system

Abstract

The system according to this embodiment aims to collect and analyze surrounding information during a disaster and support a rapid response. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects surrounding information. The analysis unit analyzes the surrounding information collected by the collection unit. The provision unit provides behavioral information based on the results obtained by the analysis unit. The monitoring unit monitors the user's behavior based on the information provided by the provision unit.

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that the collection and analysis of peripheral information in the event of a disaster were not sufficiently carried out, making it difficult to respond quickly.

[0005] The system according to the embodiment aims to collect and analyze peripheral information in the event of a disaster and support a quick response.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects surrounding information. The analysis unit analyzes the surrounding information collected by the collection unit. The provision unit provides behavioral information based on the results obtained by the analysis unit. The monitoring unit monitors the user's behavior based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze surrounding information during a disaster and support a rapid response. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system for making immediate decisions in emergencies. This system is a mechanism for disaster prevention and mitigating risks during disasters by capturing surrounding information triggered by the common language "Run!!". It enables action regardless of time or place when immediate response is needed for loved ones or those in need. It integrates geographic information and AI to enable real-time decision support and prediction through data analysis. For example, it captures surrounding information triggered by the common language "Run!!". At this time, it collects data such as geospatial information, satellite data, sensors, and camera images. For example, when a disaster such as an earthquake or typhoon occurs, it can collect surrounding geographic information and sensor data, and the AI ​​can analyze it to grasp the situation in real time. Next, the AI ​​analyzes the collected data to provide real-time decision support and prediction. For example, it can suggest evacuation routes or predict the congestion level of evacuation sites when a disaster occurs. This allows users to make accurate decisions and reduce risks during disasters. Furthermore, it provides action information based on the data analyzed by the AI. For example, by providing suggestions for evacuation routes and information on evacuation sites, users can act quickly. Furthermore, the AI ​​can monitor user behavior and provide advice as needed. This allows users to take consistent actions, further reducing risks during disasters. This system offers a groundbreaking solution for all generations, including those who have difficulty reading maps or who can read maps but find them difficult to understand. Users can act quickly and without hesitation, triggered by the common command "Run!!", gather information about their surroundings, and receive action guidance based on data analyzed by the AI. In addition, since the smartphone is held horizontally, walking safety is also ensured. As a result, this system, which enables immediate decision-making in emergencies, can reduce risks during disasters.

[0029] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects surrounding information. Surrounding information includes, but is not limited to, geospatial information, satellite data, sensors, and camera images. For example, the collection unit can collect geospatial information from GPS data and map information. The collection unit can also collect satellite data from weather satellite data and remote sensing data. Furthermore, the collection unit can collect sensor data from temperature sensors, humidity sensors, vibration sensors, etc. For example, the collection unit acquires geospatial information from GPS data and integrates it with map information. Satellite data is acquired in real time from weather satellite data and used for disaster prediction. Sensor data is acquired from temperature sensors and humidity sensors to monitor environmental changes. The analysis unit analyzes the surrounding information collected by the collection unit. The analysis is performed, for example, to provide real-time decision support or prediction, but is not limited to such examples. For example, the analysis unit analyzes the collected geospatial information and proposes evacuation routes in the event of a disaster. Furthermore, the analysis unit can analyze collected satellite data to predict typhoon paths. In addition, the analysis unit can analyze collected sensor data to predict earthquake occurrences. For example, the analysis unit analyzes geospatial information to propose optimal evacuation routes. Satellite data is used to predict typhoon paths and issue evacuation orders. Sensor data is used to predict earthquake occurrences and provide emergency notifications. The provision unit provides action information based on the results obtained by the analysis unit. Action information includes, but is not limited to, evacuation routes, evacuation locations, and action instructions. For example, the provision unit provides users with evacuation routes obtained by the analysis unit. The provision unit can also provide users with information on evacuation locations obtained by the analysis unit. Furthermore, the provision unit can also provide users with action instructions obtained by the analysis unit. For example, the provision unit displays evacuation routes on a map and guides users. Information on evacuation locations includes capacity and facility information. Action instructions are provided via voice guidance or text messages. The monitoring unit monitors user behavior based on information provided by the service provider unit.Monitoring is performed, for example, for real-time monitoring or periodic checks, but is not limited to such examples. For instance, the monitoring unit can monitor user behavior in real time and provide advice as needed. The monitoring unit can also periodically check user behavior and point out areas for improvement. Furthermore, the monitoring unit can accumulate user behavior data and analyze long-term behavioral patterns. For example, the monitoring unit can monitor user behavior in real time and suggest changes to evacuation routes. Periodic checks can point out areas for improvement based on the user's behavioral history. Accumulated behavioral data can be used to analyze long-term behavioral patterns and predict future behavior. As a result, the system according to the embodiment can mitigate risks during disasters through the collection, analysis, provision, and monitoring of surrounding information.

[0030] The collection unit collects surrounding information. This information includes, but is not limited to, geospatial information, satellite data, sensors, and camera images. For example, the collection unit can collect geospatial information from GPS data and map information. Specifically, GPS data is acquired in real time from vehicles and mobile devices and integrated with map information. This allows for accurate determination of the current location and travel route. The collection unit can also collect satellite data from weather satellite data and remote sensing data. Weather satellite data provides meteorological information such as cloud movement, rainfall, and wind speed, while remote sensing data allows for observation of changes in the ground surface and vegetation conditions. Furthermore, the collection unit can collect sensor data from temperature sensors, humidity sensors, vibration sensors, etc. Temperature sensors detect changes in ambient temperature, humidity sensors measure humidity in the air, and vibration sensors detect earthquakes and building tremors, and detect abnormal vibrations. For example, the collection unit acquires geospatial information from GPS data and integrates it with map information. This allows for accurate determination of the user's current location and travel route. Satellite data is acquired in real time from weather satellites and used for disaster prediction. Weather satellite data provides meteorological information such as cloud movement, rainfall, and wind speed, while remote sensing data can observe changes in the ground surface and vegetation conditions. Sensor data is acquired from temperature and humidity sensors to monitor environmental changes. Temperature sensors detect changes in ambient temperature, and humidity sensors measure humidity in the air. Vibration sensors detect earthquakes and building tremors and detect abnormal vibrations. As a result, the data collection unit can collect a wide range of information from diverse data sources and understand the situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provision departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the surrounding information collected by the collection unit. This analysis is performed, for example, to support real-time decision-making and make predictions, but is not limited to such examples. Specifically, the analysis unit analyzes collected geospatial information and proposes evacuation routes in the event of a disaster. Geospatial information is used to calculate the optimal evacuation route based on GPS data and map information. For example, it proposes the shortest and safest evacuation route, taking into account road congestion and passable routes. The analysis unit can also analyze collected satellite data to predict typhoon paths. Weather satellite data is used to predict typhoon paths and intensity based on information such as cloud movement, rainfall, and wind speed. Furthermore, the analysis unit can analyze collected sensor data to predict earthquake occurrences. Data from vibration sensors is used to detect minute vibrations that are precursors to earthquakes and to predict earthquake occurrences. For example, the analysis unit analyzes geospatial information and proposes the optimal evacuation route, calculating the shortest and safest evacuation route, taking into account road congestion and passable routes. Satellite data is used to predict typhoon paths and issue evacuation orders. Weather satellite data predicts typhoon paths and intensity based on information such as cloud movement, rainfall, and wind speed. Sensor data is used to predict earthquakes and issue emergency notifications. Data from vibration sensors detects minute vibrations that are precursors to earthquakes and predicts their occurrence. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can also use historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past disaster data, it can predict fluctuations in risk in specific areas and time periods and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The service provider provides action information based on the results obtained by the analysis unit. This action information includes, but is not limited to, evacuation routes, evacuation locations, and action instructions. Specifically, the service provider provides the user with evacuation routes obtained by the analysis unit. The evacuation routes are displayed on a map and guided to the user. For example, the optimal evacuation route is calculated based on the user's current location and evacuation destination and displayed on the map. The service provider can also provide the user with information on evacuation locations obtained by the analysis unit. This information includes capacity and facilities. For example, the capacity and facilities of an evacuation location are displayed, allowing the user to select an appropriate location. Furthermore, the service provider can also provide the user with action instructions obtained by the analysis unit. These instructions are provided via voice guidance or text messages. For example, evacuation route guidance and evacuation location information are provided via voice guidance or text messages, enabling the user to act quickly and appropriately. This allows the service provider to provide users with appropriate action information based on the results obtained by the analysis unit, thereby mitigating risks during disasters. Furthermore, the information provider can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can review the accuracy and method of providing information based on user feedback regarding evacuation routes and shelters. In addition, the information provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the information provider to quickly and reliably provide users with actionable information and minimize risks during disasters.

[0033] The monitoring unit monitors user behavior based on information provided by the service provider. Monitoring is performed for purposes such as real-time monitoring and periodic checks, but is not limited to these examples. Specifically, the monitoring unit monitors user behavior in real time and provides advice as needed. For example, it monitors whether users are following the correct evacuation route and provides advice to return to the correct route if they are taking the wrong path. The monitoring unit can also periodically check user behavior and point out areas for improvement. For example, it can point out areas for improvement in user behavior based on the results of evacuation drills and use this information for future drills. Furthermore, the monitoring unit can accumulate user behavior data and analyze long-term behavior patterns. For example, it can accumulate user evacuation behavior data and analyze long-term behavior patterns to predict future behavior. This allows the monitoring unit to monitor user behavior in real time and provide advice as needed. Periodic checks point out areas for improvement based on the user's behavior history. Accumulation of behavior data is used to analyze long-term behavior patterns and predict future behavior. This allows the monitoring unit to continuously monitor user behavior and provide appropriate advice, thereby mitigating risks during disasters. Furthermore, the monitoring unit can identify areas for improvement across the entire system based on user behavior data, thereby enhancing system performance. For example, it can analyze user behavior data to improve the method of providing information on evacuation routes and evacuation locations. The monitoring unit can also collect user feedback and use it to improve the system. As a result, the monitoring unit can continuously monitor user behavior and provide appropriate advice, thereby mitigating risks during disasters and improving the overall system performance.

[0034] The data collection unit can collect surrounding information such as geospatial information, satellite data, sensors, and camera images. For example, the data collection unit can collect geospatial information from GPS data and map information. The data collection unit can also collect satellite data from weather satellite data and remote sensing data. The data collection unit can also collect sensor data from temperature sensors, humidity sensors, vibration sensors, etc. The data collection unit can also collect camera images from surveillance camera images and drone images. For example, the data collection unit can acquire geospatial information from GPS data and integrate it with map information. The data collection unit can acquire weather satellite data in real time and use it for disaster prediction. The data collection unit can acquire data from temperature sensors and humidity sensors to monitor environmental changes. The data collection unit can analyze surveillance camera images to understand the situation of a disaster. In this way, by collecting surrounding information from diverse data sources, the situation during a disaster can be accurately understood. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input prompts to the generating AI to acquire geospatial information, and the generating AI can analyze and provide the geospatial information.

[0035] The analysis unit can analyze collected surrounding information to provide real-time decision support and predictions. For example, the analysis unit can analyze collected geospatial information to propose evacuation routes in the event of a disaster. The analysis unit can also analyze collected satellite data to predict the path of a typhoon. The analysis unit can also analyze collected sensor data to predict the occurrence of an earthquake. For example, the analysis unit can analyze geospatial information to propose the optimal evacuation route. The analysis unit can analyze satellite data to predict the path of a typhoon and use it to issue evacuation orders. The analysis unit can analyze sensor data to predict the occurrence of an earthquake and use it to issue emergency notifications. This enables rapid decision support and predictions through real-time analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected geospatial information into a generating AI, which can then propose the optimal evacuation route.

[0036] The information provider can provide users with information on evacuation routes and evacuation locations based on the results obtained by the analysis unit. For example, the information provider can provide users with evacuation routes obtained by the analysis unit. The information provider can also provide users with information on evacuation locations obtained by the analysis unit. The information provider can also provide users with action instructions obtained by the analysis unit. For example, the information provider can display evacuation routes on a map and guide users. The information provider can provide information on evacuation locations, including capacity and equipment information. The information provider can provide action instructions via voice guidance or text message. In this way, by providing appropriate evacuation information based on the analysis results, it supports users in taking swift action. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the evacuation routes obtained by the analysis unit into a generating AI, and the generating AI can guide users along the evacuation routes.

[0037] The monitoring unit can monitor user behavior and provide advice as needed, based on information provided by the provision unit. For example, the monitoring unit can monitor user behavior in real time and provide advice as needed. The monitoring unit can also periodically check user behavior and point out areas for improvement. The monitoring unit can also accumulate user behavior data and analyze long-term behavior patterns. For example, the monitoring unit can monitor user behavior in real time and suggest changes to evacuation routes. The monitoring unit can conduct periodic checks and point out areas for improvement based on the user's behavior history. The monitoring unit accumulates behavior data, analyzes long-term behavior patterns, and uses this to predict future behavior. This allows for further reduction of risks during disasters by monitoring user behavior and providing appropriate advice. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user behavior data into a generating AI, which can then point out areas for improvement.

[0038] The data collection unit can analyze the user's past behavioral history when collecting surrounding information and select the optimal data collection method. For example, the data collection unit can prioritize collecting information in similar situations based on the user's past evacuation routes. The data collection unit can also prioritize collecting information on evacuation sites used by the user in the past. The data collection unit can also analyze the user's past behavioral patterns and select the most effective data collection method. For example, the data collection unit can prioritize collecting information in similar situations based on the user's past evacuation routes. The data collection unit prioritizes collecting information on evacuation sites used by the user in the past. The data collection unit analyzes the user's past behavioral patterns and selects the most effective data collection method. This allows the optimal data collection method to be selected by analyzing past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into a generating AI, which can then select the optimal data collection method.

[0039] The data collection unit can prioritize the collection of different data sources depending on the type of disaster when gathering surrounding information. For example, when an earthquake occurs, the data collection unit prioritizes the collection of seismometer data and seismic wave information. When a typhoon occurs, the data collection unit can also prioritize the collection of weather satellite data and anemometer data. When a flood occurs, the data collection unit can also prioritize the collection of river water level data and rainfall data. For example, when an earthquake occurs, the data collection unit prioritizes the collection of seismometer data and seismic wave information. When a typhoon occurs, the data collection unit prioritizes the collection of weather satellite data and anemometer data. When a flood occurs, the data collection unit prioritizes the collection of river water level data and rainfall data. This enables appropriate information collection by prioritizing the collection of data sources according to the type of disaster. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data sources according to the type of disaster into a generating AI, and the generating AI can prioritize their collection.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting surrounding information. For example, if the user is in a specific area, the data collection unit will prioritize collecting disaster information for that area. If the user is on the move, the data collection unit can also prioritize collecting disaster information for their destination. If the user is in an evacuation center, the data collection unit can also prioritize collecting information about the situation at that evacuation center. For example, if the user is in a specific area, the data collection unit will prioritize collecting disaster information for that area. If the user is on the move, the data collection unit will prioritize collecting disaster information for their destination. If the user is in an evacuation center, the data collection unit will prioritize collecting information about the situation at that evacuation center. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant information.

[0041] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting surrounding information. For example, the data collection unit can collect surrounding disaster information based on location information shared by the user on social media. The data collection unit can also collect relevant disaster information based on information from accounts that the user follows on social media. The data collection unit can also collect necessary information by analyzing information that the user has posted on social media. For example, the data collection unit can collect surrounding disaster information based on location information shared by the user on social media. The data collection unit can collect relevant disaster information based on information from accounts that the user follows on social media. The data collection unit can collect necessary information by analyzing information that the user has posted on social media. This allows for the efficient collection of relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, which can then collect relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to its importance. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can perform a simplified analysis on information of low importance. The analysis unit can determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the collected information into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the type of disaster during the analysis. For example, when an earthquake occurs, the analysis unit applies an algorithm that analyzes seismic wave data. When a typhoon occurs, the analysis unit can also apply an algorithm that analyzes meteorological data. When a flood occurs, the analysis unit can also apply an algorithm that analyzes water level data. For example, when an earthquake occurs, the analysis unit applies an algorithm that analyzes seismic wave data. When a typhoon occurs, the analysis unit applies an algorithm that analyzes meteorological data. When a flood occurs, the analysis unit applies an algorithm that analyzes water level data. This makes it possible to perform an appropriate analysis by applying an analysis algorithm appropriate to the type of disaster. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input an analysis algorithm appropriate to the type of disaster into a generating AI, and the generating AI can apply it.

[0044] The analysis unit can determine the priority of analysis based on the submission date of the collected information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also lower the priority of analysis for information that has been submitted a long time ago. The analysis unit may also adjust the order of analysis according to the submission date. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may lower the priority of analysis for information that has been submitted a long time ago. The analysis unit may adjust the order of analysis according to the submission date. This allows the analysis unit to prioritize the analysis of the most recent information by determining the priority of analysis based on the submission date of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit may input the submission date of the collected information into a generating AI, and the generating AI may determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the collected information during analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit can also lower the priority of analysis for less relevant information. The analysis unit can also adjust the order of analysis according to relevance. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit lowers the priority of analysis for less relevant information. The analysis unit adjusts the order of analysis according to relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the collected information into a generating AI, and the generating AI can adjust the order of analysis.

[0046] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide a detailed explanation for highly important information. For less important information, the provider can also provide a simplified explanation. The provider can also determine the priority of provision according to importance. For example, the provider can provide a detailed explanation for highly important information. For less important information, the provider can provide a simplified explanation. The provider can determine the priority of provision according to importance. This allows important information to be provided in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the importance of the information into a generating AI, and the generating AI can adjust the level of detail of the provision.

[0047] The information provider can apply different information provision algorithms depending on the type of disaster at the time of provision. For example, when an earthquake occurs, the information provider can apply an algorithm that provides information based on seismic wave data. When a typhoon occurs, the information provider can also apply an algorithm that provides information based on meteorological data. When a flood occurs, the information provider can also apply an algorithm that provides information based on water level data. For example, when an earthquake occurs, the information provider can apply an algorithm that provides information based on seismic wave data. When a typhoon occurs, the information provider can apply an algorithm that provides information based on meteorological data. When a flood occurs, the information provider can apply an algorithm that provides information based on water level data. This makes it possible to provide appropriate information by applying an information provision algorithm according to the type of disaster. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input an information provision algorithm according to the type of disaster into a generating AI, and the generating AI can apply it.

[0048] The information provider can determine the priority of information provision based on the submission date. For example, the provider can prioritize providing the most recent information. The provider can also lower the priority of older information. The provider can also adjust the order of provision according to the submission date. For example, the provider can prioritize providing the most recent information. The provider can lower the priority of older information. The provider can adjust the order of provision according to the submission date. This allows the provider to prioritize providing the most recent information by determining the priority of provision based on the submission date. Some or all of the above processing in the information provider can be performed using AI, for example, or without AI. For example, the provider can input the submission date of the information into a generating AI, and the generating AI can determine the priority of provision.

[0049] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider can prioritize the delivery of highly relevant information. The provider can also lower the priority of less relevant information. The provider can also adjust the order of information delivery according to relevance. For example, the provider can prioritize the delivery of highly relevant information. The provider can lower the priority of less relevant information. The provider can adjust the order of information delivery according to relevance. This makes efficient information delivery possible by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the relevance of the information into a generating AI, and the generating AI can adjust the order of delivery.

[0050] The monitoring unit can analyze the user's past behavior history and select the optimal monitoring method during monitoring. For example, the monitoring unit may prioritize monitoring in similar situations based on the user's past evacuation routes. The monitoring unit may also prioritize monitoring information about evacuation sites used by the user in the past. The monitoring unit can also analyze the user's past behavior patterns and select the most effective monitoring method. For example, the monitoring unit may prioritize monitoring in similar situations based on the user's past evacuation routes. The monitoring unit may prioritize monitoring information about evacuation sites used by the user in the past. The monitoring unit may analyze the user's past behavior patterns and select the most effective monitoring method. In this way, the optimal monitoring method can be selected by analyzing past behavior history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past behavior history into a generating AI, which can then select the optimal monitoring method.

[0051] The monitoring unit can apply different monitoring methods depending on the type of disaster during monitoring. For example, when an earthquake occurs, the monitoring unit performs monitoring based on seismometer data and seismic wave information. When a typhoon occurs, the monitoring unit can also perform monitoring based on weather satellite data and anemometer data. When a flood occurs, the monitoring unit can also perform monitoring based on river water level data and rainfall data. For example, when an earthquake occurs, the monitoring unit performs monitoring based on seismometer data and seismic wave information. When a typhoon occurs, the monitoring unit performs monitoring based on weather satellite data and anemometer data. When a flood occurs, the monitoring unit performs monitoring based on river water level data and rainfall data. This makes it possible to perform appropriate monitoring by applying monitoring methods appropriate to the type of disaster. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input monitoring methods appropriate to the type of disaster into a generating AI, and the generating AI can apply them.

[0052] The monitoring unit can select the optimal monitoring method by considering the user's geographical location information during monitoring. For example, if the user is in a specific area, the monitoring unit will prioritize monitoring disaster information for that area. If the user is on the move, the monitoring unit can also prioritize monitoring disaster information for the destination. If the user is in an evacuation center, the monitoring unit can also prioritize monitoring information about the status of that evacuation center. For example, if the user is in a specific area, the monitoring unit will prioritize monitoring disaster information for that area. If the user is on the move, the monitoring unit will prioritize monitoring disaster information for the destination. If the user is in an evacuation center, the monitoring unit will prioritize monitoring information about the status of that evacuation center. This allows the optimal monitoring method to be selected by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI, which can then select the optimal monitoring method.

[0053] The monitoring unit can analyze the user's social media activity during monitoring and propose monitoring methods. For example, the monitoring unit can monitor surrounding disaster information based on location information shared by the user on social media. The monitoring unit can also monitor relevant disaster information based on information from accounts the user follows on social media. The monitoring unit can also analyze information posted by the user on social media and propose necessary monitoring methods. For example, the monitoring unit can monitor surrounding disaster information based on location information shared by the user on social media. The monitoring unit can monitor relevant disaster information based on information from accounts the user follows on social media. The monitoring unit can analyze information posted by the user on social media and propose necessary monitoring methods. In this way, by analyzing social media activity, the optimal monitoring method can be proposed. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's social media activity into a generating AI, and the generating AI can propose monitoring methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze the user's past behavioral history when collecting surrounding information and select the optimal collection method. For example, it can prioritize collecting information in similar situations based on routes the user has previously evacuated. It can also prioritize collecting information on evacuation sites the user has used in the past. By analyzing the user's past behavioral patterns, it can select the most effective information collection method. In this way, the optimal information collection method can be selected by analyzing past behavioral history.

[0056] The data collection unit can prioritize the collection of different data sources depending on the type of disaster when gathering surrounding information. For example, during an earthquake, it prioritizes the collection of seismometer data and seismic wave information. During a typhoon, it can also prioritize the collection of weather satellite data and anemometer data. During a flood, it can also prioritize the collection of river water level data and rainfall data. This allows for the collection of appropriate information by prioritizing data sources according to the type of disaster.

[0057] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when gathering surrounding information. For example, if the user is in a specific area, it can prioritize the collection of disaster information for that area. If the user is on the move, it can also prioritize the collection of disaster information for their destination. If the user is in an evacuation center, it can also prioritize the collection of information regarding the situation at that evacuation center. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant information.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, it can perform a detailed analysis on highly important information and a simplified analysis on less important information. It can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the information.

[0059] The analysis unit can apply different analysis algorithms depending on the type of disaster during the analysis. For example, during an earthquake, an algorithm that analyzes seismic wave data is applied. During a typhoon, an algorithm that analyzes meteorological data can also be applied. During a flood, an algorithm that analyzes water level data can also be applied. This allows for appropriate analysis by applying an analysis algorithm appropriate to the type of disaster.

[0060] The analysis unit can determine the priority of analysis based on when the collected information was submitted. For example, it can prioritize the analysis of the most recent information. Older information can be given a lower priority. The order of analysis can also be adjusted according to the submission date. This allows for prioritizing the analysis of the most recent information by determining the priority of analysis based on the submission date.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects surrounding information. This surrounding information includes geospatial information, satellite data, sensor data, and camera images. For example, the collection unit collects geospatial information from GPS data and map information, and satellite data from weather satellite data and remote sensing data. It also collects sensor data from temperature sensors, humidity sensors, vibration sensors, etc. Step 2: The analysis unit analyzes the surrounding information collected by the collection unit. The analysis is performed to provide real-time decision support and prediction. For example, geospatial information is analyzed to propose evacuation routes in the event of a disaster, satellite data is analyzed to predict the path of a typhoon, and sensor data is analyzed to predict the occurrence of an earthquake. Step 3: The service provider provides action information based on the results obtained by the analysis unit. This action information includes evacuation routes, evacuation locations, and action instructions. For example, the service provider provides the user with evacuation routes obtained by the analysis unit, provides information on evacuation locations, and provides action instructions via voice guidance or text message. Step 4: The monitoring unit monitors user behavior based on the information provided by the service provider. Monitoring is performed for real-time monitoring and periodic checks. For example, the monitoring unit monitors user behavior in real time, provides advice as needed, periodically checks to identify areas for improvement in behavior, and accumulates behavioral data to analyze long-term behavioral patterns.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system for making immediate decisions in emergencies. This system is a mechanism for disaster prevention and mitigating risks during disasters by capturing surrounding information triggered by the common language "Run!!". It enables action regardless of time or place when immediate response is needed for loved ones or those in need. It integrates geographic information and AI to enable real-time decision support and prediction through data analysis. For example, it captures surrounding information triggered by the common language "Run!!". At this time, it collects data such as geospatial information, satellite data, sensors, and camera images. For example, when a disaster such as an earthquake or typhoon occurs, it can collect surrounding geographic information and sensor data, and the AI ​​can analyze it to grasp the situation in real time. Next, the AI ​​analyzes the collected data to provide real-time decision support and prediction. For example, it can suggest evacuation routes or predict the congestion level of evacuation sites when a disaster occurs. This allows users to make accurate decisions and reduce risks during disasters. Furthermore, it provides action information based on the data analyzed by the AI. For example, by providing suggestions for evacuation routes and information on evacuation sites, users can act quickly. Furthermore, the AI ​​can monitor user behavior and provide advice as needed. This allows users to take consistent actions, further reducing risks during disasters. This system offers a groundbreaking solution for all generations, including those who have difficulty reading maps or who can read maps but find them difficult to understand. Users can act quickly and without hesitation, triggered by the common command "Run!!", gather information about their surroundings, and receive action guidance based on data analyzed by the AI. In addition, since the smartphone is held horizontally, walking safety is also ensured. As a result, this system, which enables immediate decision-making in emergencies, can reduce risks during disasters.

[0064] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects surrounding information. Surrounding information includes, but is not limited to, geospatial information, satellite data, sensors, and camera images. For example, the collection unit can collect geospatial information from GPS data and map information. The collection unit can also collect satellite data from weather satellite data and remote sensing data. Furthermore, the collection unit can collect sensor data from temperature sensors, humidity sensors, vibration sensors, etc. For example, the collection unit acquires geospatial information from GPS data and integrates it with map information. Satellite data is acquired in real time from weather satellite data and used for disaster prediction. Sensor data is acquired from temperature sensors and humidity sensors to monitor environmental changes. The analysis unit analyzes the surrounding information collected by the collection unit. The analysis is performed, for example, to provide real-time decision support or prediction, but is not limited to such examples. For example, the analysis unit analyzes the collected geospatial information and proposes evacuation routes in the event of a disaster. Furthermore, the analysis unit can analyze collected satellite data to predict typhoon paths. In addition, the analysis unit can analyze collected sensor data to predict earthquake occurrences. For example, the analysis unit analyzes geospatial information to propose optimal evacuation routes. Satellite data is used to predict typhoon paths and issue evacuation orders. Sensor data is used to predict earthquake occurrences and provide emergency notifications. The provision unit provides action information based on the results obtained by the analysis unit. Action information includes, but is not limited to, evacuation routes, evacuation locations, and action instructions. For example, the provision unit provides users with evacuation routes obtained by the analysis unit. The provision unit can also provide users with information on evacuation locations obtained by the analysis unit. Furthermore, the provision unit can also provide users with action instructions obtained by the analysis unit. For example, the provision unit displays evacuation routes on a map and guides users. Information on evacuation locations includes capacity and facility information. Action instructions are provided via voice guidance or text messages. The monitoring unit monitors user behavior based on information provided by the service provider unit.Monitoring is performed, for example, for real-time monitoring or periodic checks, but is not limited to such examples. For instance, the monitoring unit can monitor user behavior in real time and provide advice as needed. The monitoring unit can also periodically check user behavior and point out areas for improvement. Furthermore, the monitoring unit can accumulate user behavior data and analyze long-term behavioral patterns. For example, the monitoring unit can monitor user behavior in real time and suggest changes to evacuation routes. Periodic checks can point out areas for improvement based on the user's behavioral history. Accumulated behavioral data can be used to analyze long-term behavioral patterns and predict future behavior. As a result, the system according to the embodiment can mitigate risks during disasters through the collection, analysis, provision, and monitoring of surrounding information.

[0065] The collection unit collects surrounding information. This information includes, but is not limited to, geospatial information, satellite data, sensors, and camera images. For example, the collection unit can collect geospatial information from GPS data and map information. Specifically, GPS data is acquired in real time from vehicles and mobile devices and integrated with map information. This allows for accurate determination of the current location and travel route. The collection unit can also collect satellite data from weather satellite data and remote sensing data. Weather satellite data provides meteorological information such as cloud movement, rainfall, and wind speed, while remote sensing data allows for observation of changes in the ground surface and vegetation conditions. Furthermore, the collection unit can collect sensor data from temperature sensors, humidity sensors, vibration sensors, etc. Temperature sensors detect changes in ambient temperature, humidity sensors measure humidity in the air, and vibration sensors detect earthquakes and building tremors, and detect abnormal vibrations. For example, the collection unit acquires geospatial information from GPS data and integrates it with map information. This allows for accurate determination of the user's current location and travel route. Satellite data is acquired in real time from weather satellites and used for disaster prediction. Weather satellite data provides meteorological information such as cloud movement, rainfall, and wind speed, while remote sensing data can observe changes in the ground surface and vegetation conditions. Sensor data is acquired from temperature and humidity sensors to monitor environmental changes. Temperature sensors detect changes in ambient temperature, and humidity sensors measure humidity in the air. Vibration sensors detect earthquakes and building tremors and detect abnormal vibrations. As a result, the data collection unit can collect a wide range of information from diverse data sources and understand the situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provision departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0066] The analysis unit analyzes the surrounding information collected by the collection unit. This analysis is performed, for example, to support real-time decision-making and make predictions, but is not limited to such examples. Specifically, the analysis unit analyzes collected geospatial information and proposes evacuation routes in the event of a disaster. Geospatial information is used to calculate the optimal evacuation route based on GPS data and map information. For example, it proposes the shortest and safest evacuation route, taking into account road congestion and passable routes. The analysis unit can also analyze collected satellite data to predict typhoon paths. Weather satellite data is used to predict typhoon paths and intensity based on information such as cloud movement, rainfall, and wind speed. Furthermore, the analysis unit can analyze collected sensor data to predict earthquake occurrences. Data from vibration sensors is used to detect minute vibrations that are precursors to earthquakes and to predict earthquake occurrences. For example, the analysis unit analyzes geospatial information and proposes the optimal evacuation route, calculating the shortest and safest evacuation route, taking into account road congestion and passable routes. Satellite data is used to predict typhoon paths and issue evacuation orders. Weather satellite data predicts typhoon paths and intensity based on information such as cloud movement, rainfall, and wind speed. Sensor data is used to predict earthquakes and issue emergency notifications. Data from vibration sensors detects minute vibrations that are precursors to earthquakes and predicts their occurrence. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can also use historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past disaster data, it can predict fluctuations in risk in specific areas and time periods and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0067] The service provider provides action information based on the results obtained by the analysis unit. This action information includes, but is not limited to, evacuation routes, evacuation locations, and action instructions. Specifically, the service provider provides the user with evacuation routes obtained by the analysis unit. The evacuation routes are displayed on a map and guided to the user. For example, the optimal evacuation route is calculated based on the user's current location and evacuation destination and displayed on the map. The service provider can also provide the user with information on evacuation locations obtained by the analysis unit. This information includes capacity and facilities. For example, the capacity and facilities of an evacuation location are displayed, allowing the user to select an appropriate location. Furthermore, the service provider can also provide the user with action instructions obtained by the analysis unit. These instructions are provided via voice guidance or text messages. For example, evacuation route guidance and evacuation location information are provided via voice guidance or text messages, enabling the user to act quickly and appropriately. This allows the service provider to provide users with appropriate action information based on the results obtained by the analysis unit, thereby mitigating risks during disasters. Furthermore, the information provider can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can review the accuracy and method of providing information based on user feedback regarding evacuation routes and shelters. In addition, the information provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the information provider to quickly and reliably provide users with actionable information and minimize risks during disasters.

[0068] The monitoring unit monitors user behavior based on information provided by the service provider. Monitoring is performed for purposes such as real-time monitoring and periodic checks, but is not limited to these examples. Specifically, the monitoring unit monitors user behavior in real time and provides advice as needed. For example, it monitors whether users are following the correct evacuation route and provides advice to return to the correct route if they are taking the wrong path. The monitoring unit can also periodically check user behavior and point out areas for improvement. For example, it can point out areas for improvement in user behavior based on the results of evacuation drills and use this information for future drills. Furthermore, the monitoring unit can accumulate user behavior data and analyze long-term behavior patterns. For example, it can accumulate user evacuation behavior data and analyze long-term behavior patterns to predict future behavior. This allows the monitoring unit to monitor user behavior in real time and provide advice as needed. Periodic checks point out areas for improvement based on the user's behavior history. Accumulation of behavior data is used to analyze long-term behavior patterns and predict future behavior. This allows the monitoring unit to continuously monitor user behavior and provide appropriate advice, thereby mitigating risks during disasters. Furthermore, the monitoring unit can identify areas for improvement across the entire system based on user behavior data, thereby enhancing system performance. For example, it can analyze user behavior data to improve the method of providing information on evacuation routes and evacuation locations. The monitoring unit can also collect user feedback and use it to improve the system. As a result, the monitoring unit can continuously monitor user behavior and provide appropriate advice, thereby mitigating risks during disasters and improving the overall system performance.

[0069] The data collection unit can collect surrounding information such as geospatial information, satellite data, sensors, and camera images. For example, the data collection unit can collect geospatial information from GPS data and map information. The data collection unit can also collect satellite data from weather satellite data and remote sensing data. The data collection unit can also collect sensor data from temperature sensors, humidity sensors, vibration sensors, etc. The data collection unit can also collect camera images from surveillance camera images and drone images. For example, the data collection unit can acquire geospatial information from GPS data and integrate it with map information. The data collection unit can acquire weather satellite data in real time and use it for disaster prediction. The data collection unit can acquire data from temperature sensors and humidity sensors to monitor environmental changes. The data collection unit can analyze surveillance camera images to understand the situation of a disaster. In this way, by collecting surrounding information from diverse data sources, the situation during a disaster can be accurately understood. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input prompts to the generating AI to acquire geospatial information, and the generating AI can analyze and provide the geospatial information.

[0070] The analysis unit can analyze collected surrounding information to provide real-time decision support and predictions. For example, the analysis unit can analyze collected geospatial information to propose evacuation routes in the event of a disaster. The analysis unit can also analyze collected satellite data to predict the path of a typhoon. The analysis unit can also analyze collected sensor data to predict the occurrence of an earthquake. For example, the analysis unit can analyze geospatial information to propose the optimal evacuation route. The analysis unit can analyze satellite data to predict the path of a typhoon and use it to issue evacuation orders. The analysis unit can analyze sensor data to predict the occurrence of an earthquake and use it to issue emergency notifications. This enables rapid decision support and predictions through real-time analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected geospatial information into a generating AI, which can then propose the optimal evacuation route.

[0071] The information provider can provide users with information on evacuation routes and evacuation locations based on the results obtained by the analysis unit. For example, the information provider can provide users with evacuation routes obtained by the analysis unit. The information provider can also provide users with information on evacuation locations obtained by the analysis unit. The information provider can also provide users with action instructions obtained by the analysis unit. For example, the information provider can display evacuation routes on a map and guide users. The information provider can provide information on evacuation locations, including capacity and equipment information. The information provider can provide action instructions via voice guidance or text message. In this way, by providing appropriate evacuation information based on the analysis results, it supports users in taking swift action. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the evacuation routes obtained by the analysis unit into a generating AI, and the generating AI can guide users along the evacuation routes.

[0072] The monitoring unit can monitor user behavior and provide advice as needed, based on information provided by the provision unit. For example, the monitoring unit can monitor user behavior in real time and provide advice as needed. The monitoring unit can also periodically check user behavior and point out areas for improvement. The monitoring unit can also accumulate user behavior data and analyze long-term behavior patterns. For example, the monitoring unit can monitor user behavior in real time and suggest changes to evacuation routes. The monitoring unit can conduct periodic checks and point out areas for improvement based on the user's behavior history. The monitoring unit accumulates behavior data, analyzes long-term behavior patterns, and uses this to predict future behavior. This allows for further reduction of risks during disasters by monitoring user behavior and providing appropriate advice. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user behavior data into a generating AI, which can then point out areas for improvement.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of collecting surrounding information based on the estimated emotions. For example, if the user is tense, the data collection unit can immediately collect surrounding information to enable a quick response. If the user is relaxed, the data collection unit can also periodically collect surrounding information and provide the necessary information. If the user is in a state of panic, the data collection unit can prioritize collecting the most important information and provide it quickly. For example, if the user is tense, the data collection unit can immediately collect surrounding information to enable a quick response. If the user is relaxed, the data collection unit can periodically collect surrounding information and provide the necessary information. If the user is in a state of panic, the data collection unit can prioritize collecting the most important information and provide it quickly. This allows for more appropriate information collection by adjusting the timing of collection according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generating AI, which can adjust the timing of collection.

[0074] The data collection unit can analyze the user's past behavioral history when collecting surrounding information and select the optimal data collection method. For example, the data collection unit can prioritize collecting information in similar situations based on the user's past evacuation routes. The data collection unit can also prioritize collecting information on evacuation sites used by the user in the past. The data collection unit can also analyze the user's past behavioral patterns and select the most effective data collection method. For example, the data collection unit can prioritize collecting information in similar situations based on the user's past evacuation routes. The data collection unit prioritizes collecting information on evacuation sites used by the user in the past. The data collection unit analyzes the user's past behavioral patterns and selects the most effective data collection method. This allows the optimal data collection method to be selected by analyzing past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into a generating AI, which can then select the optimal data collection method.

[0075] The data collection unit can prioritize the collection of different data sources depending on the type of disaster when gathering surrounding information. For example, when an earthquake occurs, the data collection unit prioritizes the collection of seismometer data and seismic wave information. When a typhoon occurs, the data collection unit can also prioritize the collection of weather satellite data and anemometer data. When a flood occurs, the data collection unit can also prioritize the collection of river water level data and rainfall data. For example, when an earthquake occurs, the data collection unit prioritizes the collection of seismometer data and seismic wave information. When a typhoon occurs, the data collection unit prioritizes the collection of weather satellite data and anemometer data. When a flood occurs, the data collection unit prioritizes the collection of river water level data and rainfall data. This enables appropriate information collection by prioritizing the collection of data sources according to the type of disaster. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data sources according to the type of disaster into a generating AI, and the generating AI can prioritize their collection.

[0076] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is tense, the data collection unit will prioritize collecting information on evacuation routes and evacuation locations. If the user is relaxed, the data collection unit may also prioritize collecting information on the surrounding safety and situation reports. If the user is in a state of panic, the data collection unit may also prioritize collecting the most important evacuation information. For example, if the user is tense, the data collection unit will prioritize collecting information on evacuation routes and evacuation locations. If the user is relaxed, the data collection unit will prioritize collecting information on the surrounding safety and situation reports. If the user is in a state of panic, the data collection unit will prioritize collecting the most important evacuation information. This allows for the priority collection of more important information by determining the priority of information according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generating AI, which can then determine the priority of information.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting surrounding information. For example, if the user is in a specific area, the data collection unit will prioritize collecting disaster information for that area. If the user is on the move, the data collection unit can also prioritize collecting disaster information for their destination. If the user is in an evacuation center, the data collection unit can also prioritize collecting information about the situation at that evacuation center. For example, if the user is in a specific area, the data collection unit will prioritize collecting disaster information for that area. If the user is on the move, the data collection unit will prioritize collecting disaster information for their destination. If the user is in an evacuation center, the data collection unit will prioritize collecting information about the situation at that evacuation center. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant information.

[0078] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting surrounding information. For example, the data collection unit can collect surrounding disaster information based on location information shared by the user on social media. The data collection unit can also collect relevant disaster information based on information from accounts that the user follows on social media. The data collection unit can also collect necessary information by analyzing information that the user has posted on social media. For example, the data collection unit can collect surrounding disaster information based on location information shared by the user on social media. The data collection unit can collect relevant disaster information based on information from accounts that the user follows on social media. The data collection unit can collect necessary information by analyzing information that the user has posted on social media. This allows for the efficient collection of relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, which can then collect relevant information.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is panicking, the analysis unit can also provide analysis results that highlight the most important information. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit provides detailed analysis results. If the user is panicking, the analysis unit provides analysis results that highlight the most important information. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then adjust the presentation of the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to its importance. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can perform a simplified analysis on information of low importance. The analysis unit can determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the collected information into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the type of disaster during the analysis. For example, when an earthquake occurs, the analysis unit applies an algorithm that analyzes seismic wave data. When a typhoon occurs, the analysis unit can also apply an algorithm that analyzes meteorological data. When a flood occurs, the analysis unit can also apply an algorithm that analyzes water level data. For example, when an earthquake occurs, the analysis unit applies an algorithm that analyzes seismic wave data. When a typhoon occurs, the analysis unit applies an algorithm that analyzes meteorological data. When a flood occurs, the analysis unit applies an algorithm that analyzes water level data. This makes it possible to perform an appropriate analysis by applying an analysis algorithm appropriate to the type of disaster. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input an analysis algorithm appropriate to the type of disaster into a generating AI, and the generating AI can apply it.

[0082] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit may prioritize the analysis of evacuation routes. If the user is relaxed, the analysis unit may also prioritize the analysis of surrounding safety information. If the user is in a state of panic, the analysis unit may also prioritize the analysis of the most important information. For example, if the user is tense, the analysis unit may prioritize the analysis of evacuation routes. If the user is relaxed, the analysis unit may prioritize the analysis of surrounding safety information. If the user is in a state of panic, the analysis unit may prioritize the analysis of the most important information. This allows for the prioritization of analysis based on the user's emotions, thereby prioritizing the analysis of more important information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then determine the priority of the analysis.

[0083] The analysis unit can determine the priority of analysis based on the submission date of the collected information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also lower the priority of analysis for information that has been submitted a long time ago. The analysis unit may also adjust the order of analysis according to the submission date. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may lower the priority of analysis for information that has been submitted a long time ago. The analysis unit may adjust the order of analysis according to the submission date. This allows the analysis unit to prioritize the analysis of the most recent information by determining the priority of analysis based on the submission date of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit may input the submission date of the collected information into a generating AI, and the generating AI may determine the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the collected information during analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit can also lower the priority of analysis for less relevant information. The analysis unit can also adjust the order of analysis according to relevance. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit lowers the priority of analysis for less relevant information. The analysis unit adjusts the order of analysis according to relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the collected information into a generating AI, and the generating AI can adjust the order of analysis.

[0085] The information provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is tense, the information provider can provide simple and easily visible information. If the user is relaxed, the information provider can also provide detailed information. If the user is panicking, the information provider can also highlight the most important information. For example, if the user is tense, the information provider can provide simple and easily visible information. If the user is relaxed, the information provider can provide detailed information. If the user is panicking, the information provider can highlight the most important information. This allows for more appropriate information to be provided by adjusting the way the information is presented according to the user's emotions. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generating AI, which can then adjust the way the information is presented.

[0086] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide a detailed explanation for highly important information. For less important information, the provider can also provide a simplified explanation. The provider can also determine the priority of provision according to importance. For example, the provider can provide a detailed explanation for highly important information. For less important information, the provider can provide a simplified explanation. The provider can determine the priority of provision according to importance. This allows important information to be provided in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the importance of the information into a generating AI, and the generating AI can adjust the level of detail of the provision.

[0087] The information provider can apply different information provision algorithms depending on the type of disaster at the time of provision. For example, when an earthquake occurs, the information provider can apply an algorithm that provides information based on seismic wave data. When a typhoon occurs, the information provider can also apply an algorithm that provides information based on meteorological data. When a flood occurs, the information provider can also apply an algorithm that provides information based on water level data. For example, when an earthquake occurs, the information provider can apply an algorithm that provides information based on seismic wave data. When a typhoon occurs, the information provider can apply an algorithm that provides information based on meteorological data. When a flood occurs, the information provider can apply an algorithm that provides information based on water level data. This makes it possible to provide appropriate information by applying an information provision algorithm according to the type of disaster. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input an information provision algorithm according to the type of disaster into a generating AI, and the generating AI can apply it.

[0088] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is tense, the information provider will prioritize providing information on evacuation routes and evacuation locations. If the user is relaxed, the information provider may also prioritize providing information on the surrounding safety and situation reports. If the user is in a state of panic, the information provider may also prioritize providing the most important evacuation information. For example, if the user is tense, the information provider will prioritize providing information on evacuation routes and evacuation locations. If the user is relaxed, the information provider will prioritize providing information on the surrounding safety and situation reports. If the user is in a state of panic, the information provider will prioritize providing the most important evacuation information. In this way, by determining the priority of information according to the user's emotions, more important information can be provided preferentially. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generating AI, and the generating AI can determine the priority of the information.

[0089] The information provider can determine the priority of information provision based on the submission date. For example, the provider can prioritize providing the most recent information. The provider can also lower the priority of older information. The provider can also adjust the order of provision according to the submission date. For example, the provider can prioritize providing the most recent information. The provider can lower the priority of older information. The provider can adjust the order of provision according to the submission date. This allows the provider to prioritize providing the most recent information by determining the priority of provision based on the submission date. Some or all of the above processing in the information provider can be performed using AI, for example, or without AI. For example, the provider can input the submission date of the information into a generating AI, and the generating AI can determine the priority of provision.

[0090] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider can prioritize the delivery of highly relevant information. The provider can also lower the priority of less relevant information. The provider can also adjust the order of information delivery according to relevance. For example, the provider can prioritize the delivery of highly relevant information. The provider can lower the priority of less relevant information. The provider can adjust the order of information delivery according to relevance. This makes efficient information delivery possible by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the relevance of the information into a generating AI, and the generating AI can adjust the order of delivery.

[0091] The monitoring unit can estimate the user's emotions and adjust its monitoring method based on the estimated emotions. For example, if the user is tense, the monitoring unit can monitor frequently to enable a quick response. If the user is relaxed, the monitoring unit can also monitor periodically and provide necessary information. If the user is in a state of panic, the monitoring unit can prioritize monitoring the most important information. For example, if the user is tense, the monitoring unit can monitor frequently to enable a quick response. If the user is relaxed, the monitoring unit can monitor periodically and provide necessary information. If the user is in a state of panic, the monitoring unit prioritizes monitoring the most important information. This allows for more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI, which can then adjust the monitoring method.

[0092] The monitoring unit can analyze the user's past behavior history and select the optimal monitoring method during monitoring. For example, the monitoring unit may prioritize monitoring in similar situations based on the user's past evacuation routes. The monitoring unit may also prioritize monitoring information about evacuation sites used by the user in the past. The monitoring unit can also analyze the user's past behavior patterns and select the most effective monitoring method. For example, the monitoring unit may prioritize monitoring in similar situations based on the user's past evacuation routes. The monitoring unit may prioritize monitoring information about evacuation sites used by the user in the past. The monitoring unit may analyze the user's past behavior patterns and select the most effective monitoring method. In this way, the optimal monitoring method can be selected by analyzing past behavior history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past behavior history into a generating AI, which can then select the optimal monitoring method.

[0093] The monitoring unit can apply different monitoring methods depending on the type of disaster during monitoring. For example, when an earthquake occurs, the monitoring unit performs monitoring based on seismometer data and seismic wave information. When a typhoon occurs, the monitoring unit can also perform monitoring based on weather satellite data and anemometer data. When a flood occurs, the monitoring unit can also perform monitoring based on river water level data and rainfall data. For example, when an earthquake occurs, the monitoring unit performs monitoring based on seismometer data and seismic wave information. When a typhoon occurs, the monitoring unit performs monitoring based on weather satellite data and anemometer data. When a flood occurs, the monitoring unit performs monitoring based on river water level data and rainfall data. This makes it possible to perform appropriate monitoring by applying monitoring methods appropriate to the type of disaster. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input monitoring methods appropriate to the type of disaster into a generating AI, and the generating AI can apply them.

[0094] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is tense, the monitoring unit will prioritize monitoring evacuation routes and evacuation locations. If the user is relaxed, the monitoring unit may also prioritize monitoring surrounding safety information. If the user is in a state of panic, the monitoring unit may also prioritize monitoring the most important information. For example, if the user is tense, the monitoring unit will prioritize monitoring evacuation routes and evacuation locations. If the user is relaxed, the monitoring unit will prioritize monitoring surrounding safety information. If the user is in a state of panic, the monitoring unit will prioritize monitoring the most important information. This allows for prioritizing monitoring of more important information by determining monitoring priorities according to the user's emotions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI, which can then determine the monitoring priorities.

[0095] The monitoring unit can select the optimal monitoring method by considering the user's geographical location information during monitoring. For example, if the user is in a specific area, the monitoring unit will prioritize monitoring disaster information for that area. If the user is on the move, the monitoring unit can also prioritize monitoring disaster information for the destination. If the user is in an evacuation center, the monitoring unit can also prioritize monitoring information about the status of that evacuation center. For example, if the user is in a specific area, the monitoring unit will prioritize monitoring disaster information for that area. If the user is on the move, the monitoring unit will prioritize monitoring disaster information for the destination. If the user is in an evacuation center, the monitoring unit will prioritize monitoring information about the status of that evacuation center. This allows the optimal monitoring method to be selected by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI, which can then select the optimal monitoring method.

[0096] The monitoring unit can analyze the user's social media activity during monitoring and propose monitoring methods. For example, the monitoring unit can monitor surrounding disaster information based on location information shared by the user on social media. The monitoring unit can also monitor relevant disaster information based on information from accounts the user follows on social media. The monitoring unit can also analyze information posted by the user on social media and propose necessary monitoring methods. For example, the monitoring unit can monitor surrounding disaster information based on location information shared by the user on social media. The monitoring unit can monitor relevant disaster information based on information from accounts the user follows on social media. The monitoring unit can analyze information posted by the user on social media and propose necessary monitoring methods. In this way, by analyzing social media activity, the optimal monitoring method can be proposed. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's social media activity into a generating AI, and the generating AI can propose monitoring methods.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The data collection unit can estimate the user's emotions and adjust the timing of collecting surrounding information based on those emotions. For example, if the user is tense, it can immediately collect surrounding information to enable a quick response. If the user is relaxed, it can collect surrounding information periodically and provide the necessary information. If the user is in a state of panic, it can prioritize collecting the most important information and provide it quickly. In this way, by adjusting the timing of data collection according to the user's emotions, more appropriate information can be collected.

[0099] The data collection unit can analyze the user's past behavioral history when collecting surrounding information and select the optimal collection method. For example, it can prioritize collecting information in similar situations based on routes the user has previously evacuated. It can also prioritize collecting information on evacuation sites the user has used in the past. By analyzing the user's past behavioral patterns, it can select the most effective information collection method. In this way, the optimal information collection method can be selected by analyzing past behavioral history.

[0100] The data collection unit can prioritize the collection of different data sources depending on the type of disaster when gathering surrounding information. For example, during an earthquake, it prioritizes the collection of seismometer data and seismic wave information. During a typhoon, it can also prioritize the collection of weather satellite data and anemometer data. During a flood, it can also prioritize the collection of river water level data and rainfall data. This allows for the collection of appropriate information by prioritizing data sources according to the type of disaster.

[0101] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is anxious, it will prioritize collecting information on evacuation routes and shelters. If the user is relaxed, it can prioritize collecting information on the surrounding safety and situation reports. If the user is panicking, it can prioritize collecting the most important evacuation information. In this way, by prioritizing information according to the user's emotions, more important information can be collected first.

[0102] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when gathering surrounding information. For example, if the user is in a specific area, it can prioritize the collection of disaster information for that area. If the user is on the move, it can also prioritize the collection of disaster information for their destination. If the user is in an evacuation center, it can also prioritize the collection of information regarding the situation at that evacuation center. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant information.

[0103] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is tense, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. If the user is panicking, it can provide analysis results that highlight the most important information. By adjusting the presentation of the analysis according to the user's emotions, it can provide more appropriate analysis results.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, it can perform a detailed analysis on highly important information and a simplified analysis on less important information. It can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the information.

[0105] The analysis unit can apply different analysis algorithms depending on the type of disaster during the analysis. For example, during an earthquake, an algorithm that analyzes seismic wave data is applied. During a typhoon, an algorithm that analyzes meteorological data can also be applied. During a flood, an algorithm that analyzes water level data can also be applied. This allows for appropriate analysis by applying an analysis algorithm appropriate to the type of disaster.

[0106] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on those emotions. For example, if the user is tense, the analysis of evacuation routes will be prioritized. If the user is relaxed, the analysis of surrounding safety information may be prioritized. If the user is in a panic, the analysis of the most important information may be prioritized. In this way, by determining the priority of the analysis according to the user's emotions, more important information can be analyzed preferentially.

[0107] The analysis unit can determine the priority of analysis based on when the collected information was submitted. For example, it can prioritize the analysis of the most recent information. Older information can be given a lower priority. The order of analysis can also be adjusted according to the submission date. This allows for prioritizing the analysis of the most recent information by determining the priority of analysis based on the submission date.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The collection unit collects surrounding information. This surrounding information includes geospatial information, satellite data, sensor data, and camera images. For example, the collection unit collects geospatial information from GPS data and map information, and satellite data from weather satellite data and remote sensing data. It also collects sensor data from temperature sensors, humidity sensors, vibration sensors, etc. Step 2: The analysis unit analyzes the surrounding information collected by the collection unit. The analysis is performed to provide real-time decision support and prediction. For example, geospatial information is analyzed to propose evacuation routes in the event of a disaster, satellite data is analyzed to predict the path of a typhoon, and sensor data is analyzed to predict the occurrence of an earthquake. Step 3: The service provider provides action information based on the results obtained by the analysis unit. This action information includes evacuation routes, evacuation locations, and action instructions. For example, the service provider provides the user with evacuation routes obtained by the analysis unit, provides information on evacuation locations, and provides action instructions via voice guidance or text message. Step 4: The monitoring unit monitors user behavior based on the information provided by the service provider. Monitoring is performed for real-time monitoring and periodic checks. For example, the monitoring unit monitors user behavior in real time, provides advice as needed, periodically checks to identify areas for improvement in behavior, and accumulates behavioral data to analyze long-term behavioral patterns.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] For example, each of the multiple elements, including the collection unit, analysis unit, provision unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects surrounding information using the camera 42 and sensors of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to provide real-time decision support and prediction. The provision unit is implemented by the control unit 46A of the smart device 14 and provides behavioral information to the user based on the analysis results. The monitoring unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12 and monitors the user's behavior and provides advice as needed. 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.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] For example, each of the multiple elements, including the data collection unit, analysis unit, provision unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects surrounding information using the camera 42 and sensors of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to provide real-time decision support and prediction. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides behavioral information to the user based on the analysis results. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12 and monitors the user's behavior and provides advice as needed. 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.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] For example, each of the multiple elements, including the collection unit, analysis unit, provision unit, and monitoring unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects surrounding information using the camera 42 and sensors of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to provide real-time decision support and prediction. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides behavioral information to the user based on the analysis results. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12 and monitors the user's behavior and provides advice as needed. 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.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] For example, each of the multiple elements, including the collection unit, analysis unit, provision unit, and monitoring unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects surrounding information using the camera 42 and sensors of the robot 414, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to provide real-time decision support and prediction. The provision unit is implemented by the control unit 46A of the robot 414 and provides action information to the user based on the analysis results. The monitoring unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12 and monitors the user's actions and provides advice as needed. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A collection unit that collects surrounding information, An analysis unit analyzes the surrounding information collected by the aforementioned collection unit, A providing unit that provides behavioral information based on the results obtained by the analysis unit, The system includes a monitoring unit that monitors user behavior based on information provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects surrounding information such as geospatial information, satellite data, sensors, and camera images. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected surrounding information is analyzed to provide real-time decision support and predictions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the results obtained by the analysis unit, information on evacuation routes and evacuation locations is provided to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, Based on the information provided by the service provider, user behavior is monitored and advice is provided as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting surrounding information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting surrounding information, the system analyzes the user's past behavior history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting surrounding information, prioritize different data sources depending on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting surrounding information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering surrounding information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, different analysis algorithms are applied depending on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the collected information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, different delivery algorithms will be applied depending on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, We estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, During monitoring, the system analyzes the user's past behavior history to select the most suitable monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, different monitoring methods are applied depending on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, we analyze users' social media activity and propose monitoring methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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. A collection unit that collects surrounding information, An analysis unit analyzes the surrounding information collected by the aforementioned collection unit, A providing unit that provides behavioral information based on the results obtained by the analysis unit, The system includes a monitoring unit that monitors user behavior based on information provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects surrounding information such as geospatial information, satellite data, sensors, and camera images. The system according to feature 1.

3. The aforementioned analysis unit, The collected surrounding information is analyzed to provide real-time decision support and predictions. The system according to feature 1.

4. The aforementioned supply unit is, Based on the results obtained by the aforementioned analysis unit, the system provides the user with information on evacuation routes and evacuation locations. The system according to feature 1.

5. The monitoring unit, Based on the information provided by the aforementioned provisioning unit, user behavior is monitored and advice is provided as needed. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting surrounding information based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting surrounding information, the system analyzes the user's past behavior history to select the most suitable collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting surrounding information, prioritize different data sources depending on the type of disaster. The system according to feature 1.

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

10. The aforementioned collection unit is When collecting surrounding information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

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