system

Abstract

The system according to this embodiment aims to optimize emergency response and resource allocation during natural disasters. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects real-time data such as earthquake information and weather data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes evacuation routes and resource allocation based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that emergency response and resource allocation in the event of natural disasters were not carried out quickly and effectively.

[0005] The system according to the embodiment aims to optimize emergency response and resource allocation in the event of natural disasters.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects real-time data such as earthquake information and meteorological data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an evacuation route and resource allocation based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize emergency response and resource allocation during natural disasters. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI disaster rescue system according to an embodiment of the present invention is an application and platform for optimizing emergency response and resource allocation during natural disasters by utilizing generative AI. The AI ​​disaster rescue system analyzes vast amounts of data in real time to support instructions for rescue activities, suggestions for effective evacuation routes, and efficient resource allocation. For example, the AI ​​disaster rescue system uses generative AI to collect real-time data such as earthquake information, weather data, road traffic information, and social media posts. Next, the AI ​​disaster rescue system uses generative AI to analyze this data and propose optimal evacuation routes, shelter locations, and resource allocation. Furthermore, the AI ​​disaster rescue system uses generative AI to utilize natural language processing to communicate smoothly with citizens and rescue organizations and provide specific instructions and advice. For example, in the event of a large-scale earthquake, the AI ​​disaster rescue system immediately collects earthquake information and analyzes the situation in the affected area. Based on the results, it proposes optimal evacuation routes and optimizes the placement of shelters. The AI ​​disaster rescue system also proposes resource allocation to efficiently distribute relief supplies and rescue activities for disaster victims. In addition, the AI ​​disaster rescue system analyzes social media posts to grasp the location information of disaster victims and urgent requests for assistance, and responds quickly. This system enables a rapid and accurate response to natural disasters, minimizing damage. By ensuring the safety of citizens and streamlining rescue operations, it can save many lives and improve the overall disaster preparedness of society. In this way, the AI ​​disaster rescue system enables a rapid and accurate response to natural disasters, minimizing damage.

[0029] The AI ​​disaster rescue system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects real-time data such as earthquake information and weather data. The data collection unit can collect data such as earthquake information, weather data, road traffic information, and SNS posts. For example, the data collection unit can obtain earthquake information from seismometers and weather data from weather stations. The data collection unit can also obtain road traffic information from traffic management systems and SNS posts from social media platforms. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can use generative AI to analyze the collected data and understand the situation in the disaster area. For example, the analysis unit can analyze earthquake information to identify the epicenter and seismic intensity. The analysis unit can also analyze weather data and predict changes in weather. Furthermore, the analysis unit can analyze road traffic information and understand the situation of traffic congestion and road closures. The proposal unit proposes evacuation routes and resource allocation based on the analysis results obtained by the analysis unit. The proposal unit can, for example, use generative AI to propose optimal evacuation routes, shelter placement, and resource allocation. For example, the proposal unit can propose the optimal evacuation route based on the situation in the disaster area. It can also propose shelter placement based on the capacity and accessibility of the shelters. Furthermore, it can propose resource allocation based on the types of relief supplies and their priority. As a result, the AI ​​disaster rescue system according to this embodiment can collect and analyze real-time data and propose optimal evacuation routes and resource allocation, enabling a rapid and accurate response during a disaster.

[0030] The data collection unit collects real-time data such as earthquake information and weather data. Specifically, earthquake information is obtained from seismometers, and weather data is obtained from weather stations. Seismometers detect the occurrence of earthquakes and report the epicenter and seismic intensity in real time. Weather stations continuously observe weather data such as temperature, humidity, wind speed, and precipitation, and provide this data to the data collection unit. Furthermore, the data collection unit obtains road traffic information from the traffic management system. The traffic management system provides real-time information on road congestion, traffic accidents, and road closures, which allows for an understanding of the traffic situation in disaster-stricken areas. The data collection unit also obtains SNS posts from social media platforms. SNS posts contain real-time information from residents and eyewitnesses in disaster-stricken areas, which allows for a rapid understanding of the situation on the ground. For example, it collects photos, videos, and text messages posted by residents in disaster-stricken areas and provides this information to the analysis unit. The data collection unit centrally manages information from these diverse data sources and stores it in a database that is updated in real time. This allows the data collection unit to quickly and accurately grasp the situation during a disaster and provide the necessary information to the analysis and proposal units. Furthermore, by adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. For example, immediately after an earthquake, the frequency of data collection from seismometers can be increased to quickly identify the epicenter and seismic intensity. Also, by adjusting the frequency of meteorological data collection, it can respond to rapid changes in weather. In this way, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. Specifically, it uses generative AI to analyze the collected data and understand the situation in the disaster-stricken area. Generative AI has the ability to process vast amounts of data quickly and extract important information. For example, it can analyze earthquake information to identify the epicenter and seismic intensity. Generative AI can analyze data obtained from seismometers and quickly identify the location and intensity of earthquakes. It can also analyze meteorological data and predict changes in weather. Based on data obtained from weather stations, generative AI predicts future weather changes and understands the situation in the disaster-stricken area. Furthermore, it can analyze road traffic information to understand traffic congestion and road closures. Generative AI analyzes data obtained from traffic management systems to understand the traffic situation in the disaster-stricken area in real time. This allows the analysis unit to quickly and accurately analyze the collected data and understand the situation in the disaster-stricken area in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past earthquake data, it can predict fluctuations in risk in specific regions and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The proposal department proposes evacuation routes and resource allocations based on the analysis results obtained by the analysis department. Specifically, it can use generative AI to propose optimal evacuation routes, shelter placements, and resource allocations. The generative AI comprehensively evaluates the situation in the disaster area and calculates the optimal evacuation route. For example, it proposes the safest and fastest evacuation route based on information on road conditions and traffic congestion in the disaster area. It can also propose shelter placements based on the capacity and accessibility of shelters. The generative AI calculates the optimal shelter placement considering the capacity and facilities of each shelter. Furthermore, it can propose resource allocations based on the types of relief supplies and their priority. The generative AI evaluates the needs and resource supply situation in the disaster area and calculates the most effective resource allocation. As a result, the proposal department can propose optimal evacuation routes and resource allocations according to the situation in the disaster area, supporting a quick and accurate response. In addition, the proposal department can continuously revise its proposals based on real-time updated data to respond to the latest situation. For example, if new information such as aftershocks or sudden changes in weather comes in, the proposal department immediately incorporates the new data and updates its proposals. Furthermore, the proposal department can make more accurate proposals by taking into account the characteristics of each region and past disaster history. This allows the proposal department to always provide highly accurate proposals based on the latest information and support a swift and appropriate response.

[0033] The data collection unit can collect data such as earthquake information, weather data, road traffic information, and social media posts. For example, the data collection unit can acquire earthquake information from seismometers and weather data from weather stations. It can also acquire road traffic information from traffic management systems and social media posts from social media platforms. This allows for a more accurate understanding of the situation by collecting information from diverse data sources. 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 earthquake information acquired from seismometers into a generating AI and have the generating AI perform an analysis of the earthquake information.

[0034] The analysis unit can analyze the collected data and understand the situation in the disaster-stricken area. For example, the analysis unit can use a generative AI to analyze the collected data and understand the situation in the disaster-stricken area. For example, the analysis unit can analyze earthquake information to identify the epicenter and seismic intensity. The analysis unit can also analyze meteorological data to predict changes in weather. Furthermore, the analysis unit can analyze road traffic information to understand the situation of traffic congestion and road closures. By accurately understanding the situation in the disaster-stricken area, appropriate countermeasures can be proposed. Some or all of the above-mentioned processes in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input collected earthquake information into a generative AI and have the generative AI identify the epicenter and seismic intensity.

[0035] The proposal unit can propose optimal evacuation routes, shelter placement, and resource allocation based on the analysis results. For example, the proposal unit can use a generative AI to propose optimal evacuation routes, shelter placement, and resource allocation. For example, the proposal unit can propose optimal evacuation routes based on the situation in the disaster area. Furthermore, the proposal unit can propose shelter placement based on the shelter's capacity and accessibility. In addition, the proposal unit can propose resource allocation based on the type and priority of relief supplies. This allows for more efficient relief efforts by proposing optimal evacuation routes and resource allocation. Some or all of the above-described processes in the proposal unit may be performed using a generative AI, or without one. For example, the proposal unit can input analysis results into a generative AI and have the AI ​​propose optimal evacuation routes and resource allocation.

[0036] The SNS Analysis Department can analyze SNS posts to understand the location information of disaster victims and urgent requests for assistance. The SNS Analysis Department can, for example, use a generative AI to analyze SNS posts and understand the location information of disaster victims and urgent requests for assistance. The SNS Analysis Department can, for example, extract GPS data from SNS posts to identify the location information of disaster victims. The SNS Analysis Department can also analyze the content of posts to identify urgent requests for assistance. Furthermore, the SNS Analysis Department can determine the priority of assistance based on the frequency and type of content of posts. This allows for the rapid understanding of the location information of disaster victims and urgent requests for assistance by analyzing SNS posts. Some or all of the above processing in the SNS Analysis Department may be performed using, for example, a generative AI, or without a generative AI. For example, the SNS Analysis Department can input SNS posts into a generative AI and have the generative AI identify the location information of disaster victims and urgent requests for assistance.

[0037] The Communications Department can communicate with citizens and relief organizations. For example, the Communications Department can use generative AI to facilitate smooth communication with citizens and relief organizations. For example, the Communications Department can use natural language processing technology to provide appropriate answers to inquiries from citizens and relief organizations. The Communications Department can also provide instructions and advice in emergencies. Furthermore, the Communications Department can facilitate real-time information sharing to support rapid responses. This enables rapid responses through smooth communication with citizens and relief organizations. Some or all of the above-mentioned processes in the Communications Department may be performed using generative AI, or without it. For example, the Communications Department can input inquiries from citizens and relief organizations into a generative AI and have the AI ​​generate appropriate answers.

[0038] The data collection unit can analyze past disaster data and select the optimal data collection method. For example, the data collection unit can analyze past earthquake data and select the most effective data collection method when an earthquake occurs. It can also analyze past flood data and select the most effective data collection method when a flood occurs. Furthermore, it can analyze past typhoon data and select the most effective data collection method when a typhoon occurs. In this way, the optimal data collection method can be selected by analyzing past disaster data. 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 past disaster data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on specific regions or circumstances during data collection. For example, the collection unit can collect data only in earthquake-affected areas and filter data from other regions. It can also collect data only in flood-affected areas and filter data from other regions. Furthermore, it can collect data only in typhoon-affected areas and filter data from other regions. This allows for the efficient collection of necessary information by filtering data based on specific regions or circumstances. 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 criteria for filtering data based on specific regions or circumstances into a generating AI and leave the filtering to the generating AI.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in an earthquake-affected area, the data collection unit will prioritize the collection of earthquake information for that area. Similarly, if the user is in a flood-affected area, the data collection unit can prioritize the collection of flood information for that area. Furthermore, if the user is in a typhoon-affected area, the data collection unit can prioritize the collection of typhoon information for that area. This allows for the priority collection of highly relevant data 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 and have the generating AI perform the priority collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user has posted about an earthquake, the data collection unit will prioritize collecting earthquake information for that region. Similarly, if a user has posted about a flood, the data collection unit can prioritize collecting flood information for that region. Furthermore, if a user has posted about a typhoon, the data collection unit can prioritize collecting typhoon information for that region. This allows for the efficient collection of relevant data by analyzing a user's 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 and have the generating AI collect the relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. 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 data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an earthquake analysis algorithm to earthquake data. It can also apply a flood analysis algorithm to flood data. Furthermore, it can apply a typhoon analysis algorithm to typhoon data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, it can appropriately prioritize the analysis of data of moderate recency. This allows for the rapid provision of up-to-date information by determining the priority of analysis based on the data collection timing. 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 data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. It may also postpone the analysis of data with low relevance. Furthermore, it may moderately prioritize the analysis of data with moderate relevance. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. 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 data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The proposal unit can adjust the level of detail in its proposals based on the importance of evacuation routes and resources. For example, it can provide detailed proposals for high-importance evacuation routes, simplified proposals for low-importance routes, and proposals with a moderate level of detail for moderately important routes. By adjusting the level of detail based on the importance of evacuation routes and resources, efficient proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of evacuation routes and resources into a generation AI and have the generation AI adjust the level of detail in the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the category of evacuation routes and resources during the proposal process. For example, the proposal unit can apply an evacuation route proposal algorithm to evacuation routes. It can also apply a resource allocation proposal algorithm to resource allocation. Furthermore, it can apply a shelter placement proposal algorithm to shelter placement. By applying different proposal algorithms depending on the category of evacuation routes and resources, more accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input evacuation routes and resource categories into a generative AI and have the generative AI apply the appropriate proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the submission timing of evacuation routes and resources. For example, the proposal department may prioritize the most recent evacuation routes. It may also postpone the proposal of older evacuation routes. Furthermore, it may moderately prioritize evacuation routes of moderate newness. This allows for the rapid provision of up-to-date information by prioritizing proposals based on the submission timing of evacuation routes and resources. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of evacuation routes and resources into a generating AI and have the generating AI determine the priority of proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of evacuation routes and resources. For example, the proposal unit can prioritize proposing evacuation routes with high relevance. It can also postpone proposing evacuation routes with low relevance. Furthermore, it can moderately prioritize proposing evacuation routes with moderate relevance. This allows for efficient proposals by adjusting the order of proposals based on the relevance of evacuation routes and resources. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of evacuation routes and resources into a generating AI and have the generating AI adjust the order of proposals.

[0050] The SNS analysis unit can adjust the level of detail of its analysis based on the importance of the posts during SNS analysis. For example, the SNS analysis unit can perform a detailed analysis on posts of high importance. It can also perform a simplified analysis on posts of low importance. Furthermore, it can perform an analysis with an appropriate level of detail on posts of moderate importance. By adjusting the level of detail of the analysis based on the importance of the posts, efficient analysis becomes possible. Some or all of the above processing in the SNS analysis unit may be performed using AI, for example, or without AI. For example, the SNS analysis unit can input the importance of the posts into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0051] The SNS analysis unit can apply different analysis algorithms depending on the category of the post during SNS analysis. For example, the SNS analysis unit can apply an earthquake analysis algorithm to posts related to earthquakes. It can also apply a flood analysis algorithm to posts related to floods. Furthermore, it can apply a typhoon analysis algorithm to posts related to typhoons. By applying different analysis algorithms depending on the category of the post, more accurate analysis becomes possible. Some or all of the above processing in the SNS analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the SNS analysis unit can input the category of the post into a generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0052] The SNS analysis unit can adjust the order of analysis based on when the posts were collected during SNS analysis. For example, the SNS analysis unit can prioritize the analysis of the most recent posts. It can also postpone the analysis of older posts. Furthermore, it can moderately prioritize the analysis of posts of moderate recency. By adjusting the order of analysis based on when the posts were collected, the latest information can be provided quickly. Some or all of the above processing in the SNS analysis unit may be performed using AI, for example, or without AI. For example, the SNS analysis unit can input the post collection dates into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0053] The SNS analysis unit can adjust the order of analysis based on the relevance of posts during SNS analysis. For example, the SNS analysis unit can prioritize the analysis of posts with high relevance. It can also postpone the analysis of posts with low relevance. Furthermore, it can moderately prioritize the analysis of posts with moderate relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of posts. Some or all of the above processing in the SNS analysis unit may be performed using AI, for example, or without AI. For example, the SNS analysis unit can input the relevance of posts into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0054] The communication unit can select the optimal communication method by referring to the user's past interactions during communication. For example, the communication unit can prioritize providing communication methods (text, voice, etc.) that the user has previously preferred. The communication unit can also provide information at the optimal timing based on the user's past interactions. Furthermore, the communication unit can analyze the user's past interactions and select the most effective communication method. In this way, the optimal communication method can be selected by referring to the user's past interactions. Some or all of the above processing in the communication unit may be performed using, for example, a generative AI, or without a generative AI. For example, the communication unit can input the user's past interactions into a generative AI and have the generative AI select the optimal communication method.

[0055] The communication unit can provide optimal information during communication, taking into account the user's current situation. For example, if the user is evacuating, the communication unit will prioritize providing information related to evacuation. It can also prioritize providing information related to rescue operations if the user is engaged in rescue activities. Furthermore, if the communication unit is on standby, it can prioritize providing information related to standby. This allows the communication unit to provide optimal information by considering the user's current situation. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's current situation into a generating AI and have the generating AI provide the optimal information.

[0056] The communication unit can select the optimal communication method by considering the user's device information during communication. For example, if the user is using a smartphone, the communication unit can provide information tailored to the screen size. Furthermore, if the user is using a tablet, the communication unit can provide information optimized for a larger screen. Additionally, if the user is using a smartwatch, the communication unit can provide concise and highly visible information. This allows the system to select the optimal communication method by considering the user's device information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For instance, the communication unit can input the user's device information into a generating AI and have the generating AI select the optimal communication method.

[0057] The communication unit can provide multilingual information during communication, according to the user's language settings. For example, the communication unit can automatically set the language of the information based on the language settings of the user's device. The communication unit can also provide a language switching function if the user uses multiple languages. Furthermore, if the communication unit selects a specific language, it can provide information in that language. This enables more appropriate information provision by providing multilingual information according to the user's language settings. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's language settings into a generating AI and have the generating AI perform the multilingual information provision.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The AI ​​disaster rescue system can also be equipped with a health monitoring unit to monitor the health status of disaster victims. The health monitoring unit collects the victim's vital signs (heart rate, body temperature, blood pressure, etc.) in real time and transmits them to the analysis unit. The analysis unit can analyze the collected vital signs and evaluate the victim's health status. For example, if the heart rate is abnormally high, the analysis unit will determine that the victim is experiencing stress or panic and notify the suggestion unit. Based on the analysis results, the suggestion unit can propose appropriate medical and psychological support for the victim. This makes it possible to grasp the victim's health status in real time and provide rapid medical assistance.

[0060] The AI ​​disaster rescue system can also include an infrastructure monitoring unit to monitor the infrastructure status in the disaster area. The infrastructure monitoring unit collects real-time data on damage to infrastructure such as roads, bridges, and buildings, and transmits it to the analysis unit. The analysis unit analyzes the collected infrastructure data and can evaluate the infrastructure status in the disaster area. For example, if a road is closed, the analysis unit notifies the proposal unit of this information, and the proposal unit can suggest the optimal detour route. Also, if a bridge is damaged, the proposal unit can suggest a route that avoids that bridge. This makes it possible to grasp the infrastructure status in the disaster area in real time and provide appropriate evacuation routes.

[0061] The AI ​​disaster rescue system can also be equipped with a location tracking unit that tracks the location of disaster victims in real time. The location tracking unit collects location information from the disaster victim's smartphone or wearable device and transmits it to the analysis unit. The analysis unit analyzes the collected location information and can determine the disaster victim's current location. For example, if a disaster victim is off an evacuation route, the analysis unit notifies the suggestion unit of this information, and the suggestion unit can propose a more optimal evacuation route. Also, if a disaster victim is in a dangerous area, the suggestion unit can encourage them to evacuate from that area. This makes it possible to grasp the location of disaster victims in real time and provide appropriate evacuation support.

[0062] The AI ​​disaster rescue system can also be equipped with an environmental monitoring unit that collects environmental data from the disaster area. The environmental monitoring unit collects environmental data such as temperature, humidity, wind speed, and precipitation in real time and transmits it to the analysis unit. The analysis unit can analyze the collected environmental data and evaluate the environmental conditions of the disaster area. For example, if the temperature is extremely low, the analysis unit will notify the proposal unit of this information, and the proposal unit can propose cold weather countermeasures to the victims. Also, if there is heavy rainfall, the proposal unit can adjust evacuation routes based on this information. This makes it possible to grasp the environmental conditions of the disaster area in real time and provide appropriate responses.

[0063] The AI ​​disaster rescue system can also include a congestion monitoring unit that monitors the congestion status of evacuation centers based on the location information of disaster victims. The congestion monitoring unit collects the location information of disaster victims and transmits it to the analysis unit. The analysis unit can analyze the collected location information and evaluate the congestion status of each evacuation center. For example, if a particular evacuation center is overcrowded, the analysis unit notifies the suggestion unit of this information, and the suggestion unit can suggest other evacuation centers to disaster victims. Also, if the capacity of an evacuation center is exceeded, the suggestion unit can suggest the establishment of a new evacuation center based on this information. This makes it possible to grasp the congestion status of evacuation centers in real time and provide appropriate evacuation support.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects real-time data such as earthquake information and weather data. For example, the data collection unit obtains earthquake information from seismometers and weather data from weather stations. The data collection unit can also obtain road traffic information from traffic management systems and social media posts from social media platforms. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, a generative AI, to understand the situation in the disaster area. The analysis unit can analyze earthquake information to identify the epicenter and seismic intensity, and analyze weather data to predict changes in weather. It can also analyze road traffic information to understand the situation of traffic congestion and road closures. Step 3: The proposal unit proposes evacuation routes and resource allocations based on the analysis results obtained by the analysis unit. The proposal unit can use generating AI to propose optimal evacuation routes, shelter placements, and resource allocations. The proposal unit proposes optimal evacuation routes based on the situation in the disaster area and proposes shelter placements based on the capacity and accessibility of the shelters. Furthermore, it can also propose resource allocations based on the types of relief supplies and their allocation priorities.

[0066] (Example of form 2) An AI disaster rescue system according to an embodiment of the present invention is an application and platform for optimizing emergency response and resource allocation during natural disasters by utilizing generative AI. The AI ​​disaster rescue system analyzes vast amounts of data in real time to support instructions for rescue activities, suggestions for effective evacuation routes, and efficient resource allocation. For example, the AI ​​disaster rescue system uses generative AI to collect real-time data such as earthquake information, weather data, road traffic information, and social media posts. Next, the AI ​​disaster rescue system uses generative AI to analyze this data and propose optimal evacuation routes, shelter locations, and resource allocation. Furthermore, the AI ​​disaster rescue system uses generative AI to utilize natural language processing to communicate smoothly with citizens and rescue organizations and provide specific instructions and advice. For example, in the event of a large-scale earthquake, the AI ​​disaster rescue system immediately collects earthquake information and analyzes the situation in the affected area. Based on the results, it proposes optimal evacuation routes and optimizes the placement of shelters. The AI ​​disaster rescue system also proposes resource allocation to efficiently distribute relief supplies and rescue activities for disaster victims. In addition, the AI ​​disaster rescue system analyzes social media posts to grasp the location information of disaster victims and urgent requests for assistance, and responds quickly. This system enables a rapid and accurate response to natural disasters, minimizing damage. By ensuring the safety of citizens and streamlining rescue operations, it can save many lives and improve the overall disaster preparedness of society. In this way, the AI ​​disaster rescue system enables a rapid and accurate response to natural disasters, minimizing damage.

[0067] The AI ​​disaster rescue system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects real-time data such as earthquake information and weather data. The data collection unit can collect data such as earthquake information, weather data, road traffic information, and SNS posts. For example, the data collection unit can obtain earthquake information from seismometers and weather data from weather stations. The data collection unit can also obtain road traffic information from traffic management systems and SNS posts from social media platforms. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can use generative AI to analyze the collected data and understand the situation in the disaster area. For example, the analysis unit can analyze earthquake information to identify the epicenter and seismic intensity. The analysis unit can also analyze weather data and predict changes in weather. Furthermore, the analysis unit can analyze road traffic information and understand the situation of traffic congestion and road closures. The proposal unit proposes evacuation routes and resource allocation based on the analysis results obtained by the analysis unit. The proposal unit can, for example, use generative AI to propose optimal evacuation routes, shelter placement, and resource allocation. For example, the proposal unit can propose the optimal evacuation route based on the situation in the disaster area. It can also propose shelter placement based on the capacity and accessibility of the shelters. Furthermore, it can propose resource allocation based on the types of relief supplies and their priority. As a result, the AI ​​disaster rescue system according to this embodiment can collect and analyze real-time data and propose optimal evacuation routes and resource allocation, enabling a rapid and accurate response during a disaster.

[0068] The data collection unit collects real-time data such as earthquake information and weather data. Specifically, earthquake information is obtained from seismometers, and weather data is obtained from weather stations. Seismometers detect the occurrence of earthquakes and report the epicenter and seismic intensity in real time. Weather stations continuously observe weather data such as temperature, humidity, wind speed, and precipitation, and provide this data to the data collection unit. Furthermore, the data collection unit obtains road traffic information from the traffic management system. The traffic management system provides real-time information on road congestion, traffic accidents, and road closures, which allows for an understanding of the traffic situation in disaster-stricken areas. The data collection unit also obtains SNS posts from social media platforms. SNS posts contain real-time information from residents and eyewitnesses in disaster-stricken areas, which allows for a rapid understanding of the situation on the ground. For example, it collects photos, videos, and text messages posted by residents in disaster-stricken areas and provides this information to the analysis unit. The data collection unit centrally manages information from these diverse data sources and stores it in a database that is updated in real time. This allows the data collection unit to quickly and accurately grasp the situation during a disaster and provide the necessary information to the analysis and proposal units. Furthermore, by adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. For example, immediately after an earthquake, the frequency of data collection from seismometers can be increased to quickly identify the epicenter and seismic intensity. Also, by adjusting the frequency of meteorological data collection, it can respond to rapid changes in weather. In this way, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0069] The analysis unit analyzes the data collected by the collection unit. Specifically, it uses generative AI to analyze the collected data and understand the situation in the disaster-stricken area. Generative AI has the ability to process vast amounts of data quickly and extract important information. For example, it can analyze earthquake information to identify the epicenter and seismic intensity. Generative AI can analyze data obtained from seismometers and quickly identify the location and intensity of earthquakes. It can also analyze meteorological data and predict changes in weather. Based on data obtained from weather stations, generative AI predicts future weather changes and understands the situation in the disaster-stricken area. Furthermore, it can analyze road traffic information to understand traffic congestion and road closures. Generative AI analyzes data obtained from traffic management systems to understand the traffic situation in the disaster-stricken area in real time. This allows the analysis unit to quickly and accurately analyze the collected data and understand the situation in the disaster-stricken area in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past earthquake data, it can predict fluctuations in risk in specific regions and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0070] The proposal department proposes evacuation routes and resource allocations based on the analysis results obtained by the analysis department. Specifically, it can use generative AI to propose optimal evacuation routes, shelter placements, and resource allocations. The generative AI comprehensively evaluates the situation in the disaster area and calculates the optimal evacuation route. For example, it proposes the safest and fastest evacuation route based on information on road conditions and traffic congestion in the disaster area. It can also propose shelter placements based on the capacity and accessibility of shelters. The generative AI calculates the optimal shelter placement considering the capacity and facilities of each shelter. Furthermore, it can propose resource allocations based on the types of relief supplies and their priority. The generative AI evaluates the needs and resource supply situation in the disaster area and calculates the most effective resource allocation. As a result, the proposal department can propose optimal evacuation routes and resource allocations according to the situation in the disaster area, supporting a quick and accurate response. In addition, the proposal department can continuously revise its proposals based on real-time updated data to respond to the latest situation. For example, if new information such as aftershocks or sudden changes in weather comes in, the proposal department immediately incorporates the new data and updates its proposals. Furthermore, the proposal department can make more accurate proposals by taking into account the characteristics of each region and past disaster history. This allows the proposal department to always provide highly accurate proposals based on the latest information and support a swift and appropriate response.

[0071] The data collection unit can collect data such as earthquake information, weather data, road traffic information, and social media posts. For example, the data collection unit can acquire earthquake information from seismometers and weather data from weather stations. It can also acquire road traffic information from traffic management systems and social media posts from social media platforms. This allows for a more accurate understanding of the situation by collecting information from diverse data sources. 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 earthquake information acquired from seismometers into a generating AI and have the generating AI perform an analysis of the earthquake information.

[0072] The analysis unit can analyze the collected data and understand the situation in the disaster-stricken area. For example, the analysis unit can use a generative AI to analyze the collected data and understand the situation in the disaster-stricken area. For example, the analysis unit can analyze earthquake information to identify the epicenter and seismic intensity. The analysis unit can also analyze meteorological data to predict changes in weather. Furthermore, the analysis unit can analyze road traffic information to understand the situation of traffic congestion and road closures. By accurately understanding the situation in the disaster-stricken area, appropriate countermeasures can be proposed. Some or all of the above-mentioned processes in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input collected earthquake information into a generative AI and have the generative AI identify the epicenter and seismic intensity.

[0073] The proposal unit can propose optimal evacuation routes, shelter placement, and resource allocation based on the analysis results. For example, the proposal unit can use a generative AI to propose optimal evacuation routes, shelter placement, and resource allocation. For example, the proposal unit can propose optimal evacuation routes based on the situation in the disaster area. Furthermore, the proposal unit can propose shelter placement based on the shelter's capacity and accessibility. In addition, the proposal unit can propose resource allocation based on the type and priority of relief supplies. This allows for more efficient relief efforts by proposing optimal evacuation routes and resource allocation. Some or all of the above-described processes in the proposal unit may be performed using a generative AI, or without one. For example, the proposal unit can input analysis results into a generative AI and have the AI ​​propose optimal evacuation routes and resource allocation.

[0074] The SNS Analysis Department can analyze SNS posts to understand the location information of disaster victims and urgent requests for assistance. The SNS Analysis Department can, for example, use a generative AI to analyze SNS posts and understand the location information of disaster victims and urgent requests for assistance. The SNS Analysis Department can, for example, extract GPS data from SNS posts to identify the location information of disaster victims. The SNS Analysis Department can also analyze the content of posts to identify urgent requests for assistance. Furthermore, the SNS Analysis Department can determine the priority of assistance based on the frequency and type of content of posts. This allows for the rapid understanding of the location information of disaster victims and urgent requests for assistance by analyzing SNS posts. Some or all of the above processing in the SNS Analysis Department may be performed using, for example, a generative AI, or without a generative AI. For example, the SNS Analysis Department can input SNS posts into a generative AI and have the generative AI identify the location information of disaster victims and urgent requests for assistance.

[0075] The Communications Department can communicate with citizens and relief organizations. For example, the Communications Department can use generative AI to facilitate smooth communication with citizens and relief organizations. For example, the Communications Department can use natural language processing technology to provide appropriate answers to inquiries from citizens and relief organizations. The Communications Department can also provide instructions and advice in emergencies. Furthermore, the Communications Department can facilitate real-time information sharing to support rapid responses. This enables rapid responses through smooth communication with citizens and relief organizations. Some or all of the above-mentioned processes in the Communications Department may be performed using generative AI, or without it. For example, the Communications Department can input inquiries from citizens and relief organizations into a generative AI and have the AI ​​generate appropriate answers.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. The data collection unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, if the user is feeling anxious, the data collection unit can increase the frequency of data collection and provide up-to-date information in real time. Conversely, if the user is relaxed, the data collection unit can decrease the frequency of data collection and provide only the necessary information. Furthermore, if the user is facing an emergency, the data collection unit can immediately begin data collection and provide information quickly. This allows for more appropriate information to be provided by adjusting the timing of data collection according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0077] The data collection unit can analyze past disaster data and select the optimal data collection method. For example, the data collection unit can analyze past earthquake data and select the most effective data collection method when an earthquake occurs. It can also analyze past flood data and select the most effective data collection method when a flood occurs. Furthermore, it can analyze past typhoon data and select the most effective data collection method when a typhoon occurs. In this way, the optimal data collection method can be selected by analyzing past disaster data. 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 past disaster data into a generating AI and have the generating AI select the optimal data collection method.

[0078] The data collection unit can filter data based on specific regions or circumstances during data collection. For example, the collection unit can collect data only in earthquake-affected areas and filter data from other regions. It can also collect data only in flood-affected areas and filter data from other regions. Furthermore, it can collect data only in typhoon-affected areas and filter data from other regions. This allows for the efficient collection of necessary information by filtering data based on specific regions or circumstances. 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 criteria for filtering data based on specific regions or circumstances into a generating AI and leave the filtering to the generating AI.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. The data collection unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, if the user is feeling anxious, the data collection unit can prioritize the collection of important data and provide it quickly. Also, if the user is relaxed, the data collection unit can collect and provide only the necessary data. Furthermore, if the user is facing an emergency, the data collection unit can urgently collect and provide the most important data. This allows for the rapid provision of important information by prioritizing data according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform the data prioritization.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in an earthquake-affected area, the data collection unit will prioritize the collection of earthquake information for that area. Similarly, if the user is in a flood-affected area, the data collection unit can prioritize the collection of flood information for that area. Furthermore, if the user is in a typhoon-affected area, the data collection unit can prioritize the collection of typhoon information for that area. This allows for the priority collection of highly relevant data 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 and have the generating AI perform the priority collection of highly relevant data.

[0081] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user has posted about an earthquake, the data collection unit will prioritize collecting earthquake information for that region. Similarly, if a user has posted about a flood, the data collection unit can prioritize collecting flood information for that region. Furthermore, if a user has posted about a typhoon, the data collection unit can prioritize collecting typhoon information for that region. This allows for the efficient collection of relevant data by analyzing a user's 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 and have the generating AI collect the relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. The analysis unit can estimate the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is feeling anxious, the analysis unit can provide a simple and highly visual presentation. If the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. Furthermore, if the user is facing an emergency, the analysis unit can provide a concise presentation. By adjusting the presentation of the analysis according to the user's emotions, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. 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 data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an earthquake analysis algorithm to earthquake data. It can also apply a flood analysis algorithm to flood data. Furthermore, it can apply a typhoon analysis algorithm to typhoon data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit can estimate the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is feeling anxious, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is facing an emergency, the analysis unit can perform a rapid analysis and provide results. By adjusting the length of the analysis according to the user's emotions, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, it can appropriately prioritize the analysis of data of moderate recency. This allows for the rapid provision of up-to-date information by determining the priority of analysis based on the data collection timing. 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 data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. It may also postpone the analysis of data with low relevance. Furthermore, it may moderately prioritize the analysis of data with moderate relevance. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. 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 data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0088] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. The suggestion unit can estimate the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is feeling anxious, the suggestion unit can provide a simple and easily understandable presentation. If the user is relaxed, it can also provide a presentation that includes detailed information. Furthermore, if the user is facing an emergency, it can provide a concise presentation. This allows for more appropriate information to be provided by adjusting the presentation of the suggestion according to the user's emotions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without one. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the suggestion.

[0089] The proposal unit can adjust the level of detail in its proposals based on the importance of evacuation routes and resources. For example, it can provide detailed proposals for high-importance evacuation routes, simplified proposals for low-importance routes, and proposals with a moderate level of detail for moderately important routes. By adjusting the level of detail based on the importance of evacuation routes and resources, efficient proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of evacuation routes and resources into a generation AI and have the generation AI adjust the level of detail in the proposals.

[0090] The proposal unit can apply different proposal algorithms depending on the category of evacuation routes and resources during the proposal process. For example, the proposal unit can apply an evacuation route proposal algorithm to evacuation routes. It can also apply a resource allocation proposal algorithm to resource allocation. Furthermore, it can apply a shelter placement proposal algorithm to shelter placement. By applying different proposal algorithms depending on the category of evacuation routes and resources, more accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input evacuation routes and resource categories into a generative AI and have the generative AI apply the appropriate proposal algorithm.

[0091] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. The suggestion unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, if the user is feeling anxious, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can also provide a detailed suggestion. Furthermore, if the user is facing an emergency, the suggestion unit can quickly provide suggestions and results. By adjusting the length of the suggestion according to the user's emotions, it becomes possible to provide more appropriate information. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0092] The proposal department can determine the priority of proposals based on the submission timing of evacuation routes and resources. For example, the proposal department may prioritize the most recent evacuation routes. It may also postpone the proposal of older evacuation routes. Furthermore, it may moderately prioritize evacuation routes of moderate newness. This allows for the rapid provision of up-to-date information by prioritizing proposals based on the submission timing of evacuation routes and resources. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of evacuation routes and resources into a generating AI and have the generating AI determine the priority of proposals.

[0093] The proposal unit can adjust the order of proposals based on the relevance of evacuation routes and resources. For example, the proposal unit can prioritize proposing evacuation routes with high relevance. It can also postpone proposing evacuation routes with low relevance. Furthermore, it can moderately prioritize proposing evacuation routes with moderate relevance. This allows for efficient proposals by adjusting the order of proposals based on the relevance of evacuation routes and resources. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of evacuation routes and resources into a generating AI and have the generating AI adjust the order of proposals.

[0094] The SNS analysis unit can estimate the user's emotions and adjust the SNS analysis method based on the estimated user emotions. The SNS analysis unit can estimate the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is feeling anxious, the SNS analysis unit can provide a simple and easy-to-understand analysis method. If the user is relaxed, the SNS analysis unit can also provide an analysis method that includes detailed information. Furthermore, if the user is facing an emergency, the SNS analysis unit can provide a concise analysis method. By adjusting the SNS analysis method according to the user's emotions, it becomes possible to provide more appropriate information. Some or all of the above processing in the SNS analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the SNS analysis unit can input user emotion data into a generative AI and have the generative AI adjust the SNS analysis method.

[0095] The SNS analysis unit can adjust the level of detail of its analysis based on the importance of the posts during SNS analysis. For example, the SNS analysis unit can perform a detailed analysis on posts of high importance. It can also perform a simplified analysis on posts of low importance. Furthermore, it can perform an analysis with an appropriate level of detail on posts of moderate importance. By adjusting the level of detail of the analysis based on the importance of the posts, efficient analysis becomes possible. Some or all of the above processing in the SNS analysis unit may be performed using AI, for example, or without AI. For example, the SNS analysis unit can input the importance of the posts into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0096] The SNS analysis unit can apply different analysis algorithms depending on the category of the post during SNS analysis. For example, the SNS analysis unit can apply an earthquake analysis algorithm to posts related to earthquakes. It can also apply a flood analysis algorithm to posts related to floods. Furthermore, it can apply a typhoon analysis algorithm to posts related to typhoons. By applying different analysis algorithms depending on the category of the post, more accurate analysis becomes possible. Some or all of the above processing in the SNS analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the SNS analysis unit can input the category of the post into a generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0097] The SNS analysis unit can estimate a user's emotions and determine the priority of SNS analysis based on the estimated emotions. The SNS analysis unit can estimate a user's emotions using, for example, an emotion engine or generative AI. For example, if a user is feeling anxious, the SNS analysis unit can prioritize the analysis of important posts and provide them quickly. Also, if a user is relaxed, the SNS analysis unit can analyze and provide only the necessary posts. Furthermore, if a user is facing an emergency, the SNS analysis unit can instantly analyze and provide the most important posts. In this way, important information can be provided quickly by determining the priority of SNS analysis according to the user's emotions. Some or all of the above processing in the SNS analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the SNS analysis unit can input user emotion data into a generative AI and have the generative AI perform the determination of SNS analysis priorities.

[0098] The SNS analysis unit can adjust the order of analysis based on when the posts were collected during SNS analysis. For example, the SNS analysis unit can prioritize the analysis of the most recent posts. It can also postpone the analysis of older posts. Furthermore, it can moderately prioritize the analysis of posts of moderate recency. By adjusting the order of analysis based on when the posts were collected, the latest information can be provided quickly. Some or all of the above processing in the SNS analysis unit may be performed using AI, for example, or without AI. For example, the SNS analysis unit can input the post collection dates into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0099] The SNS analysis unit can adjust the order of analysis based on the relevance of posts during SNS analysis. For example, the SNS analysis unit can prioritize the analysis of posts with high relevance. It can also postpone the analysis of posts with low relevance. Furthermore, it can moderately prioritize the analysis of posts with moderate relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of posts. Some or all of the above processing in the SNS analysis unit may be performed using AI, for example, or without AI. For example, the SNS analysis unit can input the relevance of posts into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0100] The communication unit can estimate the user's emotions and adjust its communication methods based on those estimated emotions. The communication unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For instance, if the user is feeling anxious, the communication unit can provide information in a calm tone. Conversely, if the user is relaxed, it can provide information in a cheerful tone. Furthermore, if the user is facing an emergency, the communication unit can provide quick and concise information. This allows for more appropriate information delivery by adjusting the communication method according to the user's emotions. Some or all of the above processing in the communication unit may be performed using, for example, generative AI, or without generative AI. For example, the communication unit can input user emotion data into a generative AI and have the generative AI adjust the communication method.

[0101] The communication unit can select the optimal communication method by referring to the user's past interactions during communication. For example, the communication unit can prioritize providing communication methods (text, voice, etc.) that the user has previously preferred. The communication unit can also provide information at the optimal timing based on the user's past interactions. Furthermore, the communication unit can analyze the user's past interactions and select the most effective communication method. In this way, the optimal communication method can be selected by referring to the user's past interactions. Some or all of the above processing in the communication unit may be performed using, for example, a generative AI, or without a generative AI. For example, the communication unit can input the user's past interactions into a generative AI and have the generative AI select the optimal communication method.

[0102] The communication unit can provide optimal information during communication, taking into account the user's current situation. For example, if the user is evacuating, the communication unit will prioritize providing information related to evacuation. It can also prioritize providing information related to rescue operations if the user is engaged in rescue activities. Furthermore, if the communication unit is on standby, it can prioritize providing information related to standby. This allows the communication unit to provide optimal information by considering the user's current situation. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's current situation into a generating AI and have the generating AI provide the optimal information.

[0103] The communication unit can estimate the user's emotions and determine communication priorities based on those estimated emotions. The communication unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, if the user is feeling anxious, the communication unit will prioritize providing important information. Conversely, if the user is relaxed, the communication unit can provide only the necessary information. Furthermore, if the user is facing an emergency, the communication unit can immediately provide the most important information. This allows for the rapid delivery of important information by prioritizing communication according to the user's emotions. Some or all of the above processing in the communication unit may be performed using, for example, generative AI, or without generative AI. For example, the communication unit can input user emotion data into a generative AI and have the generative AI determine communication priorities.

[0104] The communication unit can select the optimal communication method by considering the user's device information during communication. For example, if the user is using a smartphone, the communication unit can provide information tailored to the screen size. Furthermore, if the user is using a tablet, the communication unit can provide information optimized for a larger screen. Additionally, if the user is using a smartwatch, the communication unit can provide concise and highly visible information. This allows the system to select the optimal communication method by considering the user's device information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For instance, the communication unit can input the user's device information into a generating AI and have the generating AI select the optimal communication method.

[0105] The communication unit can provide multilingual information during communication, according to the user's language settings. For example, the communication unit can automatically set the language of the information based on the language settings of the user's device. The communication unit can also provide a language switching function if the user uses multiple languages. Furthermore, if the communication unit selects a specific language, it can provide information in that language. This enables more appropriate information provision by providing multilingual information according to the user's language settings. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's language settings into a generating AI and have the generating AI perform the multilingual information provision.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The AI ​​disaster rescue system can also be equipped with a health monitoring unit to monitor the health status of disaster victims. The health monitoring unit collects the victim's vital signs (heart rate, body temperature, blood pressure, etc.) in real time and transmits them to the analysis unit. The analysis unit can analyze the collected vital signs and evaluate the victim's health status. For example, if the heart rate is abnormally high, the analysis unit will determine that the victim is experiencing stress or panic and notify the suggestion unit. Based on the analysis results, the suggestion unit can propose appropriate medical and psychological support for the victim. This makes it possible to grasp the victim's health status in real time and provide rapid medical assistance.

[0108] The AI ​​disaster rescue system can also include an emotional support unit that estimates the emotions of disaster victims and adjusts the support provided based on those estimates. This emotional support unit analyzes the victim's facial expressions and tone of voice to estimate their emotions. For example, if a victim is feeling anxious, the emotional support unit can provide relaxing music or messages. If a victim is in a state of panic, the unit can suggest breathing exercises or relaxation techniques to help them calm down. Furthermore, the emotional support unit can support the victim in contacting family and friends to help them feel more secure. This makes it possible to provide appropriate support tailored to the emotional state of the disaster victim.

[0109] The AI ​​disaster rescue system can also include an infrastructure monitoring unit to monitor the infrastructure status in the disaster area. The infrastructure monitoring unit collects real-time data on damage to infrastructure such as roads, bridges, and buildings, and transmits it to the analysis unit. The analysis unit analyzes the collected infrastructure data and can evaluate the infrastructure status in the disaster area. For example, if a road is closed, the analysis unit notifies the proposal unit of this information, and the proposal unit can suggest the optimal detour route. Also, if a bridge is damaged, the proposal unit can suggest a route that avoids that bridge. This makes it possible to grasp the infrastructure status in the disaster area in real time and provide appropriate evacuation routes.

[0110] The AI ​​disaster rescue system can also include a shelter management unit that estimates the emotions of disaster victims and adjusts the shelter environment based on those estimates. The shelter management unit analyzes the emotions of the victims and optimizes the shelter environment. For example, if a victim is feeling stressed, the shelter management unit can provide a relaxing space. If a victim is feeling anxious, the shelter management unit can provide lighting or music to create a sense of security. Furthermore, the shelter management unit can adjust the temperature and humidity to ensure the victim's comfort. This makes it possible to provide a comfortable shelter environment tailored to the emotional state of the victims.

[0111] The AI ​​disaster rescue system can also be equipped with a location tracking unit that tracks the location of disaster victims in real time. The location tracking unit collects location information from the disaster victim's smartphone or wearable device and transmits it to the analysis unit. The analysis unit analyzes the collected location information and can determine the disaster victim's current location. For example, if a disaster victim is off an evacuation route, the analysis unit notifies the suggestion unit of this information, and the suggestion unit can propose a more optimal evacuation route. Also, if a disaster victim is in a dangerous area, the suggestion unit can encourage them to evacuate from that area. This makes it possible to grasp the location of disaster victims in real time and provide appropriate evacuation support.

[0112] The AI ​​disaster rescue system can also include an emotional communication unit that estimates the emotions of disaster victims and adjusts the content of communication based on those estimated emotions. The emotional communication unit analyzes the emotions of the disaster victims and provides appropriate communication content. For example, if a disaster victim is feeling anxious, the emotional communication unit can provide reassuring messages. If a disaster victim is in a state of panic, the emotional communication unit can also provide advice to help them calm down. Furthermore, if a disaster victim is relaxed, the emotional communication unit can provide detailed information. This makes it possible to provide appropriate communication tailored to the emotional state of the disaster victims.

[0113] The AI ​​disaster rescue system can also be equipped with an environmental monitoring unit that collects environmental data from the disaster area. The environmental monitoring unit collects environmental data such as temperature, humidity, wind speed, and precipitation in real time and transmits it to the analysis unit. The analysis unit can analyze the collected environmental data and evaluate the environmental conditions of the disaster area. For example, if the temperature is extremely low, the analysis unit will notify the proposal unit of this information, and the proposal unit can propose cold weather countermeasures to the victims. Also, if there is heavy rainfall, the proposal unit can adjust evacuation routes based on this information. This makes it possible to grasp the environmental conditions of the disaster area in real time and provide appropriate responses.

[0114] The AI ​​disaster rescue system may also include an emotional resource allocation unit that estimates the emotions of disaster victims and adjusts resource allocation based on those estimated emotions. The emotional resource allocation unit analyzes the emotions of disaster victims and optimizes resource allocation. For example, if a disaster victim is feeling anxious, the emotional resource allocation unit can prioritize providing psychological support. Similarly, if a disaster victim is in a state of panic, the emotional resource allocation unit can prioritize providing medical assistance. Furthermore, if a disaster victim is relaxed, the emotional resource allocation unit can provide only the necessary resources. This makes it possible to provide appropriate resource allocation according to the emotional state of disaster victims.

[0115] The AI ​​disaster rescue system can also include a congestion monitoring unit that monitors the congestion status of evacuation centers based on the location information of disaster victims. The congestion monitoring unit collects the location information of disaster victims and transmits it to the analysis unit. The analysis unit can analyze the collected location information and evaluate the congestion status of each evacuation center. For example, if a particular evacuation center is overcrowded, the analysis unit notifies the suggestion unit of this information, and the suggestion unit can suggest other evacuation centers to disaster victims. Also, if the capacity of an evacuation center is exceeded, the suggestion unit can suggest the establishment of a new evacuation center based on this information. This makes it possible to grasp the congestion status of evacuation centers in real time and provide appropriate evacuation support.

[0116] The AI ​​disaster rescue system may also include an emotional evacuation route suggestion unit that estimates the emotions of disaster victims and adjusts the suggested evacuation routes based on those estimated emotions. The emotional evacuation route suggestion unit analyzes the emotions of disaster victims and proposes the optimal evacuation route. For example, if a disaster victim is feeling anxious, the emotional evacuation route suggestion unit can propose a safe and reassuring evacuation route. If a disaster victim is in a state of panic, the emotional evacuation route suggestion unit can also propose a route that allows for rapid evacuation. Furthermore, if a disaster victim is relaxed, the emotional evacuation route suggestion unit can provide a detailed evacuation route. This makes it possible to provide an appropriate evacuation route according to the emotional state of disaster victims.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit collects real-time data such as earthquake information and weather data. For example, the data collection unit obtains earthquake information from seismometers and weather data from weather stations. The data collection unit can also obtain road traffic information from traffic management systems and social media posts from social media platforms. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, a generative AI, to understand the situation in the disaster area. The analysis unit can analyze earthquake information to identify the epicenter and seismic intensity, and analyze weather data to predict changes in weather. It can also analyze road traffic information to understand the situation of traffic congestion and road closures. Step 3: The proposal unit proposes evacuation routes and resource allocations based on the analysis results obtained by the analysis unit. The proposal unit can use generating AI to propose optimal evacuation routes, shelter placements, and resource allocations. The proposal unit proposes optimal evacuation routes based on the situation in the disaster area and proposes shelter placements based on the capacity and accessibility of the shelters. Furthermore, it can also propose resource allocations based on the types of relief supplies and their allocation priorities.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, SNS analysis unit, and communication unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects earthquake information and weather data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal evacuation route and resource allocation. The SNS analysis unit collects SNS posts using, for example, the communication I / F 44 of the smart device 14 and analyzes them using the specific processing unit 290 of the data processing unit 12. The communication unit communicates with citizens and relief organizations using, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, SNS analysis unit, and communication unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects earthquake information and weather data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal evacuation route and resource allocation. The SNS analysis unit collects SNS posts using, for example, the communication I / F 44 of the smart glasses 214 and analyzes them using the specific processing unit 290 of the data processing unit 12. The communication unit communicates with citizens and relief organizations using, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, SNS analysis unit, and communication unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects earthquake information and weather data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal evacuation route and resource allocation. The SNS analysis unit collects SNS posts using, for example, the communication I / F 44 of the headset terminal 314 and analyzes them using the specific processing unit 290 of the data processing unit 12. The communication unit communicates with citizens and relief organizations using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, SNS analysis unit, and communication unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects earthquake information and weather data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal evacuation route and resource allocation. The SNS analysis unit collects SNS posts using, for example, the communication I / F 44 of the robot 414 and analyzes them using the specific processing unit 290 of the data processing unit 12. The communication unit communicates with citizens and relief organizations using, for example, the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) The collection unit collects real-time data such as earthquake information and weather data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes evacuation routes and resource allocations. Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as earthquake information, weather data, road traffic information, and social media posts. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data will be analyzed to understand the situation in the disaster-stricken areas. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose the optimal evacuation routes, shelter locations, and resource allocation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The facility includes a social media analysis unit that analyzes social media posts to identify the location of disaster victims and urgent requests for assistance. The system described in Appendix 1, characterized by the features described herein. (Note 6) It has a communications department to communicate with citizens and relief organizations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past disaster data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on specific regions or circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of evacuation routes and resources. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the evacuation route and resource category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of submissions regarding evacuation routes and resources. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of evacuation routes and resources. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned SNS analysis unit, We estimate user sentiment and adjust our social media analysis methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned SNS analysis unit, When analyzing social media posts, adjust the level of detail based on the importance of each post. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned SNS analysis unit, When analyzing social media posts, different analysis algorithms are applied depending on the post category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned SNS analysis unit, It estimates user sentiment and determines the priority of social media analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned SNS analysis unit, When analyzing social media posts, adjust the order of analysis based on when the posts were collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned SNS analysis unit, When analyzing social media posts, the order of analysis is adjusted based on the relevance of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned communications department, It estimates the user's emotions and adjusts the communication method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned communications department, During communication, the system selects the most suitable communication method by referring to the user's past interactions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned communications department, When communicating, provide the most relevant information by considering the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned communications department, It estimates the user's emotions and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned communications department, During communication, the system selects the optimal communication method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned communications department, During communication, provide multilingual information according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects real-time data such as earthquake information and weather data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes evacuation routes and resource allocations. Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as earthquake information, weather data, road traffic information, and social media posts. The system according to feature 1.

3. The aforementioned analysis unit, The collected data will be analyzed to understand the situation in the disaster-stricken areas. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose the optimal evacuation routes, shelter locations, and resource allocation. The system according to feature 1.

5. The aforementioned analysis unit, By analyzing social media posts, we can identify the location of disaster victims and urgent requests for assistance. The system according to feature 1.

6. It has a communications department to communicate with citizens and relief organizations. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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

9. The aforementioned collection unit is When collecting data, filtering is performed based on specific regions or circumstances. The system according to feature 1.

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

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