system

Abstract

The system according to this embodiment aims to propose an optimal evacuation method in order to improve the survival rate in emergency situations. [Solution] The system according to the embodiment comprises an analysis unit, a sensing unit, a proposal unit, and a guidance unit. The analysis unit analyzes the content of the emergency alert. The sensing unit grasps the user's current location and surrounding conditions. The proposal unit proposes the optimal evacuation method. The guidance unit provides guidance and support in real time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to propose a rapid and appropriate evacuation method in an emergency situation, and there are problems in improving the survival rate.

[0005] The system according to the embodiment aims to propose an optimal evacuation method in order to improve the survival rate in an emergency situation.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a grasping unit, a proposal unit, and a guiding unit. The analysis unit analyzes the content of an emergency alert. The grasping unit grasps the current position of the user and the surrounding situation. The proposal unit proposes an optimal evacuation method. The guiding unit provides guidance and support in real time.

Effects of the Invention

[0007] The system according to this embodiment can propose an optimal evacuation method to improve the survival rate in emergency situations. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI system according to an embodiment of the present invention is a system for improving survival rates in emergency situations. This AI system analyzes the content of emergency alerts in real time, understands the user's current location and surrounding conditions, and proposes the optimal evacuation method. Furthermore, the AI ​​system provides guidance and support in real time. For example, to respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks, the AI ​​system analyzes the content of emergency alerts and understands the specific details of the emergency. This allows the user to immediately understand the details of the emergency. Next, the AI ​​system understands the user's current location and surrounding conditions. For example, it collects and analyzes information such as the user's location, the height of surrounding buildings, their age, and seismic resistance standards. This allows the AI ​​system to identify the safest evacuation location and route for the user. Furthermore, the AI ​​system proposes multiple locations and methods that offer the highest survival rates for the user. For example, it presents multiple options, such as evacuating to a nearby tall building or moving to a distant safe location, in order of highest survival rate. This makes it easier for the user to choose the safest option. Finally, the AI ​​system guides the user in real time and supports evacuation actions. For example, it provides appropriate instructions in response to changes in the emergency. This allows the user to respond flexibly in accordance with the progression of the emergency. This system allows users to make calm decisions and take the safest evacuation actions during emergencies. The AI ​​system analyzes the situation in real time and proposes the optimal evacuation method, thereby improving survival rates and reducing anxiety. In this way, the AI ​​system can improve survival rates during emergencies.

[0029] The AI ​​system according to this embodiment comprises an analysis unit, a sensing unit, a proposal unit, and a guidance unit. The analysis unit analyzes the content of emergency alerts. The analysis unit analyzes the content of emergency alerts in order to respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks. The analysis unit uses AI to analyze the content of emergency alerts in real time and grasp the specific details of the emergency. For example, if the analysis unit receives an earthquake warning, it analyzes the epicenter and seismic intensity of the earthquake and notifies the user. If it receives a tsunami warning, it analyzes the arrival time and height of the tsunami and notifies the user. If it receives a flood warning, it analyzes the progress of the flood and the extent of the inundation and notifies the user. If it receives a missile attack warning, it analyzes the direction and speed of the incoming missile and notifies the user. The sensing unit grasps the user's current location and surrounding conditions. The sensing unit collects and analyzes information such as the user's location information, the height of surrounding buildings, their age, and seismic resistance standards. The tracking unit uses AI to grasp the user's current location and surrounding environment in real time. For example, the tracking unit uses GPS data to determine the user's current location. It uses Wi-Fi location information to determine the user's location within a building. It collects and analyzes information such as the height, age, and seismic resistance standards of surrounding buildings. The suggestion unit proposes the optimal evacuation method. The suggestion unit presents multiple options in order of highest survival rate, such as evacuating to a nearby tall building or moving to a safe place far away. The suggestion unit uses AI to propose the evacuation method with the highest survival rate for the user. For example, the suggestion unit suggests evacuating to a nearby tall building. It suggests moving to a safe place far away. It presents evacuation routes and guides the user to the safest route. The guidance unit provides guidance and support in real time. For example, the guidance unit provides appropriate instructions in response to changes in the emergency. The guidance unit uses AI to guide the user in real time and support evacuation actions. For example, the guidance unit updates the evacuation route and notifies the user as the emergency progresses. The system monitors the congestion status of evacuation shelters in real time and guides users to the most suitable evacuation shelter. This allows the AI ​​system according to this embodiment to improve survival rates during emergencies.

[0030] The analysis unit analyzes the content of emergency alerts. For example, the analysis unit analyzes the content of emergency alerts to respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks. The analysis unit uses AI to analyze the content of emergency alerts in real time and understand the specific nature of the emergency. Specifically, the AI ​​uses natural language processing technology to analyze the alert text and extract the type of emergency and its details. For example, upon receiving an earthquake warning, the AI ​​analyzes the earthquake's epicenter and intensity and notifies the user. Upon receiving a tsunami warning, the AI ​​analyzes the tsunami's arrival time and height and notifies the user. Upon receiving a flood warning, the AI ​​analyzes the flood's progress and the extent of the inundation and notifies the user. Upon receiving a missile attack warning, the AI ​​analyzes the missile's direction and speed and notifies the user. This allows the analysis unit to quickly and accurately analyze the content of emergency alerts and provide appropriate information to the user. Furthermore, the analysis unit can learn from past emergency alert data to perform even more accurate analyses. For example, it can improve the accuracy of earthquake intensity predictions in specific areas based on past earthquake data. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal alerts, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0031] The location tracking unit understands the user's current location and surrounding conditions. For example, it collects and analyzes information such as the user's location, the height and age of surrounding buildings, and seismic resistance standards. Specifically, it uses GPS data to determine the user's current location and Wi-Fi location information to determine the user's location within a building. Furthermore, it collects information such as the height and age of surrounding buildings and seismic resistance standards, and integrates and analyzes this data. The AI ​​processes this data in real time to accurately understand the user's current location and surrounding conditions. For example, in the event of an earthquake, the location tracking unit evaluates the seismic performance of the building the user is in and determines the need for evacuation. Similarly, in the event of a flood, it assesses the risk of inundation based on the surrounding topography and building heights. This allows the location tracking unit to quickly and accurately understand the user's current location and surrounding conditions, providing fundamental information for appropriate evacuation instructions. Furthermore, the location tracking unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can assess the risks of specific regions or buildings based on past earthquake data and formulate future countermeasures. Furthermore, the monitoring unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data, and issue warnings early. This allows the monitoring 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 suggestion function proposes the most suitable evacuation method. It presents multiple options, such as evacuating to a nearby tall building or moving to a distant, safe location, in order of highest survival rate. Specifically, the AI ​​calculates the optimal evacuation method based on the user's current location, surrounding conditions, and the type of emergency. For example, in the event of an earthquake, the suggestion function proposes evacuating to a nearby tall building. In the event of a tsunami, it proposes moving to a distant, safe location. In the event of a flood, it proposes evacuating to higher ground with a low risk of inundation. If a missile attack is predicted, it proposes evacuating to an underground shelter. The suggestion function presents these options in order of highest survival rate, proposing the safest evacuation method for the user. Furthermore, the suggestion function presents evacuation routes and guides the user to the safest route. For example, in the event of an earthquake, the suggestion function guides the user to a route with a low risk of building collapse. In the event of a tsunami, the suggestion function guides the user to a route with a low risk of inundation. In the event of a flood, the suggestion function guides the user to higher ground with a low risk of inundation. If a missile attack is predicted, the suggestion function guides the user to the shortest route to an underground shelter. This allows the proposal department to suggest the safest evacuation methods and routes for users, thereby improving survival rates in emergencies. Furthermore, the proposal department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas and time periods based on past evacuation data and formulate future countermeasures. In addition, the proposal department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This enables the proposal department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0033] The guidance system provides guidance and support in real time. For example, it provides appropriate instructions in response to changes in an emergency. Specifically, the AI ​​monitors the progress of the emergency in real time and provides appropriate instructions to the user. For example, in the event of an earthquake, the guidance system updates evacuation orders and notifies the user if the risk of building collapse increases. In the event of a tsunami, it updates evacuation orders and notifies the user in response to changes in the arrival time and height of the tsunami. In the event of a flood, it updates evacuation orders and notifies the user in response to the expansion of the flooded area. If a missile attack is predicted, it updates evacuation orders and notifies the user in response to changes in the direction and speed of the missile. Furthermore, the guidance system grasps the congestion status of evacuation sites in real time and guides the user to the most suitable evacuation site. For example, if an evacuation site is crowded, the guidance system suggests other evacuation sites and notifies the user. This allows the guidance system to provide appropriate instructions in response to the progress of an emergency and ensure the safety of the user. In addition, the guidance system can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past evacuation data, the system can predict risk fluctuations in specific areas and time periods and formulate future countermeasures. Furthermore, the guidance unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, enabling early warnings. This allows the guidance unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0034] The individual information collection unit collects individual information such as the user's health status and age. For example, the individual information collection unit collects health information such as the user's heart rate, blood pressure, and medical history. The individual information collection unit uses AI to monitor the user's health status in real time and notifies if there is an abnormality. For example, the individual information collection unit monitors the user's heart rate in real time and notifies if there is an abnormality. It monitors the user's blood pressure in real time and notifies if there is an abnormality. It monitors the user's body temperature in real time and notifies if there is an abnormality. The individual information collection unit collects the user's age. The individual information collection unit uses AI to suggest appropriate evacuation methods based on the user's age. For example, the individual information collection unit adjusts the evacuation route according to the user's age. For the elderly and children, it suggests safer evacuation routes. In this way, by collecting the user's individual information, it is possible to suggest more appropriate evacuation methods. Some or all of the above processing in the individual information collection unit may be performed using AI or not using AI. For example, the individual information collection unit can input the user's health status into the AI, which can then analyze the health status and detect abnormalities.

[0035] The evacuation shelter provision unit provides detailed information about evacuation shelters. For example, the evacuation shelter provision unit provides detailed information such as the address, capacity, and facilities of the evacuation shelter. The evacuation shelter provision unit uses AI to update the detailed information about evacuation shelters in real time and provide it to the user. For example, the evacuation shelter provision unit provides the address of the evacuation shelter. It provides the capacity of the evacuation shelter. It provides the facilities of the evacuation shelter. By providing detailed information about evacuation shelters, the user can select an appropriate evacuation shelter. Some or all of the above processing in the evacuation shelter provision unit may be performed using AI or not. For example, the evacuation shelter provision unit can input detailed information about evacuation shelters into AI, and the AI ​​can analyze the information and provide it to the user.

[0036] The analysis unit analyzes the content of emergency alerts to respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks. For example, if the analysis unit receives an earthquake warning, it analyzes the earthquake's epicenter and intensity and notifies the user. If it receives a tsunami warning, it analyzes the tsunami's arrival time and height and notifies the user. If it receives a flood warning, it analyzes the flood's progress and the extent of the inundation and notifies the user. If it receives a missile attack warning, it analyzes the missile's direction and speed and notifies the user. This enables analysis tailored to the type of emergency. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the content of the emergency alert into AI, which can then analyze the emergency and notify the user.

[0037] The sensing unit collects and analyzes information such as the user's location, the height and age of surrounding buildings, and earthquake resistance standards. For example, the sensing unit uses GPS data to determine the user's current location. It uses Wi-Fi location information to determine the user's location within a building. It collects and analyzes information such as the height and age of surrounding buildings and earthquake resistance standards. This allows for a detailed understanding of the user's surroundings. Some or all of the above processing in the sensing unit may be performed using AI, or it may be performed without AI. For example, the sensing unit can input the user's location information into the AI, which can then analyze the location information to understand the surroundings.

[0038] The suggestion unit presents multiple options, such as evacuating to a nearby tall building or moving to a safe place far away, in order of highest survival rate. For example, the suggestion unit might suggest evacuating to a nearby tall building, or moving to a safe place far away. It might also present evacuation routes and guide the user to the safest route. This allows the suggestion unit to propose the evacuation method with the highest survival rate for the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the evacuation options into an AI, which can then present the options in order of highest survival rate.

[0039] The analysis unit improves the accuracy of its analysis when analyzing the content of an emergency alert by referring to past emergency data. For example, the analysis unit refers to past earthquake data to predict the magnitude and impact of a current earthquake. It analyzes the current progress of a flood based on past flood data. It refers to past missile flight data to predict the direction and speed of a current missile. In this way, the accuracy of the analysis is improved by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past emergency data into an AI, and the AI ​​can analyze the data to predict the content of a current emergency.

[0040] The analysis unit applies different analysis algorithms depending on the type of emergency when analyzing the content of an emergency alert. For example, in the case of an earthquake, the analysis unit applies an earthquake wave analysis algorithm to identify the epicenter and seismic intensity. In the case of a tsunami, it applies an ocean data analysis algorithm to predict the height and arrival time of the tsunami. In the case of a flood, it applies a river data analysis algorithm to predict the rate of water level rise and the extent of inundation. This enables analysis tailored to the type of emergency. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data corresponding to the type of emergency into the AI, and the AI ​​can apply an appropriate analysis algorithm to output the analysis results.

[0041] The analysis unit adjusts the analysis results when analyzing the content of emergency alerts, taking into account the user's geographical location. For example, if the user is near the coast, the analysis unit prioritizes the tsunami risk. If the user is in a mountainous area, it prioritizes the landslide risk. If the user is in an urban area, it prioritizes the building collapse risk. This enables analysis that takes the user's geographical location into account. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's geographical location information into the AI, which can then analyze the location information and adjust the content of the emergency alert.

[0042] The analysis unit analyzes users' social media activity and supplements relevant information when analyzing the content of emergency alerts. For example, the analysis unit identifies the scope of the emergency based on location information posted by users on social media. It analyzes photos and videos shared by users on social media to understand the situation on the ground. It supplements the details of the emergency based on information shared by users with other users on social media. In this way, the accuracy of emergency alert analysis is improved by analyzing social media activity. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user social media data into AI, and the AI ​​can analyze the data to supplement the details of the emergency.

[0043] The location tracking unit selects the optimal acquisition method by referring to the user's past movement history when acquiring the user's location information. For example, the location tracking unit adjusts the frequency of location information acquisition based on places the user has frequently visited in the past. It analyzes the user's past movement patterns and selects the optimal location information acquisition method. It adjusts the location information acquisition method based on the means of transportation the user has used in the past. This improves the accuracy of location information acquisition by referring to past movement history. Some or all of the above processing in the location tracking unit may be performed using AI or not. For example, the location tracking unit can input the user's past movement data into AI, and the AI ​​can analyze the data and select the optimal location information acquisition method.

[0044] The location tracking unit applies different acquisition algorithms depending on the surrounding environment when acquiring the user's location information. For example, if the user is inside a building, the location tracking unit acquires location information using Wi-Fi or Bluetooth®. If the user is outdoors, it acquires location information using GPS. If the user is underground, it acquires location information using cell phone base station information. This makes it possible to acquire appropriate location information according to the surrounding environment. Some or all of the above processing in the location tracking unit may be performed using AI or not. For example, the location tracking unit can input data on the user's surrounding environment into the AI, which can then analyze the data and apply an appropriate acquisition algorithm.

[0045] The location tracking unit prioritizes acquiring highly relevant information by considering the user's geographical location when obtaining the user's location information. For example, if the user is near the coast, the location tracking unit prioritizes acquiring information related to tsunami risk. If the user is in a mountainous area, it prioritizes acquiring information related to landslide risk. If the user is in an urban area, it prioritizes acquiring information related to building collapse risk. This makes it possible to acquire appropriate information that takes the user's geographical location information into consideration. Some or all of the above processing in the location tracking unit may be performed using AI or not. For example, the location tracking unit can input the user's geographical location information into AI, and the AI ​​can analyze the location information and prioritize acquiring highly relevant information.

[0046] The location tracking unit analyzes the user's social media activity and obtains relevant information when acquiring the user's location information. For example, the location tracking unit identifies the scope of an emergency based on location information posted by the user on social media. It analyzes photos and videos shared by the user on social media to understand the situation on the ground. It supplements the details of the emergency based on information shared by the user with other users on social media. By analyzing social media activity, the accuracy of location information acquisition is improved. Some or all of the above processing in the location tracking unit may be performed using AI or not. For example, the location tracking unit can input the user's social media data into AI, which can then analyze the data and obtain relevant information.

[0047] The proposal department adjusts the level of detail in its proposals based on the importance of each evacuation method. For example, it provides detailed explanations for highly important evacuation methods and concise explanations for less important ones. It also adjusts the order of proposals according to their importance. This enables appropriate proposals based on the importance of each evacuation method. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data on the importance of evacuation methods into an AI, which can then analyze the data and adjust the level of detail in the proposals.

[0048] The proposal unit applies different proposal algorithms depending on the type of evacuation method. For example, when evacuating to a tall building, the proposal unit makes suggestions that take into account the building's structure and seismic resistance. When moving to a distant, safe location, it makes suggestions that take into account transportation methods and travel time. When evacuating to a temporary shelter, it makes suggestions that take into account the surrounding conditions and safety. This enables appropriate suggestions according to the type of evacuation method. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input data on the type of evacuation method into an AI, which can then analyze the data and apply an appropriate proposal algorithm.

[0049] The proposal department determines the priority of proposals based on the timing of submission of evacuation methods. For example, immediately after an emergency occurs, the proposal department prioritizes proposing the most important evacuation methods. If the emergency is ongoing, it adjusts the priority of proposals according to the situation. After the emergency is resolved, it proposes the next course of action. This ensures that appropriate priority of proposals is determined according to the timing of submission of evacuation methods. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input data on the timing of evacuation method submissions into an AI, which can then analyze the data to determine the priority of proposals.

[0050] The proposal department adjusts the order of proposals based on the relevance of the evacuation methods. For example, the proposal department prioritizes proposing the most relevant evacuation methods. Less relevant evacuation methods are postponed. The order of proposals is adjusted according to their relevance. This allows for the determination of an appropriate order of proposals based on the relevance of the evacuation methods. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data on the relevance of evacuation methods into an AI, which can then analyze the data and adjust the order of proposals.

[0051] The guidance unit adjusts the level of detail in its guidance based on the progress of the emergency. For example, immediately after an emergency occurs, the guidance unit provides concise and rapid guidance. If the emergency is ongoing, it provides detailed guidance and situation-appropriate instructions. After the emergency is resolved, it provides detailed guidance on the next steps. This enables appropriate guidance according to the progress of the emergency. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input emergency progress data into the AI, which can then analyze the data and adjust the level of detail in the guidance.

[0052] The guidance unit applies different guidance algorithms depending on the type of emergency. For example, in the case of an earthquake, the guidance unit provides guidance on building safety and evacuation routes. In the case of a tsunami, it provides guidance on evacuation routes to higher ground. In the case of a flood, it provides guidance on evacuation locations with a low risk of flooding. This enables appropriate guidance according to the type of emergency. Some or all of the above processing in the guidance unit may be performed using AI, or it may be performed without AI. For example, the guidance unit can input emergency type data into the AI, which can then analyze the data and apply an appropriate guidance algorithm.

[0053] The guidance unit adjusts the order of guidance based on the location of the emergency. For example, if the emergency is nearby, the guidance unit will quickly guide the user to an evacuation route. If the emergency is far away, it will provide detailed information and guide the user to the next action. The guidance unit adjusts the order of guidance according to the location of the emergency. This allows for the determination of an appropriate guidance order according to the location of the emergency. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input emergency location data into the AI, and the AI ​​can analyze the data to adjust the guidance order.

[0054] The guidance unit improves the accuracy of guidance by referring to relevant information about the emergency during guidance. For example, the guidance unit refers to the latest information about the emergency and updates the guidance in real time. It refers to past emergency data and provides optimal guidance. It improves the accuracy of guidance based on relevant information about the emergency. As a result, the accuracy of guidance is improved by referring to relevant information about the emergency. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input relevant information about the emergency into AI, and the AI ​​can analyze the data to improve the accuracy of guidance.

[0055] The individual information collection unit improves the accuracy of individual information collection by referring to past health data. For example, the individual information collection unit refers to the user's past health checkup data to understand their current health status. It collects necessary information based on the user's past medical history. It refers to the user's past exercise data to understand their current physical fitness. As a result, the accuracy of individual information collection is improved by referring to past health data. Some or all of the above processing in the individual information collection unit may be performed using AI or not. For example, the individual information collection unit can input the user's past health data into AI, and the AI ​​can analyze the data to understand their current health status.

[0056] The individual information collection unit monitors the user's current health status in real time when collecting individual information. For example, the individual information collection unit monitors the user's heart rate in real time and notifies if there is an abnormality. It also monitors the user's blood pressure in real time and notifies if there is an abnormality. It monitors the user's body temperature in real time and notifies if there is an abnormality. This allows for the collection of appropriate information by monitoring the user's current health status in real time. Some or all of the above processing in the individual information collection unit may be performed using AI or not using AI. For example, the individual information collection unit can input the user's health data into AI, which can then analyze the data and detect abnormalities.

[0057] The individual information collection unit prioritizes collecting highly relevant information by considering the user's geographical location when collecting individual information. For example, if the user is near the coast, the individual information collection unit prioritizes collecting information related to tsunami risk. If the user is in a mountainous area, it prioritizes collecting information related to landslide risk. If the user is in an urban area, it prioritizes collecting information related to building collapse risk. This enables the collection of appropriate information that takes the user's geographical location into account. Some or all of the above processing in the individual information collection unit may be performed using AI, or not. For example, the individual information collection unit can input the user's geographical location information into AI, which can then analyze the location information and prioritize collecting highly relevant information.

[0058] The individual information collection unit analyzes the user's social media activity and collects relevant information when collecting individual information. For example, the individual information collection unit identifies the scope of an emergency based on location information posted by the user on social media. It analyzes photos and videos shared by the user on social media to understand the situation on the ground. It supplements the details of the emergency based on information shared by the user with other users on social media. By analyzing social media activity, the accuracy of collecting individual information is improved. Some or all of the above processing in the individual information collection unit may be performed using AI or not. For example, the individual information collection unit can input the user's social media data into AI, which can then analyze the data and collect relevant information.

[0059] The evacuation shelter provision unit improves the accuracy of its provision of information by referring to past evacuation data when providing information on evacuation shelters. For example, the evacuation shelter provision unit provides the safest evacuation shelter based on past evacuation data. It predicts the congestion level of evacuation shelters based on past evacuation data. It evaluates the safety of evacuation shelters based on past evacuation data. In this way, the accuracy of providing evacuation shelters is improved by referring to past evacuation data. Some or all of the above processes in the evacuation shelter provision unit may be performed using AI or not. For example, the evacuation shelter provision unit can input past evacuation data into AI, and the AI ​​can analyze the data to provide the optimal evacuation shelter.

[0060] The evacuation shelter provision unit improves the accuracy of its provision of information by considering real-time conditions when providing information on evacuation shelters. For example, the evacuation shelter provision unit provides the optimal evacuation shelter based on the real-time congestion status of evacuation shelters. It evaluates the safety of evacuation shelters in real time and provides the optimal evacuation shelter. It provides the optimal evacuation shelter based on the real-time access status of evacuation shelters. In this way, the accuracy of providing evacuation shelters is improved by considering real-time conditions. Some or all of the above processes in the evacuation shelter provision unit may be performed using AI or not. For example, the evacuation shelter provision unit can input real-time situation data into AI, and the AI ​​can analyze the data and provide the optimal evacuation shelter.

[0061] The evacuation shelter provision unit prioritizes providing highly relevant information by considering the user's geographical location when providing information on evacuation shelters. For example, if the user is near the coast, the evacuation shelter provision unit will prioritize providing evacuation shelters related to tsunami risk. If the user is in a mountainous area, it will prioritize providing evacuation shelters related to landslide risk. If the user is in an urban area, it will prioritize providing evacuation shelters related to building collapse risk. This makes it possible to provide appropriate evacuation shelter information considering the user's geographical location. Some or all of the above processing in the evacuation shelter provision unit may be performed using AI or not. For example, the evacuation shelter provision unit can input the user's geographical location information into AI, and the AI ​​can analyze the location information and prioritize providing highly relevant evacuation shelters.

[0062] The evacuation shelter provision unit analyzes users' social media activity and provides relevant information when providing information about evacuation shelters. For example, the evacuation shelter provision unit provides the most suitable evacuation shelter based on location information posted by users on social media. It analyzes photos and videos shared by users on social media to understand the local situation. It provides detailed information about evacuation shelters based on information shared by users with other users on social media. In this way, the accuracy of evacuation shelter information provision is improved by analyzing social media activity. Some or all of the above processes in the evacuation shelter provision unit may be performed using AI or not. For example, the evacuation shelter provision unit can input user social media data into AI, and the AI ​​can analyze the data and provide relevant evacuation shelter information.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The analysis unit can also improve analysis accuracy by referring to the user's past evacuation behavior data in the event of an emergency. For example, it can predict current evacuation behavior and propose the optimal evacuation method based on evacuation behavior data from similar emergencies in the past. Based on past evacuation behavior data, it can analyze which evacuation routes users tend to choose and propose the optimal evacuation route. Based on past evacuation behavior data, it can analyze which evacuation locations users tend to choose and propose the optimal evacuation locations. In this way, by referring to past evacuation behavior data, the analysis accuracy is improved and more appropriate evacuation methods can be proposed.

[0065] The evacuation shelter provision unit can also improve the accuracy of its provision of evacuation shelter information by referring to users' past evacuation shelter selection data. For example, it can predict current evacuation shelter selections based on data of evacuation shelters previously selected by users and propose the most suitable evacuation shelter. It can analyze which evacuation shelters users tend to choose based on past evacuation shelter selection data and propose the most suitable evacuation shelter. It can also analyze the criteria users use when selecting an evacuation shelter based on past evacuation shelter selection data and propose the most suitable evacuation shelter. In this way, by referring to past evacuation shelter selection data, the accuracy of evacuation shelter provision can be improved, and more appropriate evacuation shelters can be proposed.

[0066] The location tracking unit can also improve the accuracy of acquiring user location information by referring to the user's past location data. For example, it can predict the current location based on data of places the user has visited in the past and acquire the optimal location information. It can analyze where the user is most likely to be based on past location data and acquire the optimal location information. It can analyze the patterns of the user's movement based on past location data and acquire the optimal location information. In this way, by referring to past location data, the accuracy of acquiring location information can be improved, and more appropriate location information can be obtained.

[0067] The guidance system can also improve guidance accuracy by referring to the user's past evacuation behavior data. For example, it can predict the current evacuation route based on data of evacuation routes previously selected by the user and provide optimal guidance. It can also analyze which evacuation routes users tend to choose based on past evacuation behavior data and provide optimal guidance. Furthermore, it can analyze the criteria users use when taking evacuation actions based on past evacuation behavior data and provide optimal guidance. In this way, by referring to past evacuation behavior data, guidance accuracy can be improved, and more appropriate guidance can be provided.

[0068] The proposal function can also improve the accuracy of its suggestions by referring to the user's past evacuation behavior data. For example, it can predict the current evacuation method based on data of evacuation methods the user has previously selected and make the optimal suggestion. It can analyze which evacuation methods the user tends to choose based on past evacuation behavior data and make the optimal suggestion. It can analyze the criteria the user uses when selecting an evacuation method based on past evacuation behavior data and make the optimal suggestion. In this way, by referring to past evacuation behavior data, the accuracy of the suggestions can be improved, and more appropriate suggestions can be made.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The analysis unit analyzes the content of emergency alerts. The analysis unit uses AI to analyze the content of emergency alerts in real time to respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks, and to understand the specific details of the emergency. For example, if an earthquake warning is received, the unit analyzes the epicenter and intensity of the earthquake and notifies the user. If a tsunami warning is received, the unit analyzes the arrival time and height of the tsunami and notifies the user. If a flood warning is received, the unit analyzes the progress of the flood and the extent of the inundation and notifies the user. If a missile attack warning is received, the unit analyzes the direction and speed of the incoming missile and notifies the user. Step 2: The sensing unit grasps the user's current location and surrounding environment. The sensing unit collects information such as the user's location, the height, age, and seismic standards of surrounding buildings, and analyzes it in real time using AI. For example, it uses GPS data to determine the user's current location and Wi-Fi location information to determine the user's location within a building. Furthermore, it collects and analyzes information such as the height, age, and seismic standards of surrounding buildings. Step 3: The suggestion unit proposes the optimal evacuation method. The suggestion unit presents multiple options, such as evacuating to a nearby tall building or moving to a safe location far away, in order of highest survival rate. The suggestion unit uses AI to propose the evacuation method with the highest survival rate for the user. For example, it suggests evacuating to a nearby tall building or moving to a safe location far away, and then presents evacuation routes to guide the user to the safest route. Step 4: The guidance department provides real-time guidance and support. The guidance department provides appropriate instructions in response to changes in the emergency, uses AI to guide users in real time, and supports evacuation actions. For example, it updates evacuation routes as the emergency progresses and notifies users. It also monitors the congestion status of evacuation sites in real time and guides users to the most suitable evacuation site.

[0071] (Example of form 2) An AI system according to an embodiment of the present invention is a system for improving survival rates in emergency situations. This AI system analyzes the content of emergency alerts in real time, understands the user's current location and surrounding conditions, and proposes the optimal evacuation method. Furthermore, the AI ​​system provides guidance and support in real time. For example, to respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks, the AI ​​system analyzes the content of emergency alerts and understands the specific details of the emergency. This allows the user to immediately understand the details of the emergency. Next, the AI ​​system understands the user's current location and surrounding conditions. For example, it collects and analyzes information such as the user's location, the height of surrounding buildings, their age, and seismic resistance standards. This allows the AI ​​system to identify the safest evacuation location and route for the user. Furthermore, the AI ​​system proposes multiple locations and methods that offer the highest survival rates for the user. For example, it presents multiple options, such as evacuating to a nearby tall building or moving to a distant safe location, in order of highest survival rate. This makes it easier for the user to choose the safest option. Finally, the AI ​​system guides the user in real time and supports evacuation actions. For example, it provides appropriate instructions in response to changes in the emergency. This allows the user to respond flexibly in accordance with the progression of the emergency. This system allows users to make calm decisions and take the safest evacuation actions during emergencies. The AI ​​system analyzes the situation in real time and proposes the optimal evacuation method, thereby improving survival rates and reducing anxiety. In this way, the AI ​​system can improve survival rates during emergencies.

[0072] The AI ​​system according to this embodiment comprises an analysis unit, a sensing unit, a proposal unit, and a guidance unit. The analysis unit analyzes the content of emergency alerts. The analysis unit analyzes the content of emergency alerts in order to respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks. The analysis unit uses AI to analyze the content of emergency alerts in real time and grasp the specific details of the emergency. For example, if the analysis unit receives an earthquake warning, it analyzes the epicenter and seismic intensity of the earthquake and notifies the user. If it receives a tsunami warning, it analyzes the arrival time and height of the tsunami and notifies the user. If it receives a flood warning, it analyzes the progress of the flood and the extent of the inundation and notifies the user. If it receives a missile attack warning, it analyzes the direction and speed of the incoming missile and notifies the user. The sensing unit grasps the user's current location and surrounding conditions. The sensing unit collects and analyzes information such as the user's location information, the height of surrounding buildings, their age, and seismic resistance standards. The tracking unit uses AI to grasp the user's current location and surrounding environment in real time. For example, the tracking unit uses GPS data to determine the user's current location. It uses Wi-Fi location information to determine the user's location within a building. It collects and analyzes information such as the height, age, and seismic resistance standards of surrounding buildings. The suggestion unit proposes the optimal evacuation method. The suggestion unit presents multiple options in order of highest survival rate, such as evacuating to a nearby tall building or moving to a safe place far away. The suggestion unit uses AI to propose the evacuation method with the highest survival rate for the user. For example, the suggestion unit suggests evacuating to a nearby tall building. It suggests moving to a safe place far away. It presents evacuation routes and guides the user to the safest route. The guidance unit provides guidance and support in real time. For example, the guidance unit provides appropriate instructions in response to changes in the emergency. The guidance unit uses AI to guide the user in real time and support evacuation actions. For example, the guidance unit updates the evacuation route and notifies the user as the emergency progresses. The system monitors the congestion status of evacuation shelters in real time and guides users to the most suitable evacuation shelter. This allows the AI ​​system according to this embodiment to improve survival rates during emergencies.

[0073] The analysis unit analyzes the content of emergency alerts. For example, the analysis unit analyzes the content of emergency alerts to respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks. The analysis unit uses AI to analyze the content of emergency alerts in real time and understand the specific nature of the emergency. Specifically, the AI ​​uses natural language processing technology to analyze the alert text and extract the type of emergency and its details. For example, upon receiving an earthquake warning, the AI ​​analyzes the earthquake's epicenter and intensity and notifies the user. Upon receiving a tsunami warning, the AI ​​analyzes the tsunami's arrival time and height and notifies the user. Upon receiving a flood warning, the AI ​​analyzes the flood's progress and the extent of the inundation and notifies the user. Upon receiving a missile attack warning, the AI ​​analyzes the missile's direction and speed and notifies the user. This allows the analysis unit to quickly and accurately analyze the content of emergency alerts and provide appropriate information to the user. Furthermore, the analysis unit can learn from past emergency alert data to perform even more accurate analyses. For example, it can improve the accuracy of earthquake intensity predictions in specific areas based on past earthquake data. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal alerts, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0074] The location tracking unit understands the user's current location and surrounding conditions. For example, it collects and analyzes information such as the user's location, the height and age of surrounding buildings, and seismic resistance standards. Specifically, it uses GPS data to determine the user's current location and Wi-Fi location information to determine the user's location within a building. Furthermore, it collects information such as the height and age of surrounding buildings and seismic resistance standards, and integrates and analyzes this data. The AI ​​processes this data in real time to accurately understand the user's current location and surrounding conditions. For example, in the event of an earthquake, the location tracking unit evaluates the seismic performance of the building the user is in and determines the need for evacuation. Similarly, in the event of a flood, it assesses the risk of inundation based on the surrounding topography and building heights. This allows the location tracking unit to quickly and accurately understand the user's current location and surrounding conditions, providing fundamental information for appropriate evacuation instructions. Furthermore, the location tracking unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can assess the risks of specific regions or buildings based on past earthquake data and formulate future countermeasures. Furthermore, the monitoring unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data, and issue warnings early. This allows the monitoring 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.

[0075] The suggestion function proposes the most suitable evacuation method. It presents multiple options, such as evacuating to a nearby tall building or moving to a distant, safe location, in order of highest survival rate. Specifically, the AI ​​calculates the optimal evacuation method based on the user's current location, surrounding conditions, and the type of emergency. For example, in the event of an earthquake, the suggestion function proposes evacuating to a nearby tall building. In the event of a tsunami, it proposes moving to a distant, safe location. In the event of a flood, it proposes evacuating to higher ground with a low risk of inundation. If a missile attack is predicted, it proposes evacuating to an underground shelter. The suggestion function presents these options in order of highest survival rate, proposing the safest evacuation method for the user. Furthermore, the suggestion function presents evacuation routes and guides the user to the safest route. For example, in the event of an earthquake, the suggestion function guides the user to a route with a low risk of building collapse. In the event of a tsunami, the suggestion function guides the user to a route with a low risk of inundation. In the event of a flood, the suggestion function guides the user to higher ground with a low risk of inundation. If a missile attack is predicted, the suggestion function guides the user to the shortest route to an underground shelter. This allows the proposal department to suggest the safest evacuation methods and routes for users, thereby improving survival rates in emergencies. Furthermore, the proposal department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas and time periods based on past evacuation data and formulate future countermeasures. In addition, the proposal department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This enables the proposal department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0076] The guidance system provides guidance and support in real time. For example, it provides appropriate instructions in response to changes in an emergency. Specifically, the AI ​​monitors the progress of the emergency in real time and provides appropriate instructions to the user. For example, in the event of an earthquake, the guidance system updates evacuation orders and notifies the user if the risk of building collapse increases. In the event of a tsunami, it updates evacuation orders and notifies the user in response to changes in the arrival time and height of the tsunami. In the event of a flood, it updates evacuation orders and notifies the user in response to the expansion of the flooded area. If a missile attack is predicted, it updates evacuation orders and notifies the user in response to changes in the direction and speed of the missile. Furthermore, the guidance system grasps the congestion status of evacuation sites in real time and guides the user to the most suitable evacuation site. For example, if an evacuation site is crowded, the guidance system suggests other evacuation sites and notifies the user. This allows the guidance system to provide appropriate instructions in response to the progress of an emergency and ensure the safety of the user. In addition, the guidance system can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past evacuation data, the system can predict risk fluctuations in specific areas and time periods and formulate future countermeasures. Furthermore, the guidance unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, enabling early warnings. This allows the guidance unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0077] The individual information collection unit collects individual information such as the user's health status and age. For example, the individual information collection unit collects health information such as the user's heart rate, blood pressure, and medical history. The individual information collection unit uses AI to monitor the user's health status in real time and notifies if there is an abnormality. For example, the individual information collection unit monitors the user's heart rate in real time and notifies if there is an abnormality. It monitors the user's blood pressure in real time and notifies if there is an abnormality. It monitors the user's body temperature in real time and notifies if there is an abnormality. The individual information collection unit collects the user's age. The individual information collection unit uses AI to suggest appropriate evacuation methods based on the user's age. For example, the individual information collection unit adjusts the evacuation route according to the user's age. For the elderly and children, it suggests safer evacuation routes. In this way, by collecting the user's individual information, it is possible to suggest more appropriate evacuation methods. Some or all of the above processing in the individual information collection unit may be performed using AI or not using AI. For example, the individual information collection unit can input the user's health status into the AI, which can then analyze the health status and detect abnormalities.

[0078] The evacuation shelter provision unit provides detailed information about evacuation shelters. For example, the evacuation shelter provision unit provides detailed information such as the address, capacity, and facilities of the evacuation shelter. The evacuation shelter provision unit uses AI to update the detailed information about evacuation shelters in real time and provide it to the user. For example, the evacuation shelter provision unit provides the address of the evacuation shelter. It provides the capacity of the evacuation shelter. It provides the facilities of the evacuation shelter. By providing detailed information about evacuation shelters, the user can select an appropriate evacuation shelter. Some or all of the above processing in the evacuation shelter provision unit may be performed using AI or not. For example, the evacuation shelter provision unit can input detailed information about evacuation shelters into AI, and the AI ​​can analyze the information and provide it to the user.

[0079] The analysis unit analyzes the content of emergency alerts to respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks. For example, if the analysis unit receives an earthquake warning, it analyzes the earthquake's epicenter and intensity and notifies the user. If it receives a tsunami warning, it analyzes the tsunami's arrival time and height and notifies the user. If it receives a flood warning, it analyzes the flood's progress and the extent of the inundation and notifies the user. If it receives a missile attack warning, it analyzes the missile's direction and speed and notifies the user. This enables analysis tailored to the type of emergency. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the content of the emergency alert into AI, which can then analyze the emergency and notify the user.

[0080] The sensing unit collects and analyzes information such as the user's location, the height and age of surrounding buildings, and earthquake resistance standards. For example, the sensing unit uses GPS data to determine the user's current location. It uses Wi-Fi location information to determine the user's location within a building. It collects and analyzes information such as the height and age of surrounding buildings and earthquake resistance standards. This allows for a detailed understanding of the user's surroundings. Some or all of the above processing in the sensing unit may be performed using AI, or it may be performed without AI. For example, the sensing unit can input the user's location information into the AI, which can then analyze the location information to understand the surroundings.

[0081] The suggestion unit presents multiple options, such as evacuating to a nearby tall building or moving to a safe place far away, in order of highest survival rate. For example, the suggestion unit might suggest evacuating to a nearby tall building, or moving to a safe place far away. It might also present evacuation routes and guide the user to the safest route. This allows the suggestion unit to propose the evacuation method with the highest survival rate for the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the evacuation options into an AI, which can then present the options in order of highest survival rate.

[0082] The analysis unit estimates the user's emotions and adjusts the emergency alert analysis method based on the estimated user emotions. For example, if the user is panicking, the AI ​​prioritizes providing concise and clear information. If the user is calm, it provides detailed information to deepen their understanding of the situation. If the user is feeling anxious, it provides information using reassuring language. This enables appropriate emergency alert analysis tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 or not. For example, the analysis unit can input user emotion data into the AI, which can analyze the emotions and adjust the emergency alert analysis method.

[0083] The analysis unit improves the accuracy of its analysis when analyzing the content of an emergency alert by referring to past emergency data. For example, the analysis unit refers to past earthquake data to predict the magnitude and impact of a current earthquake. It analyzes the current progress of a flood based on past flood data. It refers to past missile flight data to predict the direction and speed of a current missile. In this way, the accuracy of the analysis is improved by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past emergency data into an AI, and the AI ​​can analyze the data to predict the content of a current emergency.

[0084] The analysis unit applies different analysis algorithms depending on the type of emergency when analyzing the content of an emergency alert. For example, in the case of an earthquake, the analysis unit applies an earthquake wave analysis algorithm to identify the epicenter and seismic intensity. In the case of a tsunami, it applies an ocean data analysis algorithm to predict the height and arrival time of the tsunami. In the case of a flood, it applies a river data analysis algorithm to predict the rate of water level rise and the extent of inundation. This enables analysis tailored to the type of emergency. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data corresponding to the type of emergency into the AI, and the AI ​​can apply an appropriate analysis algorithm to output the analysis results.

[0085] The analysis unit estimates the user's emotions and determines the priority of emergency alerts based on the estimated emotions. For example, if the user is panicking, the analysis unit prioritizes providing the most important information. If the user is calm, it provides detailed information sequentially. If the user is feeling anxious, it prioritizes providing information that provides reassurance. This allows for the determination of appropriate emergency alert priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 or not. For example, the analysis unit can input user emotion data into an AI, which can analyze the emotions and determine the priority of emergency alerts.

[0086] The analysis unit adjusts the analysis results when analyzing the content of emergency alerts, taking into account the user's geographical location. For example, if the user is near the coast, the analysis unit prioritizes the tsunami risk. If the user is in a mountainous area, it prioritizes the landslide risk. If the user is in an urban area, it prioritizes the building collapse risk. This enables analysis that takes the user's geographical location into account. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's geographical location information into the AI, which can then analyze the location information and adjust the content of the emergency alert.

[0087] The analysis unit analyzes users' social media activity and supplements relevant information when analyzing the content of emergency alerts. For example, the analysis unit identifies the scope of the emergency based on location information posted by users on social media. It analyzes photos and videos shared by users on social media to understand the situation on the ground. It supplements the details of the emergency based on information shared by users with other users on social media. In this way, the accuracy of emergency alert analysis is improved by analyzing social media activity. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user social media data into AI, and the AI ​​can analyze the data to supplement the details of the emergency.

[0088] The sensing unit estimates the user's emotions and adjusts the timing of location information acquisition based on the estimated emotions. For example, if the user is in a panic state, the sensing unit frequently acquires location information to confirm safety. If the user is calm, it acquires location information at normal intervals. If the user is feeling anxious, it acquires location information at appropriate intervals to provide a sense of security. This makes it possible to acquire appropriate location 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 sensing unit may be performed using AI or not. For example, the sensing unit can input user emotion data into an AI, which can analyze the emotions and adjust the timing of location information acquisition.

[0089] The location tracking unit selects the optimal acquisition method by referring to the user's past movement history when acquiring the user's location information. For example, the location tracking unit adjusts the frequency of location information acquisition based on places the user has frequently visited in the past. It analyzes the user's past movement patterns and selects the optimal location information acquisition method. It adjusts the location information acquisition method based on the means of transportation the user has used in the past. This improves the accuracy of location information acquisition by referring to past movement history. Some or all of the above processing in the location tracking unit may be performed using AI or not. For example, the location tracking unit can input the user's past movement data into AI, and the AI ​​can analyze the data and select the optimal location information acquisition method.

[0090] The location tracking unit applies different acquisition algorithms depending on the surrounding environment when acquiring the user's location information. For example, if the user is inside a building, the location tracking unit acquires location information using Wi-Fi or Bluetooth. If the user is outdoors, it acquires location information using GPS. If the user is underground, it acquires location information using cell phone base station information. This makes it possible to acquire appropriate location information according to the surrounding environment. Some or all of the above processing in the location tracking unit may be performed using AI or not. For example, the location tracking unit can input data on the user's surrounding environment into the AI, which can then analyze the data and apply an appropriate acquisition algorithm.

[0091] The sensing unit estimates the user's emotions and determines the priority of location information based on the estimated emotions. For example, if the user is in a state of panic, the sensing unit prioritizes acquiring the most important location information. If the user is calm, it acquires location information with the normal priority. If the user is feeling anxious, it prioritizes acquiring location information that provides a sense of security. This allows for the determination of an appropriate priority of location 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 sensing unit may be performed using AI or not. For example, the sensing unit can input user emotion data into an AI, which can analyze the emotions and determine the priority of location information.

[0092] The location tracking unit prioritizes acquiring highly relevant information by considering the user's geographical location when obtaining the user's location information. For example, if the user is near the coast, the location tracking unit prioritizes acquiring information related to tsunami risk. If the user is in a mountainous area, it prioritizes acquiring information related to landslide risk. If the user is in an urban area, it prioritizes acquiring information related to building collapse risk. This makes it possible to acquire appropriate information that takes the user's geographical location information into consideration. Some or all of the above processing in the location tracking unit may be performed using AI or not. For example, the location tracking unit can input the user's geographical location information into AI, and the AI ​​can analyze the location information and prioritize acquiring highly relevant information.

[0093] The location tracking unit analyzes the user's social media activity and obtains relevant information when acquiring the user's location information. For example, the location tracking unit identifies the scope of an emergency based on location information posted by the user on social media. It analyzes photos and videos shared by the user on social media to understand the situation on the ground. It supplements the details of the emergency based on information shared by the user with other users on social media. By analyzing social media activity, the accuracy of location information acquisition is improved. Some or all of the above processing in the location tracking unit may be performed using AI or not. For example, the location tracking unit can input the user's social media data into AI, which can then analyze the data and obtain relevant information.

[0094] The suggestion unit estimates the user's emotions and adjusts the way the suggestion is expressed based on the estimated emotions. For example, if the user is panicking, the suggestion unit will make a concise and clear suggestion. If the user is calm, it will make a detailed suggestion. If the user is feeling anxious, it will make a reassuring suggestion. This makes it possible to express appropriate suggestions 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can analyze the emotions and adjust the way the suggestion is expressed.

[0095] The proposal department adjusts the level of detail in its proposals based on the importance of each evacuation method. For example, it provides detailed explanations for highly important evacuation methods and concise explanations for less important ones. It also adjusts the order of proposals according to their importance. This enables appropriate proposals based on the importance of each evacuation method. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data on the importance of evacuation methods into an AI, which can then analyze the data and adjust the level of detail in the proposals.

[0096] The proposal unit applies different proposal algorithms depending on the type of evacuation method. For example, when evacuating to a tall building, the proposal unit makes suggestions that take into account the building's structure and seismic resistance. When moving to a distant, safe location, it makes suggestions that take into account transportation methods and travel time. When evacuating to a temporary shelter, it makes suggestions that take into account the surrounding conditions and safety. This enables appropriate suggestions according to the type of evacuation method. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input data on the type of evacuation method into an AI, which can then analyze the data and apply an appropriate proposal algorithm.

[0097] The suggestion unit estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. For example, if the user is panicking, the suggestion unit will provide a short, concise suggestion. If the user is calm, it will provide a detailed suggestion. If the user is feeling anxious, it will provide a reassuring suggestion. This allows for the determination of an appropriate suggestion length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can analyze the emotions and adjust the length of the suggestion.

[0098] The proposal department determines the priority of proposals based on the timing of submission of evacuation methods. For example, immediately after an emergency occurs, the proposal department prioritizes proposing the most important evacuation methods. If the emergency is ongoing, it adjusts the priority of proposals according to the situation. After the emergency is resolved, it proposes the next course of action. This ensures that appropriate priority of proposals is determined according to the timing of submission of evacuation methods. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input data on the timing of evacuation method submissions into an AI, which can then analyze the data to determine the priority of proposals.

[0099] The proposal department adjusts the order of proposals based on the relevance of the evacuation methods. For example, the proposal department prioritizes proposing the most relevant evacuation methods. Less relevant evacuation methods are postponed. The order of proposals is adjusted according to their relevance. This allows for the determination of an appropriate order of proposals based on the relevance of the evacuation methods. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data on the relevance of evacuation methods into an AI, which can then analyze the data and adjust the order of proposals.

[0100] The guidance unit estimates the user's emotions and adjusts the way the guidance is presented based on the estimated emotions. For example, if the user is in a state of panic, the guidance unit provides concise and clear guidance. If the user is calm, it provides detailed guidance. If the user is feeling anxious, it provides reassuring guidance. This enables the appropriate expression of guidance 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 guidance unit may be performed using AI or not. For example, the guidance unit can input user emotion data into AI, and the AI ​​can analyze the emotions and adjust the way the guidance is presented.

[0101] The guidance unit adjusts the level of detail in its guidance based on the progress of the emergency. For example, immediately after an emergency occurs, the guidance unit provides concise and rapid guidance. If the emergency is ongoing, it provides detailed guidance and situation-appropriate instructions. After the emergency is resolved, it provides detailed guidance on the next steps. This enables appropriate guidance according to the progress of the emergency. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input emergency progress data into the AI, which can then analyze the data and adjust the level of detail in the guidance.

[0102] The guidance unit applies different guidance algorithms depending on the type of emergency. For example, in the case of an earthquake, the guidance unit provides guidance on building safety and evacuation routes. In the case of a tsunami, it provides guidance on evacuation routes to higher ground. In the case of a flood, it provides guidance on evacuation locations with a low risk of flooding. This enables appropriate guidance according to the type of emergency. Some or all of the above processing in the guidance unit may be performed using AI, or it may be performed without AI. For example, the guidance unit can input emergency type data into the AI, which can then analyze the data and apply an appropriate guidance algorithm.

[0103] The guidance unit estimates the user's emotions and determines the priority of guidance based on the estimated emotions. For example, if the user is in a state of panic, the guidance unit will prioritize providing the most important guidance. If the user is calm, guidance will be provided in the normal priority order. If the user is feeling anxious, guidance that provides reassurance will be prioritized. This allows for the determination of appropriate guidance 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 guidance unit may be performed using AI or not. For example, the guidance unit can input user emotion data into an AI, which can analyze the emotions and determine the priority of guidance.

[0104] The guidance unit adjusts the order of guidance based on the location of the emergency. For example, if the emergency is nearby, the guidance unit will quickly guide the user to an evacuation route. If the emergency is far away, it will provide detailed information and guide the user to the next action. The guidance unit adjusts the order of guidance according to the location of the emergency. This allows for the determination of an appropriate guidance order according to the location of the emergency. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input emergency location data into the AI, and the AI ​​can analyze the data to adjust the guidance order.

[0105] The guidance unit improves the accuracy of guidance by referring to relevant information about the emergency during guidance. For example, the guidance unit refers to the latest information about the emergency and updates the guidance in real time. It refers to past emergency data and provides optimal guidance. It improves the accuracy of guidance based on relevant information about the emergency. As a result, the accuracy of guidance is improved by referring to relevant information about the emergency. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input relevant information about the emergency into AI, and the AI ​​can analyze the data to improve the accuracy of guidance.

[0106] The individual information collection unit estimates the user's emotions and adjusts the method of collecting individual information based on the estimated user emotions. For example, if the user is in a state of panic, the individual information collection unit collects concise and rapid information. If the user is calm, it collects detailed information. If the user is feeling anxious, it collects information that provides reassurance. This enables the collection of appropriate individual 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 individual information collection unit may be performed using AI or not. For example, the individual information collection unit can input user emotion data into an AI, which can analyze the emotions and adjust the method of collecting individual information.

[0107] The individual information collection unit improves the accuracy of individual information collection by referring to past health data. For example, the individual information collection unit refers to the user's past health checkup data to understand their current health status. It collects necessary information based on the user's past medical history. It refers to the user's past exercise data to understand their current physical fitness. As a result, the accuracy of individual information collection is improved by referring to past health data. Some or all of the above processing in the individual information collection unit may be performed using AI or not. For example, the individual information collection unit can input the user's past health data into AI, and the AI ​​can analyze the data to understand their current health status.

[0108] The individual information collection unit monitors the user's current health status in real time when collecting individual information. For example, the individual information collection unit monitors the user's heart rate in real time and notifies if there is an abnormality. It also monitors the user's blood pressure in real time and notifies if there is an abnormality. It monitors the user's body temperature in real time and notifies if there is an abnormality. This allows for the collection of appropriate information by monitoring the user's current health status in real time. Some or all of the above processing in the individual information collection unit may be performed using AI or not using AI. For example, the individual information collection unit can input the user's health data into AI, which can then analyze the data and detect abnormalities.

[0109] The individual information collection unit estimates the user's emotions and determines the priority of individual information based on the estimated user emotions. For example, if the user is in a state of panic, the individual information collection unit prioritizes collecting the most important information. If the user is calm, it collects information in the usual order of priority. If the user is feeling anxious, it prioritizes collecting information that provides reassurance. This allows for the determination of an appropriate priority of individual 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 individual information collection unit may be performed using AI or not. For example, the individual information collection unit can input user emotion data into an AI, which can analyze the emotions and determine the priority of individual information.

[0110] The individual information collection unit prioritizes collecting highly relevant information by considering the user's geographical location when collecting individual information. For example, if the user is near the coast, the individual information collection unit prioritizes collecting information related to tsunami risk. If the user is in a mountainous area, it prioritizes collecting information related to landslide risk. If the user is in an urban area, it prioritizes collecting information related to building collapse risk. This enables the collection of appropriate information that takes the user's geographical location into account. Some or all of the above processing in the individual information collection unit may be performed using AI, or not. For example, the individual information collection unit can input the user's geographical location information into AI, which can then analyze the location information and prioritize collecting highly relevant information.

[0111] The individual information collection unit analyzes the user's social media activity and collects relevant information when collecting individual information. For example, the individual information collection unit identifies the scope of an emergency based on location information posted by the user on social media. It analyzes photos and videos shared by the user on social media to understand the situation on the ground. It supplements the details of the emergency based on information shared by the user with other users on social media. By analyzing social media activity, the accuracy of collecting individual information is improved. Some or all of the above processing in the individual information collection unit may be performed using AI or not. For example, the individual information collection unit can input the user's social media data into AI, which can then analyze the data and collect relevant information.

[0112] The shelter provision unit estimates the user's emotions and adjusts the method of providing shelter based on the estimated emotions. For example, if the user is in a state of panic, the shelter provision unit will provide a simple and clear description of the shelter. If the user is calm, it will provide a detailed description of the shelter. If the user is feeling anxious, it will provide a reassuring description of the shelter. This makes it possible to provide an appropriate shelter 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 shelter provision unit may be performed using AI or not. For example, the shelter provision unit can input user emotion data into AI, and the AI ​​can analyze the emotions and adjust the method of providing shelter.

[0113] The evacuation shelter provision unit improves the accuracy of its provision of information by referring to past evacuation data when providing information on evacuation shelters. For example, the evacuation shelter provision unit provides the safest evacuation shelter based on past evacuation data. It predicts the congestion level of evacuation shelters based on past evacuation data. It evaluates the safety of evacuation shelters based on past evacuation data. In this way, the accuracy of providing evacuation shelters is improved by referring to past evacuation data. Some or all of the above processes in the evacuation shelter provision unit may be performed using AI or not. For example, the evacuation shelter provision unit can input past evacuation data into AI, and the AI ​​can analyze the data to provide the optimal evacuation shelter.

[0114] The evacuation shelter provision unit improves the accuracy of its provision of information by considering real-time conditions when providing information on evacuation shelters. For example, the evacuation shelter provision unit provides the optimal evacuation shelter based on the real-time congestion status of evacuation shelters. It evaluates the safety of evacuation shelters in real time and provides the optimal evacuation shelter. It provides the optimal evacuation shelter based on the real-time access status of evacuation shelters. In this way, the accuracy of providing evacuation shelters is improved by considering real-time conditions. Some or all of the above processes in the evacuation shelter provision unit may be performed using AI or not. For example, the evacuation shelter provision unit can input real-time situation data into AI, and the AI ​​can analyze the data and provide the optimal evacuation shelter.

[0115] The shelter provision unit estimates the user's emotions and determines the priority of shelters based on the estimated emotions. For example, if the user is in a state of panic, the shelter provision unit will prioritize providing the most important shelters. If the user is calm, shelters will be provided in the normal priority order. If the user is feeling anxious, shelters that provide a sense of security will be prioritized. This allows for the determination of an appropriate priority order of shelters 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 shelter provision unit may be performed using AI or not. For example, the shelter provision unit can input user emotion data into an AI, which can analyze the emotions and determine the priority order of shelters.

[0116] The evacuation shelter provision unit prioritizes providing highly relevant information by considering the user's geographical location when providing information on evacuation shelters. For example, if the user is near the coast, the evacuation shelter provision unit will prioritize providing evacuation shelters related to tsunami risk. If the user is in a mountainous area, it will prioritize providing evacuation shelters related to landslide risk. If the user is in an urban area, it will prioritize providing evacuation shelters related to building collapse risk. This makes it possible to provide appropriate evacuation shelter information considering the user's geographical location. Some or all of the above processing in the evacuation shelter provision unit may be performed using AI or not. For example, the evacuation shelter provision unit can input the user's geographical location information into AI, and the AI ​​can analyze the location information and prioritize providing highly relevant evacuation shelters.

[0117] The evacuation shelter provision unit analyzes users' social media activity and provides relevant information when providing information about evacuation shelters. For example, the evacuation shelter provision unit provides the most suitable evacuation shelter based on location information posted by users on social media. It analyzes photos and videos shared by users on social media to understand the local situation. It provides detailed information about evacuation shelters based on information shared by users with other users on social media. In this way, the accuracy of evacuation shelter information provision is improved by analyzing social media activity. Some or all of the above processes in the evacuation shelter provision unit may be performed using AI or not. For example, the evacuation shelter provision unit can input user social media data into AI, and the AI ​​can analyze the data and provide relevant evacuation shelter information.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The analysis unit can also improve analysis accuracy by referring to the user's past evacuation behavior data in the event of an emergency. For example, it can predict current evacuation behavior and propose the optimal evacuation method based on evacuation behavior data from similar emergencies in the past. Based on past evacuation behavior data, it can analyze which evacuation routes users tend to choose and propose the optimal evacuation route. Based on past evacuation behavior data, it can analyze which evacuation locations users tend to choose and propose the optimal evacuation locations. In this way, by referring to past evacuation behavior data, the analysis accuracy is improved and more appropriate evacuation methods can be proposed.

[0120] The individual information collection unit can also estimate the user's emotions and adjust the frequency of health monitoring based on the estimated emotions. For example, if the user is in a panic state, the frequency of heart rate and blood pressure monitoring is increased to detect abnormalities early. If the user is calm, health status is checked at the normal monitoring frequency. If the user is feeling anxious, monitoring is performed at an appropriate frequency to provide reassurance. This enables appropriate health monitoring in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the individual information collection unit may be performed using AI or not.

[0121] The evacuation shelter provision unit can also improve the accuracy of its provision of evacuation shelter information by referring to users' past evacuation shelter selection data. For example, it can predict current evacuation shelter selections based on data of evacuation shelters previously selected by users and propose the most suitable evacuation shelter. It can analyze which evacuation shelters users tend to choose based on past evacuation shelter selection data and propose the most suitable evacuation shelter. It can also analyze the criteria users use when selecting an evacuation shelter based on past evacuation shelter selection data and propose the most suitable evacuation shelter. In this way, by referring to past evacuation shelter selection data, the accuracy of evacuation shelter provision can be improved, and more appropriate evacuation shelters can be proposed.

[0122] The analysis unit can also estimate the user's emotions in the event of an emergency and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is in a state of panic, a concise and clear notification will be provided to promptly encourage evacuation. If the user is calm, detailed analysis results will be provided to deepen their understanding of the situation. If the user is feeling anxious, the notification will be made using reassuring language. This makes it possible to provide appropriate analysis results in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI.

[0123] The location tracking unit can also improve the accuracy of acquiring user location information by referring to the user's past location data. For example, it can predict the current location based on data of places the user has visited in the past and acquire the optimal location information. It can analyze where the user is most likely to be based on past location data and acquire the optimal location information. It can analyze the patterns of the user's movement based on past location data and acquire the optimal location information. In this way, by referring to past location data, the accuracy of acquiring location information can be improved, and more appropriate location information can be obtained.

[0124] The suggestion section can also estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is in a state of panic, it can quickly make suggestions to encourage evacuation. If the user is calm, it can assess the situation and make suggestions at an appropriate time. If the user is feeling anxious, it can make suggestions at a time that provides reassurance. This allows for the determination of appropriate suggestion timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the suggestion section may be performed using AI or not.

[0125] The guidance system can also improve guidance accuracy by referring to the user's past evacuation behavior data. For example, it can predict the current evacuation route based on data of evacuation routes previously selected by the user and provide optimal guidance. It can also analyze which evacuation routes users tend to choose based on past evacuation behavior data and provide optimal guidance. Furthermore, it can analyze the criteria users use when taking evacuation actions based on past evacuation behavior data and provide optimal guidance. In this way, by referring to past evacuation behavior data, guidance accuracy can be improved, and more appropriate guidance can be provided.

[0126] The analysis unit can also estimate the user's emotions in the event of an emergency and prioritize the analysis results based on the estimated emotions. For example, if the user is in a state of panic, the most important analysis results will be notified first. If the user is calm, detailed analysis results will be notified sequentially. If the user is feeling anxious, analysis results that provide reassurance will be notified first. This allows for the determination of an appropriate priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0127] The sensing unit can also estimate the user's emotions when acquiring the user's location information and adjust the method of acquiring the location information based on the estimated emotions. For example, if the user is in a state of panic, the location information is acquired quickly to confirm their safety. If the user is calm, the location information is acquired in the usual way. If the user is feeling anxious, the location information is acquired in a way that provides reassurance. This makes it possible to acquire appropriate location 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. Some or all of the above processing in the sensing unit may be performed using AI or not.

[0128] The proposal function can also improve the accuracy of its suggestions by referring to the user's past evacuation behavior data. For example, it can predict the current evacuation method based on data of evacuation methods the user has previously selected and make the optimal suggestion. It can analyze which evacuation methods the user tends to choose based on past evacuation behavior data and make the optimal suggestion. It can analyze the criteria the user uses when selecting an evacuation method based on past evacuation behavior data and make the optimal suggestion. In this way, by referring to past evacuation behavior data, the accuracy of the suggestions can be improved, and more appropriate suggestions can be made.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The analysis unit analyzes the content of emergency alerts. The analysis unit uses AI to analyze the content of emergency alerts in real time to respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks, and to understand the specific details of the emergency. For example, if an earthquake warning is received, the unit analyzes the epicenter and intensity of the earthquake and notifies the user. If a tsunami warning is received, the unit analyzes the arrival time and height of the tsunami and notifies the user. If a flood warning is received, the unit analyzes the progress of the flood and the extent of the inundation and notifies the user. If a missile attack warning is received, the unit analyzes the direction and speed of the incoming missile and notifies the user. Step 2: The sensing unit grasps the user's current location and surrounding environment. The sensing unit collects information such as the user's location, the height, age, and seismic standards of surrounding buildings, and analyzes it in real time using AI. For example, it uses GPS data to determine the user's current location and Wi-Fi location information to determine the user's location within a building. Furthermore, it collects and analyzes information such as the height, age, and seismic standards of surrounding buildings. Step 3: The suggestion unit proposes the optimal evacuation method. The suggestion unit presents multiple options, such as evacuating to a nearby tall building or moving to a safe location far away, in order of highest survival rate. The suggestion unit uses AI to propose the evacuation method with the highest survival rate for the user. For example, it suggests evacuating to a nearby tall building or moving to a safe location far away, and then presents evacuation routes to guide the user to the safest route. Step 4: The guidance department provides real-time guidance and support. The guidance department provides appropriate instructions in response to changes in the emergency, uses AI to guide users in real time, and supports evacuation actions. For example, it updates evacuation routes as the emergency progresses and notifies users. It also monitors the congestion status of evacuation sites in real time and guides users to the most suitable evacuation site.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the analysis unit, understanding unit, proposal unit, guidance unit, individual information collection unit, and evacuation location provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit analyzes the content of the emergency alert using the processor 46 of the smart device 14 and grasps the specific details of the emergency using the identification processing unit 290 of the data processing unit 12. The understanding unit identifies the user's current location using GPS data and Wi-Fi location information from the smart device 14 and analyzes the surrounding situation using the data processing unit 12. The proposal unit proposes the optimal evacuation method using the identification processing unit 290 of the data processing unit 12 and presents it to the user using the control unit 46A of the smart device 14. The guidance unit provides guidance and support in real time using the control unit 46A of the smart device 14. The individual information collection unit collects the user's health status using the sensors of the smart device 14 and analyzes it using the data processing unit 12. The evacuation shelter provision unit provides detailed information about evacuation shelters via the specific processing unit 290 of the data processing device 12, and presents it to the user via the control unit 46A 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.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the analysis unit, understanding unit, proposal unit, guidance unit, individual information collection unit, and evacuation location provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit analyzes the content of the emergency alert using the processor 46 of the smart glasses 214, and the specific details of the emergency are grasped by the identification processing unit 290 of the data processing unit 12. The understanding unit identifies the user's current location using GPS data and Wi-Fi location information from the smart glasses 214, and the surrounding situation is analyzed by the data processing unit 12. The proposal unit proposes the optimal evacuation method using the identification processing unit 290 of the data processing unit 12, and presents it to the user using the control unit 46A of the smart glasses 214. The guidance unit provides guidance and support in real time using the control unit 46A of the smart glasses 214. The individual information collection unit collects the user's health status using the sensors of the smart glasses 214 and analyzes it using the data processing unit 12. The evacuation shelter provision unit provides detailed information about evacuation shelters via the specific processing unit 290 of the data processing device 12, and presents it to the user via the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the analysis unit, information gathering unit, proposal unit, guidance unit, individual information collection unit, and evacuation location provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit analyzes the content of the emergency alert using the processor 46 of the headset terminal 314 and grasps the specific details of the emergency using the identification processing unit 290 of the data processing unit 12. The information gathering unit identifies the user's current location using GPS data and Wi-Fi location information from the headset terminal 314 and analyzes the surrounding situation using the data processing unit 12. The proposal unit proposes the optimal evacuation method using the identification processing unit 290 of the data processing unit 12 and presents it to the user using the control unit 46A of the headset terminal 314. The guidance unit provides guidance and support in real time using the control unit 46A of the headset terminal 314. The individual information collection unit collects the user's health status using the sensors of the headset terminal 314 and analyzes it using the data processing unit 12. The evacuation shelter provision unit provides detailed information about evacuation shelters via the specific processing unit 290 of the data processing device 12, and presents it to the user via the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the analysis unit, understanding unit, proposal unit, guidance unit, individual information collection unit, and evacuation location provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit analyzes the content of the emergency alert using the robot 414's processor 46 and grasps the specific details of the emergency using the data processing unit 12's identification processing unit 290. The understanding unit identifies the user's current location using the robot 414's GPS data and Wi-Fi location information and analyzes the surrounding situation using the data processing unit 12. The proposal unit proposes the optimal evacuation method using the data processing unit 12's identification processing unit 290 and presents it to the user using the robot 414's control unit 46A. The guidance unit provides guidance and support in real time using the robot 414's control unit 46A. The individual information collection unit collects the user's health status using the robot 414's sensors and analyzes it using the data processing unit 12. The evacuation site provision unit provides detailed information about evacuation sites via the specific processing unit 290 of the data processing device 12, and presents it to the user via the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) The analysis unit analyzes the contents of the emergency alert, A sensing unit that grasps the user's current location and surrounding environment, The proposal department, which proposes the most suitable evacuation method, It includes an information desk that provides real-time guidance and support. A system characterized by the following features. (Note 2) It is equipped with an individual information collection unit that collects individual information such as the user's health status and age. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a section that provides detailed information about evacuation sites. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, To respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks, we analyze the content of emergency alerts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The gripping part is, The system collects and analyzes information such as the user's location, the height of surrounding buildings, their age, and earthquake resistance standards. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Present multiple options in order of highest survival rate, such as evacuating to a nearby tall building or moving to a safe location far away. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method of emergency alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing the content of emergency alerts, we improve the accuracy of the analysis by referring to past emergency data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing the content of an emergency alert, different analysis algorithms are applied depending on the type of emergency. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates user sentiment and prioritizes emergency alerts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing the content of emergency alerts, the analysis results are adjusted to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing the content of emergency alerts, we analyze users' social media activity to supplement relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The gripping part is, The system estimates the user's emotions and adjusts the timing of location data acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The gripping part is, When acquiring a user's location information, the system selects the optimal acquisition method by referring to their past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The gripping part is, When acquiring a user's location information, different acquisition algorithms are applied depending on the surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The gripping part is, It estimates the user's emotions and prioritizes location information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The gripping part is, When acquiring user location information, the system prioritizes acquiring highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The gripping part is, When obtaining a user's location information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the evacuation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the type of evacuation method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, the priority of proposals will be determined based on the timing of submission of evacuation methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the evacuation methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is The system estimates the user's emotions and adjusts the way guidance is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is When providing guidance, adjust the level of detail based on the progress of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is When providing guidance, different guidance algorithms are applied depending on the type of emergency. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide section is The system estimates the user's emotions and determines the priority of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide section is When providing directions, the order of directions will be adjusted based on the location of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned guide section is When providing guidance, we will refer to relevant emergency information to improve the accuracy of the guidance. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned individual information collection unit is: We estimate the user's emotions and adjust the method of collecting individual information based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned individual information collection unit is: When collecting individual information, past health data is referenced to improve collection accuracy. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned individual information collection unit is: When collecting individual information, the user's current health status is monitored in real time. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned individual information collection unit is: It estimates the user's emotions and prioritizes individual information based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned individual information collection unit is: When collecting individual information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned individual information collection unit is: When collecting individual information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned evacuation shelter provision department, The system estimates the user's emotions and adjusts how evacuation shelters are provided based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned evacuation shelter provision department, When providing information on evacuation sites, we will improve the accuracy of the information provided by referring to past evacuation data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned evacuation shelter provision department, When providing information on evacuation shelters, we will improve the accuracy of the information provided by taking real-time conditions into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned evacuation shelter provision department, The system estimates the user's emotions and determines the priority of evacuation locations based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned evacuation shelter provision department, When providing information about evacuation shelters, we prioritize providing highly relevant information by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned evacuation shelter provision department, When providing information about evacuation shelters, we analyze users' social media activity and provide relevant information. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0203] 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 analysis unit analyzes the contents of the emergency alert, A sensing unit that grasps the user's current location and surrounding environment, The proposal department, which proposes the most suitable evacuation method, It includes an information desk that provides real-time guidance and support. A system characterized by the following features.

2. It is equipped with an individual information collection unit that collects individual information such as the user's health status and age. The system according to feature 1.

3. It includes a section that provides detailed information about evacuation sites. The system according to feature 1.

4. The aforementioned analysis unit, To respond to emergencies such as earthquakes, tsunamis, floods, and missile attacks, we analyze the content of emergency alerts. The system according to feature 1.

5. The gripping part is, The system collects and analyzes information such as the user's location, the height of surrounding buildings, their age, and earthquake resistance standards. The system according to feature 1.

6. The aforementioned proposal section is, Present multiple options in order of highest survival rate, such as evacuating to a nearby tall building or moving to a safe location far away. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method of emergency alerts based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing the content of emergency alerts, we improve the accuracy of the analysis by referring to past emergency data. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing the content of an emergency alert, different analysis algorithms are applied depending on the type of emergency. The system according to feature 1.

10. The aforementioned analysis unit, It estimates user sentiment and prioritizes emergency alerts based on the estimated user sentiment. The system according to feature 1.

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