system

Abstract

The system according to this embodiment aims to facilitate the rapid and efficient application of insurance and subsidies after a disaster. [Solution] The system according to the embodiment comprises an imaging unit, an analysis unit, a generation unit, and a submission unit. The imaging unit captures data of the disaster area. The analysis unit analyzes the data captured by the imaging unit and evaluates the extent of the damage. The generation unit automatically generates insurance and subsidy application documents based on the damage evaluation by the analysis unit. The submission unit submits the application documents generated by the generation unit to insurance companies and the government.

Description

Technical Field

[0006]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that the application for insurance and subsidies after a disaster was complicated and time-consuming.

[0005] The system according to the embodiment aims to quickly and efficiently apply for insurance and subsidies after a disaster.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an imaging unit, an analysis unit, a generation unit, and a submission unit. The imaging unit captures data of the disaster area. The analysis unit analyzes the data captured by the imaging unit and evaluates the extent of the damage. The generation unit automatically generates insurance and subsidy application documents based on the damage evaluation by the analysis unit. The submission unit submits the application documents generated by the generation unit to insurance companies and the government. [Effects of the Invention]

[0007] The system according to this embodiment enables the rapid and efficient application of insurance and subsidies after a disaster. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The post-disaster insurance and subsidy application system according to an embodiment of the present invention is a system that captures data of the disaster-stricken area with a 360-degree camera, analyzes the data with AI to evaluate the extent of the damage, and automatically generates and submits insurance and subsidy application documents. This system enables the insurance and subsidy application process to be carried out quickly and efficiently by capturing data of the disaster-stricken area, analyzing it with AI, and automatically generating and submitting application documents. For example, data is captured using a 360-degree camera in the disaster-stricken area after a disaster. For example, the interior and exterior of damaged houses are captured in detail with a 360-degree camera. This data is input into the AI. Next, the AI ​​analyzes the input data and evaluates the extent of the damage. For example, the AI ​​analyzes the extent of collapse and damage to houses and performs a quantitative evaluation. Based on the evaluation results, insurance and subsidy application documents are automatically generated. For example, the insurance amount and subsidy amount according to the extent of the damage are calculated and entered into the application documents. Finally, the generated application documents are submitted to insurance companies and the government to complete the application process. This system allows disaster victims to receive insurance payments and subsidies quickly. Furthermore, insurance companies and governments can address the shortage of personnel for damage assessment and streamline procedures. In addition, by utilizing AI-generated data, damage assessments can be performed quickly and accurately through the analysis of video data, and detailed reports can be automatically generated. This will enable faster and more efficient support for disaster victims. As a result, post-disaster insurance and subsidy application systems will be able to provide support to disaster victims quickly and efficiently.

[0029] The post-disaster insurance and subsidy application system according to this embodiment comprises a shooting unit, an analysis unit, a generation unit, and a submission unit. The shooting unit captures data of the disaster area. Data of the disaster area includes, but is not limited to, photographs, videos, and sensor data. The shooting unit captures detailed data of the disaster area using, for example, a 360-degree camera. For example, the interior and exterior of damaged houses can be captured in detail with a 360-degree camera. The shooting unit can also capture a wide area of ​​the disaster using a drone. For example, a drone can be flown to capture the entire disaster area from above. Furthermore, the shooting unit can collect environmental data of the disaster area using sensors. For example, environmental data such as temperature, humidity, and atmospheric pressure can be collected by sensors. The analysis unit analyzes the data captured by the shooting unit and evaluates the extent of the damage. The analysis is performed by, for example, image analysis, data mining, and machine learning algorithms, but is not limited to these methods. For example, the analysis unit can use image analysis techniques to evaluate the extent of collapse and damage to houses. The analysis unit can also use data mining techniques to extract damage patterns. For example, common damage patterns can be identified from data in disaster-stricken areas. Furthermore, the analysis unit can predict damage using machine learning algorithms. For example, it can predict future damage based on past data. The generation unit automatically generates insurance and subsidy application documents based on the damage assessment by the analysis unit. Automatic generation is performed by methods such as template-based generation and AI generation, but is not limited to these examples. For example, the generation unit can automatically generate application documents using template-based generation. The generation unit can also automatically generate application documents using AI. For example, the AI ​​can calculate insurance and subsidy amounts according to the damage situation and include them in the application documents. Furthermore, the generation unit can automatically generate detailed reports using generation AI. For example, the generation AI can create a detailed report on the damage situation and attach it to the application documents. The submission unit submits the application documents generated by the generation unit to insurance companies and the government.Submissions may be made by methods such as email, online forms, or API integration, but are not limited to these examples. For instance, the submitting unit can send application documents to insurance companies or governments via email. Alternatively, the submitting unit can submit application documents using online forms. For example, it can access the insurance company or government website, fill out the online form, and submit it. Furthermore, the submitting unit can submit application documents using API integration. For example, it can integrate with the insurance company or government system via API to automatically send application documents. This allows the post-disaster insurance and subsidy application system according to this embodiment to provide support to disaster victims quickly and efficiently.

[0030] The photography unit captures data from the disaster area. This data includes, but is not limited to, photographs, videos, and sensor data. The photography unit can capture detailed data from the disaster area using, for example, 360-degree cameras. Specifically, 360-degree cameras can capture detailed images of the interior and exterior of damaged houses. 360-degree cameras have a wide field of view and can capture the entire disaster area at once, making them very effective in understanding the full extent of the damage. The photography unit can also use drones to photograph wide areas of the disaster. Drones provide an aerial view, allowing for the assessment of damage that is not visible from the ground. For example, a drone can be flown to photograph the entire disaster area from above. This allows for a rapid assessment of the extent of the damage over a wide area. Furthermore, the photography unit can collect environmental data from the disaster area using sensors. For example, sensors can collect environmental data such as temperature, humidity, and atmospheric pressure. This environmental data is important for a more detailed understanding of the situation in the disaster area. For example, changes in temperature and humidity can help assess the sanitary conditions and the risk of secondary disasters in the disaster area. The camera crew can collect this data in real time and transmit it to a central database. This allows for a quick and accurate assessment of the situation in the disaster area, enabling appropriate responses.

[0031] The analysis unit analyzes data captured by the imaging unit to assess the extent of the damage. The analysis is performed using methods such as image analysis, data mining, and machine learning algorithms, but is not limited to these examples. Specifically, image analysis technology can be used to assess the degree of collapse and damage to buildings. Image analysis technology can analyze photographs and videos of the disaster area and automatically detect damaged areas and the extent of collapse. For example, image analysis algorithms can identify structural damage to buildings and assess the extent and severity of that damage. The analysis unit can also extract damage patterns using data mining technology. Data mining technology can extract useful information from large amounts of data and identify common patterns and trends in damage. For example, it can find common damage patterns from data of the disaster area. Furthermore, the analysis unit can predict damage using machine learning algorithms. Machine learning algorithms can predict future damage based on past data. For example, a model trained on past disaster data can be used to predict the probability of damage occurring in specific areas or under specific conditions. This allows the analysis unit to quickly and accurately analyze the collected data and assess the extent of the damage. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit automatically generates insurance and subsidy application documents based on the damage assessment performed by the analysis unit. Automatic generation is performed using methods such as template-based generation and AI-based generation, but is not limited to these examples. Specifically, the generation unit can automatically generate application documents using template-based generation. Template-based generation is a method of quickly and accurately creating application documents by embedding analysis results into predefined templates. For example, insurance and subsidy amounts based on the damage can be automatically calculated based on the template and included in the application documents. The generation unit can also automatically generate application documents using AI. The AI ​​can calculate insurance and subsidy amounts based on the damage and include them in the application documents. For example, the AI ​​can create a detailed report of the damage and attach it to the application documents. Furthermore, the generation unit can automatically generate detailed reports using generation AI. The generation AI can create a detailed report of the damage and attach it to the application documents. For example, the generation AI can analyze photos and videos of the damage and create a detailed report. This allows the generation unit to quickly and accurately automatically generate application documents, reducing the burden on disaster victims.

[0033] The submission unit submits the application documents generated by the generation unit to insurance companies and governments. Submissions are made by methods such as email, online forms, and API integration, but are not limited to these. Specifically, the submission unit can send application documents to insurance companies and governments using email. Email is a means of sending application documents quickly and reliably, and insurance company and government officials can immediately review them. The submission unit can also submit application documents using online forms. Online forms are a method of accessing the insurance company or government website, filling out the application documents, and submitting them. For example, the submission unit can access the insurance company or government website, fill out the application documents on the online form, and submit them. Furthermore, the submission unit can also submit application documents using API integration. API integration is a method of integrating with the insurance company or government system and automatically sending application documents. For example, the submission unit can integrate with the insurance company or government system via API and automatically send application documents. This allows the submission unit to submit application documents quickly and reliably, and to efficiently provide support to disaster victims. In addition, the submission unit can track the status of the application documents after submission and notify the victims. For example, information such as whether the application documents have been received, whether the review is in progress, and whether the results have been released can be notified to the disaster victims. This allows the submitting department to keep the disaster victims informed of the status of their application at all times, enabling them to proceed with the application process with peace of mind.

[0034] The generation unit can automatically generate detailed reports using a generation AI. For example, the generation unit can create a detailed report on the damage situation using the generation AI. For example, the generation AI can create a detailed report on the damage situation and attach it to the application documents. The generation unit can also generate detailed reports on the damage situation quickly and accurately using the generation AI. For example, the generation AI can quickly and accurately create a detailed report on the damage situation and attach it to the application documents. Furthermore, the generation unit can automatically update the detailed report on the damage situation using the generation AI. For example, the generation AI can automatically update the detailed report on the damage situation and reflect the latest information in the application documents. In this way, detailed reports can be generated quickly and accurately by using the generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input damage situation data into the generation AI and have the generation AI execute the generation of a detailed report.

[0035] The imaging unit can capture detailed data of the disaster area. For example, the imaging unit can use a 360-degree camera to capture detailed data of the disaster area. For example, it can capture detailed images of the interior and exterior of damaged houses with a 360-degree camera. The imaging unit can also use a drone to photograph a wide area of ​​the disaster. For example, it can fly a drone to photograph the entire disaster area from above. Furthermore, the imaging unit can collect environmental data of the disaster area using sensors. For example, it can collect environmental data such as temperature, humidity, and atmospheric pressure using sensors. By capturing detailed data of the disaster area, it becomes possible to assess the extent of the damage more accurately. Some or all of the above processing in the imaging unit may be performed using AI, for example, or without AI. For example, the imaging unit can input data captured by a 360-degree camera into a generating AI and have the generating AI perform a detailed analysis of the captured data.

[0036] The analysis unit can quantitatively evaluate the extent of house collapse and damage. For example, the analysis unit can use image analysis technology to evaluate the extent of house collapse and damage. For example, it can use image analysis technology to quantify the extent of house collapse and evaluate the extent of damage. The analysis unit can also extract damage patterns using data mining technology. For example, it can find common damage patterns from data of the affected area. Furthermore, the analysis unit can predict damage using machine learning algorithms. For example, it can predict future damage based on past data. This makes it possible to accurately grasp the extent of damage by quantitatively evaluating the extent of house collapse and damage. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the extent of house collapse and damage into a generating AI and have the generating AI perform a quantitative evaluation.

[0037] The generation unit can calculate insurance and subsidy amounts according to the extent of the damage. For example, the generation unit can calculate insurance and subsidy amounts according to the extent of the damage and include them in the application documents. The generation unit can also quickly calculate insurance and subsidy amounts according to the extent of the damage. For example, it can quickly calculate insurance and subsidy amounts according to the extent of the damage and include them in the application documents. Furthermore, the generation unit can automatically update insurance and subsidy amounts according to the extent of the damage. For example, it can automatically update insurance and subsidy amounts according to the extent of the damage and reflect the latest information in the application documents. This ensures that appropriate compensation is provided quickly by calculating insurance and subsidy amounts according to the extent of the damage. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input damage data into a generation AI and have the generation AI calculate insurance and subsidy amounts.

[0038] The submission unit can submit the generated application documents to insurance companies and governments. For example, the submission unit can send the application documents to insurance companies and governments via email. The submission unit can also submit the application documents using online forms. For example, it can access the insurance company's or government's website, fill out the online form, and submit it. Furthermore, the submission unit can submit the application documents using API integration. For example, it can integrate with the insurance company's or government's system via API and automatically send the application documents. This allows for faster receipt of insurance payments and subsidies by quickly submitting the generated application documents. Some or all of the above processes in the submission unit may be performed using AI, for example, or not using AI. For example, the submission unit can input the generated application documents into a generation AI and have the generation AI execute the timing and method of submission.

[0039] The camera unit can select the optimal shooting method when taking pictures, taking into account the environmental conditions of the disaster area. For example, the camera unit can use a waterproof cover when taking pictures in rainy weather. The camera unit can also use an infrared camera when taking pictures at night. Furthermore, the camera unit can use a tripod to ensure stable shooting when there are strong winds. By selecting the optimal shooting method according to the environmental conditions of the disaster area, accurate data can be obtained. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input environmental condition data of the disaster area into a generating AI and have the generating AI select the optimal shooting method.

[0040] The camera unit can automatically identify and focus on important areas in the disaster zone during filming. For example, it can automatically identify and photograph in detail the collapsed parts of houses. The camera unit can also prioritize filming areas with severe damage. Furthermore, it can focus on filming areas directly related to the lives of disaster victims (such as kitchens and bedrooms). By focusing on filming important areas in the disaster zone, it becomes possible to accurately grasp the extent of the damage. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input data from the disaster zone into a generating AI and have the generating AI identify and film important areas.

[0041] The camera unit can prioritize photographing highly relevant locations while considering the geographical location information of the disaster area. For example, the camera unit can prioritize photographing the central area of ​​the disaster area. The camera unit can also photograph surrounding areas of the disaster area. Furthermore, the camera unit can identify and photograph important locations while considering the geographical characteristics of the disaster area. By considering the geographical location information of the disaster area while taking photographs, highly relevant data can be obtained. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input geographical location data of the disaster area into a generating AI and have the generating AI identify and photograph highly relevant locations.

[0042] The photography team can analyze social media activity in the disaster area during filming and photograph relevant locations. For example, the photography team can prioritize photographing locations that are trending on social media. The photography team can also use photos and videos posted by disaster victims as reference for filming. Furthermore, the photography team can analyze social media hashtags to identify and photograph relevant locations. By analyzing social media activity and filming accordingly, important locations in the disaster area can be identified. Some or all of the above processing by the photography team may be performed using AI, or not. For example, the photography team can input social media data into a generating AI and have the generating AI identify and photograph relevant locations.

[0043] The analysis unit can adjust the level of detail of the analysis based on the severity of the damage. For example, the analysis unit can perform a detailed analysis on areas with significant damage. The analysis unit can also perform a simplified analysis on areas with minor damage. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the severity of the damage. By adjusting the level of detail of the analysis according to the severity of the damage, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input damage severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of damage during the analysis. For example, the analysis unit can apply a structural analysis algorithm for the collapse of houses. For example, the analysis unit can apply a flood inundation analysis algorithm for flood damage. For example, the analysis unit can apply a burnout analysis algorithm for fire damage. For example, the analysis unit can apply a burnout analysis algorithm for fire damage. By applying an analysis algorithm according to the category of damage, accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input damage category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the timing of the damage. For example, the analysis unit can prioritize the analysis of the most recent damage. The analysis unit can also postpone the analysis of past damage. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the timing of the damage. This allows for a rapid response to the latest damage by determining the priority of analysis based on the timing of the damage. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input damage timing data into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the damages during the analysis. For example, the analysis unit can prioritize the analysis of direct damages. The analysis unit can also postpone the analysis of indirect damages. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the damages. By adjusting the order of analysis based on the relevance of the damages, damages with high relevance can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input damage relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The generation unit can adjust the level of detail in the report based on the severity of the damage during generation. For example, the generation unit can generate a detailed report for serious damage. The generation unit can also generate a concise report for minor damage. Furthermore, the generation unit can adjust the level of detail in the report in stages according to the severity of the damage. This allows for the provision of appropriate information by adjusting the level of detail in the report according to the severity of the damage. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input damage severity data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0048] The generation unit can apply different generation algorithms depending on the category of damage during generation. For example, the generation unit can apply a structural analysis algorithm for house collapse. For example, the generation unit can apply a flood inundation analysis algorithm for flood damage. For example, the generation unit can apply a burnout analysis algorithm for fire damage. For example, the generation unit can apply a burnout analysis algorithm for fire damage. By applying a generation algorithm according to the category of damage, accurate reports can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input damage category data into a generation AI and cause the generation AI to execute the application of different generation algorithms.

[0049] The generation unit can determine the priority of reports based on when the damage occurred during generation. For example, the generation unit can prioritize generating reports for the most recent damage. The generation unit can also postpone reporting on past damage. Furthermore, the generation unit can adjust the priority of reports in stages according to when the damage occurred. This allows for a quick response to the latest damage by determining the priority of reports based on when the damage occurred. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on when the damage occurred into a generation AI and have the generation AI determine the priority of reports.

[0050] The generation unit can adjust the order of reports based on the relevance of the damage during generation. For example, the generation unit can prioritize generating reports for direct damage. The generation unit can also postpone generating reports for indirect damage. Furthermore, the generation unit can adjust the order of reports in stages according to the relevance of the damage. By adjusting the order of reports based on the relevance of the damage, it is possible to prioritize the provision of highly relevant information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the relevance of the damage into a generation AI and have the generation AI perform the adjustment of the report order.

[0051] The submission unit can select the optimal submission method at the time of submission, taking into account the acceptance status of insurance companies and governments. For example, the submission unit can submit documents in accordance with the acceptance hours of insurance companies. The submission unit can also check the acceptance status of governments and submit documents at the optimal time. Furthermore, the submission unit can adjust the submission method according to the acceptance status of insurance companies and governments. This enables a swift process by selecting the optimal submission method according to the acceptance status of insurance companies and governments. Some or all of the above processing in the submission unit may be performed using AI, or not. For example, the submission unit can input data on the acceptance status of insurance companies and governments into a generating AI and have the generating AI select the optimal submission method.

[0052] The submission unit can automatically identify important sections of the submitted documents and submit them with emphasis. For example, the submission unit can automatically identify the most important sections of the submitted documents and submit them with priority. The submission unit can also highlight important sections of the submitted documents when submitting them. Furthermore, the submission unit can automatically identify important sections of the submitted documents and add supplementary information as needed before submitting them. This enables faster review by focusing on submitting important sections of the documents. Some or all of the above processing in the submission unit may be performed using AI, for example, or not. For example, the submission unit can input the data of the submitted documents into a generating AI and have the generating AI perform the identification and submission of important sections.

[0053] The submission unit can prioritize submissions to highly relevant locations, taking into account the geographical location information of insurance companies and governments. For example, it can prioritize submissions to locations close to the insurance company's location. It can also prioritize submissions to locations close to the relevant government department. Furthermore, the submission unit can select the optimal submission destination, taking into account the geographical location information of insurance companies and governments. This enables faster processing by considering the geographical location information of insurance companies and governments during submission. Some or all of the above processing in the submission unit may be performed using AI, for example, or not using AI. For example, the submission unit can input geographical location data of insurance companies and governments into a generating AI and have the generating AI identify and submit to highly relevant locations.

[0054] The submitting department can analyze the social media activities of insurance companies and governments and submit the relevant sections at the time of submission. For example, the submitting department can analyze the social media activities of insurance companies and governments and submit the most relevant sections. The submitting department can also prioritize submitting sections that are trending on social media. For example, the submitting department can prioritize submitting sections that are trending on social media. Furthermore, the submitting department can adjust the content of the submitted documents based on the social media activities of insurance companies and governments. For example, the submitting department can adjust the content of the submitted documents based on the social media activities of insurance companies and governments. This allows for the provision of highly relevant information by analyzing and submitting social media activities. Some or all of the above processing by the submitting department may be performed using AI, for example, or not using AI. For example, the submitting department can input social media data of insurance companies and governments into a generating AI and have the generating AI identify and submit the relevant sections.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The photography department can select the optimal shooting method when capturing data from disaster-stricken areas, taking into account the environmental conditions of the area. For example, in rainy weather, they can use waterproof covers for shooting. They can also use infrared cameras at night. Furthermore, in strong winds, they can use tripods to ensure stable shooting. By selecting the optimal shooting method according to the environmental conditions of the disaster area, they can acquire accurate data.

[0057] The analysis unit can adjust the level of detail of the analysis based on the severity of the damage when analyzing data from disaster-stricken areas. For example, it can perform detailed analysis on areas with severe damage, and simplified analysis on areas with minor damage. Furthermore, it can adjust the level of detail of the analysis in stages according to the severity of the damage. This allows for the provision of appropriate analysis results by adjusting the level of detail according to the severity of the damage.

[0058] The generation unit can apply different generation algorithms depending on the category of damage. For example, a structural analysis algorithm can be applied to house collapses. A flood analysis algorithm can be applied to flood damage. Furthermore, a burnt-out analysis algorithm can be applied to fire damage. By applying a generation algorithm appropriate to the category of damage, accurate reports can be provided.

[0059] The submission system can automatically identify and prioritize the most important parts of a submitted document. For example, it can automatically identify the most crucial sections of a document and submit them with priority. It can also highlight important sections of a document before submission. Furthermore, it can automatically identify important sections of a document and add supplementary information as needed. This allows for faster review by focusing on the most important parts of the document.

[0060] The submission department can select the most suitable submission method at the time of submission, taking into account the acceptance status of insurance companies and governments. For example, submissions can be made in accordance with the insurance company's acceptance hours. Alternatively, the government's acceptance status can be checked, and submissions can be made at the optimal time. Furthermore, the submission method can be adjusted according to the acceptance status of insurance companies and governments. This allows for faster processing by selecting the most suitable submission method according to the acceptance status of insurance companies and governments.

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

[0062] Step 1: The photography team captures data from the disaster area. This data includes photographs, videos, and sensor data. For example, a 360-degree camera can be used to capture detailed data from the disaster area. Drones can also be used to photograph a wide area of ​​the disaster. Furthermore, sensors can be used to collect environmental data from the disaster area (temperature, humidity, atmospheric pressure, etc.). Step 2: The analysis unit analyzes the data captured by the imaging unit and evaluates the extent of the damage. The analysis is performed using methods such as image analysis, data mining, and machine learning algorithms. For example, image analysis technology can be used to evaluate the degree of collapse and damage to houses, and data mining technology can be used to extract damage patterns. Machine learning algorithms can also be used to predict damage. Step 3: The generation unit automatically generates insurance and subsidy application documents based on the damage assessment performed by the analysis unit. Automatic generation is performed using methods such as template-based generation or AI-based generation. For example, application documents can be automatically generated using template-based generation, and the AI ​​can calculate the insurance and subsidy amounts according to the damage and include them in the application documents. Furthermore, a detailed report can be automatically generated using the generation AI and attached to the application documents. Step 4: The submission unit submits the application documents generated by the generation unit to the insurance company or government. Submission can be done via email, online form, API integration, etc. For example, application documents can be sent to the insurance company or government via email, or submitted using an online form. Furthermore, application documents can be automatically submitted using API integration.

[0063] (Example of form 2) The post-disaster insurance and subsidy application system according to an embodiment of the present invention is a system that captures data of the disaster-stricken area with a 360-degree camera, analyzes the data with AI to evaluate the extent of the damage, and automatically generates and submits insurance and subsidy application documents. This system enables the insurance and subsidy application process to be carried out quickly and efficiently by capturing data of the disaster-stricken area, analyzing it with AI, and automatically generating and submitting application documents. For example, data is captured using a 360-degree camera in the disaster-stricken area after a disaster. For example, the interior and exterior of damaged houses are captured in detail with a 360-degree camera. This data is input into the AI. Next, the AI ​​analyzes the input data and evaluates the extent of the damage. For example, the AI ​​analyzes the extent of collapse and damage to houses and performs a quantitative evaluation. Based on the evaluation results, insurance and subsidy application documents are automatically generated. For example, the insurance amount and subsidy amount according to the extent of the damage are calculated and entered into the application documents. Finally, the generated application documents are submitted to insurance companies and the government to complete the application process. This system allows disaster victims to receive insurance payments and subsidies quickly. Furthermore, insurance companies and governments can address the shortage of personnel for damage assessment and streamline procedures. In addition, by utilizing AI-generated data, damage assessments can be performed quickly and accurately through the analysis of video data, and detailed reports can be automatically generated. This will enable faster and more efficient support for disaster victims. As a result, post-disaster insurance and subsidy application systems will be able to provide support to disaster victims quickly and efficiently.

[0064] The post-disaster insurance and subsidy application system according to this embodiment comprises a shooting unit, an analysis unit, a generation unit, and a submission unit. The shooting unit captures data of the disaster area. Data of the disaster area includes, but is not limited to, photographs, videos, and sensor data. The shooting unit captures detailed data of the disaster area using, for example, a 360-degree camera. For example, the interior and exterior of damaged houses can be captured in detail with a 360-degree camera. The shooting unit can also capture a wide area of ​​the disaster using a drone. For example, a drone can be flown to capture the entire disaster area from above. Furthermore, the shooting unit can collect environmental data of the disaster area using sensors. For example, environmental data such as temperature, humidity, and atmospheric pressure can be collected by sensors. The analysis unit analyzes the data captured by the shooting unit and evaluates the extent of the damage. The analysis is performed by, for example, image analysis, data mining, and machine learning algorithms, but is not limited to these methods. For example, the analysis unit can use image analysis techniques to evaluate the extent of collapse and damage to houses. The analysis unit can also use data mining techniques to extract damage patterns. For example, common damage patterns can be identified from data in disaster-stricken areas. Furthermore, the analysis unit can predict damage using machine learning algorithms. For example, it can predict future damage based on past data. The generation unit automatically generates insurance and subsidy application documents based on the damage assessment by the analysis unit. Automatic generation is performed by methods such as template-based generation and AI generation, but is not limited to these examples. For example, the generation unit can automatically generate application documents using template-based generation. The generation unit can also automatically generate application documents using AI. For example, the AI ​​can calculate insurance and subsidy amounts according to the damage situation and include them in the application documents. Furthermore, the generation unit can automatically generate detailed reports using generation AI. For example, the generation AI can create a detailed report on the damage situation and attach it to the application documents. The submission unit submits the application documents generated by the generation unit to insurance companies and the government.Submissions may be made by methods such as email, online forms, or API integration, but are not limited to these examples. For instance, the submitting unit can send application documents to insurance companies or governments via email. Alternatively, the submitting unit can submit application documents using online forms. For example, it can access the insurance company or government website, fill out the online form, and submit it. Furthermore, the submitting unit can submit application documents using API integration. For example, it can integrate with the insurance company or government system via API to automatically send application documents. This allows the post-disaster insurance and subsidy application system according to this embodiment to provide support to disaster victims quickly and efficiently.

[0065] The photography unit captures data from the disaster area. This data includes, but is not limited to, photographs, videos, and sensor data. The photography unit can capture detailed data from the disaster area using, for example, 360-degree cameras. Specifically, 360-degree cameras can capture detailed images of the interior and exterior of damaged houses. 360-degree cameras have a wide field of view and can capture the entire disaster area at once, making them very effective in understanding the full extent of the damage. The photography unit can also use drones to photograph wide areas of the disaster. Drones provide an aerial view, allowing for the assessment of damage that is not visible from the ground. For example, a drone can be flown to photograph the entire disaster area from above. This allows for a rapid assessment of the extent of the damage over a wide area. Furthermore, the photography unit can collect environmental data from the disaster area using sensors. For example, sensors can collect environmental data such as temperature, humidity, and atmospheric pressure. This environmental data is important for a more detailed understanding of the situation in the disaster area. For example, changes in temperature and humidity can help assess the sanitary conditions and the risk of secondary disasters in the disaster area. The camera crew can collect this data in real time and transmit it to a central database. This allows for a quick and accurate assessment of the situation in the disaster area, enabling appropriate responses.

[0066] The analysis unit analyzes data captured by the imaging unit to assess the extent of the damage. The analysis is performed using methods such as image analysis, data mining, and machine learning algorithms, but is not limited to these examples. Specifically, image analysis technology can be used to assess the degree of collapse and damage to buildings. Image analysis technology can analyze photographs and videos of the disaster area and automatically detect damaged areas and the extent of collapse. For example, image analysis algorithms can identify structural damage to buildings and assess the extent and severity of that damage. The analysis unit can also extract damage patterns using data mining technology. Data mining technology can extract useful information from large amounts of data and identify common patterns and trends in damage. For example, it can find common damage patterns from data of the disaster area. Furthermore, the analysis unit can predict damage using machine learning algorithms. Machine learning algorithms can predict future damage based on past data. For example, a model trained on past disaster data can be used to predict the probability of damage occurring in specific areas or under specific conditions. This allows the analysis unit to quickly and accurately analyze the collected data and assess the extent of the damage. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The generation unit automatically generates insurance and subsidy application documents based on the damage assessment performed by the analysis unit. Automatic generation is performed using methods such as template-based generation and AI-based generation, but is not limited to these examples. Specifically, the generation unit can automatically generate application documents using template-based generation. Template-based generation is a method of quickly and accurately creating application documents by embedding analysis results into predefined templates. For example, insurance and subsidy amounts based on the damage can be automatically calculated based on the template and included in the application documents. The generation unit can also automatically generate application documents using AI. The AI ​​can calculate insurance and subsidy amounts based on the damage and include them in the application documents. For example, the AI ​​can create a detailed report of the damage and attach it to the application documents. Furthermore, the generation unit can automatically generate detailed reports using generation AI. The generation AI can create a detailed report of the damage and attach it to the application documents. For example, the generation AI can analyze photos and videos of the damage and create a detailed report. This allows the generation unit to quickly and accurately automatically generate application documents, reducing the burden on disaster victims.

[0068] The submission unit submits the application documents generated by the generation unit to insurance companies and governments. Submissions are made by methods such as email, online forms, and API integration, but are not limited to these. Specifically, the submission unit can send application documents to insurance companies and governments using email. Email is a means of sending application documents quickly and reliably, and insurance company and government officials can immediately review them. The submission unit can also submit application documents using online forms. Online forms are a method of accessing the insurance company or government website, filling out the application documents, and submitting them. For example, the submission unit can access the insurance company or government website, fill out the application documents on the online form, and submit them. Furthermore, the submission unit can also submit application documents using API integration. API integration is a method of integrating with the insurance company or government system and automatically sending application documents. For example, the submission unit can integrate with the insurance company or government system via API and automatically send application documents. This allows the submission unit to submit application documents quickly and reliably, and to efficiently provide support to disaster victims. In addition, the submission unit can track the status of the application documents after submission and notify the victims. For example, information such as whether the application documents have been received, whether the review is in progress, and whether the results have been released can be notified to the disaster victims. This allows the submitting department to keep the disaster victims informed of the status of their application at all times, enabling them to proceed with the application process with peace of mind.

[0069] The generation unit can automatically generate detailed reports using a generation AI. For example, the generation unit can create a detailed report on the damage situation using the generation AI. For example, the generation AI can create a detailed report on the damage situation and attach it to the application documents. The generation unit can also generate detailed reports on the damage situation quickly and accurately using the generation AI. For example, the generation AI can quickly and accurately create a detailed report on the damage situation and attach it to the application documents. Furthermore, the generation unit can automatically update the detailed report on the damage situation using the generation AI. For example, the generation AI can automatically update the detailed report on the damage situation and reflect the latest information in the application documents. In this way, detailed reports can be generated quickly and accurately by using the generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input damage situation data into the generation AI and have the generation AI execute the generation of a detailed report.

[0070] The imaging unit can capture detailed data of the disaster area. For example, the imaging unit can use a 360-degree camera to capture detailed data of the disaster area. For example, it can capture detailed images of the interior and exterior of damaged houses with a 360-degree camera. The imaging unit can also use a drone to photograph a wide area of ​​the disaster. For example, it can fly a drone to photograph the entire disaster area from above. Furthermore, the imaging unit can collect environmental data of the disaster area using sensors. For example, it can collect environmental data such as temperature, humidity, and atmospheric pressure using sensors. By capturing detailed data of the disaster area, it becomes possible to assess the extent of the damage more accurately. Some or all of the above processing in the imaging unit may be performed using AI, for example, or without AI. For example, the imaging unit can input data captured by a 360-degree camera into a generating AI and have the generating AI perform a detailed analysis of the captured data.

[0071] The analysis unit can quantitatively evaluate the extent of house collapse and damage. For example, the analysis unit can use image analysis technology to evaluate the extent of house collapse and damage. For example, it can use image analysis technology to quantify the extent of house collapse and evaluate the extent of damage. The analysis unit can also extract damage patterns using data mining technology. For example, it can find common damage patterns from data of the affected area. Furthermore, the analysis unit can predict damage using machine learning algorithms. For example, it can predict future damage based on past data. This makes it possible to accurately grasp the extent of damage by quantitatively evaluating the extent of house collapse and damage. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the extent of house collapse and damage into a generating AI and have the generating AI perform a quantitative evaluation.

[0072] The generation unit can calculate insurance and subsidy amounts according to the extent of the damage. For example, the generation unit can calculate insurance and subsidy amounts according to the extent of the damage and include them in the application documents. The generation unit can also quickly calculate insurance and subsidy amounts according to the extent of the damage. For example, it can quickly calculate insurance and subsidy amounts according to the extent of the damage and include them in the application documents. Furthermore, the generation unit can automatically update insurance and subsidy amounts according to the extent of the damage. For example, it can automatically update insurance and subsidy amounts according to the extent of the damage and reflect the latest information in the application documents. This ensures that appropriate compensation is provided quickly by calculating insurance and subsidy amounts according to the extent of the damage. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input damage data into a generation AI and have the generation AI calculate insurance and subsidy amounts.

[0073] The submission unit can submit the generated application documents to insurance companies and governments. For example, the submission unit can send the application documents to insurance companies and governments via email. The submission unit can also submit the application documents using online forms. For example, it can access the insurance company's or government's website, fill out the online form, and submit it. Furthermore, the submission unit can submit the application documents using API integration. For example, it can integrate with the insurance company's or government's system via API and automatically send the application documents. This allows for faster receipt of insurance payments and subsidies by quickly submitting the generated application documents. Some or all of the above processes in the submission unit may be performed using AI, for example, or not using AI. For example, the submission unit can input the generated application documents into a generation AI and have the generation AI execute the timing and method of submission.

[0074] The camera unit can estimate the user's emotions and adjust the timing of shooting based on the estimated emotions. For example, if the user is feeling stressed, the camera unit can start shooting at a time when the user is relaxed. If the user is calm, the camera unit can take its time to take detailed shots. Furthermore, if the user is in a hurry, the camera unit can quickly shoot only the necessary parts. This reduces the burden on the user by adjusting the timing of shooting according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the shooting unit can input user emotion data into a generating AI and have the AI ​​adjust the timing of the shots.

[0075] The camera unit can select the optimal shooting method when taking pictures, taking into account the environmental conditions of the disaster area. For example, the camera unit can use a waterproof cover when taking pictures in rainy weather. The camera unit can also use an infrared camera when taking pictures at night. Furthermore, the camera unit can use a tripod to ensure stable shooting when there are strong winds. By selecting the optimal shooting method according to the environmental conditions of the disaster area, accurate data can be obtained. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input environmental condition data of the disaster area into a generating AI and have the generating AI select the optimal shooting method.

[0076] The camera unit can automatically identify and focus on important areas in the disaster zone during filming. For example, it can automatically identify and photograph in detail the collapsed parts of houses. The camera unit can also prioritize filming areas with severe damage. Furthermore, it can focus on filming areas directly related to the lives of disaster victims (such as kitchens and bedrooms). By focusing on filming important areas in the disaster zone, it becomes possible to accurately grasp the extent of the damage. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input data from the disaster zone into a generating AI and have the generating AI identify and film important areas.

[0077] The camera unit can estimate the user's emotions and determine the priority of areas to photograph based on the estimated emotions. For example, if the user is feeling anxious, the camera unit can prioritize photographing the areas with the greatest damage. If the user is calm, the camera unit can also photograph the entire damage evenly. Furthermore, if the user is in a hurry, the camera unit can photograph only the most important areas. This allows for photography that meets the user's needs by prioritizing areas according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the shooting unit can input user emotion data into a generating AI and have the AI ​​determine the priority of shooting locations.

[0078] The camera unit can prioritize photographing highly relevant locations while considering the geographical location information of the disaster area. For example, the camera unit can prioritize photographing the central area of ​​the disaster area. The camera unit can also photograph surrounding areas of the disaster area. Furthermore, the camera unit can identify and photograph important locations while considering the geographical characteristics of the disaster area. By considering the geographical location information of the disaster area while taking photographs, highly relevant data can be obtained. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input geographical location data of the disaster area into a generating AI and have the generating AI identify and photograph highly relevant locations.

[0079] The photography team can analyze social media activity in the disaster area during filming and photograph relevant locations. For example, the photography team can prioritize photographing locations that are trending on social media. The photography team can also use photos and videos posted by disaster victims as reference for filming. Furthermore, the photography team can analyze social media hashtags to identify and photograph relevant locations. By analyzing social media activity and filming accordingly, important locations in the disaster area can be identified. Some or all of the above processing by the photography team may be performed using AI, or not. For example, the photography team can input social media data into a generating AI and have the generating AI identify and photograph relevant locations.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can use a concise and easy-to-understand presentation. For example, if the user is feeling anxious, the analysis unit can use a concise and easy-to-understand presentation. The analysis unit can also provide detailed analysis results if the user is calm. For example, if the user is calm, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0081] The analysis unit can adjust the level of detail of the analysis based on the severity of the damage. For example, the analysis unit can perform a detailed analysis on areas with significant damage. The analysis unit can also perform a simplified analysis on areas with minor damage. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the severity of the damage. By adjusting the level of detail of the analysis according to the severity of the damage, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input damage severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of damage during the analysis. For example, the analysis unit can apply a structural analysis algorithm for the collapse of houses. For example, the analysis unit can apply a flood inundation analysis algorithm for flood damage. For example, the analysis unit can apply a burnout analysis algorithm for fire damage. For example, the analysis unit can apply a burnout analysis algorithm for fire damage. By applying an analysis algorithm according to the category of damage, accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input damage category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is feeling anxious, the analysis unit can provide a reassuring analysis result. For example, if the user is feeling anxious, the analysis unit can provide a reassuring analysis result. In this way, by adjusting the length of the analysis according to the user's emotions, analysis results that meet the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the timing of the damage. For example, the analysis unit can prioritize the analysis of the most recent damage. The analysis unit can also postpone the analysis of past damage. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the timing of the damage. This allows for a rapid response to the latest damage by determining the priority of analysis based on the timing of the damage. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input damage timing data into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the damages during the analysis. For example, the analysis unit can prioritize the analysis of direct damages. The analysis unit can also postpone the analysis of indirect damages. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the damages. By adjusting the order of analysis based on the relevance of the damages, damages with high relevance can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input damage relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The generation unit can estimate the user's emotions and adjust the presentation of the generated report based on the estimated emotions. For example, if the user is feeling anxious, the generation unit can use a concise and easy-to-understand presentation. The generation unit can also provide a detailed report if the user is calm. Furthermore, if the user is in a hurry, the generation unit can provide a concise and to-the-point report. By adjusting the presentation of the report according to the user's emotions, a report that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the report is presented.

[0087] The generation unit can adjust the level of detail in the report based on the severity of the damage during generation. For example, the generation unit can generate a detailed report for serious damage. The generation unit can also generate a concise report for minor damage. Furthermore, the generation unit can adjust the level of detail in the report in stages according to the severity of the damage. This allows for the provision of appropriate information by adjusting the level of detail in the report according to the severity of the damage. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input damage severity data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0088] The generation unit can apply different generation algorithms depending on the category of damage during generation. For example, the generation unit can apply a structural analysis algorithm for house collapse. For example, the generation unit can apply a flood inundation analysis algorithm for flood damage. For example, the generation unit can apply a burnout analysis algorithm for fire damage. For example, the generation unit can apply a burnout analysis algorithm for fire damage. By applying a generation algorithm according to the category of damage, accurate reports can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input damage category data into a generation AI and cause the generation AI to execute the application of different generation algorithms.

[0089] The generation unit can estimate the user's emotions and adjust the length of the generated report based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise report. For example, if the user is in a hurry, the generation unit can generate a short, concise report. The generation unit can also generate a detailed report if the user is relaxed. For example, if the user is anxious, the generation unit can generate a reassuring report. In this way, by adjusting the length of the report according to the user's emotions, a report tailored to the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the report.

[0090] The generation unit can determine the priority of reports based on when the damage occurred during generation. For example, the generation unit can prioritize generating reports for the most recent damage. The generation unit can also postpone reporting on past damage. Furthermore, the generation unit can adjust the priority of reports in stages according to when the damage occurred. This allows for a quick response to the latest damage by determining the priority of reports based on when the damage occurred. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on when the damage occurred into a generation AI and have the generation AI determine the priority of reports.

[0091] The generation unit can adjust the order of reports based on the relevance of the damage during generation. For example, the generation unit can prioritize generating reports for direct damage. The generation unit can also postpone generating reports for indirect damage. Furthermore, the generation unit can adjust the order of reports in stages according to the relevance of the damage. By adjusting the order of reports based on the relevance of the damage, it is possible to prioritize the provision of highly relevant information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the relevance of the damage into a generation AI and have the generation AI perform the adjustment of the report order.

[0092] The submission unit can estimate the user's emotions and adjust the submission timing based on the estimated emotions. For example, if the user is feeling anxious, the submission unit can submit at a time that provides reassurance. The submission unit can also submit quickly if the user is calm. Furthermore, if the user is in a hurry, the submission unit can submit in the shortest possible time. This reduces the user's burden by adjusting the submission timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the submission unit may be performed using AI or not. For example, the submission unit can input user emotion data into a generative AI and have the generative AI adjust the submission timing.

[0093] The submission unit can select the optimal submission method at the time of submission, taking into account the acceptance status of insurance companies and governments. For example, the submission unit can submit documents in accordance with the acceptance hours of insurance companies. The submission unit can also check the acceptance status of governments and submit documents at the optimal time. Furthermore, the submission unit can adjust the submission method according to the acceptance status of insurance companies and governments. This enables a swift process by selecting the optimal submission method according to the acceptance status of insurance companies and governments. Some or all of the above processing in the submission unit may be performed using AI, or not. For example, the submission unit can input data on the acceptance status of insurance companies and governments into a generating AI and have the generating AI select the optimal submission method.

[0094] The submission unit can automatically identify important sections of the submitted documents and submit them with emphasis. For example, the submission unit can automatically identify the most important sections of the submitted documents and submit them with priority. The submission unit can also highlight important sections of the submitted documents when submitting them. Furthermore, the submission unit can automatically identify important sections of the submitted documents and add supplementary information as needed before submitting them. This enables faster review by focusing on submitting important sections of the documents. Some or all of the above processing in the submission unit may be performed using AI, for example, or not. For example, the submission unit can input the data of the submitted documents into a generating AI and have the generating AI perform the identification and submission of important sections.

[0095] The submission unit can estimate the user's emotions and prioritize the documents to be submitted based on the estimated emotions. For example, if the user is feeling anxious, the submission unit can prioritize submitting the most important documents. For example, if the user is feeling anxious, the submission unit can prioritize submitting all documents equally. For example, if the user is calm, the submission unit can prioritize submitting all documents equally. Furthermore, if the user is in a hurry, the submission unit can prioritize submitting the documents that can be processed most quickly. For example, if the user is in a hurry, the submission unit can prioritize submitting the documents that can be processed most quickly. This allows for submissions that meet the user's needs by prioritizing the documents to be submitted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the submission unit may be performed using AI, for example, or not using AI. For example, the submission unit can input user emotion data into a generating AI and have the AI ​​determine the priority of the submitted documents.

[0096] The submission unit can prioritize submissions to highly relevant locations, taking into account the geographical location information of insurance companies and governments. For example, it can prioritize submissions to locations close to the insurance company's location. It can also prioritize submissions to locations close to the relevant government department. Furthermore, the submission unit can select the optimal submission destination, taking into account the geographical location information of insurance companies and governments. This enables faster processing by considering the geographical location information of insurance companies and governments during submission. Some or all of the above processing in the submission unit may be performed using AI, for example, or not using AI. For example, the submission unit can input geographical location data of insurance companies and governments into a generating AI and have the generating AI identify and submit to highly relevant locations.

[0097] The submitting department can analyze the social media activities of insurance companies and governments and submit the relevant sections at the time of submission. For example, the submitting department can analyze the social media activities of insurance companies and governments and submit the most relevant sections. The submitting department can also prioritize submitting sections that are trending on social media. For example, the submitting department can prioritize submitting sections that are trending on social media. Furthermore, the submitting department can adjust the content of the submitted documents based on the social media activities of insurance companies and governments. For example, the submitting department can adjust the content of the submitted documents based on the social media activities of insurance companies and governments. This allows for the provision of highly relevant information by analyzing and submitting social media activities. Some or all of the above processing by the submitting department may be performed using AI, for example, or not using AI. For example, the submitting department can input social media data of insurance companies and governments into a generating AI and have the generating AI identify and submit the relevant sections.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the emotions of disaster victims when analyzing data from disaster-stricken areas and determine the priority of the analysis based on those estimated emotions. For example, if a disaster victim is experiencing strong anxiety, the system can prioritize the analysis of the most severely damaged areas. If the disaster victim is calm, the system can analyze the overall damage situation evenly. Furthermore, if the disaster victim is in a hurry, the system can quickly analyze only the necessary areas. In this way, by determining the priority of the analysis according to the emotions of the disaster victims, it becomes possible to conduct analysis that meets their needs.

[0100] The photography team can estimate the emotions of disaster victims when capturing data from disaster areas and adjust their photography methods based on these estimates. For example, if a victim is feeling stressed, they can start photography at a time when the victim is likely to relax. If the victim is calm, they can take their time to capture detailed images. Furthermore, if the victim is in a hurry, they can quickly photograph only the necessary areas. By adjusting the photography method according to the victim's emotions, the burden on the victim can be reduced.

[0101] The generation unit can estimate the victim's emotions when calculating insurance or subsidy amounts based on the extent of the damage, and adjust the calculation method based on those estimated emotions. For example, if the victim is feeling anxious, a simple and easy-to-understand calculation method can be used. If the victim is calm, a detailed calculation method can be used. Furthermore, if the victim is in a hurry, a method for rapid calculation can be used. In this way, by adjusting the calculation method according to the victim's emotions, it is possible to provide calculation results that are easy for the victim to understand.

[0102] The submission department can estimate the victim's emotions when submitting the generated application documents to insurance companies or the government, and adjust the timing of submission based on those estimated emotions. For example, if the victim is feeling anxious, the documents can be submitted at a time that will provide reassurance. If the victim is calm, the documents can be submitted quickly. Furthermore, if the victim is in a hurry, the documents can be submitted in the shortest possible time. In this way, the burden on the victim can be reduced by adjusting the timing of submission according to their emotions.

[0103] The analysis unit can estimate the emotions of disaster victims when analyzing data from disaster-stricken areas and adjust the presentation of the analysis based on these estimated emotions. For example, if a disaster victim is feeling anxious, a concise and easy-to-understand presentation can be used. If the disaster victim is calm, detailed analysis results can be provided. Furthermore, if the disaster victim is in a hurry, concise analysis results can be provided. In this way, by adjusting the presentation of the analysis according to the emotions of the disaster victims, it is possible to provide analysis results that are easy for disaster victims to understand.

[0104] The photography department can select the optimal shooting method when capturing data from disaster-stricken areas, taking into account the environmental conditions of the area. For example, in rainy weather, they can use waterproof covers for shooting. They can also use infrared cameras at night. Furthermore, in strong winds, they can use tripods to ensure stable shooting. By selecting the optimal shooting method according to the environmental conditions of the disaster area, they can acquire accurate data.

[0105] The analysis unit can adjust the level of detail of the analysis based on the severity of the damage when analyzing data from disaster-stricken areas. For example, it can perform detailed analysis on areas with severe damage, and simplified analysis on areas with minor damage. Furthermore, it can adjust the level of detail of the analysis in stages according to the severity of the damage. This allows for the provision of appropriate analysis results by adjusting the level of detail according to the severity of the damage.

[0106] The generation unit can apply different generation algorithms depending on the category of damage. For example, a structural analysis algorithm can be applied to house collapses. A flood analysis algorithm can be applied to flood damage. Furthermore, a burnt-out analysis algorithm can be applied to fire damage. By applying a generation algorithm appropriate to the category of damage, accurate reports can be provided.

[0107] The submission system can automatically identify and prioritize the most important parts of a submitted document. For example, it can automatically identify the most crucial sections of a document and submit them with priority. It can also highlight important sections of a document before submission. Furthermore, it can automatically identify important sections of a document and add supplementary information as needed. This allows for faster review by focusing on the most important parts of the document.

[0108] The submission department can select the most suitable submission method at the time of submission, taking into account the acceptance status of insurance companies and governments. For example, submissions can be made in accordance with the insurance company's acceptance hours. Alternatively, the government's acceptance status can be checked, and submissions can be made at the optimal time. Furthermore, the submission method can be adjusted according to the acceptance status of insurance companies and governments. This allows for faster processing by selecting the most suitable submission method according to the acceptance status of insurance companies and governments.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The photography team captures data from the disaster area. This data includes photographs, videos, and sensor data. For example, a 360-degree camera can be used to capture detailed data from the disaster area. Drones can also be used to photograph a wide area of ​​the disaster. Furthermore, sensors can be used to collect environmental data from the disaster area (temperature, humidity, atmospheric pressure, etc.). Step 2: The analysis unit analyzes the data captured by the imaging unit and evaluates the extent of the damage. The analysis is performed using methods such as image analysis, data mining, and machine learning algorithms. For example, image analysis technology can be used to evaluate the degree of collapse and damage to houses, and data mining technology can be used to extract damage patterns. Machine learning algorithms can also be used to predict damage. Step 3: The generation unit automatically generates insurance and subsidy application documents based on the damage assessment performed by the analysis unit. Automatic generation is performed using methods such as template-based generation or AI-based generation. For example, application documents can be automatically generated using template-based generation, and the AI ​​can calculate the insurance and subsidy amounts according to the damage and include them in the application documents. Furthermore, a detailed report can be automatically generated using the generation AI and attached to the application documents. Step 4: The submission unit submits the application documents generated by the generation unit to the insurance company or government. Submission can be done via email, online form, API integration, etc. For example, application documents can be sent to the insurance company or government via email, or submitted using an online form. Furthermore, application documents can be automatically submitted using API integration.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the imaging unit, analysis unit, generation unit, and submission unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the smart device 14 or a drone to capture data of the disaster area. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to analyze the captured data and evaluate the extent of the damage. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to automatically generate insurance and subsidy application documents based on the evaluation results. The submission unit is implemented by, for example, the control unit 46A of the smart device 14 to submit the generated application documents to insurance companies and the government. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the imaging unit, analysis unit, generation unit, and submission unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the smart glasses 214 or a drone to capture data of the disaster area. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the captured data and evaluates the extent of the damage. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which automatically generates insurance and subsidy application documents based on the evaluation results. The submission unit is implemented, for example, in the control unit 46A of the smart glasses 214, which submits the generated application documents to insurance companies and the government. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the imaging unit, analysis unit, generation unit, and submission unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the headset terminal 314 or a drone to capture data of the disaster area. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 to analyze the captured data and evaluate the extent of the damage. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 to automatically generate insurance and subsidy application documents based on the evaluation results. The submission unit is implemented in the control unit 46A of the headset terminal 314 to submit the generated application documents to insurance companies and the government. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the imaging unit, analysis unit, generation unit, and submission unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the robot 414 or a drone to capture data of the disaster area. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to analyze the captured data and evaluate the extent of the damage. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to automatically generate insurance and subsidy application documents based on the evaluation results. The submission unit is implemented by, for example, the control unit 46A of the robot 414 to submit the generated application documents to insurance companies and the government. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The photography team, which takes pictures of the disaster area, An analysis unit analyzes the data captured by the aforementioned imaging unit and evaluates the extent of the damage, A generation unit that automatically generates insurance and subsidy application documents based on the damage assessment performed by the aforementioned analysis unit, The system comprises a submission unit that submits the application documents generated by the generation unit to insurance companies and the government. A system characterized by the following features. (Note 2) The generating unit is Use generative AI to automatically generate detailed reports. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned imaging unit is Detailed photographs of the disaster-stricken area The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Quantitatively assess the extent of house collapse and damage. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Calculate insurance and subsidy amounts based on the extent of the damage. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned submission section, Submit the generated application documents to the insurance company or the government. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is When filming, the optimal filming method is selected considering the environmental conditions of the disaster-stricken area. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is During filming, the system automatically identifies and focuses on important locations in the disaster area. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is The system estimates the user's emotions and prioritizes the locations to photograph based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is During filming, we prioritize filming locations that are highly relevant, taking into account the geographical location information of the disaster-stricken area. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned imaging unit is During filming, we analyze social media activity in the disaster-stricken areas and film relevant locations. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the severity of the damage. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of damage. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the damage occurred. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relevance of the damage. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate user sentiment and adjust how reports are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail in the report is adjusted based on the severity of the damage. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of damage. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates user sentiment and adjusts the length of the report generated based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, report priorities are determined based on when the damage occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of reports is adjusted based on the relevance of the damage. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned submission section, It estimates the user's emotions and adjusts the submission timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned submission section, When submitting, select the most suitable submission method considering the acceptance status of insurance companies and government agencies. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned submission section, When submitting documents, the system automatically identifies the most important parts and focuses on submitting them. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned submission section, It estimates the user's emotions and prioritizes the documents to be submitted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned submission section, When submitting, prioritize submitting the most relevant sections, taking into account the geographical location information of insurance companies and government agencies. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned submission section, When submitting, analyze the social media activities of insurance companies and governments and submit the relevant sections. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The photography team, which takes pictures of the disaster area, An analysis unit analyzes the data captured by the aforementioned imaging unit and evaluates the extent of the damage, A generation unit that automatically generates insurance and subsidy application documents based on the damage assessment performed by the aforementioned analysis unit, The system comprises a submission unit that submits the application documents generated by the generation unit to insurance companies and the government. A system characterized by the following features.

2. The generating unit is Use generation AI to automatically generate detailed reports. The system according to feature 1.

3. The aforementioned imaging unit is Detailed photographs of the disaster-stricken area The system according to feature 1.

4. The aforementioned analysis unit, Quantitatively assess the extent of house collapse and damage. The system according to feature 1.

5. The generating unit is Calculate insurance and subsidy amounts based on the extent of the damage. The system according to feature 1.

6. The aforementioned submission section, Submit the generated application documents to the insurance company or the government. The system according to feature 1.

7. The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system according to feature 1.

8. The aforementioned imaging unit is When filming, the optimal filming method is selected considering the environmental conditions of the disaster-stricken area. The system according to feature 1.

9. The aforementioned imaging unit is During filming, the system automatically identifies and focuses on important locations in the disaster area. The system according to feature 1.

Images