system

Abstract

The system according to the embodiment aims to identify specific disaster prevention measures and evacuation methods based on the region and living environment, and to provide damage forecasts. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an identification unit, a prediction unit, and a provision unit. The reception unit receives information about the region and living environment from a user. The analysis unit analyzes the information received by the reception unit. The identification unit identifies insufficient disaster prevention measures or emergency escape routes and evacuation locations based on the information analyzed by the analysis unit. The prediction unit performs damage prediction based on the information identified by the identification unit. The provision unit provides the user with the prediction results obtained by the prediction unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to identify specific disaster prevention measures and evacuation methods based on the region and living environment, and there is room for improvement.

[0005] The system according to the embodiment aims to identify specific disaster prevention measures and evacuation methods based on the region and living environment, and to provide damage forecasts. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, a prediction unit, and a provision unit. The reception unit receives information about the region and living environment from the user. The analysis unit analyzes the information received by the reception unit. The identification unit identifies insufficient disaster prevention measures or emergency escape routes and evacuation locations based on the information analyzed by the analysis unit. The prediction unit performs damage prediction based on the information identified by the identification unit. The provision unit provides the user with the prediction results obtained by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify specific disaster prevention measures and evacuation methods based on the region and living environment, and provide damage forecasts. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A disaster prevention support system according to an embodiment of the present invention allows a user to input information about their local area and living environment, thereby identifying insufficient disaster prevention measures, emergency escape methods, and evacuation locations, and predicting damage. In this disaster prevention support system, a user inputs information about their local area and living environment, and the system analyzes the information to identify insufficient disaster prevention measures, emergency escape methods, and evacuation locations. The system then performs a damage prediction and provides the results to the user. For example, a user inputs information about the area in which they live, such as the topography, building structure, and family composition. This information is input into the system, and the system identifies insufficient disaster prevention measures, emergency escape methods, and evacuation locations based on the local topography, building structure, and family composition. For example, the system determines how to evacuate in the event of a disaster and which evacuation locations are safe. Furthermore, the system predicts damage in the event of a disaster based on information about the local topography, building structure, and family composition. For example, it predicts the expected level of damage in the event of a disaster and which areas are particularly dangerous. Finally, the system provides the analysis results and damage prediction to the user. Based on the information provided by the system, users can strengthen their disaster prevention measures and confirm emergency escape methods and evacuation locations. This increases users' disaster prevention awareness. For example, users can learn in detail how to evacuate in the event of a disaster and which evacuation sites are safe. This will raise users' awareness of disaster prevention and strengthen their disaster preparedness. This allows the disaster prevention support system to strengthen disaster prevention measures based on the user's region and living environment, and enable them to take appropriate action in an emergency.

[0029] A disaster prevention support system according to an embodiment includes a reception unit, an analysis unit, an identification unit, a prediction unit, and a provision unit. The reception unit receives information about a region and living environment from a user. The reception unit receives information such as the topography of the region where the user lives, the structure of buildings, and family composition. For example, the reception unit provides an interface for inputting topographical information about the region where the user lives. The reception unit can also provide a form for inputting structural information about buildings. The reception unit can also provide options for inputting family composition information. The analysis unit analyzes the information received by the reception unit. The analysis unit identifies insufficient disaster prevention measures, emergency escape routes, and evacuation locations based on information such as the topography of the region, the structure of buildings, and family composition. For example, the analysis unit analyzes topographical information to assess the risk of floods and landslides. The analysis unit can also analyze structural information about buildings to assess the risk of collapse during an earthquake. The analysis unit can also analyze family composition information to determine evacuation priorities. The identification unit identifies insufficient disaster prevention measures, emergency escape routes, and evacuation locations based on the information analyzed by the analysis unit. The identification unit identifies how to evacuate in the event of a disaster, which evacuation site is safe, etc. For example, the identification unit recommends that a user living in an area with a high flood risk evacuate to higher ground. The identification unit can also recommend highly earthquake-resistant evacuation sites to a user living in an area with a high earthquake risk. Furthermore, the identification unit can propose evacuation plans according to family composition. The prediction unit predicts damage in the event of a disaster based on the information identified by the identification unit. The prediction unit predicts damage in the event of a disaster based on information such as the local topography, building structure, and family composition. For example, the prediction unit predicts the inundation area in the event of a flood. The prediction unit can also predict the risk of building collapse in the event of an earthquake. Furthermore, the prediction unit can perform evacuation simulations according to family composition. The provision unit provides the prediction results obtained by the prediction unit to the user. The provision unit provides the analysis results and damage predictions to the user. For example, the provision unit notifies the user that the user lives in an area with a high flood risk.The providing unit can also notify the user that they live in an area with a high earthquake risk. Furthermore, the providing unit can also suggest an evacuation plan to the user based on their family structure. This allows the disaster prevention support system according to the embodiment to strengthen disaster prevention measures based on the user's area and living environment and take appropriate action in an emergency.

[0030] The reception unit can accept information about the topography, building structure, family composition, and other related information of the area where the user lives. The reception unit, for example, provides an interface for inputting topographical information about the area where the user lives. For example, the reception unit has a map display function for inputting topographical information. The reception unit can also provide a form for inputting structural information about the building. For example, the reception unit provides options for inputting information such as the number of floors and structural materials of the building. The reception unit can also provide options for inputting family composition information. For example, the reception unit provides a form for inputting information such as the number of family members and their age composition. The reception unit can also accept other related information. For example, the reception unit provides an interface for inputting information such as weather information and past disaster history. This allows the reception unit to accept detailed information about the user's area and living environment.

[0031] The analysis unit can analyze the local topography, building structure, family composition, etc. based on the information received by the reception unit. The analysis unit, for example, analyzes topographical information to evaluate the risk of floods and landslides. For example, the analysis unit simulates the flood inundation area based on topographical data. The analysis unit can also analyze building structural information to evaluate the risk of collapse during an earthquake. For example, the analysis unit simulates the risk of collapse during an earthquake based on information such as the number of floors and structural materials of a building. Furthermore, the analysis unit can analyze family composition information to determine priorities for evacuation. For example, the analysis unit determines priorities for evacuation based on information such as the number of family members and age composition. This allows the analysis unit to perform detailed analysis of information related to the local area and living environment.

[0032] The identification unit can identify how to evacuate in the event of a disaster and which evacuation locations are safe, based on the information analyzed by the analysis unit. For example, the identification unit recommends evacuation to higher ground for a user living in an area with a high risk of flooding. For example, the identification unit identifies an evacuation route to higher ground based on the extent of flood inundation. The identification unit can also recommend highly earthquake-resistant evacuation locations for a user living in an area with a high risk of earthquakes. For example, the identification unit identifies highly earthquake-resistant evacuation locations based on the risk of collapse during an earthquake. Furthermore, the identification unit can propose evacuation plans based on family composition. For example, the identification unit determines evacuation priorities based on information such as the number of family members and their age composition, and proposes an evacuation plan. This allows the identification unit to identify evacuation methods and safe evacuation locations in the event of a disaster.

[0033] The prediction unit can predict damage in the event of a disaster based on the information identified by the identification unit. The prediction unit, for example, predicts the inundation area in the event of a flood. For example, the prediction unit simulates the inundation area in the event of a flood based on topographical data. The prediction unit can also predict the risk of building collapse in the event of an earthquake. For example, the prediction unit simulates the risk of collapse in the event of an earthquake based on information such as the number of floors and structural materials of a building. Furthermore, the prediction unit can also perform evacuation simulations based on family composition. For example, the prediction unit performs evacuation simulations based on information such as the number of family members and age composition, and evaluates the risks during evacuation. This allows the prediction unit to predict damage in the event of a disaster.

[0034] The providing unit can provide the user with the prediction results obtained by the prediction unit. For example, the providing unit notifies the user that they live in an area with a high risk of flooding. For example, the providing unit notifies the user of the flood risk based on the flood inundation area. The providing unit can also notify the user that they live in an area with a high risk of earthquakes. For example, the providing unit notifies the user of the earthquake risk based on the risk of collapse during an earthquake. Furthermore, the providing unit can also suggest an evacuation plan to the user based on family composition. For example, the providing unit suggests an evacuation plan based on information such as the number of family members and age composition. In this way, the providing unit can provide the user with the damage prediction results.

[0035] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, automatically displays information that the user has frequently input in the past as a candidate. For example, the reception unit automatically displays information that the user has frequently input as a candidate based on the past input history. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit preferentially suggests input methods that the user has used in the past based on the past input history. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past input history. For example, the reception unit predicts and suggests information that will be used in a specific time period based on the past input history. In this way, the reception unit can improve input efficiency by selecting the optimal input method based on the past input history.

[0036] The reception unit can filter the region and living environment information based on the user's current living situation and areas of interest when inputting the information. For example, if the user is raising a child, the reception unit prioritizes input of information related to the safety of the child. For example, the reception unit detects that the user is raising a child and prioritizes input of information related to the safety of the child. Furthermore, if the user is elderly, the reception unit can also prioritize input of disaster prevention measures information for elderly people. For example, the reception unit detects that the user is elderly and prioritizes input of disaster prevention measures information for elderly people. Furthermore, if the user has a pet, the reception unit can also prioritize input of information related to the evacuation of the pet. For example, the reception unit detects that the user has a pet and prioritizes input of information related to the evacuation of the pet. In this way, the reception unit can improve the accuracy of input by prioritizes input of information according to the user's living situation and areas of interest.

[0037] When inputting regional and living environment information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user lives near the coast, the reception unit causes information regarding tsunami countermeasures to be prioritized. For example, the reception unit causes information regarding tsunami countermeasures to be prioritized based on the user's geographical location information. Furthermore, if the user lives in a mountainous area, the reception unit can also prioritize inputting information regarding landslide countermeasures. For example, the reception unit causes information regarding landslide countermeasures to be prioritized based on the user's geographical location information. Furthermore, if the user lives in an urban area, the reception unit can also prioritize inputting information regarding earthquake countermeasures. For example, the reception unit causes information regarding earthquake countermeasures to be prioritized based on the user's geographical location information. In this way, the reception unit can improve the accuracy of input by preferentially inputting highly relevant information based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and input related information when inputting region and living environment information. For example, if the user has posted about disaster prevention on social media, the reception unit can prompt the user to input information based on the content of the posts. For example, the reception unit can analyze the user's social media activity and prompt the user to input information based on the content of the posts about disaster prevention. Furthermore, if the user has shared information about a specific region on social media, the reception unit can also prompt the user to input information related to that region. For example, the reception unit can analyze the user's social media activity and prompt the user to input information about the specific region. Furthermore, if the user has published information about their family structure on social media, the reception unit can also simplify the input based on that information. For example, the reception unit can analyze the user's social media activity and simplify the input based on the information about their family structure. In this way, the reception unit can improve the accuracy of input by prompting the user to input related information based on the user's social media activity.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit evaluates the importance of the information and performs a detailed analysis on the information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. For example, the analysis unit evaluates the importance of the information and performs a simplified analysis on the information of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. For example, the analysis unit evaluates the importance of the information and performs an analysis with an appropriate level of detail on information of medium importance. In this way, the analysis unit can perform an efficient analysis by adjusting the level of detail of the analysis according to the importance of the information.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a terrain analysis algorithm to terrain information. For example, the analysis unit applies a terrain analysis algorithm to analyze the terrain information. The analysis unit can also apply a building analysis algorithm to building structural information. For example, the analysis unit applies a building analysis algorithm to analyze building structural information. Furthermore, the analysis unit can also apply a demographic analysis algorithm to family composition information. For example, the analysis unit applies a demographic analysis algorithm to analyze family composition information. In this way, the analysis unit can improve the accuracy of the analysis by applying an analysis algorithm depending on the category of information.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the information. The analysis unit, for example, prioritizes analysis of the latest information. For example, the analysis unit prioritizes analysis of the latest information based on the time of submission of the information. The analysis unit can also analyze older information with a lower priority. For example, the analysis unit analyzes older information with a lower priority based on the time of submission of the information. Furthermore, the analysis unit can analyze information that has been submitted at a medium time with a moderate priority. For example, the analysis unit analyzes medium information with a moderate priority based on the time of submission of the information. In this way, the analysis unit can perform efficient analysis by determining the priority of analysis based on the time of submission of the information.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. For example, the analysis unit evaluates the relevance of the information and prioritizes analysis of information with high relevance. The analysis unit can also postpone the order of analysis of information with low relevance. For example, the analysis unit evaluates the relevance of the information and postpones the order of analysis of information with low relevance. Furthermore, the analysis unit can analyze information with medium relevance in an appropriate order. For example, the analysis unit evaluates the relevance of the information and analyzes information with medium relevance in an appropriate order. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the information.

[0043] The identification unit can improve the accuracy of identification by taking into account the interrelationships of information during identification. The identification unit, for example, combines topographical information and building structural information to improve the accuracy of identification. For example, the identification unit combines topographical information and building structural information to analyze and improve the accuracy of identification. The identification unit can also combine family composition information and regional demographic information to improve the accuracy of identification. For example, the identification unit combines family composition information and regional demographic information to analyze and improve the accuracy of identification. Furthermore, the identification unit can also combine past disaster data and current regional information to improve the accuracy of identification. For example, the identification unit combines past disaster data and current regional information to analyze and improve the accuracy of identification. In this way, the identification unit can improve the accuracy of identification by taking into account the interrelationships of information.

[0044] The identification unit can perform the identification by taking into consideration attribute information of the person who submitted the information. For example, if the person who submitted the information is elderly, the identification unit identifies an evacuation site for elderly people. For example, the identification unit identifies an evacuation site for elderly people based on the attribute information of the person who submitted the information. Furthermore, if the person who submitted the information is raising children, the identification unit can also identify an evacuation site that takes into consideration the safety of children. For example, the identification unit identifies an evacuation site that takes into consideration the safety of children based on the attribute information of the person who submitted the information. Furthermore, if the person who submitted the information has pets, the identification unit can also identify an evacuation site that allows pets. For example, the identification unit identifies an evacuation site that allows pets based on the attribute information of the person who submitted the information. In this way, the identification unit can improve the accuracy of the identification by taking into consideration the attribute information of the person who submitted the information.

[0045] The identification unit can perform the identification by taking into consideration the geographical distribution of information. For example, the identification unit prioritizes identifying geographically close evacuation sites. For example, the identification unit prioritizes identifying geographically close evacuation sites based on the geographical distribution of information. The identification unit can also lower the priority of identifying geographically distant evacuation sites. For example, the identification unit lowers the priority of geographically distant evacuation sites based on the geographical distribution of information. Furthermore, the identification unit can also identify evacuation sites that are at a medium geographical distance with a moderate priority. For example, the identification unit identifies evacuation sites that are at a medium distance with a moderate priority based on the geographical distribution of information. In this way, the identification unit can improve the accuracy of the identification by taking into consideration the geographical distribution of information.

[0046] During identification, the identification unit can improve the accuracy of the identification by referring to literature related to the information. The identification unit, for example, improves the accuracy of the identification by referring to literature related to past disasters. For example, the identification unit improves the accuracy of the identification by referring to literature related to past disasters. The identification unit can also improve the accuracy of the identification by referring to the latest research on disaster prevention measures. For example, the identification unit improves the accuracy of the identification by referring to the latest research on disaster prevention measures. Furthermore, the identification unit can also improve the accuracy of the identification by referring to disaster prevention cases in other regions. For example, the identification unit improves the accuracy of the identification by referring to disaster prevention cases in other regions. In this way, the identification unit can improve the accuracy of the identification by referring to literature related to the information.

[0047] When making a prediction, the prediction unit can optimize the current prediction by referring to past prediction data. The prediction unit, for example, optimizes the current prediction based on past disaster prediction data. For example, the prediction unit optimizes the current prediction by referring to past disaster prediction data. The prediction unit can also optimize the current prediction by referring to past damage data. For example, the prediction unit optimizes the current prediction by referring to past damage data. Furthermore, the prediction unit can also optimize the current prediction based on past evacuation data. For example, the prediction unit optimizes the current prediction by referring to past evacuation data. In this way, the prediction unit can optimize the current prediction by referring to past prediction data.

[0048] The prediction unit can apply different prediction methods to each information category during prediction. For example, the prediction unit applies a terrain prediction method to terrain information. For example, the prediction unit applies a terrain prediction method to predict the terrain information. The prediction unit can also apply a building prediction method to building structural information. For example, the prediction unit applies a building prediction method to predict building structural information. The prediction unit can also apply a demographic prediction method to family composition information. For example, the prediction unit applies a demographic prediction method to predict family composition information. In this way, the prediction unit can improve the accuracy of prediction by applying a prediction method according to the information category.

[0049] During prediction, the prediction unit can analyze a change in the prediction based on the time of submission of the information. The prediction unit, for example, analyzes a change in the prediction based on the latest information. For example, the prediction unit analyzes a change in the prediction based on the time of submission of the information and the latest information. The prediction unit can also underestimate a change in the prediction for old information. For example, the prediction unit underestimates a change in the prediction for old information based on the time of submission of the information. Furthermore, the prediction unit can analyze a moderate change in the prediction for information submitted at a medium time. For example, the prediction unit moderately analyzes a change in the prediction for medium information based on the time of submission of the information. In this way, the prediction unit can improve the accuracy of the prediction by analyzing a change in the prediction based on the time of submission of the information.

[0050] When making a prediction, the prediction unit can analyze the prediction by referring to market data related to the information. For example, the prediction unit predicts the economic impact of a disaster based on the related market data. For example, the prediction unit predicts the economic impact of a disaster by referring to the related market data. The prediction unit can also predict demand for evacuation sites by referring to the related market data. For example, the prediction unit predicts demand for evacuation sites based on the related market data. Furthermore, the prediction unit can also predict post-disaster reconstruction plans based on the related market data. For example, the prediction unit predicts post-disaster reconstruction plans by referring to the related market data. In this way, the prediction unit can improve the accuracy of the prediction by referring to the market data related to the information.

[0051] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between the information when providing the information. The providing unit, for example, combines topographical information and building structural information to improve the accuracy of the information provided. For example, the providing unit combines topographical information and building structural information to analyze the information provided, thereby improving the accuracy of the information provided. The providing unit can also combine family composition information and regional demographic information to improve the accuracy of the information provided. For example, the providing unit combines family composition information and regional demographic information to analyze the information provided, thereby improving the accuracy of the information provided. Furthermore, the providing unit can also combine past disaster data and current regional information to improve the accuracy of the information provided. For example, the providing unit combines past disaster data and current regional information to analyze the information provided, thereby improving the accuracy of the information provided. This allows the providing unit to improve the accuracy of the information provided by taking into account the interrelationships between the information.

[0052] The providing unit can provide the information while taking into consideration attribute information of the person who submitted the information. For example, if the submitter is an elderly person, the providing unit provides disaster prevention information for elderly people. For example, the providing unit provides disaster prevention information for elderly people based on the attribute information of the submitter. Furthermore, if the submitter is raising children, the providing unit can also provide disaster prevention information that takes into consideration the safety of children. For example, the providing unit provides disaster prevention information that takes into consideration the safety of children based on the attribute information of the submitter. Furthermore, if the submitter has pets, the providing unit can also provide evacuation information that allows pets to be brought along. For example, the providing unit provides evacuation information that allows pets to be brought along based on the attribute information of the submitter. In this way, the providing unit can improve the accuracy of the information provided by taking into consideration the attribute information of the person who submitted the information.

[0053] The providing unit can provide the information taking into consideration the geographical distribution of the information when providing the information. For example, the providing unit can provide information on geographically close evacuation sites with priority. For example, the providing unit can provide information on geographically close evacuation sites with priority based on the geographical distribution of the information. The providing unit can also lower the priority of providing information on geographically distant evacuation sites. For example, the providing unit can lower the priority of information on geographically distant evacuation sites based on the geographical distribution of the information. Furthermore, the providing unit can provide information on evacuation sites that are at a medium geographical distance with a moderate priority. For example, the providing unit can provide information on evacuation sites that are at a medium distance with a moderate priority based on the geographical distribution of the information. In this way, the providing unit can improve the accuracy of the provision by taking into consideration the geographical distribution of the information.

[0054] The providing unit can improve the accuracy of the information provided by referring to literature related to the information when providing the information. The providing unit can, for example, improve the accuracy of the information provided by referring to literature related to past disasters. For example, the providing unit can improve the accuracy of the information provided by referring to literature related to past disasters. The providing unit can also improve the accuracy of the information provided by referring to the latest research on disaster prevention measures. For example, the providing unit can improve the accuracy of the information provided by referring to the latest research on disaster prevention measures. Furthermore, the providing unit can also improve the accuracy of the information provided by referring to disaster prevention examples in other regions. For example, the providing unit can improve the accuracy of the information provided by referring to disaster prevention examples in other regions. In this way, the providing unit can improve the accuracy of the information provided by referring to literature related to the information.

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

[0056] The disaster prevention support system can further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, blood pressure, and body temperature, and provides it to the analysis unit. The analysis unit can evaluate the user's health condition based on this health data and identify risks during evacuation. For example, for a user with a high heart rate, the analysis unit can suggest a reasonable evacuation route. Furthermore, for a user with high blood pressure, the system can prioritize medical support at the evacuation site. Furthermore, for a user with an abnormally high body temperature, the system can suggest cooling measures at the evacuation site. This allows the health management unit to provide appropriate disaster prevention measures according to the user's health condition.

[0057] The disaster prevention support system can further include a history management unit that records the user's past evacuation history. The history management unit records what evacuation actions the user has taken in the past and provides this information to the analysis unit. The analysis unit can analyze the user's evacuation behavior trends based on this history data and suggest the optimal behavior for the next evacuation. For example, a notification urging an earlier evacuation can be sent to a user who has delayed evacuation in the past. A detailed evacuation route can also be provided to a user who has taken the wrong evacuation route in the past. Furthermore, a guideline for behavior at the evacuation site can be provided to a user who has experienced confusion at an evacuation site in the past. This allows the history management unit to provide appropriate disaster prevention measures based on the user's past evacuation behavior.

[0058] The disaster prevention support system can further include a lifestyle management unit that monitors the user's lifestyle. The lifestyle management unit collects data on the user's diet, exercise, sleep, etc. and provides it to the analysis unit. The analysis unit can evaluate the user's lifestyle based on this data and identify risks during evacuation. For example, for a user who is not getting enough exercise, the analysis unit can suggest an evacuation route that takes physical strength into consideration. Also, for a user who is sleep-deprived, it can prioritize resting at an evacuation site. Furthermore, it can suggest nutritional supplements at an evacuation site to a user who has irregular eating habits. In this way, the lifestyle management unit can provide appropriate disaster prevention measures according to the user's lifestyle.

[0059] The disaster prevention support system may further include a communication support unit that supports the user in contacting family and friends. The communication support unit collects the user's contact information and provides it to the analysis unit. Based on this information, the analysis unit can suggest methods for smoothly communicating with family and friends in the event of a disaster. For example, when a disaster occurs, the analysis unit can automatically send a message to family and friends to check on their safety. It can also suggest a meeting place at an evacuation shelter. It can also suggest alternative means in case communication is not possible. In this way, the communication support unit can support the user in smoothly communicating with family and friends in the event of a disaster.

[0060] The disaster prevention support system may further include a pet management unit that manages information about the user's pets. The pet management unit collects information such as the type, number, and health status of the user's pets and provides it to the analysis unit. Based on this information, the analysis unit can identify risks to pets during evacuation. For example, for a user who owns a large dog, the analysis unit can suggest an appropriate route to take their pet during evacuation. Also, if the pet's health is poor, the analysis unit can provide priority medical support at the evacuation site. Furthermore, for a user who owns multiple pets, the analysis unit can suggest pet management methods during evacuation. This allows the pet management unit to provide appropriate disaster prevention measures tailored to the user's pets.

[0061] The disaster prevention support system can further include a community collaboration unit that supports collaboration with the user's local community. The community collaboration unit collects information about the user's local community and provides it to the analysis unit. Based on this information, the analysis unit can suggest ways to collaborate with the local community. For example, it can provide information about local evacuation drills. It can also suggest ways to contact local disaster prevention leaders. It can also suggest ways to share local disaster prevention resources. In this way, the community collaboration unit allows the user to collaborate with the local community to strengthen disaster prevention measures.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit receives information about the area and living environment from the user. Specifically, it receives information such as the topography of the area where the user lives, the structure of buildings, and family composition. For example, the reception unit provides an interface for inputting topographical information about the area where the user lives, and provides a form for inputting building structure information and options for inputting family composition information. Step 2: The analysis unit analyzes the information received by the reception unit. Specifically, it performs analysis to identify insufficient disaster prevention measures and emergency escape routes and evacuation sites based on information such as the local topography, building structure, and family composition. For example, it analyzes topographical information to assess the risk of floods and landslides, analyzes building structure information to assess the risk of collapse during an earthquake, and analyzes family composition information to determine evacuation priorities. Step 3: The identification unit identifies insufficient disaster prevention measures, emergency escape methods, and evacuation locations based on the information analyzed by the analysis unit. Specifically, it identifies how to evacuate in the event of a disaster and which evacuation locations are safe. For example, it recommends evacuating to higher ground for users living in areas with a high risk of flooding, and recommends earthquake-resistant evacuation locations for users living in areas with a high risk of earthquakes. It also proposes evacuation plans based on family composition. Step 4: The prediction unit predicts damage in the event of a disaster based on the information identified by the identification unit. Specifically, it predicts damage in the event of a disaster based on information such as the local topography, building structure, and family composition. For example, it predicts the extent of flooding in the event of a flood, predicts the risk of building collapse in the event of an earthquake, and performs evacuation simulations based on family composition. Step 5: The provision unit provides the user with the prediction results obtained by the prediction unit. Specifically, it provides the user with the analysis results and damage predictions. For example, it notifies the user that they live in an area with a high flood risk, notifies them that they live in an area with a high earthquake risk, and suggests an evacuation plan based on their family composition.

[0064] (Example 2) A disaster prevention support system according to an embodiment of the present invention allows a user to input information about their local area and living environment, thereby identifying insufficient disaster prevention measures, emergency escape methods, and evacuation locations, and predicting damage. In this disaster prevention support system, a user inputs information about their local area and living environment, and the system analyzes the information to identify insufficient disaster prevention measures, emergency escape methods, and evacuation locations. The system then performs a damage prediction and provides the results to the user. For example, a user inputs information about the area in which they live, such as the topography, building structure, and family composition. This information is input into the system, and the system identifies insufficient disaster prevention measures, emergency escape methods, and evacuation locations based on the local topography, building structure, and family composition. For example, the system determines how to evacuate in the event of a disaster and which evacuation locations are safe. Furthermore, the system predicts damage in the event of a disaster based on information about the local topography, building structure, and family composition. For example, it predicts the expected level of damage in the event of a disaster and which areas are particularly dangerous. Finally, the system provides the analysis results and damage prediction to the user. Based on the information provided by the system, users can strengthen their disaster prevention measures and confirm emergency escape methods and evacuation locations. This increases users' disaster prevention awareness. For example, users can learn in detail how to evacuate in the event of a disaster and which evacuation sites are safe. This will raise users' awareness of disaster prevention and strengthen their disaster preparedness. This allows the disaster prevention support system to strengthen disaster prevention measures based on the user's region and living environment, and enable them to take appropriate action in an emergency.

[0065] A disaster prevention support system according to an embodiment includes a reception unit, an analysis unit, an identification unit, a prediction unit, and a provision unit. The reception unit receives information about a region and living environment from a user. The reception unit receives information such as the topography of the region where the user lives, the structure of buildings, and family composition. For example, the reception unit provides an interface for inputting topographical information about the region where the user lives. The reception unit can also provide a form for inputting structural information about buildings. The reception unit can also provide options for inputting family composition information. The analysis unit analyzes the information received by the reception unit. The analysis unit identifies insufficient disaster prevention measures, emergency escape routes, and evacuation locations based on information such as the topography of the region, the structure of buildings, and family composition. For example, the analysis unit analyzes topographical information to assess the risk of floods and landslides. The analysis unit can also analyze structural information about buildings to assess the risk of collapse during an earthquake. The analysis unit can also analyze family composition information to determine evacuation priorities. The identification unit identifies insufficient disaster prevention measures, emergency escape routes, and evacuation locations based on the information analyzed by the analysis unit. The identification unit identifies how to evacuate in the event of a disaster, which evacuation site is safe, etc. For example, the identification unit recommends that a user living in an area with a high flood risk evacuate to higher ground. The identification unit can also recommend highly earthquake-resistant evacuation sites to a user living in an area with a high earthquake risk. Furthermore, the identification unit can propose evacuation plans according to family composition. The prediction unit predicts damage in the event of a disaster based on the information identified by the identification unit. The prediction unit predicts damage in the event of a disaster based on information such as the local topography, building structure, and family composition. For example, the prediction unit predicts the inundation area in the event of a flood. The prediction unit can also predict the risk of building collapse in the event of an earthquake. Furthermore, the prediction unit can perform evacuation simulations according to family composition. The provision unit provides the prediction results obtained by the prediction unit to the user. The provision unit provides the analysis results and damage predictions to the user. For example, the provision unit notifies the user that the user lives in an area with a high flood risk.The providing unit can also notify the user that they live in an area with a high earthquake risk. Furthermore, the providing unit can also suggest an evacuation plan to the user based on their family structure. This allows the disaster prevention support system according to the embodiment to strengthen disaster prevention measures based on the user's area and living environment and take appropriate action in an emergency.

[0066] The reception unit can accept information about the topography, building structure, family composition, and other related information of the area where the user lives. The reception unit, for example, provides an interface for inputting topographical information about the area where the user lives. For example, the reception unit has a map display function for inputting topographical information. The reception unit can also provide a form for inputting structural information about the building. For example, the reception unit provides options for inputting information such as the number of floors and structural materials of the building. The reception unit can also provide options for inputting family composition information. For example, the reception unit provides a form for inputting information such as the number of family members and their age composition. The reception unit can also accept other related information. For example, the reception unit provides an interface for inputting information such as weather information and past disaster history. This allows the reception unit to accept detailed information about the user's area and living environment.

[0067] The analysis unit can analyze the local topography, building structure, family composition, etc. based on the information received by the reception unit. The analysis unit, for example, analyzes topographical information to evaluate the risk of floods and landslides. For example, the analysis unit simulates the flood inundation area based on topographical data. The analysis unit can also analyze building structural information to evaluate the risk of collapse during an earthquake. For example, the analysis unit simulates the risk of collapse during an earthquake based on information such as the number of floors and structural materials of a building. Furthermore, the analysis unit can analyze family composition information to determine priorities for evacuation. For example, the analysis unit determines priorities for evacuation based on information such as the number of family members and age composition. This allows the analysis unit to perform detailed analysis of information related to the local area and living environment.

[0068] The identification unit can identify how to evacuate in the event of a disaster and which evacuation locations are safe, based on the information analyzed by the analysis unit. For example, the identification unit recommends evacuation to higher ground for a user living in an area with a high risk of flooding. For example, the identification unit identifies an evacuation route to higher ground based on the extent of flood inundation. The identification unit can also recommend highly earthquake-resistant evacuation locations for a user living in an area with a high risk of earthquakes. For example, the identification unit identifies highly earthquake-resistant evacuation locations based on the risk of collapse during an earthquake. Furthermore, the identification unit can propose evacuation plans based on family composition. For example, the identification unit determines evacuation priorities based on information such as the number of family members and their age composition, and proposes an evacuation plan. This allows the identification unit to identify evacuation methods and safe evacuation locations in the event of a disaster.

[0069] The prediction unit can predict damage in the event of a disaster based on the information identified by the identification unit. The prediction unit, for example, predicts the inundation area in the event of a flood. For example, the prediction unit simulates the inundation area in the event of a flood based on topographical data. The prediction unit can also predict the risk of building collapse in the event of an earthquake. For example, the prediction unit simulates the risk of collapse in the event of an earthquake based on information such as the number of floors and structural materials of a building. Furthermore, the prediction unit can also perform evacuation simulations based on family composition. For example, the prediction unit performs evacuation simulations based on information such as the number of family members and age composition, and evaluates the risks during evacuation. This allows the prediction unit to predict damage in the event of a disaster.

[0070] The providing unit can provide the user with the prediction results obtained by the prediction unit. For example, the providing unit notifies the user that they live in an area with a high risk of flooding. For example, the providing unit notifies the user of the flood risk based on the flood inundation area. The providing unit can also notify the user that they live in an area with a high risk of earthquakes. For example, the providing unit notifies the user of the earthquake risk based on the risk of collapse during an earthquake. Furthermore, the providing unit can also suggest an evacuation plan to the user based on family composition. For example, the providing unit suggests an evacuation plan based on information such as the number of family members and age composition. In this way, the providing unit can provide the user with the damage prediction results.

[0071] The reception unit can estimate the user's emotions and adjust the timing of input of area and living environment information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit divides the input into stages and makes the input happen in stages. For example, the reception unit detects that the user is feeling stressed using an emotion estimation algorithm and adjusts the input to divide and make the input happen in stages. The reception unit can also allow the user to input all information at once if the user is relaxed. For example, the reception unit detects that the user is relaxed using an emotion estimation algorithm and adjusts the input to allow all information to be input at once. Furthermore, if the user is in a hurry, the reception unit can prioritize input of only the most important information. For example, the reception unit detects that the user is in a hurry using an emotion estimation algorithm and adjusts the input to prioritize input of only the most important information. In this way, the reception unit can reduce the input burden by adjusting the input timing according to the user's emotions.

[0072] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, automatically displays information that the user has frequently input in the past as a candidate. For example, the reception unit automatically displays information that the user has frequently input as a candidate based on the past input history. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit preferentially suggests input methods that the user has used in the past based on the past input history. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past input history. For example, the reception unit predicts and suggests information that will be used in a specific time period based on the past input history. In this way, the reception unit can improve input efficiency by selecting the optimal input method based on the past input history.

[0073] The reception unit can filter the region and living environment information based on the user's current living situation and areas of interest when inputting the information. For example, if the user is raising a child, the reception unit prioritizes input of information related to the safety of the child. For example, the reception unit detects that the user is raising a child and prioritizes input of information related to the safety of the child. Furthermore, if the user is elderly, the reception unit can also prioritize input of disaster prevention measures information for elderly people. For example, the reception unit detects that the user is elderly and prioritizes input of disaster prevention measures information for elderly people. Furthermore, if the user has a pet, the reception unit can also prioritize input of information related to the evacuation of the pet. For example, the reception unit detects that the user has a pet and prioritizes input of information related to the evacuation of the pet. In this way, the reception unit can improve the accuracy of input by prioritizes input of information according to the user's living situation and areas of interest.

[0074] The reception unit can estimate the user's emotion and determine the priority of information to be input based on the estimated user's emotion. For example, when the user is nervous, the reception unit allows the most important information to be input preferentially. For example, the reception unit detects that the user is nervous using an emotion estimation algorithm and allows the most important information to be input preferentially. Furthermore, when the user is relaxed, the reception unit can also allow detailed information to be input. For example, the reception unit detects that the user is relaxed using an emotion estimation algorithm and allows the user to input detailed information. Furthermore, when the user is in a hurry, the reception unit can also allow only the minimum necessary information to be input. For example, the reception unit detects that the user is in a hurry using an emotion estimation algorithm and allows the user to input only the minimum necessary information. In this way, the reception unit can determine the priority of information to be input according to the user's emotion, allowing important information to be input preferentially.

[0075] When inputting regional and living environment information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user lives near the coast, the reception unit causes information regarding tsunami countermeasures to be prioritized. For example, the reception unit causes information regarding tsunami countermeasures to be prioritized based on the user's geographical location information. Furthermore, if the user lives in a mountainous area, the reception unit can also prioritize inputting information regarding landslide countermeasures. For example, the reception unit causes information regarding landslide countermeasures to be prioritized based on the user's geographical location information. Furthermore, if the user lives in an urban area, the reception unit can also prioritize inputting information regarding earthquake countermeasures. For example, the reception unit causes information regarding earthquake countermeasures to be prioritized based on the user's geographical location information. In this way, the reception unit can improve the accuracy of input by preferentially inputting highly relevant information based on the user's geographical location information.

[0076] The reception unit can analyze the user's social media activity and input related information when inputting region and living environment information. For example, if the user has posted about disaster prevention on social media, the reception unit can prompt the user to input information based on the content of the posts. For example, the reception unit can analyze the user's social media activity and prompt the user to input information based on the content of the posts about disaster prevention. Furthermore, if the user has shared information about a specific region on social media, the reception unit can also prompt the user to input information related to that region. For example, the reception unit can analyze the user's social media activity and prompt the user to input information about the specific region. Furthermore, if the user has published information about their family structure on social media, the reception unit can also simplify the input based on that information. For example, the reception unit can analyze the user's social media activity and simplify the input based on the information about their family structure. In this way, the reception unit can improve the accuracy of input by prompting the user to input related information based on the user's social media activity.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible analysis result. For example, the analysis unit detects that the user is nervous using an emotion estimation algorithm and provides a simple and highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit detects that the user is relaxed using an emotion estimation algorithm and provides a detailed analysis result. Furthermore, the analysis unit can also provide a summary of the analysis result if the user is in a hurry. For example, the analysis unit detects that the user is in a hurry using an emotion estimation algorithm and provides a summary of the analysis result. In this way, the analysis unit can adjust the way the analysis is presented based on the user's emotions, thereby facilitating understanding of the analysis result.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit evaluates the importance of the information and performs a detailed analysis on the information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. For example, the analysis unit evaluates the importance of the information and performs a simplified analysis on the information of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. For example, the analysis unit evaluates the importance of the information and performs an analysis with an appropriate level of detail on information of medium importance. In this way, the analysis unit can perform an efficient analysis by adjusting the level of detail of the analysis according to the importance of the information.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a terrain analysis algorithm to terrain information. For example, the analysis unit applies a terrain analysis algorithm to analyze the terrain information. The analysis unit can also apply a building analysis algorithm to building structural information. For example, the analysis unit applies a building analysis algorithm to analyze building structural information. Furthermore, the analysis unit can also apply a demographic analysis algorithm to family composition information. For example, the analysis unit applies a demographic analysis algorithm to analyze family composition information. In this way, the analysis unit can improve the accuracy of the analysis by applying an analysis algorithm depending on the category of information.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a short and concise analysis result. For example, the analysis unit detects that the user is nervous using an emotion estimation algorithm and provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit detects that the user is relaxed using an emotion estimation algorithm and provides a detailed analysis result. Furthermore, the analysis unit can also provide a concise analysis result if the user is in a hurry. For example, the analysis unit detects that the user is in a hurry using an emotion estimation algorithm and provides a concise analysis result. In this way, the analysis unit can adjust the length of the analysis according to the user's emotions, thereby facilitating understanding of the analysis result.

[0081] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the information. The analysis unit, for example, prioritizes analysis of the latest information. For example, the analysis unit prioritizes analysis of the latest information based on the time of submission of the information. The analysis unit can also analyze older information with a lower priority. For example, the analysis unit analyzes older information with a lower priority based on the time of submission of the information. Furthermore, the analysis unit can analyze information that has been submitted at a medium time with a moderate priority. For example, the analysis unit analyzes medium information with a moderate priority based on the time of submission of the information. In this way, the analysis unit can perform efficient analysis by determining the priority of analysis based on the time of submission of the information.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. For example, the analysis unit evaluates the relevance of the information and prioritizes analysis of information with high relevance. The analysis unit can also postpone the order of analysis of information with low relevance. For example, the analysis unit evaluates the relevance of the information and postpones the order of analysis of information with low relevance. Furthermore, the analysis unit can analyze information with medium relevance in an appropriate order. For example, the analysis unit evaluates the relevance of the information and analyzes information with medium relevance in an appropriate order. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the information.

[0083] The identification unit can estimate the user's emotion and adjust the identification criteria based on the estimated user's emotion. For example, if the user is nervous, the identification unit provides a simple, highly visible criterion. For example, the identification unit detects that the user is nervous using an emotion estimation algorithm and provides a simple, highly visible criterion. The identification unit can also provide detailed criteria if the user is relaxed. For example, the identification unit detects that the user is relaxed using an emotion estimation algorithm and provides a detailed criterion. Furthermore, the identification unit can also provide criteria that emphasize the main points if the user is in a hurry. For example, the identification unit detects that the user is in a hurry using an emotion estimation algorithm and provides a criterion that emphasizes the main points. In this way, the identification unit can adjust the identification criteria according to the user's emotion, thereby facilitating understanding of the identification results.

[0084] The identification unit can improve the accuracy of identification by taking into account the interrelationships of information during identification. The identification unit, for example, combines topographical information and building structural information to improve the accuracy of identification. For example, the identification unit combines topographical information and building structural information to analyze and improve the accuracy of identification. The identification unit can also combine family composition information and regional demographic information to improve the accuracy of identification. For example, the identification unit combines family composition information and regional demographic information to analyze and improve the accuracy of identification. Furthermore, the identification unit can also combine past disaster data and current regional information to improve the accuracy of identification. For example, the identification unit combines past disaster data and current regional information to analyze and improve the accuracy of identification. In this way, the identification unit can improve the accuracy of identification by taking into account the interrelationships of information.

[0085] The identification unit can perform the identification by taking into consideration attribute information of the person who submitted the information. For example, if the person who submitted the information is elderly, the identification unit identifies an evacuation site for elderly people. For example, the identification unit identifies an evacuation site for elderly people based on the attribute information of the person who submitted the information. Furthermore, if the person who submitted the information is raising children, the identification unit can also identify an evacuation site that takes into consideration the safety of children. For example, the identification unit identifies an evacuation site that takes into consideration the safety of children based on the attribute information of the person who submitted the information. Furthermore, if the person who submitted the information has pets, the identification unit can also identify an evacuation site that allows pets. For example, the identification unit identifies an evacuation site that allows pets based on the attribute information of the person who submitted the information. In this way, the identification unit can improve the accuracy of the identification by taking into consideration the attribute information of the person who submitted the information.

[0086] The identification unit can estimate the user's emotion and adjust the order in which specific results are displayed based on the estimated user's emotion. For example, if the user is nervous, the identification unit displays the most important result first. For example, the identification unit detects that the user is nervous using an emotion estimation algorithm and displays the most important result first. The identification unit can also sequentially display detailed results if the user is relaxed. For example, the identification unit detects that the user is relaxed using an emotion estimation algorithm and displays detailed results sequentially. Furthermore, the identification unit can also display results that emphasize the main points first if the user is in a hurry. For example, the identification unit detects that the user is in a hurry using an emotion estimation algorithm and displays results that emphasize the main points first. In this way, the identification unit can adjust the order in which specific results are displayed according to the user's emotion, thereby facilitating understanding of the specific results.

[0087] The identification unit can perform the identification by taking into consideration the geographical distribution of information. For example, the identification unit prioritizes identifying geographically close evacuation sites. For example, the identification unit prioritizes identifying geographically close evacuation sites based on the geographical distribution of information. The identification unit can also lower the priority of identifying geographically distant evacuation sites. For example, the identification unit lowers the priority of geographically distant evacuation sites based on the geographical distribution of information. Furthermore, the identification unit can also identify evacuation sites that are at a medium geographical distance with a moderate priority. For example, the identification unit identifies evacuation sites that are at a medium distance with a moderate priority based on the geographical distribution of information. In this way, the identification unit can improve the accuracy of the identification by taking into consideration the geographical distribution of information.

[0088] During identification, the identification unit can improve the accuracy of the identification by referring to literature related to the information. The identification unit, for example, improves the accuracy of the identification by referring to literature related to past disasters. For example, the identification unit improves the accuracy of the identification by referring to literature related to past disasters. The identification unit can also improve the accuracy of the identification by referring to the latest research on disaster prevention measures. For example, the identification unit improves the accuracy of the identification by referring to the latest research on disaster prevention measures. Furthermore, the identification unit can also improve the accuracy of the identification by referring to disaster prevention cases in other regions. For example, the identification unit improves the accuracy of the identification by referring to disaster prevention cases in other regions. In this way, the identification unit can improve the accuracy of the identification by referring to literature related to the information.

[0089] The prediction unit can estimate the user's emotions and adjust the display method of the prediction based on the estimated user's emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible display method. For example, the prediction unit detects that the user is nervous using an emotion estimation algorithm and provides a simple and highly visible display method. The prediction unit can also provide a detailed prediction result if the user is relaxed. For example, the prediction unit detects that the user is relaxed using an emotion estimation algorithm and provides a detailed prediction result. Furthermore, the prediction unit can also provide a prediction result that focuses on the main points if the user is in a hurry. For example, the prediction unit detects that the user is in a hurry using an emotion estimation algorithm and provides a prediction result that focuses on the main points. In this way, the prediction unit can adjust the display method of the prediction according to the user's emotions, thereby facilitating understanding of the prediction result.

[0090] When making a prediction, the prediction unit can optimize the current prediction by referring to past prediction data. The prediction unit, for example, optimizes the current prediction based on past disaster prediction data. For example, the prediction unit optimizes the current prediction by referring to past disaster prediction data. The prediction unit can also optimize the current prediction by referring to past damage data. For example, the prediction unit optimizes the current prediction by referring to past damage data. Furthermore, the prediction unit can also optimize the current prediction based on past evacuation data. For example, the prediction unit optimizes the current prediction by referring to past evacuation data. In this way, the prediction unit can optimize the current prediction by referring to past prediction data.

[0091] The prediction unit can apply different prediction methods to each information category during prediction. For example, the prediction unit applies a terrain prediction method to terrain information. For example, the prediction unit applies a terrain prediction method to predict the terrain information. The prediction unit can also apply a building prediction method to building structural information. For example, the prediction unit applies a building prediction method to predict building structural information. The prediction unit can also apply a demographic prediction method to family composition information. For example, the prediction unit applies a demographic prediction method to predict family composition information. In this way, the prediction unit can improve the accuracy of prediction by applying a prediction method according to the information category.

[0092] The prediction unit can estimate the user's emotions and adjust the importance of the predictions based on the estimated user emotions. For example, when the user is nervous, the prediction unit prioritizes displaying the most important prediction results. For example, the prediction unit detects that the user is nervous using an emotion estimation algorithm and prioritizes displaying the most important prediction results. The prediction unit can also sequentially display detailed prediction results when the user is relaxed. For example, the prediction unit detects that the user is relaxed using an emotion estimation algorithm and sequentially display detailed prediction results. Furthermore, when the user is in a hurry, the prediction unit can also prioritize displaying prediction results that emphasize the main points. For example, the prediction unit detects that the user is in a hurry using an emotion estimation algorithm and prioritizes displaying prediction results that emphasize the main points. In this way, the prediction unit can prioritize providing important prediction results by adjusting the importance of the predictions according to the user's emotions.

[0093] During prediction, the prediction unit can analyze a change in the prediction based on the time of submission of the information. The prediction unit, for example, analyzes a change in the prediction based on the latest information. For example, the prediction unit analyzes a change in the prediction based on the time of submission of the information and the latest information. The prediction unit can also underestimate a change in the prediction for old information. For example, the prediction unit underestimates a change in the prediction for old information based on the time of submission of the information. Furthermore, the prediction unit can analyze a moderate change in the prediction for information submitted at a medium time. For example, the prediction unit moderately analyzes a change in the prediction for medium information based on the time of submission of the information. In this way, the prediction unit can improve the accuracy of the prediction by analyzing a change in the prediction based on the time of submission of the information.

[0094] When making a prediction, the prediction unit can analyze the prediction by referring to market data related to the information. For example, the prediction unit predicts the economic impact of a disaster based on the related market data. For example, the prediction unit predicts the economic impact of a disaster by referring to the related market data. The prediction unit can also predict demand for evacuation sites by referring to the related market data. For example, the prediction unit predicts demand for evacuation sites based on the related market data. Furthermore, the prediction unit can also predict post-disaster reconstruction plans based on the related market data. For example, the prediction unit predicts post-disaster reconstruction plans by referring to the related market data. In this way, the prediction unit can improve the accuracy of the prediction by referring to the market data related to the information.

[0095] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit prioritizes providing the most important information. For example, the providing unit detects that the user is nervous using an emotion estimation algorithm and prioritizes providing the most important information. The providing unit can also sequentially provide detailed information when the user is relaxed. For example, the providing unit detects that the user is relaxed using an emotion estimation algorithm and sequentially provides detailed information. Furthermore, when the user is in a hurry, the providing unit can also prioritize providing information that emphasizes the main points. For example, the providing unit detects that the user is in a hurry using an emotion estimation algorithm and prioritizes providing information that emphasizes the main points. In this way, the providing unit can prioritize providing important information by determining the priority of information to be provided according to the user's emotion.

[0096] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between the information when providing the information. The providing unit, for example, combines topographical information and building structural information to improve the accuracy of the information provided. For example, the providing unit combines topographical information and building structural information to analyze the information provided, thereby improving the accuracy of the information provided. The providing unit can also combine family composition information and regional demographic information to improve the accuracy of the information provided. For example, the providing unit combines family composition information and regional demographic information to analyze the information provided, thereby improving the accuracy of the information provided. Furthermore, the providing unit can also combine past disaster data and current regional information to improve the accuracy of the information provided. For example, the providing unit combines past disaster data and current regional information to analyze the information provided, thereby improving the accuracy of the information provided. This allows the providing unit to improve the accuracy of the information provided by taking into account the interrelationships between the information.

[0097] The providing unit can provide the information while taking into consideration attribute information of the person who submitted the information. For example, if the submitter is an elderly person, the providing unit provides disaster prevention information for elderly people. For example, the providing unit provides disaster prevention information for elderly people based on the attribute information of the submitter. Furthermore, if the submitter is raising children, the providing unit can also provide disaster prevention information that takes into consideration the safety of children. For example, the providing unit provides disaster prevention information that takes into consideration the safety of children based on the attribute information of the submitter. Furthermore, if the submitter has pets, the providing unit can also provide evacuation information that allows pets to be brought along. For example, the providing unit provides evacuation information that allows pets to be brought along based on the attribute information of the submitter. In this way, the providing unit can improve the accuracy of the information provided by taking into consideration the attribute information of the person who submitted the information.

[0098] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. For example, the providing unit detects that the user is nervous using an emotion estimation algorithm and provides a simple, highly visible display method. The providing unit can also provide detailed information when the user is relaxed. For example, the providing unit detects that the user is relaxed using an emotion estimation algorithm and provides detailed information. Furthermore, the providing unit can also provide information that is concise when the user is in a hurry. For example, the providing unit detects that the user is in a hurry using an emotion estimation algorithm and provides information that is concise. In this way, the providing unit can promote understanding of the information by adjusting the display method of the information to be provided according to the user's emotion.

[0099] The providing unit can provide the information taking into consideration the geographical distribution of the information when providing the information. For example, the providing unit can provide information on geographically close evacuation sites with priority. For example, the providing unit can provide information on geographically close evacuation sites with priority based on the geographical distribution of the information. The providing unit can also lower the priority of providing information on geographically distant evacuation sites. For example, the providing unit can lower the priority of information on geographically distant evacuation sites based on the geographical distribution of the information. Furthermore, the providing unit can provide information on evacuation sites that are at a medium geographical distance with a moderate priority. For example, the providing unit can provide information on evacuation sites that are at a medium distance with a moderate priority based on the geographical distribution of the information. In this way, the providing unit can improve the accuracy of the provision by taking into consideration the geographical distribution of the information.

[0100] The providing unit can improve the accuracy of the information provided by referring to literature related to the information when providing the information. The providing unit can, for example, improve the accuracy of the information provided by referring to literature related to past disasters. For example, the providing unit can improve the accuracy of the information provided by referring to literature related to past disasters. The providing unit can also improve the accuracy of the information provided by referring to the latest research on disaster prevention measures. For example, the providing unit can improve the accuracy of the information provided by referring to the latest research on disaster prevention measures. Furthermore, the providing unit can also improve the accuracy of the information provided by referring to disaster prevention examples in other regions. For example, the providing unit can improve the accuracy of the information provided by referring to disaster prevention examples in other regions. In this way, the providing unit can improve the accuracy of the information provided by referring to literature related to the information. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, prediction unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and provides an interface for the user to input information about the local area and living environment. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the information received from the reception unit. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies insufficient disaster prevention measures and emergency escape routes and evacuation locations based on the analyzed information. The prediction unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and predicts damage in the event of a disaster. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the prediction results to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, prediction unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for the user to voice-input information about the area and living environment. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the information received from the reception unit. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies insufficient disaster prevention measures and emergency escape routes and evacuation locations based on the analyzed information. The prediction unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and predicts damage in the event of a disaster. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the prediction results to the user. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, prediction unit, and provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and provides an interface for the user to voice-input information about the area and living environment. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the information received from the reception unit. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies insufficient disaster prevention measures and emergency escape routes and evacuation locations based on the analyzed information. The prediction unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and predicts damage in the event of a disaster. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides the prediction results to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, prediction unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and provides an interface for the user to voice-input information about the area and living environment. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the information received from the reception unit. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies insufficient disaster prevention measures and emergency escape routes and evacuation locations based on the analyzed information. The prediction unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and predicts damage in the event of a disaster. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the prediction results to the user.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The disaster prevention support system can further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, blood pressure, and body temperature, and provides it to the analysis unit. The analysis unit can evaluate the user's health condition based on this health data and identify risks during evacuation. For example, for a user with a high heart rate, the analysis unit can suggest a reasonable evacuation route. Furthermore, for a user with high blood pressure, the system can prioritize medical support at the evacuation site. Furthermore, for a user with an abnormally high body temperature, the system can suggest cooling measures at the evacuation site. This allows the health management unit to provide appropriate disaster prevention measures according to the user's health condition.

[0103] The disaster prevention support system can further include a history management unit that records the user's past evacuation history. The history management unit records what evacuation actions the user has taken in the past and provides this information to the analysis unit. The analysis unit can analyze the user's evacuation behavior trends based on this history data and suggest the optimal behavior for the next evacuation. For example, a notification urging an earlier evacuation can be sent to a user who has delayed evacuation in the past. A detailed evacuation route can also be provided to a user who has taken the wrong evacuation route in the past. Furthermore, a guideline for behavior at the evacuation site can be provided to a user who has experienced confusion at an evacuation site in the past. This allows the history management unit to provide appropriate disaster prevention measures based on the user's past evacuation behavior.

[0104] The disaster prevention support system can further include a training suggestion unit that estimates the user's emotions and suggests evacuation drills based on the estimated emotions. The training suggestion unit provides the user's emotion data to the analysis unit, and the analysis unit suggests evacuation drills according to the user's emotional state. For example, if the user is feeling anxious, a simple evacuation drill can be suggested. Also, if the user is relaxed, a detailed evacuation drill can be suggested. Furthermore, if the user is feeling stressed, relaxation training to reduce stress can be suggested. In this way, the training suggestion unit can provide appropriate evacuation drills according to the user's emotional state.

[0105] The disaster prevention support system can further include a lifestyle management unit that monitors the user's lifestyle. The lifestyle management unit collects data on the user's diet, exercise, sleep, etc. and provides it to the analysis unit. The analysis unit can evaluate the user's lifestyle based on this data and identify risks during evacuation. For example, for a user who is not getting enough exercise, the analysis unit can suggest an evacuation route that takes physical strength into consideration. Also, for a user who is sleep-deprived, it can prioritize resting at an evacuation site. Furthermore, it can suggest nutritional supplements at an evacuation site to a user who has irregular eating habits. In this way, the lifestyle management unit can provide appropriate disaster prevention measures according to the user's lifestyle.

[0106] The disaster prevention support system can further include a communication support unit that estimates the user's emotions and supports communication during evacuation based on the estimated emotions. The communication support unit provides the user's emotional data to the analysis unit, which then suggests a communication method according to the user's emotional state. For example, if the user is feeling anxious, the analysis unit can send a message that gives a sense of security. If the user is relaxed, the analysis unit can provide detailed evacuation information. Furthermore, if the user is feeling stressed, the analysis unit can provide advice on how to reduce stress. In this way, the communication support unit can support appropriate communication according to the user's emotional state.

[0107] The disaster prevention support system may further include a communication support unit that supports the user in contacting family and friends. The communication support unit collects the user's contact information and provides it to the analysis unit. Based on this information, the analysis unit can suggest methods for smoothly communicating with family and friends in the event of a disaster. For example, when a disaster occurs, the analysis unit can automatically send a message to family and friends to check on their safety. It can also suggest a meeting place at an evacuation shelter. It can also suggest alternative means in case communication is not possible. In this way, the communication support unit can support the user in smoothly communicating with family and friends in the event of a disaster.

[0108] The disaster prevention support system can further include a stress management unit that estimates the user's emotions and performs stress management during evacuation based on the estimated emotions. The stress management unit provides the user's emotional data to the analysis unit, which then suggests a stress management method according to the user's emotional state. For example, if the user is feeling high stress, the analysis unit can suggest a relaxation method. If the user is feeling anxious, the analysis unit can also send a message that gives a sense of security. Furthermore, if the user is feeling tense, the analysis unit can suggest breathing techniques to relieve tension. In this way, the stress management unit can provide appropriate stress management according to the user's emotional state.

[0109] The disaster prevention support system may further include a pet management unit that manages information about the user's pets. The pet management unit collects information such as the type, number, and health status of the user's pets and provides it to the analysis unit. Based on this information, the analysis unit can identify risks to pets during evacuation. For example, for a user who owns a large dog, the analysis unit can suggest an appropriate route to take their pet during evacuation. Also, if the pet's health is poor, the analysis unit can provide priority medical support at the evacuation site. Furthermore, for a user who owns multiple pets, the analysis unit can suggest pet management methods during evacuation. This allows the pet management unit to provide appropriate disaster prevention measures tailored to the user's pets.

[0110] The disaster prevention support system can further include an information providing unit that estimates the user's emotions and provides information during evacuation based on the estimated emotions. The information providing unit provides the user's emotional data to the analysis unit, and the analysis unit suggests an information providing method according to the user's emotional state. For example, if the user is feeling anxious, information that gives a sense of security can be provided. Also, if the user is relaxed, detailed evacuation information can be provided. Furthermore, if the user is feeling stressed, advice for reducing stress can be provided. This allows the information providing unit to provide appropriate information according to the user's emotional state.

[0111] The disaster prevention support system can further include a community collaboration unit that supports collaboration with the user's local community. The community collaboration unit collects information about the user's local community and provides it to the analysis unit. Based on this information, the analysis unit can suggest ways to collaborate with the local community. For example, it can provide information about local evacuation drills. It can also suggest ways to contact local disaster prevention leaders. It can also suggest ways to share local disaster prevention resources. In this way, the community collaboration unit allows the user to collaborate with the local community to strengthen disaster prevention measures.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit receives information about the area and living environment from the user. Specifically, it receives information such as the topography of the area where the user lives, the structure of buildings, and family composition. For example, the reception unit provides an interface for inputting topographical information about the area where the user lives, and provides a form for inputting building structure information and options for inputting family composition information. Step 2: The analysis unit analyzes the information received by the reception unit. Specifically, it performs analysis to identify insufficient disaster prevention measures and emergency escape routes and evacuation sites based on information such as the local topography, building structure, and family composition. For example, it analyzes topographical information to assess the risk of floods and landslides, analyzes building structure information to assess the risk of collapse during an earthquake, and analyzes family composition information to determine evacuation priorities. Step 3: The identification unit identifies insufficient disaster prevention measures, emergency escape methods, and evacuation locations based on the information analyzed by the analysis unit. Specifically, it identifies how to evacuate in the event of a disaster and which evacuation locations are safe. For example, it recommends evacuating to higher ground for users living in areas with a high risk of flooding, and recommends earthquake-resistant evacuation locations for users living in areas with a high risk of earthquakes. It also proposes evacuation plans based on family composition. Step 4: The prediction unit predicts damage in the event of a disaster based on the information identified by the identification unit. Specifically, it predicts damage in the event of a disaster based on information such as the local topography, building structure, and family composition. For example, it predicts the extent of flooding in the event of a flood, predicts the risk of building collapse in the event of an earthquake, and performs evacuation simulations based on family composition. Step 5: The provision unit provides the user with the prediction results obtained by the prediction unit. Specifically, it provides the user with the analysis results and damage predictions. For example, it notifies the user that they live in an area with a high flood risk, notifies them that they live in an area with a high earthquake risk, and suggests an evacuation plan based on their family composition.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information about the region and living environment from a user; an analysis unit that analyzes the information received by the reception unit; an identification unit that identifies insufficient disaster prevention measures or emergency escape routes and evacuation sites based on the information analyzed by the analysis unit; a prediction unit that performs damage prediction based on the information identified by the identification unit; a providing unit that provides a user with the prediction result obtained by the prediction unit. A system characterized by:

2. The reception unit Accepts information about the area where the user lives, such as the topography, building structure, family composition, and other relevant information.

2. The system of claim 1.

3. The analysis unit Based on the information received by the reception unit, the topography of the area, the structure of buildings, family composition, etc. are analyzed.

2. The system of claim 1.

4. The identification unit Based on the information analyzed by the analysis unit, how to evacuate in the event of a disaster and which evacuation site is safe are identified.

2. The system of claim 1.

5. The prediction unit Based on the information identified by the identification unit, damage prediction is performed in the event of a disaster.

2. The system of claim 1.

6. The providing unit The prediction result obtained by the prediction unit is provided to the user.

2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of inputting local and living environment information based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

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