System

Abstract

An object of the system according to the embodiment is to quickly and effectively perform information collection and handling of a business office when a disaster occurs.SOLUTION: A system according to an embodiment includes a disaster prediction unit, a real-time information collection unit, a disaster-stricken office identification unit, a report creation unit, an action presentation unit, and a knowledge accumulation unit. The disaster-forecasting unit uses the generated AI to integrate the business office information into the geographic information system, and forecasts disasters. The real-time information collection unit collects disaster information in real time when a disaster occurs. The disaster-stricken office specification unit specifies a disaster-stricken office and aggregates peripheral information thereof. The report creation unit creates a report summarizing a situation at the time of occurrence of a disaster. The action presentation unit presents an action to be taken next. The knowledge accumulation unit accumulates past disaster response data and generates training and a response manual.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that businesses are unable to quickly and effectively gather information and respond when a disaster occurs.

[0005] The system according to the embodiment aims to enable a business to quickly and effectively collect information and respond to a disaster when it occurs. [Means for solving the problem]

[0006] The system according to the embodiment comprises a disaster prediction unit, a real-time information collection unit, a damaged office identification unit, a report creation unit, an action suggestion unit, and a knowledge accumulation unit. The disaster prediction unit uses a generation AI to integrate business establishment information into a geographic information system and perform disaster prediction. The real-time information collection unit collects disaster information in real time when a disaster occurs. The damaged office identification unit identifies damaged offices and consolidates information about their surrounding areas. The report creation unit creates a report summarizing the situation when a disaster occurs. The action suggestion unit suggests the next action to be taken. The knowledge accumulation unit accumulates past disaster response data and generates training and response manuals. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and effectively collect information and respond to disasters at business establishments when they occur. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A disaster response system according to an embodiment of the present invention is a system that realizes disaster predictions for business establishments nationwide, visualization and sharing of disaster information, and knowledge accumulation. This system integrates business establishment information into a geographic information system, and a generation AI performs disaster predictions, collects disaster information in real time, identifies affected offices, consolidates information about their surrounding areas, creates reports, suggests next actions to be taken, and accumulates knowledge. This enables the disaster response system to realize efficient and rapid disaster response for business establishments nationwide.

[0029] A disaster response system according to an embodiment includes a disaster prediction unit, a real-time information collection unit, a damaged office identification unit, a report creation unit, an action presentation unit, and a knowledge accumulation unit. The disaster prediction unit uses a generation AI to integrate business establishment information into a geographic information system and perform disaster prediction. For example, the generation AI analyzes disaster-related data such as meteorological data and earthquake data to predict the risk of a disaster. The generation AI can also analyze typhoon path predictions and earthquake occurrence probability and issue advance warnings to businesses. The real-time information collection unit uses the generation AI to collect disaster information in real time when a disaster occurs. For example, the generation AI collects disaster information from social media, news sites, and government agency announcements and immediately notifies businesses. The generation AI can also grasp the disaster situation based on real-time information at the time of the disaster. The damaged office identification unit uses the generation AI to identify damaged businesses when a disaster occurs and aggregate information about the surrounding area. For example, the generation AI collects map information, traffic information, evacuation shelter information, etc. of the affected area and provides it to businesses. The generation AI can also identify affected offices and consolidate information. The report creation unit uses the generation AI to create reports summarizing the situation at the time of a disaster and provide them to management and relevant parties. For example, the generation AI can generate reports including the damage situation, response status, and future outlook, and share them with relevant parties. The action suggestion unit uses the generation AI to generate a to-do list indicating the next actions to be taken when a disaster occurs. For example, the generation AI can provide specific instructions to offices, such as evacuation orders, safety checks, and recovery procedures. The knowledge accumulation unit uses the generation AI to accumulate past disaster response data and generate training and response manuals based on that data. For example, the generation AI can analyze past disaster response successes and failures and propose optimal response methods. This enables the disaster response system to realize efficient and rapid disaster response for offices nationwide. For example, it can issue advance warnings before a disaster occurs, collect information in real time when a disaster occurs, identify affected offices, and consolidate the information. The system also provides reports summarizing the situation to management and relevant parties, specifying the next actions to be taken.Furthermore, by accumulating past disaster response data and using that data to generate training and response manuals, the disaster response capabilities of businesses can be improved.

[0030] The disaster prediction unit uses the generation AI to combine past disaster data with current weather data to identify patterns of disaster occurrence and improve prediction accuracy. For example, the generation AI analyzes past typhoon data and current weather data to predict the typhoon's path and intensity. For example, it learns past typhoon path patterns and makes predictions based on current weather conditions. The generation AI also combines past earthquake data with current geological data to evaluate the risk of earthquake occurrence. For example, it analyzes current geological conditions based on the epicenters and seismic intensity of past earthquakes to make predictions. The generation AI also analyzes past flood data and current rainfall data to predict the risk of flood occurrence. For example, it learns past flood patterns and makes predictions based on current rainfall and topographical data. This makes it possible to identify disaster occurrence patterns and improve prediction accuracy.

[0031] The disaster prediction unit uses the generation AI to analyze geological data or urban development data, and can perform a detailed assessment of disaster risk in a specific area. For example, the generation AI analyzes geological data to assess the risk of earthquakes and landslides. For example, it predicts the disaster risk in a specific area based on the geological structure and strength of the ground. The generation AI also analyzes urban development data to assess the risk of floods and fires. For example, it predicts the disaster risk based on the city's drainage system and building density. The generation AI also combines geological data and urban development data to assess complex disaster risks. For example, it comprehensively predicts the risk of fires and floods caused by earthquakes. This allows for a detailed assessment of disaster risk in a specific area.

[0032] The real-time information collection unit uses the generation AI to analyze real-time data from drones and sensors, enabling an immediate grasp of the disaster situation. For example, the generation AI in the real-time information collection unit analyzes video data from drones to grasp the disaster situation in real time. For example, it analyzes aerial video of the disaster area to assess the damage situation. The generation AI also analyzes data from sensors to immediately grasp the disaster situation. For example, it analyzes data from seismometers and water level gauges to assess the earthquake and flood situation. The generation AI also integrates data from drones and sensors to comprehensively grasp the disaster situation. For example, it combines aerial video with data from ground sensors to assess the damage situation. This allows an immediate grasp of the disaster situation.

[0033] When the real-time information collection unit uses the generation AI to collect disaster information, it can also analyze voice data and image data to provide detailed information. For example, when the generation AI collects disaster information, the real-time information collection unit analyzes voice data to understand the situation in the disaster-stricken area. For example, it analyzes emergency calls from the disaster-stricken area and voice reports from the local area. The generation AI also analyzes image data to understand the disaster situation in detail. For example, it analyzes photos and videos of the disaster area to assess the damage situation. The generation AI also integrates voice data and image data to comprehensively understand the disaster situation. For example, it combines voice reports and photos from the local area to assess the damage situation. This makes it possible to provide detailed information.

[0034] The real-time information collection unit uses the generation AI to integrate data from different disaster information sources and provide information from a global perspective. In the real-time information collection unit, for example, the generation AI collects disaster information from international organizations and provides information from a global perspective. For example, it integrates disaster information from the United Nations and WHO. It also collects disaster information from non-governmental organizations and the generation AI provides comprehensive information. For example, it integrates information from the Red Cross and international relief organizations. It also integrates data from different disaster information sources and the generation AI grasps the disaster situation from a global perspective. For example, it combines information from international organizations and non-governmental organizations and provides it. This makes it possible to provide information from a global perspective.

[0035] The damaged office identification unit can use the generation AI to analyze the location information of damaged offices and propose the optimal evacuation route. For example, the generation AI analyzes the location information of damaged offices and proposes the optimal evacuation route. For example, it calculates the shortest route to an evacuation shelter based on map data of the affected area. The generation AI also analyzes traffic information and proposes the optimal route taking into account the congestion status of evacuation routes. For example, it calculates a detour route based on information on traffic congestion and road closures. The generation AI also analyzes real-time weather data and proposes an evacuation route that takes weather conditions into account. For example, it calculates a route that avoids the effects of heavy rain and strong winds. This makes it possible to propose the optimal evacuation route.

[0036] The damaged office identification unit can use the generation AI to analyze the status of infrastructure surrounding the damaged office and support recovery planning. For example, the generation AI analyzes the power supply status of infrastructure surrounding the damaged office and supports recovery planning. For example, it identifies priority recovery areas based on the power outage area and recovery forecast. The generation AI also analyzes the status of the communication network and supports communication recovery planning for the damaged office. For example, it determines the priority of communication recovery based on the area where communication failures have occurred and recovery forecast. The generation AI also analyzes the status of infrastructure such as water and gas and supports comprehensive recovery planning for the damaged office. For example, it integrates the recovery forecast of each infrastructure and proposes an optimal recovery schedule. This can support recovery planning.

[0037] The Damaged Office Identification Department uses drones and robots to identify damaged offices and gather information using the generation AI, allowing for a detailed understanding of the situation on site. For example, the generation AI uses drones to collect aerial footage of damaged offices to understand the detailed situation on site. For example, it analyzes the damage to buildings in the affected area and evacuation routes. The generation AI also uses robots to investigate the internal situation of damaged offices and collect detailed information. For example, it identifies the damage inside the building and the locations of evacuees. The generation AI also integrates data from drones and robots to understand the overall situation of the damaged offices. For example, it combines aerial footage with internal investigation data to assess the damage situation. This allows for a detailed understanding of the situation on site.

[0038] The damaged office identification unit can use the generation AI to work with other disaster response systems when identifying damaged offices and aggregating information. For example, the generation AI in the damaged office identification unit works with a medical support system to understand the health status of employees in damaged offices. For example, it identifies employees who need emergency medical support based on medical data. The generation AI also works with a relief supplies management system to understand the demand for supplies in damaged offices. For example, it identifies the type and quantity of supplies needed and supports their rapid supply. The generation AI also works with other disaster response systems to develop comprehensive support plans for damaged offices. For example, it proposes a plan that integrates medical support, supply of supplies, and evacuation support. This allows it to work with other disaster response systems.

[0039] The report creation department can use the generation AI to analyze the disaster situation in detail and create a report that proposes specific countermeasures to management. For example, the report creation department uses the generation AI to analyze the disaster situation in detail and create a report that proposes specific countermeasures to management. For example, the report creation department describes in detail the damage situation in the affected area and recovery plans. The generation AI also analyzes the economic impact and creates a report that proposes specific countermeasures to management. For example, it evaluates the economic losses and recovery costs caused by the disaster. The generation AI also analyzes legal risks and creates a report that proposes specific countermeasures to management. For example, it describes in detail the legal risks caused by the disaster and countermeasures. This makes it possible to create a report that proposes specific countermeasures.

[0040] The report creation unit can use the generation AI to take into account economic impacts and legal risks when analyzing a disaster situation and create a comprehensive report. For example, when the generation AI analyzes a disaster situation, the report creation unit takes into account economic impacts and creates a comprehensive report. For example, it evaluates economic losses and recovery costs caused by the disaster. The generation AI also analyzes legal risks and creates a comprehensive report. For example, it details legal risks caused by the disaster and countermeasures. The generation AI also integrates economic impacts and legal risks and creates a comprehensive report. For example, it comprehensively evaluates economic losses and legal risks caused by the disaster. This allows for the creation of a comprehensive report.

[0041] The report creation department can use the generation AI to refer to benchmark data from other companies and industries when creating reports and perform comparative analysis. For example, the generation AI refers to disaster response data from other companies and performs comparative analysis. For example, the report creation department evaluates its own response measures based on the disaster response status and recovery plans of other companies in the same industry. The generation AI also refers to benchmark data from the entire industry and performs comparative analysis. For example, the generation AI evaluates its own response measures based on the disaster response status and recovery plans of the entire industry. The generation AI also integrates benchmark data from other companies and industries and performs comprehensive comparative analysis. For example, the generation AI evaluates its own response measures based on data from multiple companies and industries. This makes it possible to perform comparative analysis.

[0042] The action suggestion unit can use the generation AI to analyze the disaster response flow and update the optimal response procedures in real time. For example, the generation AI in the action suggestion unit analyzes the disaster response flow and updates the optimal response procedures in real time. For example, the response procedures are adjusted based on the progression of the disaster and new information. The generation AI also analyzes past disaster response data and proposes the optimal response procedures. For example, the response procedures are optimized based on past successes and failures. The generation AI also analyzes real-time disaster information and dynamically updates the response procedures. For example, the response procedures are adjusted according to the damage situation and evacuation situation. This makes it possible to update the optimal response procedures in real time.

[0043] When analyzing the disaster response flow using the generation AI, the action suggestion unit can refer to past success stories and failure stories to propose the optimal response. For example, the action suggestion unit uses the generation AI to analyze past success stories and propose the optimal response. For example, it proposes a response based on procedures and methods that have been successful in past disaster responses. The generation AI can also analyze past failure stories and propose the optimal response. For example, it can propose a response to avoid procedures and methods that have failed in past disaster responses. The generation AI can also integrate success stories and failure stories to propose the optimal response. For example, it can comprehensively evaluate the factors that contributed to success and failure and propose the optimal response. This makes it possible to propose the optimal response.

[0044] The knowledge accumulation unit can use the generation AI to analyze past disaster response data and propose optimal training programs. For example, the generation AI in the knowledge accumulation unit analyzes past disaster response data and proposes optimal training programs. For example, it designs training content based on past successes and failures. The generation AI also analyzes disaster response simulation data and proposes training programs. For example, it designs practical training content based on the simulation results. The generation AI also analyzes employee skill data and proposes individually optimized training programs. For example, it designs training content according to the skill level of each employee. This makes it possible to propose optimal training programs.

[0045] When accumulating knowledge using the generation AI, the knowledge accumulation unit can evaluate the reliability of data and prioritize the accumulation of highly reliable information. For example, the knowledge accumulation unit evaluates the reliability of disaster response data collected by the generation AI and prioritizes the accumulation of highly reliable information. For example, it prioritizes information from official announcements and reliable news sources. In addition, an algorithm for evaluating data reliability is developed, and the generation AI automatically evaluates the reliability of disaster response data. For example, it evaluates based on the reliability of the information source and the consistency of the information. In addition, the generation AI integrates data from multiple sources and extracts highly reliable information. For example, it analyzes multiple news sources and social media posts to identify highly reliable information. This allows the accumulation of highly reliable information to be prioritized.

[0046] When using the generation AI to generate training and response manuals, the knowledge accumulation unit can utilize visual data to provide them in a visually easy-to-understand format. For example, the generation AI can generate training and response manuals in video format and provide them in a visually easy-to-understand format. For example, disaster response procedures can be explained using videos. The generation AI can also use illustrations to provide training and response manuals visually. For example, it can illustrate evacuation routes and response procedures. The generation AI can also integrate visual data to generate training and response manuals that are visually easy to understand. For example, it can provide them in a visually easy-to-understand format.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The disaster response system can further include an energy management unit. The energy management unit provides functions for optimizing energy consumption at business facilities when a disaster occurs. For example, the generative AI analyzes the power consumption data of business facilities and proposes optimal energy allocation to ensure the necessary energy supply. The energy management unit can also make proposals to promote the use of renewable energy. For example, it can recommend the use of solar power generation and wind power generation to improve the stability of energy supply during disasters. The energy management unit can also monitor energy consumption during disasters in real time and issue instructions to reduce energy consumption as necessary. This allows for efficient energy management during disasters.

[0049] The disaster response system can further include a communication support unit. The communication support unit provides functions to facilitate communication both inside and outside the workplace when a disaster occurs. For example, the generative AI can optimize communication methods between employees within the workplace and support rapid information sharing. The communication support unit can also make suggestions to facilitate communication with external organizations. For example, it can secure communication methods with government agencies and relief organizations and quickly transmit necessary information. The communication support unit can also diversify communication methods during a disaster and provide multiple means of communication. For example, it can utilize telephone, email, chat apps, etc. to ensure the transmission of information. This can facilitate communication during a disaster.

[0050] The disaster response system can further include a health management department. The health management department provides functions to monitor the health status of employees at a workplace in the event of a disaster and take appropriate measures. For example, the generative AI analyzes employee health data in real time and responds immediately if an abnormality is detected. The health management department can also assess health risks during a disaster and propose preventive measures. For example, it can assess the risk of infectious diseases and implement preventive measures. The health management department can also provide programs to support employees' recovery after a disaster. For example, it can implement health checks and rehabilitation programs. This helps maintain employee health and improve response capabilities in the event of a disaster.

[0051] The disaster response system can further include an education and training department. The education and training department provides functions for providing disaster response training to employees at the business. For example, the generative AI analyzes past disaster response data and designs an optimal training program. The education and training department can also provide practical training using simulations. For example, it can conduct disaster simulations using virtual reality (VR) to allow employees to experience actual disaster situations. The education and training department can also evaluate the effectiveness of the training and improve the training program as necessary. For example, it can collect feedback after the training and optimize the training content. This can improve employees' disaster response capabilities.

[0052] The disaster response system can further include a data analysis unit. The data analysis unit analyzes data collected when a disaster occurs and provides a function for identifying areas for improvement in disaster response. For example, the generative AI analyzes disaster response data and identifies response delays and problems. The data analysis unit can also evaluate the effectiveness of disaster response and propose improvement measures. For example, it can make proposals for accelerating and streamlining the response. The data analysis unit can also identify best practices for disaster response and make proposals for deployment to other business locations. For example, it can share response measures based on successful cases with other business locations. This can lead to improvements in disaster response.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The disaster prediction unit uses the generation AI to integrate business information into the geographic information system and perform disaster predictions. For example, the generation AI analyzes disaster-related data such as meteorological and earthquake data to predict the risk of disasters occurring. The generation AI can also analyze typhoon path predictions and the probability of earthquakes occurring, and issue advance warnings to businesses. Step 2: The real-time information collection unit uses the generation AI to collect disaster information in real time when a disaster occurs. For example, the generation AI collects disaster information from social media, news sites, and government agency announcements, and immediately notifies businesses. The generation AI can also grasp the disaster situation based on real-time information when a disaster occurs. Step 3: The damaged office identification unit uses the generation AI to identify affected business offices when a disaster occurs and aggregate information about the surrounding area. For example, the generation AI collects map information, traffic information, and evacuation shelter information for the affected area and provides this information to the business offices. The generation AI can also identify affected offices and aggregate information. Step 4: The report creation department uses the generation AI to create a report summarizing the situation at the time of the disaster and provide it to management and relevant parties. For example, the generation AI can generate a report including the damage situation, response status, future outlook, etc., and share it with relevant parties. Step 5: The action suggestion unit uses the generation AI to generate a to-do list showing the next actions to be taken in the event of a disaster. For example, the generation AI can provide specific instructions to businesses, such as evacuation orders, safety checks, and recovery procedures. Step 6: The knowledge accumulation unit uses the generation AI to accumulate past disaster response data and generate training and response manuals based on that data. For example, the generation AI can analyze past cases of success and failure in disaster response and propose optimal response methods.

[0055] (Example 2) A disaster response system according to an embodiment of the present invention is a system that realizes disaster predictions for business establishments nationwide, visualization and sharing of disaster information, and knowledge accumulation. This system integrates business establishment information into a geographic information system, and a generation AI performs disaster predictions, collects disaster information in real time, identifies affected offices, consolidates information about their surrounding areas, creates reports, suggests next actions to be taken, and accumulates knowledge. This enables the disaster response system to realize efficient and rapid disaster response for business establishments nationwide.

[0056] A disaster response system according to an embodiment includes a disaster prediction unit, a real-time information collection unit, a damaged office identification unit, a report creation unit, an action presentation unit, and a knowledge accumulation unit. The disaster prediction unit uses a generation AI to integrate business establishment information into a geographic information system and perform disaster prediction. For example, the generation AI analyzes disaster-related data such as meteorological data and earthquake data to predict the risk of a disaster. The generation AI can also analyze typhoon path predictions and earthquake occurrence probability and issue advance warnings to businesses. The real-time information collection unit uses the generation AI to collect disaster information in real time when a disaster occurs. For example, the generation AI collects disaster information from social media, news sites, and government agency announcements and immediately notifies businesses. The generation AI can also grasp the disaster situation based on real-time information at the time of the disaster. The damaged office identification unit uses the generation AI to identify damaged businesses when a disaster occurs and aggregate information about the surrounding area. For example, the generation AI collects map information, traffic information, evacuation shelter information, etc. of the affected area and provides it to businesses. The generation AI can also identify affected offices and consolidate information. The report creation unit uses the generation AI to create reports summarizing the situation at the time of a disaster and provide them to management and relevant parties. For example, the generation AI can generate reports including the damage situation, response status, and future outlook, and share them with relevant parties. The action suggestion unit uses the generation AI to generate a to-do list indicating the next actions to be taken when a disaster occurs. For example, the generation AI can provide specific instructions to offices, such as evacuation orders, safety checks, and recovery procedures. The knowledge accumulation unit uses the generation AI to accumulate past disaster response data and generate training and response manuals based on that data. For example, the generation AI can analyze past disaster response successes and failures and propose optimal response methods. This enables the disaster response system to realize efficient and rapid disaster response for offices nationwide. For example, it can issue advance warnings before a disaster occurs, collect information in real time when a disaster occurs, identify affected offices, and consolidate the information. The system also provides reports summarizing the situation to management and relevant parties, specifying the next actions to be taken.Furthermore, by accumulating past disaster response data and using that data to generate training and response manuals, the disaster response capabilities of businesses can be improved.

[0057] The disaster prediction unit uses the generation AI to combine past disaster data with current weather data to identify patterns of disaster occurrence and improve prediction accuracy. For example, the generation AI analyzes past typhoon data and current weather data to predict the typhoon's path and intensity. For example, it learns past typhoon path patterns and makes predictions based on current weather conditions. The generation AI also combines past earthquake data with current geological data to evaluate the risk of earthquake occurrence. For example, it analyzes current geological conditions based on the epicenters and seismic intensity of past earthquakes to make predictions. The generation AI also analyzes past flood data and current rainfall data to predict the risk of flood occurrence. For example, it learns past flood patterns and makes predictions based on current rainfall and topographical data. This makes it possible to identify disaster occurrence patterns and improve prediction accuracy.

[0058] The disaster prediction unit uses the generation AI to analyze geological data or urban development data, and can perform a detailed assessment of disaster risk in a specific area. For example, the generation AI analyzes geological data to assess the risk of earthquakes and landslides. For example, it predicts the disaster risk in a specific area based on the geological structure and strength of the ground. The generation AI also analyzes urban development data to assess the risk of floods and fires. For example, it predicts the disaster risk based on the city's drainage system and building density. The generation AI also combines geological data and urban development data to assess complex disaster risks. For example, it comprehensively predicts the risk of fires and floods caused by earthquakes. This allows for a detailed assessment of disaster risk in a specific area.

[0059] The disaster prediction unit uses the emotion estimation function to analyze residents' emotional reactions to disaster predictions and improve the acceptability of the prediction information. For example, when the generation AI provides disaster prediction information, the disaster prediction unit analyzes residents' emotional reactions in real time and evaluates the acceptability of the information. For example, it calculates an emotion score by analyzing residents' facial expressions and voices. The emotion estimation function is also used to optimize the method of providing disaster prediction information. For example, it adds positive messages to alleviate information that makes residents feel anxious. It also adjusts the timing and content of providing disaster prediction information based on residents' emotional reaction data. For example, it provides information at a time when residents are most likely to accept it. This makes it possible to improve the acceptability of disaster prediction information.

[0060] The real-time information collection unit uses the generation AI to analyze real-time data from drones and sensors, enabling an immediate grasp of the disaster situation. For example, the generation AI in the real-time information collection unit analyzes video data from drones to grasp the disaster situation in real time. For example, it analyzes aerial video of the disaster area to assess the damage situation. The generation AI also analyzes data from sensors to immediately grasp the disaster situation. For example, it analyzes data from seismometers and water level gauges to assess the earthquake and flood situation. The generation AI also integrates data from drones and sensors to comprehensively grasp the disaster situation. For example, it combines aerial video with data from ground sensors to assess the damage situation. This allows an immediate grasp of the disaster situation.

[0061] The real-time information collection unit can use the emotion estimation function to analyze the emotional reactions of users who receive disaster information and adjust the urgency of the information. For example, when the generation AI provides disaster information, the real-time information collection unit analyzes the user's emotional reactions in real time and adjusts the urgency of the information. For example, it calculates an emotion score by analyzing the user's facial expressions and voice. It also uses the emotion estimation function to adjust the way in which disaster information is provided. For example, it adds a positive message to alleviate information that makes the user feel anxious. It also adjusts the timing and content of providing disaster information based on the user's emotional reaction data. For example, it provides information at a time when the user is most receptive. This makes it possible to adjust the urgency of the information.

[0062] When the real-time information collection unit uses the generation AI to collect disaster information, it can also analyze voice data and image data to provide detailed information. For example, when the generation AI collects disaster information, the real-time information collection unit analyzes voice data to understand the situation in the disaster-stricken area. For example, it analyzes emergency calls from the disaster-stricken area and voice reports from the local area. The generation AI also analyzes image data to understand the disaster situation in detail. For example, it analyzes photos and videos of the disaster area to assess the damage situation. The generation AI also integrates voice data and image data to comprehensively understand the disaster situation. For example, it combines voice reports and photos from the local area to assess the damage situation. This makes it possible to provide detailed information.

[0063] The real-time information collection unit uses the generation AI to integrate data from different disaster information sources and provide information from a global perspective. In the real-time information collection unit, for example, the generation AI collects disaster information from international organizations and provides information from a global perspective. For example, it integrates disaster information from the United Nations and WHO. It also collects disaster information from non-governmental organizations and the generation AI provides comprehensive information. For example, it integrates information from the Red Cross and international relief organizations. It also integrates data from different disaster information sources and the generation AI grasps the disaster situation from a global perspective. For example, it combines information from international organizations and non-governmental organizations and provides it. This makes it possible to provide information from a global perspective.

[0064] The damaged office identification unit can use the generation AI to analyze the location information of damaged offices and propose the optimal evacuation route. For example, the generation AI analyzes the location information of damaged offices and proposes the optimal evacuation route. For example, it calculates the shortest route to an evacuation shelter based on map data of the affected area. The generation AI also analyzes traffic information and proposes the optimal route taking into account the congestion status of evacuation routes. For example, it calculates a detour route based on information on traffic congestion and road closures. The generation AI also analyzes real-time weather data and proposes an evacuation route that takes weather conditions into account. For example, it calculates a route that avoids the effects of heavy rain and strong winds. This makes it possible to propose the optimal evacuation route.

[0065] The damaged office identification unit can use the generation AI to analyze the status of infrastructure surrounding the damaged office and support recovery planning. For example, the generation AI analyzes the power supply status of infrastructure surrounding the damaged office and supports recovery planning. For example, it identifies priority recovery areas based on the power outage area and recovery forecast. The generation AI also analyzes the status of the communication network and supports communication recovery planning for the damaged office. For example, it determines the priority of communication recovery based on the area where communication failures have occurred and recovery forecast. The generation AI also analyzes the status of infrastructure such as water and gas and supports comprehensive recovery planning for the damaged office. For example, it integrates the recovery forecast of each infrastructure and proposes an optimal recovery schedule. This can support recovery planning.

[0066] The Damaged Office Identification Department uses drones and robots to identify damaged offices and gather information using the generation AI, allowing for a detailed understanding of the situation on site. For example, the generation AI uses drones to collect aerial footage of damaged offices to understand the detailed situation on site. For example, it analyzes the damage to buildings in the affected area and evacuation routes. The generation AI also uses robots to investigate the internal situation of damaged offices and collect detailed information. For example, it identifies the damage inside the building and the locations of evacuees. The generation AI also integrates data from drones and robots to understand the overall situation of the damaged offices. For example, it combines aerial footage with internal investigation data to assess the damage situation. This allows for a detailed understanding of the situation on site.

[0067] The damaged office identification unit can use the generation AI to work with other disaster response systems when identifying damaged offices and aggregating information. For example, the generation AI in the damaged office identification unit works with a medical support system to understand the health status of employees in damaged offices. For example, it identifies employees who need emergency medical support based on medical data. The generation AI also works with a relief supplies management system to understand the demand for supplies in damaged offices. For example, it identifies the type and quantity of supplies needed and supports their rapid supply. The generation AI also works with other disaster response systems to develop comprehensive support plans for damaged offices. For example, it proposes a plan that integrates medical support, supply of supplies, and evacuation support. This allows it to work with other disaster response systems.

[0068] The affected office identification unit can use the emotion estimation function to monitor the emotions of employees in affected offices in real time and propose appropriate support measures. For example, the affected office identification unit uses a generative AI to monitor the emotions of employees in affected offices in real time and propose appropriate support measures. For example, it can analyze employees' facial expressions and voices to calculate an emotion score and propose psychological support. It can also use the emotion estimation function to evaluate employees' stress levels and propose support measures to reduce stress. For example, it can propose relaxation methods or the provision of counseling. It can also propose appropriate communication methods based on employees' emotional response data. For example, it can suggest messages and timing that employees will find most receptive. This makes it possible to propose appropriate support measures.

[0069] The report creation department can use the generation AI to analyze the disaster situation in detail and create a report that proposes specific countermeasures to management. For example, the report creation department uses the generation AI to analyze the disaster situation in detail and create a report that proposes specific countermeasures to management. For example, the report creation department describes in detail the damage situation in the affected area and recovery plans. The generation AI also analyzes the economic impact and creates a report that proposes specific countermeasures to management. For example, it evaluates the economic losses and recovery costs caused by the disaster. The generation AI also analyzes legal risks and creates a report that proposes specific countermeasures to management. For example, it describes in detail the legal risks caused by the disaster and countermeasures. This makes it possible to create a report that proposes specific countermeasures.

[0070] The report creation unit can use the generation AI to take into account economic impacts and legal risks when analyzing a disaster situation and create a comprehensive report. For example, when the generation AI analyzes a disaster situation, the report creation unit takes into account economic impacts and creates a comprehensive report. For example, it evaluates economic losses and recovery costs caused by the disaster. The generation AI also analyzes legal risks and creates a comprehensive report. For example, it details legal risks caused by the disaster and countermeasures. The generation AI also integrates economic impacts and legal risks and creates a comprehensive report. For example, it comprehensively evaluates economic losses and legal risks caused by the disaster. This allows for the creation of a comprehensive report.

[0071] The report creation unit can use the emotion estimation function to analyze the emotional reactions of management who receive the report and optimize the report content. For example, the report creation unit uses a generation AI to analyze the emotional reactions of management who receive the report in real time and optimize the report content. For example, it calculates an emotion score by analyzing the facial expressions and voice of management. It also uses the emotion estimation function to adjust the report content. For example, it adds a positive message to alleviate information that makes management feel anxious. It also adjusts the timing and content of the report based on the emotional reaction data of management. For example, it provides the report at a time when management is most receptive. This makes it possible to optimize the report content.

[0072] The report creation department can use the generation AI to refer to benchmark data from other companies and industries when creating reports and perform comparative analysis. For example, the generation AI refers to disaster response data from other companies and performs comparative analysis. For example, the report creation department evaluates its own response measures based on the disaster response status and recovery plans of other companies in the same industry. The generation AI also refers to benchmark data from the entire industry and performs comparative analysis. For example, the generation AI evaluates its own response measures based on the disaster response status and recovery plans of the entire industry. The generation AI also integrates benchmark data from other companies and industries and performs comprehensive comparative analysis. For example, the generation AI evaluates its own response measures based on data from multiple companies and industries. This makes it possible to perform comparative analysis.

[0073] The action suggestion unit can use the generation AI to analyze the disaster response flow and update the optimal response procedures in real time. For example, the generation AI in the action suggestion unit analyzes the disaster response flow and updates the optimal response procedures in real time. For example, the response procedures are adjusted based on the progression of the disaster and new information. The generation AI also analyzes past disaster response data and proposes the optimal response procedures. For example, the response procedures are optimized based on past successes and failures. The generation AI also analyzes real-time disaster information and dynamically updates the response procedures. For example, the response procedures are adjusted according to the damage situation and evacuation situation. This makes it possible to update the optimal response procedures in real time.

[0074] When analyzing the disaster response flow using the generation AI, the action suggestion unit can refer to past success stories and failure stories to propose the optimal response. For example, the action suggestion unit uses the generation AI to analyze past success stories and propose the optimal response. For example, it proposes a response based on procedures and methods that have been successful in past disaster responses. The generation AI can also analyze past failure stories and propose the optimal response. For example, it can propose a response to avoid procedures and methods that have failed in past disaster responses. The generation AI can also integrate success stories and failure stories to propose the optimal response. For example, it can comprehensively evaluate the factors that contributed to success and failure and propose the optimal response. This makes it possible to propose the optimal response.

[0075] The action suggestion unit can use the emotion estimation function to analyze the emotions of employees following the disaster response flow and suggest measures to reduce stress. For example, the action suggestion unit uses a generation AI to analyze the emotions of employees following the disaster response flow in real time and suggest measures to reduce stress. For example, it calculates an emotion score by analyzing the employee's facial expressions and voice. It also uses the emotion estimation function to evaluate the employee's stress level and suggest measures to reduce stress. For example, it suggests providing relaxation methods or counseling. It also suggests appropriate communication methods based on the employee's emotional response data. For example, it suggests messages and timing that are most receptive to employees. This makes it possible to suggest measures to reduce stress.

[0076] The knowledge accumulation unit can use the generation AI to analyze past disaster response data and propose optimal training programs. For example, the generation AI in the knowledge accumulation unit analyzes past disaster response data and proposes optimal training programs. For example, it designs training content based on past successes and failures. The generation AI also analyzes disaster response simulation data and proposes training programs. For example, it designs practical training content based on the simulation results. The generation AI also analyzes employee skill data and proposes individually optimized training programs. For example, it designs training content according to the skill level of each employee. This makes it possible to propose optimal training programs.

[0077] When accumulating knowledge using the generation AI, the knowledge accumulation unit can evaluate the reliability of data and prioritize the accumulation of highly reliable information. For example, the knowledge accumulation unit evaluates the reliability of disaster response data collected by the generation AI and prioritizes the accumulation of highly reliable information. For example, it prioritizes information from official announcements and reliable news sources. In addition, an algorithm for evaluating data reliability is developed, and the generation AI automatically evaluates the reliability of disaster response data. For example, it evaluates based on the reliability of the information source and the consistency of the information. In addition, the generation AI integrates data from multiple sources and extracts highly reliable information. For example, it analyzes multiple news sources and social media posts to identify highly reliable information. This allows the accumulation of highly reliable information to be prioritized.

[0078] The knowledge accumulation unit can use the emotion estimation function to analyze the emotions of training participants and evaluate the effectiveness of the training program. For example, the knowledge accumulation unit uses a generation AI to analyze the emotions of training participants in real time and evaluate the effectiveness of the training program. For example, it analyzes the participants' facial expressions and voices to calculate an emotion score. The emotion estimation function can also be used to evaluate the effectiveness of the training program. For example, it can evaluate the participants' stress levels and satisfaction and adjust the training content. It can also identify areas for improvement in the training program based on the emotional response data of the training participants. For example, it can strengthen the training content that participants find most effective. This makes it possible to evaluate the effectiveness of the training program.

[0079] When using the generation AI to generate training and response manuals, the knowledge accumulation unit can utilize visual data to provide them in a visually easy-to-understand format. For example, the generation AI can generate training and response manuals in video format and provide them in a visually easy-to-understand format. For example, disaster response procedures can be explained using videos. The generation AI can also use illustrations to provide training and response manuals visually. For example, it can illustrate evacuation routes and response procedures. The generation AI can also integrate visual data to generate training and response manuals that are visually easy to understand. For example, it can provide them in a visually easy-to-understand format.

[0080] The knowledge accumulation unit can use the emotion estimation function to monitor the emotions of training participants in real time and evaluate the effectiveness of the training program. For example, the knowledge accumulation unit uses a generation AI to monitor the emotions of training participants in real time and evaluate the effectiveness of the training program. For example, it analyzes the participants' facial expressions and voices to calculate an emotion score. It also uses the emotion estimation function to evaluate the effectiveness of the training program. For example, it evaluates the participants' stress levels and satisfaction and adjusts the training content. It also identifies areas for improvement in the training program based on the emotional response data of the training participants. For example, it strengthens the training content that participants find most effective. This makes it possible to evaluate the effectiveness of the training program.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The disaster response system can further include an energy management unit. The energy management unit provides functions for optimizing energy consumption at business facilities when a disaster occurs. For example, the generative AI analyzes the power consumption data of business facilities and proposes optimal energy allocation to ensure the necessary energy supply. The energy management unit can also make proposals to promote the use of renewable energy. For example, it can recommend the use of solar power generation and wind power generation to improve the stability of energy supply during disasters. The energy management unit can also monitor energy consumption during disasters in real time and issue instructions to reduce energy consumption as necessary. This allows for efficient energy management during disasters.

[0083] The disaster response system can further include a communication support unit. The communication support unit provides functions to facilitate communication both inside and outside the workplace when a disaster occurs. For example, the generative AI can optimize communication methods between employees within the workplace and support rapid information sharing. The communication support unit can also make suggestions to facilitate communication with external organizations. For example, it can secure communication methods with government agencies and relief organizations and quickly transmit necessary information. The communication support unit can also diversify communication methods during a disaster and provide multiple means of communication. For example, it can utilize telephone, email, chat apps, etc. to ensure the transmission of information. This can facilitate communication during a disaster.

[0084] The disaster response system can further include a psychological support unit. The psychological support unit provides psychological support to employees at the workplace when a disaster occurs. For example, the generative AI can monitor employees' stress levels in real time and provide psychological support as needed. The psychological support unit can also make suggestions to maintain employees' mental health. For example, it can recommend relaxation methods or counseling. The psychological support unit can also provide programs to support employees' psychological recovery after a disaster. For example, it can implement stress management and mental health training. This can help maintain employees' psychological health and improve their ability to respond to disasters.

[0085] The disaster response system can further include a health management department. The health management department provides functions to monitor the health status of employees at a workplace in the event of a disaster and take appropriate measures. For example, the generative AI analyzes employee health data in real time and responds immediately if an abnormality is detected. The health management department can also assess health risks during a disaster and propose preventive measures. For example, it can assess the risk of infectious diseases and implement preventive measures. The health management department can also provide programs to support employees' recovery after a disaster. For example, it can implement health checks and rehabilitation programs. This helps maintain employee health and improve response capabilities in the event of a disaster.

[0086] The disaster response system can further include an education and training department. The education and training department provides functions for providing disaster response training to employees at the business. For example, the generative AI analyzes past disaster response data and designs an optimal training program. The education and training department can also provide practical training using simulations. For example, it can conduct disaster simulations using virtual reality (VR) to allow employees to experience actual disaster situations. The education and training department can also evaluate the effectiveness of the training and improve the training program as necessary. For example, it can collect feedback after the training and optimize the training content. This can improve employees' disaster response capabilities.

[0087] The disaster response system can also use emotion estimation functions to analyze the emotional reactions of residents who receive disaster information, improving the acceptability of the information. For example, when the generation AI provides disaster prediction information, it can analyze residents' emotional reactions in real time to evaluate how easily the information is accepted. For example, it can calculate an emotion score by analyzing residents' facial expressions and voice. The emotion estimation function can also be used to optimize the way disaster prediction information is provided. For example, it can add positive messages to alleviate information that makes residents feel anxious. It can also adjust the timing and content of providing disaster prediction information based on residents' emotional reaction data. For example, it can provide information at a time when residents are most likely to accept it. This can improve the acceptability of disaster prediction information.

[0088] The disaster response system can also use an emotion estimation function to analyze the emotional reactions of users who receive disaster information and adjust the urgency of the information. For example, when the generation AI provides disaster information, it can analyze the user's emotional reactions in real time and adjust the urgency of the information. For example, it can calculate an emotion score by analyzing the user's facial expressions and voice. The emotion estimation function can also be used to adjust the way disaster information is provided. For example, it can add a positive message to alleviate information that makes the user feel anxious. It can also adjust the timing and content of disaster information provided based on the user's emotional reaction data. For example, it can provide information at a time when the user is most receptive. This makes it possible to adjust the urgency of the information.

[0089] The disaster response system can also use emotion estimation functions to monitor the emotions of employees in affected offices in real time and propose appropriate support measures. For example, the generative AI can monitor the emotions of employees in affected offices in real time and propose appropriate support measures. For example, it can analyze employees' facial expressions and voices to calculate an emotion score and propose psychological support. The emotion estimation function can also be used to evaluate employees' stress levels and propose support measures to reduce stress. For example, it can suggest relaxation methods or the provision of counseling. It can also suggest appropriate communication methods based on employees' emotional response data. For example, it can suggest the messages and timing that employees will find most receptive. This makes it possible to propose appropriate support measures.

[0090] The disaster response system can also use the emotion estimation function to monitor the emotions of training participants in real time and evaluate the effectiveness of the training program. For example, the generative AI can monitor the emotions of training participants in real time and evaluate the effectiveness of the training program. For example, it can analyze the participants' facial expressions and voices to calculate an emotion score. The emotion estimation function can also be used to evaluate the effectiveness of the training program. For example, it can evaluate the participants' stress levels and satisfaction and adjust the training content. It can also identify areas for improvement in the training program based on the emotional response data of the training participants. For example, it can strengthen the training content that participants find most effective. This makes it possible to evaluate the effectiveness of the training program.

[0091] The disaster response system can further include a data analysis unit. The data analysis unit analyzes data collected when a disaster occurs and provides a function for identifying areas for improvement in disaster response. For example, the generative AI analyzes disaster response data and identifies response delays and problems. The data analysis unit can also evaluate the effectiveness of disaster response and propose improvement measures. For example, it can make proposals for accelerating and streamlining the response. The data analysis unit can also identify best practices for disaster response and make proposals for deployment to other business locations. For example, it can share response measures based on successful cases with other business locations. This can lead to improvements in disaster response.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The disaster prediction unit uses the generation AI to integrate business information into the geographic information system and perform disaster predictions. For example, the generation AI analyzes disaster-related data such as meteorological and earthquake data to predict the risk of disasters occurring. The generation AI can also analyze typhoon path predictions and the probability of earthquakes occurring, and issue advance warnings to businesses. Step 2: The real-time information collection unit uses the generation AI to collect disaster information in real time when a disaster occurs. For example, the generation AI collects disaster information from social media, news sites, and government agency announcements, and immediately notifies businesses. The generation AI can also grasp the disaster situation based on real-time information when a disaster occurs. Step 3: The damaged office identification unit uses the generation AI to identify affected business offices when a disaster occurs and aggregate information about the surrounding area. For example, the generation AI collects map information, traffic information, and evacuation shelter information for the affected area and provides this information to the business offices. The generation AI can also identify affected offices and aggregate information. Step 4: The report creation department uses the generation AI to create a report summarizing the situation at the time of the disaster and provide it to management and relevant parties. For example, the generation AI can generate a report including the damage situation, response status, future outlook, etc., and share it with relevant parties. Step 5: The action suggestion unit uses the generation AI to generate a to-do list showing the next actions to be taken in the event of a disaster. For example, the generation AI can provide specific instructions to businesses, such as evacuation orders, safety checks, and recovery procedures. Step 6: The knowledge accumulation unit uses the generation AI to accumulate past disaster response data and generate training and response manuals based on that data. For example, the generation AI can analyze past cases of success and failure in disaster response and propose optimal response methods.

[0094] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a 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.

[0129] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A disaster prediction department that uses generation AI to integrate business information into a geographic information system and make disaster predictions; a real-time information collection unit that collects disaster information in real time when a disaster occurs; a disaster-affected office identification department that identifies disaster-affected offices and collects information about their surrounding areas; A report writing department that creates reports summarizing the situation when a disaster occurs; an action suggestion unit that suggests the next action to be taken; A knowledge accumulation unit that accumulates past disaster response data and generates training and response manuals. A system characterized by:

2. The disaster prediction unit The generation AI is used to combine the past disaster data with current weather data to identify patterns of disaster occurrence and improve forecast accuracy.

2. The system of claim 1.

3. The real-time information collection unit The generative AI will be used to analyze real-time data from drones and sensors to instantly grasp the disaster situation.

2. The system of claim 1.

4. The disaster-affected office identification department The generation AI is used to analyze the location information of the affected office and propose the optimal evacuation route.

2. The system of claim 1.

5. The report creation unit The generation AI is used to analyze the disaster situation in detail and create the report proposing specific countermeasures to management.

2. The system of claim 1.

6. The action presentation unit The generative AI will be used to analyze disaster response flows and update optimal response procedures in real time.

2. The system of claim 1.

7. The knowledge accumulation unit The generative AI is used to analyze the past disaster response data and propose optimal training programs.

2. The system of claim 1.

8. The disaster prediction unit Analyze residents' emotional reactions to the disaster forecasts to improve the acceptability of the forecast information.

2. The system of claim 1.

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