disaster. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider may input disaster severity data into AI and adjust the level of detail of the instructions based on the severity.

[0048] The service provider applies different instruction algorithms depending on the type of disaster when providing instructions. For example, in the case of an earthquake, the service provider applies an evacuation instruction algorithm specifically for earthquakes. In the case of a flood, the service provider may apply an evacuation instruction algorithm specifically for floods. In the case of a fire, the service provider may also apply an evacuation instruction algorithm specifically for fires. This allows the service provider to provide appropriate evacuation instructions according to the type of disaster. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input disaster type data into AI and apply an instruction algorithm appropriate to the type.

[0049] The information provider determines the priority of instructions based on the timing of the disaster when providing them. For example, immediately after a disaster occurs, the information provider prioritizes providing rapid evacuation instructions. After a certain period of time has elapsed since the disaster occurred, the information provider can provide detailed evacuation instructions. The information provider can also provide instructions with appropriate priorities depending on the timing of the disaster. This allows the information provider to provide instructions with appropriate priorities depending on the timing of the disaster. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input disaster timing data into AI and determine the priority of instructions based on the timing of the disaster.

[0050] The service provider adjusts the order of instructions based on the relevance of the disasters when providing them. For example, the service provider may prioritize instructions for disasters that have a direct impact. The service provider may postpone instructions for disasters that have an indirect impact. The service provider may also provide instructions in an appropriate order according to the relevance of the disasters. This allows the service provider to provide instructions in an appropriate order according to the relevance of the disasters. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input disaster relevance data into AI and adjust the order of instructions based on relevance.

[0051] The navigation unit selects the optimal display method by referring to the user's past evacuation history when displaying navigation information. For example, the navigation unit provides the optimal display method based on the evacuation routes the user has used in the past. The navigation unit can select a display method that avoids congestion based on the user's past evacuation history. The navigation unit can also analyze the user's past evacuation history and provide the most efficient display method. In this way, the navigation unit can provide the optimal navigation display by utilizing past evacuation history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without using AI. For example, the navigation unit can input the user's past evacuation history data into AI and select the optimal display method.

[0052] The navigation unit displays the optimal evacuation route when displaying navigation information, taking into account the user's current location. For example, the navigation unit displays the optimal evacuation route based on the user's current location. The navigation unit can also display routes to nearby evacuation shelters, taking into account the user's location information. The navigation unit can also display evacuation routes limited to a specific area based on the user's location information. In this way, the navigation unit can provide the optimal evacuation route based on the user's location information. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's location information data into AI and display the optimal evacuation route.

[0053] The navigation unit selects the optimal display method when displaying navigation information, taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit provides a display method that matches the screen size. If the user is using a tablet, the navigation unit can provide a display method optimized for a larger screen. If the user is using a smartwatch, the navigation unit can also provide a concise and highly visible display method. This enables the navigation unit to display navigation information optimized for the user's device. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's device information into AI and select the optimal display method.

[0054] The navigation unit analyzes the user's social media activity when displaying navigation information to show the optimal evacuation route. For example, the navigation unit analyzes the user's social media posts and displays relevant evacuation routes. The navigation unit can also analyze posts from the user's followers and friends to display reliable evacuation routes. The navigation unit can also analyze trends on social media and display the latest evacuation routes. In this way, the navigation unit can utilize social media to provide the optimal evacuation route. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input social media data into AI to display the optimal evacuation route.

[0055] The individual optimization unit provides optimal instructions by referring to the user's past health data during individual optimization. For example, the individual optimization unit proposes the optimal evacuation method based on the user's past health data. The individual optimization unit can adjust the evacuation route considering the user's health condition. The individual optimization unit can also refer to the user's past health data and provide appropriate evacuation instructions. In this way, the individual optimization unit can provide optimal instructions by utilizing past health data. Some or all of the above processing in the individual optimization unit may be performed using AI, for example, or without using AI. For example, the individual optimization unit can input the user's past health data into AI and provide optimal instructions.

[0056] The individual optimization unit provides optimal instructions while considering the user's geographical location information during individual optimization. For example, the individual optimization unit proposes the optimal evacuation route based on the user's current location. The individual optimization unit can provide information on nearby evacuation shelters while considering the user's location information. The individual optimization unit can also provide instructions limited to a specific area based on the user's geographical location information. In this way, the individual optimization unit can provide optimal instructions based on the user's location information. Some or all of the above processing in the individual optimization unit may be performed using AI, for example, or without using AI. For example, the individual optimization unit can input the user's location information data into AI and provide optimal instructions.

[0057] The information provision department provides optimal information by referring to past disaster data when providing information. For example, the information provision department refers to past earthquake data to provide optimal information. The information provision department can select an efficient information provision method based on information provision patterns during past disasters. The information provision department can also determine the optimal information provision method for a specific region from past disaster data. This enables the information provision department to provide optimal information by utilizing past data. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can input past disaster data into AI to select the optimal information provision method.

[0058] The information provision unit provides optimal information by considering the user's geographical location when providing information. For example, the information provision unit provides optimal information based on the user's current location. The information provision unit can provide information on nearby evacuation shelters by considering the user's location. The information provision unit can also provide information limited to a specific area based on the user's geographical location. In this way, the information provision unit can provide optimal information based on the user's location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI. For example, the information provision unit can input the user's location data into AI and provide optimal information.

[0059] Visual evacuation route displays using AR technology select the optimal display method by referring to the user's past evacuation history during AR display. For example, visual evacuation route displays using AR technology can provide the optimal AR display based on the evacuation route the user has used in the past. Visual evacuation route displays using AR technology can select an AR display that avoids congestion based on the user's past evacuation history. Visual evacuation route displays using AR technology can also analyze the user's past evacuation history and provide the most efficient AR display. In this way, visual evacuation route displays using AR technology can provide the optimal AR display by utilizing past evacuation history. Some or all of the above processing in visual evacuation route displays using AR technology may be performed using AI, for example, or not using AI. For example, visual evacuation route displays using AR technology can input the user's past evacuation history data into AI and select the optimal display method.

[0060] Visual evacuation route displays using AR technology select the optimal display method by considering the user's geographical location information during AR display. For example, visual evacuation route displays using AR technology can provide the optimal AR display based on the user's current location. Visual evacuation route displays using AR technology can display routes to nearby evacuation shelters in AR, taking into account the user's location information. Visual evacuation route displays using AR technology can also provide AR displays limited to specific areas based on the user's geographical location information. In this way, visual evacuation route displays using AR technology can provide the optimal AR display based on the user's location information. Some or all of the above processing in visual evacuation route displays using AR technology may be performed using AI, for example, or without AI. For example, visual evacuation route displays using AR technology can input the user's location information data into AI and select the optimal display method.

[0061] The voice recognition-based emergency operation selects the optimal recognition method by referring to the user's past voice command history during voice recognition. For example, the voice recognition-based emergency operation provides the optimal recognition method based on the user's past voice command history. The voice recognition-based emergency operation can select a recognition method that responds quickly from the user's voice command history. The voice recognition-based emergency operation can also analyze the user's past voice command history and provide the most efficient recognition method. In this way, the voice recognition-based emergency operation can provide optimal voice recognition by utilizing past voice command history. Some or all of the above processing in the voice recognition-based emergency operation may be performed using AI, for example, or without AI. For example, the voice recognition-based emergency operation can input the user's past voice command history data into AI and select the optimal recognition method.

[0062] The voice recognition-based emergency assistance system selects the optimal recognition method by considering the user's device information during voice recognition. For example, if the user is using a smartphone, the voice recognition-based emergency assistance system can provide a recognition method that matches the device's microphone performance. If the user is using a tablet, the voice recognition-based emergency assistance system can provide a recognition method optimized for the large screen. If the user is using a smartwatch, the voice recognition-based emergency assistance system can also provide a concise and rapid recognition method. This enables voice recognition optimized for the user's device. Some or all of the above processing in the voice recognition-based emergency assistance system may be performed using AI, for example, or without AI. For example, the voice recognition-based emergency assistance system can input the user's device information into AI to select the optimal recognition method.

[0063] The multilingual real-time translation function selects the optimal translation method by referring to the user's past language usage history during translation. For example, the multilingual real-time translation function provides the optimal translation method based on the user's past language usage history. The multilingual real-time translation function can select a translation method that responds quickly from the user's language usage history. The multilingual real-time translation function can also analyze the user's past language usage history and provide the most efficient translation method. This allows the multilingual real-time translation function to provide the optimal translation by utilizing past language usage history. Some or all of the above-described processes in the multilingual real-time translation function may be performed using AI, for example, or without AI. For example, the multilingual real-time translation function can input the user's past language usage history data into AI to select the optimal translation method.

[0064] The multilingual real-time translation function selects the optimal translation method by considering the user's geographical location information during translation. For example, the multilingual real-time translation function provides the optimal translation method based on the user's current location. The multilingual real-time translation function can translate information about nearby evacuation shelters by considering the user's location information. The multilingual real-time translation function can also provide translations limited to specific regions based on the user's geographical location information. In this way, the multilingual real-time translation function can provide the optimal translation based on the user's location information. Some or all of the above processing in the multilingual real-time translation function may be performed using AI, for example, or without AI. For example, the multilingual real-time translation function can input the user's geographical location data into AI and select the optimal translation method.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The disaster response system can also include a communications unit to support users in contacting family and friends. This unit manages user contact information and provides a means for rapid communication during a disaster. For example, it can provide an automated message sending function to inform family and friends of the user's current situation while they are evacuating. The communications unit can also notify family and friends when the user arrives at a safe location. Furthermore, the communications unit can prioritize displaying the people the user wants to contact in an emergency, facilitating quick communication. In this way, the disaster response system can help users quickly connect with family and friends and gain a sense of security.

[0067] The disaster response system may also include a pet support unit that provides evacuation instructions that take into consideration the safety of the user's pets. The pet support unit manages information about the user's pets and provides instructions for evacuating with pets during a disaster. For example, the pet support unit can provide information on pet shelters and pet-friendly evacuation routes. It can also monitor the pet's health and provide necessary medical support. Furthermore, the pet support unit can advise users on precautions and necessary preparations when evacuating with their pets. In this way, the disaster response system can help users and their pets evacuate safely.

[0068] The disaster response system can also include a vehicle support unit that provides evacuation instructions taking into account the user's vehicle information. The vehicle support unit manages the location and status of the user's vehicle and supports evacuation using the vehicle during a disaster. For example, the vehicle support unit can monitor the vehicle's fuel level and battery status and suggest the optimal evacuation route. Furthermore, based on the vehicle's location information, the vehicle support unit can provide the nearest evacuation shelter or safe parking location. In addition, the vehicle support unit can advise the user on precautions and necessary preparations when evacuating using their vehicle. In this way, the disaster response system can help users evacuate safely using their vehicles.

[0069] The disaster response system may also include a stockpile management unit that provides evacuation instructions taking into account the user's food and water reserves. The stockpile management unit manages the user's food and water reserves and provides instructions for supplying necessary goods during a disaster. For example, the stockpile management unit can monitor the user's reserves and provide a list of necessary supplies. It can also advise users on what items they should bring with them when evacuating. Furthermore, the stockpile management unit can provide information to help users secure necessary supplies at evacuation centers. This allows the disaster response system to support users in securing necessary supplies and evacuating safely.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The collection unit collects disaster information. The collection unit can collect disaster information such as earthquake information, flood information, and fire information. The collection unit can collect disaster information in various ways, such as using sensors or collecting from social media. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze disaster information using, for example, data mining or machine learning algorithms. Based on the results of the analysis of the disaster information, the analysis unit generates evacuation orders to be provided to the user. Step 3: The provisioning unit provides evacuation instructions based on the analysis results obtained by the analysis unit. The provisioning unit can, for example, set the notification method and the format of the information to be provided, and then provide evacuation instructions to the user. Step 4: The navigation unit conveys the evacuation instructions provided by the service provider to the user. The navigation unit can convey the evacuation instructions to the user by methods such as voice guidance or map display.

[0072] (Example of form 2) An embodiment of the present invention provides a disaster response system that is an AI chatbot that organizes the situation when an earthquake occurs and provides personalized action guidelines. This disaster response system uses a multimodal AI that integrates real-time disaster information, evacuation shelter information, and safety confirmation functions. This AI comprehensively processes voice, images, and text and provides optimal evacuation instructions tailored to the individual's situation via AR display and voice. This overcomes impaired judgment and language barriers during panic and guides appropriate actions through real-time situation analysis. For example, the disaster response system provides real-time disaster information, evacuation shelter information, and safety confirmation functions when an earthquake occurs. The disaster response system comprehensively processes voice, images, and text and provides optimal evacuation instructions tailored to the individual's situation via AR display and voice. This overcomes impaired judgment and language barriers during panic and guides appropriate actions through real-time situation analysis. Next, the disaster response system provides individually optimized instructions according to the user's health condition and physical abilities. For example, a user who has difficulty moving physically is instructed to move to a nearby evacuation shelter or safe place. Furthermore, the disaster response system uses AI to analyze the damage situation in the surrounding area and provides information to local governments and rescue teams in real time. This enables rapid rescue operations. In addition, the disaster response system features visual evacuation route display using AR technology, easy operation in emergencies using voice recognition, and real-time multilingual translation functionality. This allows users to intuitively understand evacuation routes and act quickly. Through this system, the aim is to minimize human casualties during disasters and realize a society where everyone can live with peace of mind. For example, in the event of an earthquake, the disaster response system will provide evacuation instructions in real time, supporting users in evacuating safely. The disaster response system also uses AI to monitor congestion and safety at evacuation centers and suggests appropriate evacuation destinations, enabling efficient operation of evacuation centers. In this way, the disaster response system can solve problems such as information overload and insufficiency, impaired judgment due to panic, and delays in appropriate action due to language barriers during earthquakes.

[0073] The disaster response system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a navigation unit. The collection unit collects disaster information. The collection unit can collect disaster information such as earthquake information, flood information, and fire information. The collection unit can collect disaster information in various ways, such as by using sensors or by collecting from social media. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze disaster information using, for example, data mining or machine learning algorithms. Based on the analysis results of the disaster information, the analysis unit generates evacuation instructions to be provided to the user. The provision unit provides evacuation instructions based on the analysis results obtained by the analysis unit. The provision unit can provide evacuation instructions to the user by setting, for example, the notification method and the format of the information to be provided. The navigation unit conveys the evacuation instructions provided by the provision unit to the user. The navigation unit can convey evacuation instructions to the user by methods such as voice guidance or map display. As a result, the disaster response system according to this embodiment can provide the user with optimal evacuation instructions by integrating the collection, analysis, provision, and navigation of disaster information.

[0074] The data collection unit collects disaster information. For example, it can collect disaster information such as earthquake information, flood information, and fire information. Specifically, for earthquake information, it uses seismometers and acceleration sensors to detect the occurrence of earthquakes and identify their intensity and epicenter. For flood information, it uses river water level sensors and rain gauges to monitor water level rises and rainfall to assess flood risk. For fire information, it uses smoke sensors and temperature sensors to detect the occurrence of fires and identify their location and scale. The data collection unit collects data from these sensors in real time and transmits it to a central database. The data collection unit also collects information from social media. For example, it collects posts related to disasters from platforms and extracts useful information using text mining techniques. This allows the data collection unit to collect not only official sensor information but also real-time information from the general public. Furthermore, the data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provision departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0075] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze disaster information using data mining and machine learning algorithms. Specifically, it uses data mining techniques to extract patterns and trends from collected data and predict the probability of disaster occurrence and the scope of impact. Machine learning algorithms learn from past disaster data and evaluate disaster risk based on new data. For example, it analyzes data on earthquake epicenters and seismic intensity to predict the probability of aftershocks. It also analyzes flood water level data and rainfall data to predict the speed of flood progression and the scope of impact. For fire information, it analyzes data from smoke sensors and temperature sensors to evaluate the speed of fire spread and the need for evacuation. Based on these analysis results, the analysis unit generates evacuation orders to be provided to users. Furthermore, the analysis unit can continuously revise its analysis results based on data that is updated in real time, enabling it to respond to the latest situation. For example, if new earthquake, flood, or fire information is collected, the analysis unit immediately incorporates the new data and updates the analysis results. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0076] The service provider provides evacuation orders based on the analysis results obtained by the analysis unit. The service provider can, for example, set the notification method and the format of the information provided to deliver evacuation orders to users. Specifically, it can send push notifications to users' devices, providing information on the necessity of evacuation and evacuation destinations. Possible notification methods include smartphone app notifications, SMS, email, and voice calls. The service provider selects the most appropriate notification method based on the user's location and situation to ensure reliable information transmission. The format of the information provided can also be flexibly configured. For example, it can combine text notifications with maps, images, and voice guidance to provide information in an easy-to-understand manner for users. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information provided. For example, it can revise evacuation routes and improve the content of instructions based on feedback from users who have received evacuation orders. This allows the service provider to provide users with quick and appropriate evacuation orders, minimizing the risk of disaster. Additionally, the service provider can reliably transmit information using multiple communication methods. For example, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the service provider to quickly and reliably provide users with action instructions, minimizing the risk of disaster.

[0077] The navigation unit conveys evacuation instructions provided by the service provider to the user. The navigation unit can convey evacuation instructions to the user through methods such as voice guidance and map display. Specifically, it displays the optimal evacuation route based on the user's current location and evacuation destination via a smartphone app, and provides warnings through voice guidance and vibration notifications. The navigation unit calculates and displays the optimal evacuation route based on real-time updated map information. For example, it suggests detour routes to avoid areas affected by floods or fires, helping users evacuate safely. The navigation unit also monitors the user's movement and can modify the evacuation route as needed. For example, if a user deviates from the designated evacuation route or if new disaster information is collected, the navigation unit immediately calculates a new route and notifies the user. Furthermore, the navigation unit can consider congestion when multiple users evacuate simultaneously and provide the optimal evacuation route. This allows the navigation unit to provide users with quick and appropriate evacuation instructions and support safe evacuation. Additionally, the navigation unit can collect user feedback and continuously improve the accuracy and effectiveness of its navigation functions. For example, based on feedback from users who have received evacuation orders, evacuation routes and guidance content can be revised. This allows the navigation system to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0078] The analysis unit includes an individual optimization unit that provides individually optimized instructions tailored to the user's health condition and physical abilities. The individual optimization unit, for example, evaluates the user's health condition, such as heart rate, blood pressure, and medical history, and provides optimal evacuation instructions. The individual optimization unit can also evaluate the user's physical abilities, such as walking speed, endurance, and muscle strength, and provide optimal evacuation instructions. This allows the analysis unit to provide optimal evacuation instructions tailored to the user's health condition and physical abilities. Some or all of the above-described processes in the individual optimization unit may be performed using AI, for example, or without AI. For example, the individual optimization unit can input the user's health data into AI to generate optimal evacuation instructions.

[0079] The analysis unit includes an information provision unit that analyzes the damage situation in the surrounding area and provides information to local governments and rescue teams in real time. The information provision unit evaluates the damage situation in the surrounding area, such as the extent of damage to buildings and the passability of roads, and provides information to local governments and rescue teams. The information provision unit can analyze the damage situation in real time and support rapid rescue operations. As a result, the analysis unit can analyze the damage situation in the surrounding area in real time and support rapid rescue operations. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI. For example, the information provision unit can input damage data into AI and provide information in real time.

[0080] The navigation unit displays evacuation routes visually using AR technology. The navigation unit displays evacuation routes visually, for example, using a smartphone app or a dedicated device. The navigation unit can display evacuation routes using AR technology so that users can intuitively understand them. In this way, the navigation unit can enable users to intuitively understand evacuation routes by visually displaying them. Some or all of the above-described processes in the visual display of evacuation routes using AR technology may be performed using, for example, a generative AI, or not using a generative AI. For example, the navigation unit can input evacuation route data into a generative AI and generate a visual display of the evacuation route.

[0081] The navigation unit performs easy operation in emergencies using voice recognition. The navigation unit achieves easy operation in emergencies by using, for example, deep learning-based voice recognition technology and keyword spotting technology. The navigation unit can support emergency operations using voice recognition technology so that users can easily operate it by voice. This allows the navigation unit to quickly receive evacuation instructions even in emergencies through easy operation using voice recognition. Some or all of the above-described processes in easy operation in emergencies using voice recognition may be performed using, for example, a generative AI, or not using a generative AI. For example, the navigation unit can input voice data into a generative AI to achieve easy operation using voice recognition.

[0082] The navigation unit is equipped with a multilingual real-time translation function. The navigation unit achieves multilingual real-time translation using, for example, machine translation or translation memory technology. The navigation unit can provide a multilingual real-time translation function so that users can receive evacuation instructions in different languages. In this way, the navigation unit can overcome language barriers and provide appropriate evacuation instructions through multilingual support. Some or all of the above-described processing in the multilingual real-time translation function may be performed using, for example, a generative AI, or without a generative AI. For example, the navigation unit can input translation data into a generative AI to achieve multilingual real-time translation.

[0083] The information collection unit estimates the user's emotions and adjusts the frequency of disaster information collection based on the estimated emotions. The information collection unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. If the user is in a state of panic, the information collection unit increases the collection frequency to provide the latest information quickly. If the user is calm, the information collection unit can maintain a normal collection frequency and avoid providing excessive information. If the user is feeling anxious, the information collection unit can appropriately adjust the collection frequency and prioritize the collection of information that provides a sense of security. In this way, the information collection unit can provide appropriate information by adjusting the information collection frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then adjust the frequency of information collection based on those emotions.

[0084] The data collection unit analyzes past disaster data and selects the optimal information collection method. For example, the data collection unit analyzes past earthquake data to identify the most reliable information sources. The data collection unit can analyze information collection patterns during past disasters and select efficient collection methods. The data collection unit can also determine the optimal timing for information collection in a specific area based on past disaster data. This enables the data collection unit to efficiently collect information by utilizing past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past disaster data into AI to select the optimal information collection method.

[0085] The data collection unit filters disaster information based on the user's current location. For example, the data collection unit prioritizes collecting the most relevant disaster information based on the user's current location. The data collection unit can collect information on nearby evacuation shelters based on the user's location. The data collection unit can also provide disaster information limited to a specific area, taking the user's location into consideration. This allows the data collection unit to provide highly relevant information based on the user's location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location information into AI and perform location-based information filtering.

[0086] The data collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user is in a state of panic, the data collection unit will prioritize collecting information about evacuation shelters. If the user is calm, the data collection unit can prioritize collecting detailed disaster information. If the user is feeling anxious, the data collection unit can prioritize collecting information that provides reassurance. In this way, the data collection unit can provide appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and determine the priority of information based on emotions.

[0087] The data collection unit analyzes users' social media activity and collects relevant information when collecting disaster information. For example, the data collection unit analyzes users' social media posts and collects relevant disaster information. The data collection unit can analyze posts from users' followers and friends to collect reliable information. The data collection unit can also analyze trends on social media to collect the latest disaster information. This allows the data collection unit to utilize social media to collect the latest disaster information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into AI and collect relevant disaster information.

[0088] The data collection unit selects the optimal data collection method when collecting disaster information, taking into account the user's device information. For example, if the user is using a smartphone, the data collection unit utilizes mobile data to collect information. If the user is using a tablet, the data collection unit can collect information optimized for a large screen. If the user is using a smartwatch, the data collection unit can also collect concise and highly visible information. This enables the data collection unit to collect information optimized for the user's device. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's device information into AI to select the optimal data collection method.

[0089] The analysis unit estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated emotions. For example, if the user is in a state of panic, the analysis unit increases the accuracy of the analysis to provide results quickly. If the user is calm, the analysis unit can perform the analysis with normal accuracy. If the user is feeling anxious, the analysis unit can appropriately adjust the accuracy of the analysis to provide a sense of security. In this way, the analysis unit can provide appropriate analysis results by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and adjust the accuracy of the analysis based on emotions.

[0090] The analysis unit optimizes the analysis algorithm by referring to past disaster data during the analysis. For example, the analysis unit selects the optimal analysis algorithm by referring to past earthquake data. The analysis unit can optimize the algorithm based on the analysis results from past disasters. The analysis unit can also determine the optimal method of analysis for a specific region from past disaster data. This allows the analysis unit to optimize the analysis algorithm by utilizing past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past disaster data into AI and select the optimal analysis algorithm.

[0091] The analysis unit performs its analysis while considering the user's current health status and physical abilities. For example, the analysis unit adjusts the content of evacuation instructions considering the user's health status. The analysis unit can propose the optimal evacuation route considering the user's physical abilities. The analysis unit can also analyze appropriate evacuation methods based on the user's health data. This enables the analysis unit to perform analysis according to the user's health status and physical abilities. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health data into AI and perform analysis based on health status and physical abilities.

[0092] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is in a state of panic, the analysis unit provides a simple and highly visible display method. If the user is in a calm state, the analysis unit can provide a display method that includes detailed information. If the user is feeling anxious, the analysis unit can provide a display method that provides a sense of security. In this way, the analysis unit can provide appropriate information by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and adjust the display method based on the emotions.

[0093] The analysis unit performs analysis while considering the user's geographical location information. For example, the analysis unit analyzes the optimal evacuation route based on the user's current location. The analysis unit can provide information on nearby evacuation shelters while considering the user's location information. The analysis unit can also perform analysis limited to a specific region based on the user's geographical location information. This allows the analysis unit to provide highly relevant analysis results based on the user's location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's location information into AI and perform analysis based on the location information.

[0094] The analysis unit improves the accuracy of its analysis by analyzing the user's social media activity during the analysis process. For example, the analysis unit analyzes the user's social media posts and incorporates relevant information into the analysis. The analysis unit can also analyze posts from the user's followers and friends and use reliable information in the analysis. The analysis unit can also analyze trends on social media and incorporate the latest information into the analysis. In this way, the analysis unit can improve the accuracy of its analysis by utilizing social media. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input social media data into AI to improve the accuracy of its analysis.

[0095] The service provider estimates the user's emotions and adjusts the way evacuation instructions are presented based on the estimated emotions. For example, if the user is in a state of panic, the service provider can provide concise and clear evacuation instructions. If the user is calm, the service provider can provide detailed evacuation instructions. If the user is feeling anxious, the service provider can provide reassuring evacuation instructions. In this way, the service provider can provide appropriate evacuation instructions by adjusting the way they are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and adjust the way evacuation instructions are presented based on those emotions.

[0096] The information provider adjusts the level of detail of the instructions based on the severity of the disaster when providing them. For example, in the case of a major disaster, the information provider may provide detailed evacuation instructions. In the case of a minor disaster, the information provider may provide concise evacuation instructions. The information provider may also provide evacuation instructions with an appropriate level of detail depending on the severity of the disaster. This allows the information provider to provide evacuation instructions with an appropriate level of detail depending on the severity of the disaster. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider may input disaster severity data into AI and adjust the level of detail of the instructions based on the severity.

[0097] The service provider applies different instruction algorithms depending on the type of disaster when providing instructions. For example, in the case of an earthquake, the service provider applies an evacuation instruction algorithm specifically for earthquakes. In the case of a flood, the service provider may apply an evacuation instruction algorithm specifically for floods. In the case of a fire, the service provider may also apply an evacuation instruction algorithm specifically for fires. This allows the service provider to provide appropriate evacuation instructions according to the type of disaster. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input disaster type data into AI and apply an instruction algorithm appropriate to the type.

[0098] The service provider estimates the user's emotions and determines the priority of instructions to be provided based on the estimated emotions. For example, if the user is in a state of panic, the service provider will prioritize providing the most important instructions. If the user is calm, the service provider can provide instructions in the usual order of priority. If the user is feeling anxious, the service provider can prioritize providing instructions that provide reassurance. In this way, the service provider can provide appropriate evacuation instructions by determining the priority of instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and determine the priority of instructions based on emotions.

[0099] The information provider determines the priority of instructions based on the timing of the disaster when providing them. For example, immediately after a disaster occurs, the information provider prioritizes providing rapid evacuation instructions. After a certain period of time has elapsed since the disaster occurred, the information provider can provide detailed evacuation instructions. The information provider can also provide instructions with appropriate priorities depending on the timing of the disaster. This allows the information provider to provide instructions with appropriate priorities depending on the timing of the disaster. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input disaster timing data into AI and determine the priority of instructions based on the timing of the disaster.

[0100] The service provider adjusts the order of instructions based on the relevance of the disasters when providing them. For example, the service provider may prioritize instructions for disasters that have a direct impact. The service provider may postpone instructions for disasters that have an indirect impact. The service provider may also provide instructions in an appropriate order according to the relevance of the disasters. This allows the service provider to provide instructions in an appropriate order according to the relevance of the disasters. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input disaster relevance data into AI and adjust the order of instructions based on relevance.

[0101] The navigation unit estimates the user's emotions and adjusts the navigation display method based on the estimated user emotions. For example, if the user is in a state of panic, the navigation unit provides a simple and highly visible display method. If the user is calm, the navigation unit can provide a display method that includes detailed information. If the user is feeling anxious, the navigation unit can provide a display method that provides a sense of security. In this way, the navigation unit can provide appropriate navigation by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user emotion data into a generative AI and adjust the display method based on the emotions.

[0102] The navigation unit selects the optimal display method by referring to the user's past evacuation history when displaying navigation information. For example, the navigation unit provides the optimal display method based on the evacuation routes the user has used in the past. The navigation unit can select a display method that avoids congestion based on the user's past evacuation history. The navigation unit can also analyze the user's past evacuation history and provide the most efficient display method. In this way, the navigation unit can provide the optimal navigation display by utilizing past evacuation history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without using AI. For example, the navigation unit can input the user's past evacuation history data into AI and select the optimal display method.

[0103] The navigation unit displays the optimal evacuation route when displaying navigation information, taking into account the user's current location. For example, the navigation unit displays the optimal evacuation route based on the user's current location. The navigation unit can also display routes to nearby evacuation shelters, taking into account the user's location information. The navigation unit can also display evacuation routes limited to a specific area based on the user's location information. In this way, the navigation unit can provide the optimal evacuation route based on the user's location information. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's location information data into AI and display the optimal evacuation route.

[0104] The navigation unit estimates the user's emotions and adjusts the navigation procedure based on the estimated emotions. For example, if the user is in a state of panic, the navigation unit can provide simplified procedures. If the user is calm, the navigation unit can provide detailed procedures. If the user is feeling anxious, the navigation unit can provide reassuring procedures. In this way, the navigation unit can provide appropriate navigation by adjusting the procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user emotion data into a generative AI and adjust the procedure based on the emotions.

[0105] The navigation unit selects the optimal display method when displaying navigation information, taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit provides a display method that matches the screen size. If the user is using a tablet, the navigation unit can provide a display method optimized for a larger screen. If the user is using a smartwatch, the navigation unit can also provide a concise and highly visible display method. This enables the navigation unit to display navigation information optimized for the user's device. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's device information into AI and select the optimal display method.

[0106] The navigation unit analyzes the user's social media activity when displaying navigation information to show the optimal evacuation route. For example, the navigation unit analyzes the user's social media posts and displays relevant evacuation routes. The navigation unit can also analyze posts from the user's followers and friends to display reliable evacuation routes. The navigation unit can also analyze trends on social media and display the latest evacuation routes. In this way, the navigation unit can utilize social media to provide the optimal evacuation route. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input social media data into AI to display the optimal evacuation route.

[0107] The individual optimization unit estimates the user's emotions and adjusts the individual optimization method based on the estimated user emotions. For example, if the user is in a state of panic, the individual optimization unit can provide quick and concise instructions. If the user is in a calm state, the individual optimization unit can provide detailed instructions. If the user is feeling anxious, the individual optimization unit can provide reassuring instructions. In this way, the individual optimization unit can provide appropriate instructions by adjusting the individual optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the individual optimization unit may be performed using AI, for example, or without AI. For example, the individual optimization unit can input user emotion data into a generative AI and adjust the individual optimization method based on emotions.

[0108] The individual optimization unit provides optimal instructions by referring to the user's past health data during individual optimization. For example, the individual optimization unit proposes the optimal evacuation method based on the user's past health data. The individual optimization unit can adjust the evacuation route considering the user's health condition. The individual optimization unit can also refer to the user's past health data and provide appropriate evacuation instructions. In this way, the individual optimization unit can provide optimal instructions by utilizing past health data. Some or all of the above processing in the individual optimization unit may be performed using AI, for example, or without using AI. For example, the individual optimization unit can input the user's past health data into AI and provide optimal instructions.

[0109] The individual optimization unit estimates the user's emotions and determines the priority of individual optimization based on the estimated user emotions. For example, if the user is in a state of panic, the individual optimization unit will prioritize providing the most important instructions. If the user is calm, the individual optimization unit can provide instructions with normal priority. If the user is feeling anxious, the individual optimization unit can prioritize providing instructions that provide a sense of security. In this way, the individual optimization unit can provide appropriate instructions by determining priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the individual optimization unit may be performed using AI, for example, or without AI. For example, the individual optimization unit can input user emotion data into a generative AI and determine priority based on emotions.

[0110] The individual optimization unit provides optimal instructions while considering the user's geographical location information during individual optimization. For example, the individual optimization unit proposes the optimal evacuation route based on the user's current location. The individual optimization unit can provide information on nearby evacuation shelters while considering the user's location information. The individual optimization unit can also provide instructions limited to a specific area based on the user's geographical location information. In this way, the individual optimization unit can provide optimal instructions based on the user's location information. Some or all of the above processing in the individual optimization unit may be performed using AI, for example, or without using AI. For example, the individual optimization unit can input the user's location information data into AI and provide optimal instructions.

[0111] The information provision unit estimates the user's emotions and adjusts the method of information provision based on the estimated emotions. For example, if the user is in a state of panic, the information provision unit can provide concise and clear information. If the user is calm, the information provision unit can provide detailed information. If the user is feeling anxious, the information provision unit can provide reassuring information. In this way, the information provision unit can provide appropriate information by adjusting the method of information provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input user emotion data into a generative AI and adjust the method of information provision based on emotions.

[0112] The information provision department provides optimal information by referring to past disaster data when providing information. For example, the information provision department refers to past earthquake data to provide optimal information. The information provision department can select an efficient information provision method based on information provision patterns during past disasters. The information provision department can also determine the optimal information provision method for a specific region from past disaster data. This enables the information provision department to provide optimal information by utilizing past data. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can input past disaster data into AI to select the optimal information provision method.

[0113] The information provision unit estimates the user's emotions and determines the priority of information provision based on the estimated emotions. For example, if the user is in a state of panic, the information provision unit will prioritize providing the most important information. If the user is calm, the information provision unit can provide information in the usual priority order. If the user is feeling anxious, the information provision unit can prioritize providing information that provides reassurance. In this way, the information provision unit can provide appropriate information by determining the priority of information provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input user emotion data into a generative AI and determine the priority of information provision based on emotions.

[0114] The information provision unit provides optimal information by considering the user's geographical location when providing information. For example, the information provision unit provides optimal information based on the user's current location. The information provision unit can provide information on nearby evacuation shelters by considering the user's location. The information provision unit can also provide information limited to a specific area based on the user's geographical location. In this way, the information provision unit can provide optimal information based on the user's location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI. For example, the information provision unit can input the user's location data into AI and provide optimal information.

[0115] Visual evacuation route displays using AR technology estimate the user's emotions and adjust the AR display method based on the estimated user emotions. For example, if the user is in a state of panic, the visual evacuation route displays using AR technology can provide a simple and highly visible AR display. If the user is calm, the visual evacuation route displays using AR technology can provide an AR display that includes detailed information. If the user is feeling anxious, the visual evacuation route displays using AR technology can provide a reassuring AR display. In this way, visual evacuation route displays using AR technology can provide an appropriate evacuation route by adjusting the AR display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in visual evacuation route displays using AR technology may be performed using AI, for example, or without using AI. For example, visual evacuation route displays using AR technology can input user emotion data into a generating AI and adjust the AR display method based on those emotions.

[0116] Visual evacuation route displays using AR technology select the optimal display method by referring to the user's past evacuation history during AR display. For example, visual evacuation route displays using AR technology can provide the optimal AR display based on the evacuation route the user has used in the past. Visual evacuation route displays using AR technology can select an AR display that avoids congestion based on the user's past evacuation history. Visual evacuation route displays using AR technology can also analyze the user's past evacuation history and provide the most efficient AR display. In this way, visual evacuation route displays using AR technology can provide the optimal AR display by utilizing past evacuation history. Some or all of the above processing in visual evacuation route displays using AR technology may be performed using AI, for example, or not using AI. For example, visual evacuation route displays using AR technology can input the user's past evacuation history data into AI and select the optimal display method.

[0117] The visual evacuation route display using AR technology estimates the user's emotions and determines the priority of the AR display based on the estimated user emotions. For example, if the user is in a state of panic, the visual evacuation route display using AR technology will prioritize the display of the most important information. If the user is calm, the visual evacuation route display using AR technology can provide the AR display with the normal priority. If the user is feeling anxious, the visual evacuation route display using AR technology can prioritize the display of information that provides a sense of security. In this way, the visual evacuation route display using AR technology can provide an appropriate evacuation route by determining the priority of the AR display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visual evacuation route display using AR technology may be performed using AI, for example, or without using AI. For example, visual evacuation route displays using AR technology can input user emotion data into a generating AI to determine the priority of AR displays based on those emotions.

[0118] Visual evacuation route displays using AR technology select the optimal display method by considering the user's geographical location information during AR display. For example, visual evacuation route displays using AR technology can provide the optimal AR display based on the user's current location. Visual evacuation route displays using AR technology can display routes to nearby evacuation shelters in AR, taking into account the user's location information. Visual evacuation route displays using AR technology can also provide AR displays limited to specific areas based on the user's geographical location information. In this way, visual evacuation route displays using AR technology can provide the optimal AR display based on the user's location information. Some or all of the above processing in visual evacuation route displays using AR technology may be performed using AI, for example, or without AI. For example, visual evacuation route displays using AR technology can input the user's location information data into AI and select the optimal display method.

[0119] The voice recognition-based emergency ease of use system estimates the user's emotions and adjusts the accuracy of voice recognition based on the estimated emotions. For example, if the user is in a panic state, the voice recognition-based emergency ease of use system increases the accuracy of voice recognition to respond quickly. If the user is calm, the voice recognition-based emergency ease of use system can perform voice recognition with normal accuracy. If the user is feeling anxious, the voice recognition-based emergency ease of use system can appropriately adjust the accuracy of voice recognition to provide reassurance. In this way, the voice recognition-based emergency ease of use system enables appropriate voice operation by adjusting the accuracy of voice recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice recognition-based emergency ease of use system may be performed using AI, for example, or without AI. For example, for emergency operations using voice recognition, user emotion data can be input into a generating AI, allowing for adjustment of the accuracy of voice recognition based on those emotions.

[0120] The voice recognition-based emergency operation selects the optimal recognition method by referring to the user's past voice command history during voice recognition. For example, the voice recognition-based emergency operation provides the optimal recognition method based on the user's past voice command history. The voice recognition-based emergency operation can select a recognition method that responds quickly from the user's voice command history. The voice recognition-based emergency operation can also analyze the user's past voice command history and provide the most efficient recognition method. In this way, the voice recognition-based emergency operation can provide optimal voice recognition by utilizing past voice command history. Some or all of the above processing in the voice recognition-based emergency operation may be performed using AI, for example, or without AI. For example, the voice recognition-based emergency operation can input the user's past voice command history data into AI and select the optimal recognition method.

[0121] Emergency ease of use via voice recognition estimates the user's emotions and determines the priority of voice recognition based on the estimated emotions. For example, if the user is in a panic state, emergency ease of use via voice recognition will prioritize the recognition of the most important voice commands. If the user is calm, emergency ease of use via voice recognition can recognize voice commands with normal priority. If the user is feeling anxious, emergency ease of use via voice recognition can prioritize the recognition of voice commands that provide reassurance. In this way, emergency ease of use via voice recognition enables appropriate voice operation by determining the priority of voice recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in emergency ease of use via voice recognition may be performed using AI, for example, or not using AI. For example, emergency ease of use via voice recognition can input user emotion data into a generative AI and determine the priority of voice recognition based on emotions.

[0122] The voice recognition-based emergency assistance system selects the optimal recognition method by considering the user's device information during voice recognition. For example, if the user is using a smartphone, the voice recognition-based emergency assistance system can provide a recognition method that matches the device's microphone performance. If the user is using a tablet, the voice recognition-based emergency assistance system can provide a recognition method optimized for the large screen. If the user is using a smartwatch, the voice recognition-based emergency assistance system can also provide a concise and rapid recognition method. This enables voice recognition optimized for the user's device. Some or all of the above processing in the voice recognition-based emergency assistance system may be performed using AI, for example, or without AI. For example, the voice recognition-based emergency assistance system can input the user's device information into AI to select the optimal recognition method.

[0123] The multilingual real-time translation function estimates the user's emotions and adjusts the accuracy of the translation based on the estimated emotions. For example, if the user is in a state of panic, the multilingual real-time translation function will increase the accuracy of the translation and respond quickly. If the user is calm, the multilingual real-time translation function can translate with normal accuracy. If the user is feeling anxious, the multilingual real-time translation function can appropriately adjust the accuracy of the translation to provide reassurance. In this way, the multilingual real-time translation function can provide appropriate translations by adjusting the accuracy of the translation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the multilingual real-time translation function may be performed using AI, for example, or not using AI. For example, the multilingual real-time translation function can input user emotion data into a generative AI and adjust the accuracy of the translation based on emotions.

[0124] The multilingual real-time translation function selects the optimal translation method by referring to the user's past language usage history during translation. For example, the multilingual real-time translation function provides the optimal translation method based on the user's past language usage history. The multilingual real-time translation function can select a translation method that responds quickly from the user's language usage history. The multilingual real-time translation function can also analyze the user's past language usage history and provide the most efficient translation method. This allows the multilingual real-time translation function to provide the optimal translation by utilizing past language usage history. Some or all of the above-described processes in the multilingual real-time translation function may be performed using AI, for example, or without AI. For example, the multilingual real-time translation function can input the user's past language usage history data into AI to select the optimal translation method.

[0125] The multilingual real-time translation function estimates the user's emotions and determines translation priorities based on the estimated emotions. For example, if the user is in a state of panic, the multilingual real-time translation function will prioritize translating the most important information. If the user is calm, the multilingual real-time translation function can provide translations with normal priorities. If the user is feeling anxious, the multilingual real-time translation function can prioritize translating information that provides reassurance. In this way, the multilingual real-time translation function can provide appropriate translations by determining translation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual real-time translation function may be performed using AI, for example, or not using AI. For example, the multilingual real-time translation function can input user emotion data into a generative AI and determine translation priorities based on emotions.

[0126] The multilingual real-time translation function selects the optimal translation method by considering the user's geographical location information during translation. For example, the multilingual real-time translation function provides the optimal translation method based on the user's current location. The multilingual real-time translation function can translate information about nearby evacuation shelters by considering the user's location information. The multilingual real-time translation function can also provide translations limited to specific regions based on the user's geographical location information. In this way, the multilingual real-time translation function can provide the optimal translation based on the user's location information. Some or all of the above processing in the multilingual real-time translation function may be performed using AI, for example, or without AI. For example, the multilingual real-time translation function can input the user's geographical location data into AI and select the optimal translation method.

[0127] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0128] The disaster response system can also include a psychological support unit that provides evacuation instructions while considering the user's psychological state. This unit monitors the user's stress and anxiety levels in real time and provides appropriate psychological support. For example, if a user is experiencing high stress, the unit can suggest breathing exercises or simple mental exercises to help them relax. If a user is feeling anxious, the unit can provide reassuring messages or music. Furthermore, if a user is calm, the unit can provide detailed information to support them in acting with confidence. This allows the disaster response system to provide appropriate support tailored to the user's psychological state, enabling them to act calmly.

[0129] The disaster response system can also include a communications unit to support users in contacting family and friends. This unit manages user contact information and provides a means for rapid communication during a disaster. For example, it can provide an automated message sending function to inform family and friends of the user's current situation while they are evacuating. The communications unit can also notify family and friends when the user arrives at a safe location. Furthermore, the communications unit can prioritize displaying the people the user wants to contact in an emergency, facilitating quick communication. In this way, the disaster response system can help users quickly connect with family and friends and gain a sense of security.

[0130] The disaster response system may also include a pet support unit that provides evacuation instructions that take into consideration the safety of the user's pets. The pet support unit manages information about the user's pets and provides instructions for evacuating with pets during a disaster. For example, the pet support unit can provide information on pet shelters and pet-friendly evacuation routes. It can also monitor the pet's health and provide necessary medical support. Furthermore, the pet support unit can advise users on precautions and necessary preparations when evacuating with their pets. In this way, the disaster response system can help users and their pets evacuate safely.

[0131] The disaster response system can also include a vehicle support unit that provides evacuation instructions taking into account the user's vehicle information. The vehicle support unit manages the location and status of the user's vehicle and supports evacuation using the vehicle during a disaster. For example, the vehicle support unit can monitor the vehicle's fuel level and battery status and suggest the optimal evacuation route. Furthermore, based on the vehicle's location information, the vehicle support unit can provide the nearest evacuation shelter or safe parking location. In addition, the vehicle support unit can advise the user on precautions and necessary preparations when evacuating using their vehicle. In this way, the disaster response system can help users evacuate safely using their vehicles.

[0132] The disaster response system may also include a stockpile management unit that provides evacuation instructions taking into account the user's food and water reserves. The stockpile management unit manages the user's food and water reserves and provides instructions for supplying necessary goods during a disaster. For example, the stockpile management unit can monitor the user's reserves and provide a list of necessary supplies. It can also advise users on what items they should bring with them when evacuating. Furthermore, the stockpile management unit can provide information to help users secure necessary supplies at evacuation centers. This allows the disaster response system to support users in securing necessary supplies and evacuating safely.

[0133] The disaster response system may also include a timing adjustment unit that estimates the user's emotions and adjusts the timing of evacuation orders based on those emotions. The timing adjustment unit monitors the user's emotional state in real time and provides evacuation orders at the appropriate time. For example, if the user is in a state of panic, the timing adjustment unit provides evacuation orders quickly and provides necessary information while the user calms down. If the user is calm, the timing adjustment unit can provide detailed evacuation orders and support the user in acting with confidence. Furthermore, if the user is feeling anxious, the timing adjustment unit can provide reassuring messages and help the user act calmly. In this way, the disaster response system can provide evacuation orders at the appropriate time according to the user's emotions and support the user in acting calmly.

[0134] The disaster response system may also include a content adjustment unit that estimates the user's emotions and adjusts the content of evacuation instructions based on those emotions. The content adjustment unit monitors the user's emotional state in real time and provides appropriate evacuation instructions. For example, if the user is in a state of panic, the content adjustment unit provides concise and clear evacuation instructions to support the user in taking swift action. If the user is calm, the content adjustment unit can provide detailed evacuation instructions to help the user act with confidence. Furthermore, if the user is feeling anxious, the content adjustment unit can provide reassuring messages to support the user in taking calm action. In this way, the disaster response system can provide appropriate evacuation instructions tailored to the user's emotions and support the user in taking calm action.

[0135] The disaster response system may also include a format adjustment unit that estimates the user's emotions and adjusts the format of evacuation instructions based on those emotions. The format adjustment unit monitors the user's emotional state in real time and provides evacuation instructions in an appropriate format. For example, if the user is in a panic state, the format adjustment unit provides evacuation instructions using visually easy-to-understand graphics and icons to help the user understand them quickly. If the user is calm, the format adjustment unit can provide detailed text information to help the user act with confidence. Furthermore, if the user is feeling anxious, the format adjustment unit can provide evacuation instructions using reassuring colors and designs to help the user act calmly. In this way, the disaster response system can provide evacuation instructions in an appropriate format according to the user's emotions and help the user act calmly.

[0136] The disaster response system may also include a frequency adjustment unit that estimates the user's emotions and adjusts the frequency of evacuation orders based on those emotions. The frequency adjustment unit monitors the user's emotional state in real time and provides evacuation orders at appropriate frequencies. For example, if the user is in a panic state, the frequency adjustment unit provides evacuation orders frequently to support the user in taking swift action. If the user is calm, the frequency adjustment unit can provide evacuation orders at normal frequencies to support the user in taking confident action. Furthermore, if the user is feeling anxious, the frequency adjustment unit can provide reassuring messages at appropriate frequencies to support the user in taking calm action. In this way, the disaster response system can provide evacuation orders at appropriate frequencies according to the user's emotions and support the user in taking calm action.

[0137] The disaster response system may also include a priority adjustment unit that estimates the user's emotions and adjusts the priority of evacuation orders based on those emotions. The priority adjustment unit monitors the user's emotional state in real time and provides evacuation orders with appropriate priorities. For example, if the user is in a panic state, the priority adjustment unit will prioritize providing the most important evacuation orders to support the user in taking swift action. If the user is calm, the priority adjustment unit can provide evacuation orders with normal priorities to support the user in taking confident action. Furthermore, if the user is feeling anxious, the priority adjustment unit can prioritize providing reassuring evacuation orders to support the user in taking calm action. In this way, the disaster response system can provide evacuation orders with appropriate priorities according to the user's emotions and support the user in taking calm action.

[0138] The following briefly describes the processing flow for example form 2.

[0139] Step 1: The collection unit collects disaster information. The collection unit can collect disaster information such as earthquake information, flood information, and fire information. The collection unit can collect disaster information in various ways, such as using sensors or collecting from social media. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze disaster information using, for example, data mining or machine learning algorithms. Based on the results of the analysis of the disaster information, the analysis unit generates evacuation orders to be provided to the user. Step 3: The provisioning unit provides evacuation instructions based on the analysis results obtained by the analysis unit. The provisioning unit can, for example, set the notification method and the format of the information to be provided, and then provide evacuation instructions to the user. Step 4: The navigation unit conveys the evacuation instructions provided by the service provider to the user. The navigation unit can convey the evacuation instructions to the user by methods such as voice guidance or map display.

[0140] 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.

[0141] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0142] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and navigation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects disaster information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected disaster information and generates evacuation instructions to be provided to the user. The provision unit is implemented in the control unit 46A of the smart device 14, which provides evacuation instructions based on the analysis results. The navigation unit conveys evacuation instructions to the user visually and audibly using the display 40A and speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0145] 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.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0147] 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.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0149] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] 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.

[0151] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and navigation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects disaster information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected disaster information and generates evacuation instructions to be provided to the user. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides evacuation instructions based on the analysis results. The navigation unit conveys evacuation instructions to the user visually and audibly using, for example, the display and speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0161] 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.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0163] 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.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0165] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] 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.

[0167] 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.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and navigation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects disaster information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected disaster information and generates evacuation instructions to be provided to the user. The provision unit is implemented in the control unit 46A of the headset terminal 314, which provides evacuation instructions based on the analysis results. The navigation unit conveys evacuation instructions to the user visually and audibly using the display 343 and speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0177] 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.

[0178] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0179] 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.

[0180] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0181] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0182] 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.

[0183] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0184] 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.

[0185] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0186] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0187] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0188] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0189] 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.

[0190] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0192] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and navigation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects disaster information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected disaster information and generates evacuation instructions to be provided to the user. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides evacuation instructions based on the analysis results. The navigation unit conveys evacuation instructions to the user visually and audibly using, for example, the display and speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0193] 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.

[0194] Figure 9 shows the 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.

[0195] 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.

[0196] 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.

[0197] 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, and motorcycles, 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 based, for example, 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.

[0198] 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."

[0199] 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.

[0200] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0209] 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 other things 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.

[0210] 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.

[0211] (Note 1) The collection unit that collects disaster information, An analysis unit analyzes the information collected by the aforementioned collection unit, A provisioning unit provides evacuation orders based on the analysis results obtained by the aforementioned analysis unit, The system includes a navigation unit that conveys evacuation instructions provided by the aforementioned supply unit to the user. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It features an individual optimization unit that provides instructions tailored to the user's health condition and physical abilities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It has an information provision department that analyzes the extent of damage in the surrounding area and provides information to local governments and rescue teams in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned navigation unit is Visual evacuation route display using AR technology. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned navigation unit is Enables easy operation in emergencies using voice recognition. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned navigation unit is Features multilingual real-time translation capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates user sentiment and adjusts the frequency of disaster information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past disaster data and select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting disaster information, filtering is performed based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting disaster information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting disaster information, the optimal collection method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past disaster data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the user's current health status and physical abilities are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we analyze users' social media activity to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way evacuation orders are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing instructions, adjust the level of detail based on the severity of the disaster. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, different instruction algorithms will be applied depending on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the instructions to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing instructions, prioritize them based on when the disaster occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing instructions, adjust the order of instructions based on the relevance of the disaster. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned navigation unit is It estimates the user's emotions and adjusts how navigation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned navigation unit is When displaying navigation information, the system selects the optimal display method by referring to the user's past evacuation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned navigation unit is When displaying navigation, the system will show the optimal evacuation route, taking into account the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned navigation unit is It estimates the user's emotions and adjusts the navigation steps based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned navigation unit is When displaying navigation, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned navigation unit is When displaying navigation, the system analyzes the user's social media activity to show the optimal evacuation route. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned individual optimization unit, It estimates the user's emotions and adjusts the individual optimization method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned individual optimization unit, During individual optimization, the system provides optimal guidance by referencing the user's past health data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned individual optimization unit, It estimates the user's emotions and determines the priority of individual optimization based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned individual optimization unit, When optimizing individually, the system provides optimal instructions while considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned information provision unit, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned information provision unit, When providing information, we refer to past disaster data to provide the most relevant information. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned information provision unit, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned information provision unit, When providing information, we will consider the user's geographical location to provide the most relevant information. The system described in Appendix 3, characterized by the features described herein. (Note 39) The visual display of evacuation routes using the aforementioned AR technology is It estimates the user's emotions and adjusts the AR display method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The visual display of evacuation routes using the aforementioned AR technology is When displaying AR content, the system selects the optimal display method by referring to the user's past evacuation history. The system described in Appendix 4, characterized by the features described herein. (Note 41) The visual display of evacuation routes using the aforementioned AR technology is It estimates the user's emotions and determines the priority of AR displays based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The visual display of evacuation routes using the aforementioned AR technology is When displaying AR content, the optimal display method is selected considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned voice recognition-based emergency operation is, It estimates the user's emotions and adjusts the accuracy of speech recognition based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned voice recognition-based emergency operation is, During speech recognition, the system selects the optimal recognition method by referring to the user's past voice command history. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned voice recognition-based emergency operation is, It estimates the user's emotions and determines the priority of speech recognition based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 46) The aforementioned voice recognition-based emergency operation is, During speech recognition, the system selects the optimal recognition method by considering the user's device information. The system described in Appendix 5, characterized by the features described herein. (Note 47) The aforementioned multilingual real-time translation function is It estimates the user's emotions and adjusts the accuracy of the translation based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 48) The aforementioned multilingual real-time translation function is During translation, the system selects the optimal translation method by referring to the user's past language usage history. The system described in Appendix 6, characterized by the features described herein. (Note 49) The aforementioned multilingual real-time translation function is It estimates the user's emotions and determines translation priorities based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 50) The aforementioned multilingual real-time translation function is During translation, the system selects the optimal translation method by considering the user's geographical location. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit that collects disaster information, An analysis unit analyzes the information collected by the aforementioned collection unit, A provisioning unit provides evacuation orders based on the analysis results obtained by the aforementioned analysis unit, The system includes a navigation unit that conveys evacuation instructions provided by the aforementioned supply unit to the user. A system characterized by the following features.

2. The aforementioned analysis unit, It features an individual optimization unit that provides instructions tailored to the user's health condition and physical abilities. The system according to feature 1.

3. The aforementioned analysis unit, It has an information provision department that analyzes the extent of damage in the surrounding area and provides information to local governments and rescue teams in real time. The system according to feature 1.

4. The aforementioned navigation unit is Visual evacuation route display using AR technology. The system according to feature 1.

5. The aforementioned navigation unit is Enables easy operation in emergencies using voice recognition. The system according to feature 1.

6. The aforementioned navigation unit is Features multilingual real-time translation capabilities. The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the frequency of disaster information collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is Analyze past disaster data and select the optimal information gathering method. The system according to feature 1.

9. The aforementioned collection unit is When collecting disaster information, filtering is performed based on the user's current location. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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