System

Abstract

An object of a system according to an embodiment is to evaluate a disaster prevention risk from a photograph provided by a user and propose an appropriate disaster prevention product.SOLUTION: A system includes a photograph recognition part, a structure analysis part, a disaster prevention risk evaluation part, and a product proposal part. The photo recognition unit recognizes a photo provided by a user. The structure analysis unit analyzes detailed information on the arrangement of furniture, the type of building material, and the position of a window from the photograph recognized by the photograph recognition unit. The disaster prevention risk evaluation unit evaluates a specific risk that may occur at the time of a disaster such as an earthquake or a flood based on the detailed information analyzed by the structure analysis unit. The product proposal part proposes a disaster prevention product recommended on the basis of the risk evaluated by the disaster prevention risk evaluation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to adequately assess disaster risk specifically from photos provided by users and suggest appropriate disaster prevention products.

[0005] The system according to the embodiment aims to evaluate disaster prevention risks based on photos provided by users and to suggest appropriate disaster prevention products. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo recognition unit, a structural analysis unit, a disaster prevention risk assessment unit, and a product proposal unit. The photo recognition unit recognizes photos provided by users. The structural analysis unit analyzes detailed information such as furniture layout, types of building materials, and window positions from the photos recognized by the photo recognition unit. The disaster prevention risk assessment unit evaluates specific risks that may occur in the event of a disaster such as an earthquake or flood based on the detailed information analyzed by the structural analysis unit. The product proposal unit recommends disaster prevention products based on the risks assessed by the disaster prevention risk assessment unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate disaster prevention risks from photos provided by users and suggest appropriate disaster prevention products. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A disaster prevention risk assessment system according to an embodiment of the present invention automatically analyzes photos provided by users, and a generation AI acquires detailed information such as furniture layout, building material types, and window locations, assesses disaster prevention risks, and recommends disaster prevention products. As a result, the disaster prevention risk assessment system can propose specific disaster prevention measures based on the user's living environment and reduce disaster risks.

[0029] A disaster prevention risk assessment system according to an embodiment includes a photo recognition unit, a structural analysis unit, a disaster prevention risk assessment unit, and a product proposal unit. The photo recognition unit recognizes photos provided by a user. For example, the photo recognition unit recognizes indoor and exterior photos and acquires data for analysis. The photo recognition unit can also recognize photos taken from a specific angle. The structural analysis unit analyzes detailed information about furniture layout, building material types, and window locations from the photos recognized by the photo recognition unit. For example, the structural analysis unit analyzes furniture layout to identify locations that are likely to collapse during an earthquake. The structural analysis unit can also analyze building material types to identify materials that are vulnerable to flooding. The structural analysis unit can also analyze window locations to assess disaster risks. The disaster prevention risk assessment unit assesses specific risks that may occur in the event of a disaster such as an earthquake or flood based on the detailed information analyzed by the structural analysis unit. For example, the disaster prevention risk assessment unit assesses the risk if furniture is positioned in a location that is likely to collapse during an earthquake. The disaster prevention risk assessment unit can also assess the risk if building materials are vulnerable to flooding. Furthermore, the disaster prevention risk assessment unit evaluates the risk if the window's location is dangerous in the event of a disaster. The product proposal unit proposes recommended disaster prevention products based on the risks assessed by the disaster prevention risk assessment unit. For example, the product proposal unit proposes furniture fasteners and earthquake-resistant mats as earthquake countermeasures. The product proposal unit can also propose waterproof sheets and drainage pumps as flood countermeasures. The product proposal unit can also propose evacuation equipment in the event of a disaster. In this way, the disaster prevention risk assessment system according to the embodiment can propose specific disaster prevention measures based on the user's living environment and reduce the risk of disaster.

[0030] The structural analysis unit can analyze the color, shape, and age of furniture to perform risk assessment. For example, the generative AI analyzes the color, shape, and age of furniture from a photo, and performs risk assessment based on these detailed attributes. For example, old furniture may be prone to falling over during an earthquake, so the age of the furniture is taken into account when assessing risk. The structural analysis unit can also analyze the color of furniture and assess the risk if a particular color has low visibility during a disaster. The structural analysis unit can also analyze the shape of furniture and assess the risk if a particular shape is dangerous during a disaster. This allows for more precise risk assessment by analyzing the detailed attributes of furniture.

[0031] The structural analysis unit can analyze the exterior of a building and the surrounding environment to perform a risk assessment. For example, the generation AI in the structural analysis unit analyzes the exterior of a building and the surrounding environment to perform a risk assessment that takes into account neighboring buildings and the terrain. For example, if there is a high risk of an adjacent building collapsing, the impact can be evaluated. The structural analysis unit can also analyze the exterior of a building and evaluate the risk if a specific exterior poses a danger in the event of a disaster. The structural analysis unit can also analyze the surrounding environment and evaluate the risk if a specific environment poses a danger in the event of a disaster. This makes it possible to perform a comprehensive risk assessment by analyzing the exterior of a building and the surrounding environment.

[0032] The photo recognition unit can assess disaster risk from videos provided by users. For example, the photo recognition unit uses photo recognition technology to analyze dynamic information about furniture placement and building materials from videos provided by users and perform risk assessment. For example, it analyzes how furniture moves in the video to assess the risk in the event of an earthquake. The photo recognition unit can also analyze how building materials change in the video to assess the risk in the event of a flood. The photo recognition unit can also analyze how window positions change in the video to assess the risk in the event of a disaster. This makes it possible to assess risk based on dynamic information from videos.

[0033] The structural analysis unit can analyze not only the interior of a building but also external spaces such as gardens and parking lots to propose disaster prevention measures. For example, the generation AI can analyze not only the interior of a building but also external spaces such as gardens and parking lots to propose comprehensive disaster prevention measures. For example, it can evaluate risks based on the plants in the garden and the placement of vehicles in the parking lot. The structural analysis unit can also analyze the area of ​​the garden and the type of plants to evaluate the risk in the event of a disaster. The structural analysis unit can also analyze the placement of vehicles in the parking lot to evaluate the risk in the event of a disaster. This makes it possible to comprehensively analyze the interior and external spaces of a building, enabling more comprehensive disaster prevention measures.

[0034] The disaster prevention risk assessment unit can refer to past disaster data and predict risks under similar conditions. For example, the generation AI in the disaster prevention risk assessment unit refers to past disaster data and predicts risks under similar conditions. For example, it evaluates earthquake risk in the same area based on past earthquake data. The disaster prevention risk assessment unit can also refer to past flood data and predict risks under similar conditions. The disaster prevention risk assessment unit can also refer to past fire data and predict risks under similar conditions. In this way, by referring to past disaster data, more accurate risk predictions are possible.

[0035] The disaster prevention risk assessment unit can conduct risk assessments by taking into account the structural characteristics of a building, such as earthquake resistance and fire resistance. For example, the disaster prevention risk assessment unit uses a generation AI to analyze a building's earthquake resistance and fire resistance and reflect this in the risk assessment. For example, a building with earthquake-resistant construction may be assessed as having a low earthquake risk. The disaster prevention risk assessment unit can also assess that a building with high fire resistance has a low fire risk. The disaster prevention risk assessment unit can also analyze the structural characteristics of a building and reflect this in the risk assessment. This allows for more precise risk assessments by taking into account the structural characteristics of a building.

[0036] The disaster prevention risk assessment unit can analyze not only buildings but also individual items such as furniture and home appliances to conduct detailed risk assessments. For example, the generation AI can analyze not only buildings but also individual items such as furniture and home appliances to conduct detailed risk assessments. For example, it can identify furniture that is likely to fall over during an earthquake or home appliances that are dangerous in the event of a fire. The disaster prevention risk assessment unit can also analyze the type and placement of furniture and reflect this in the risk assessment. The disaster prevention risk assessment unit can also analyze the age and placement of home appliances and reflect this in the risk assessment. This makes it possible to conduct more detailed risk assessments by analyzing not only buildings but also individual items such as furniture and home appliances.

[0037] The disaster prevention risk assessment unit can provide an individually optimized risk assessment by taking into account the user's lifestyle habits and behavioral patterns. For example, the disaster prevention risk assessment unit uses a generation AI to analyze the user's lifestyle habits and behavioral patterns and provide an individually optimized risk assessment. For example, the disaster prevention risk assessment unit can assess risk based on the layout of rooms and furniture frequently used by the user. The disaster prevention risk assessment unit can also analyze the user's wake-up time and meal times and reflect these in the risk assessment. The disaster prevention risk assessment unit can also analyze the user's frequency of going out and travel routes and reflect these in the risk assessment. This makes it possible to provide an individually optimized risk assessment by taking into account the user's lifestyle habits and behavioral patterns.

[0038] The product proposal unit can provide customization options according to the user's budget and preferences. In the product proposal unit, for example, the generation AI takes into account the user's budget and customizes the disaster prevention products it proposes. For example, it proposes cost-effective products to users on a low budget. The product proposal unit can also customize the disaster prevention products it proposes by taking into account the user's preferences. For example, it can propose products with specific colors or designs. The product proposal unit can also customize the disaster prevention products it proposes by taking into account the user's functional preferences. For example, it can propose products with specific functions. This makes it possible to increase user satisfaction by providing customization options according to the user's budget and preferences.

[0039] The product proposal department can propose disaster prevention products that take into account the characteristics of the user's region. For example, the product proposal department uses a generation AI to propose appropriate disaster prevention products by taking into account the climate and topography of the user's region. For example, it can propose waterproof sheets and drainage pumps in areas prone to flooding. The product proposal department can also propose earthquake-resistant goods in areas prone to earthquakes. The product proposal department can also propose fire alarms and fire extinguishers in areas with a high risk of fire. In this way, more appropriate disaster prevention products can be proposed by taking into account the characteristics of the user's region.

[0040] The product proposal unit can propose disaster prevention products that meet the individual needs of the user, such as their family composition and whether or not they have pets. For example, the product proposal unit uses a generation AI to consider the user's family composition and propose appropriate disaster prevention products. For example, for households with small children, it can propose disaster prevention products for children. The product proposal unit can also propose disaster prevention products for pets for households with pets. The product proposal unit can also propose disaster prevention products that meet the individual needs of users with special requirements. For example, it can propose special disaster prevention products to users with disabilities. This allows for more appropriate disaster prevention measures to be taken by proposing disaster prevention products that meet the individual needs of the user, such as their family composition and whether or not they have pets.

[0041] The product proposal unit can provide recommendations that take into account the user's past purchase history and usage status. For example, the product proposal unit uses a generation AI to analyze the user's past purchase history and propose appropriate disaster prevention products. For example, it can propose products that are compatible with products purchased in the past. The product proposal unit can also analyze the user's usage status and propose appropriate disaster prevention products. For example, it can propose disaster prevention goods related to frequently used products. The product proposal unit can also provide recommendations based on the user's purchase history and usage status. This makes it possible to propose more appropriate disaster prevention products by taking into account the user's past purchase history and usage status.

[0042] The report generation unit can add visual elements to make the report easier for users to understand. For example, the report generation unit can add graphs and illustrations to the report generated by the generation AI to make it easier for users to understand. For example, the report generation unit can display risk assessment results in a graph. The report generation unit can also use illustrations to visually explain specific disaster prevention measures. The report generation unit can also use icons to highlight important information. In this way, adding visual elements makes it easier for users to understand the risk assessment results.

[0043] The report generation unit can customize the content of the report according to the user's expertise and level of understanding. For example, the generation AI in the report generation unit customizes the content of the report by taking into account the user's expertise and level of understanding. For example, it provides easy-to-understand explanations without using technical jargon. The report generation unit can also customize the content of the report by taking into account the user's occupation and educational background. For example, it provides detailed technical information for engineers. The report generation unit can also customize the content of the report based on the user's past feedback. This makes it possible to provide more appropriate information by customizing the content of the report according to the user's expertise and level of understanding.

[0044] The report generation unit can expand the report provision method to include not only paper and digital media, but also audio guide and video formats. For example, the report generation unit provides reports generated by the generation AI not only in paper and digital media, but also in audio guide and video formats. For example, the report generation unit can explain the risk assessment results using audio guide. The report generation unit can also explain specific disaster prevention measures in video format. The report generation unit can also provide information in real time using a streaming service. This diversifies the methods of providing reports, making it possible to provide information that meets user needs.

[0045] The report generation unit can continuously update the contents of the report based on user feedback. In the report generation unit, for example, the generation AI continuously updates the contents of the report based on user feedback. For example, the risk assessment results are revised to reflect the user's opinions. The report generation unit can also periodically update the contents of the report to provide the latest information. The report generation unit can also continuously update the contents of the report using the system's automatic update function. In this way, by continuously updating the contents of the report based on user feedback, it is possible to always provide the latest information.

[0046] The Public Disaster Prevention Information Coordination Department can acquire public disaster prevention information in real time and provide it to users. For example, the Public Disaster Prevention Information Coordination Department will build a system in which a generation AI acquires public disaster prevention information in real time and provides it to users. For example, it will immediately notify users of the latest earthquake information and flood warnings. The Public Disaster Prevention Information Coordination Department can also acquire government disaster prevention information and data from the Japan Meteorological Agency in real time and provide it to users. The Public Disaster Prevention Information Coordination Department can also acquire disaster prevention manuals from local governments in real time and provide them to users. In this way, the latest disaster prevention information can be provided by acquiring public disaster prevention information in real time and providing it to users.

[0047] The public disaster prevention information collaboration unit can integrate public disaster prevention information with the user's living environment information and propose specific disaster prevention measures. The public disaster prevention information collaboration unit, for example, builds a system that integrates public disaster prevention information with the user's living environment information and proposes specific disaster prevention measures. For example, it proposes measures based on the earthquake risk of the region and the earthquake resistance of buildings. The public disaster prevention information collaboration unit can also propose measures based on the flood risk of the region and the waterproofing of buildings. The public disaster prevention information collaboration unit can also propose measures based on the fire risk of the region and the fire resistance of buildings. In this way, by integrating public disaster prevention information with the user's living environment information, it is possible to propose more specific disaster prevention measures.

[0048] The public disaster prevention information collaboration department can expand collaboration with public disaster prevention information to include at least one of local community information and volunteer information, and propose comprehensive disaster prevention measures. The public disaster prevention information collaboration department, for example, integrates public disaster prevention information with local community information to build a system that proposes comprehensive disaster prevention measures. For example, it provides information on local evacuation shelters and volunteer activity information. The public disaster prevention information collaboration department can also integrate local disaster prevention plans and community communication networks to propose comprehensive disaster prevention measures. The public disaster prevention information collaboration department can also integrate volunteer recruitment information and activity details to propose comprehensive disaster prevention measures. In this way, by expanding collaboration with public disaster prevention information to include local community information, volunteer information, and the like, more comprehensive disaster prevention measures can be proposed.

[0049] The public disaster prevention information collaboration unit can customize public disaster prevention information according to the user's living environment and provide individually optimized information. The public disaster prevention information collaboration unit, for example, builds a system that customizes public disaster prevention information according to the user's living environment and provides individually optimized information. For example, information is provided based on the earthquake risk of the region and the earthquake resistance of buildings. The public disaster prevention information collaboration unit can also provide information based on the flood risk of the region and the waterproofing of buildings. The public disaster prevention information collaboration unit can also provide information based on the fire risk of the region and the fire resistance of buildings. In this way, by customizing public disaster prevention information according to the user's living environment, it is possible to provide more appropriate information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The disaster prevention risk assessment system can also monitor the user's health condition and reflect it in the risk assessment. For example, if the user has a chronic illness such as high blood pressure or heart disease, the system can suggest evacuation routes and evacuation locations taking that health condition into account. It can also suggest necessary medical supplies and medicines depending on the user's health condition. This makes it possible to plan disaster prevention measures that take the user's health condition into account.

[0052] The disaster prevention risk assessment system can also provide individually optimized risk assessments by taking into account the user's lifestyle habits and behavioral patterns. For example, risk can be assessed based on the layout of rooms and furniture frequently used by the user. The system can also analyze the user's wake-up time and meal times and reflect these in the risk assessment. It can also analyze the user's frequency of going out and their travel routes and reflect these in the risk assessment. This makes it possible to provide individually optimized risk assessments by taking into account the user's lifestyle habits and behavioral patterns.

[0053] The disaster prevention risk assessment system can also suggest disaster prevention products according to the individual needs of the user, such as their family structure and whether they have pets. For example, disaster prevention products for children can be suggested for households with small children. Disaster prevention products for pets can also be suggested for households with pets. Disaster prevention products according to the individual needs of users with special requirements can also be suggested. For example, special disaster prevention products can be suggested for users with disabilities. This allows for more appropriate disaster prevention measures to be taken by suggesting disaster prevention products according to the individual needs of the user, such as their family structure and whether they have pets.

[0054] The disaster prevention risk assessment system can also provide recommendations that take into account a user's past purchase history and usage status. For example, it can suggest products that are compatible with products previously purchased. It can also analyze a user's usage status and suggest appropriate disaster prevention products. For example, it can suggest disaster prevention goods related to frequently used products. It can also provide recommendations based on a user's purchase history and usage status. This makes it possible to suggest more appropriate disaster prevention products by taking into account a user's past purchase history and usage status.

[0055] The disaster prevention risk assessment system can also suggest disaster prevention products that take into account the characteristics of the user's region. For example, it can suggest waterproof sheets and drainage pumps in areas prone to flooding. It can also suggest earthquake-resistant goods in areas prone to earthquakes. It can also suggest fire alarms and fire extinguishers in areas with a high risk of fire. In this way, it can suggest more appropriate disaster prevention products by taking into account the characteristics of the user's region.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The photo recognition unit recognizes photos provided by the user. For example, it recognizes indoor and exterior photos and acquires data for analysis. It can also recognize photos taken from specific angles. Step 2: The structural analysis unit analyzes detailed information from the photos recognized by the photo recognition unit, such as furniture placement, types of building materials, and window locations. For example, it analyzes furniture placement to identify locations that are likely to collapse during an earthquake. It can also analyze types of building materials to identify materials that are vulnerable to flooding. It also analyzes window locations to assess risks in the event of a disaster. Step 3: The disaster prevention risk assessment unit evaluates specific risks that may arise in the event of a disaster such as an earthquake or flood based on the detailed information analyzed by the structural analysis unit. For example, if furniture is positioned in a way that makes it likely to collapse during an earthquake, that risk is assessed. If building materials are vulnerable to flooding, that risk can also be assessed. Also, if the location of windows is dangerous during a disaster, that risk is assessed. Step 4: The Product Proposal Department recommends disaster prevention products based on the risks assessed by the Disaster Prevention Risk Assessment Department. For example, they may recommend furniture fixing devices and earthquake-resistant mats as earthquake countermeasures, waterproof sheets and drainage pumps as flood countermeasures, or evacuation equipment for disasters.

[0058] (Example 2) A disaster prevention risk assessment system according to an embodiment of the present invention automatically analyzes photos provided by users, and a generation AI acquires detailed information such as furniture layout, building material types, and window locations, assesses disaster prevention risks, and recommends disaster prevention products. As a result, the disaster prevention risk assessment system can propose specific disaster prevention measures based on the user's living environment and reduce disaster risks.

[0059] A disaster prevention risk assessment system according to an embodiment includes a photo recognition unit, a structural analysis unit, a disaster prevention risk assessment unit, and a product proposal unit. The photo recognition unit recognizes photos provided by a user. For example, the photo recognition unit recognizes indoor and exterior photos and acquires data for analysis. The photo recognition unit can also recognize photos taken from a specific angle. The structural analysis unit analyzes detailed information about furniture layout, building material types, and window locations from the photos recognized by the photo recognition unit. For example, the structural analysis unit analyzes furniture layout to identify locations that are likely to collapse during an earthquake. The structural analysis unit can also analyze building material types to identify materials that are vulnerable to flooding. The structural analysis unit can also analyze window locations to assess disaster risks. The disaster prevention risk assessment unit assesses specific risks that may occur in the event of a disaster such as an earthquake or flood based on the detailed information analyzed by the structural analysis unit. For example, the disaster prevention risk assessment unit assesses the risk if furniture is positioned in a location that is likely to collapse during an earthquake. The disaster prevention risk assessment unit can also assess the risk if building materials are vulnerable to flooding. Furthermore, the disaster prevention risk assessment unit evaluates the risk if the window's location is dangerous in the event of a disaster. The product proposal unit proposes recommended disaster prevention products based on the risks assessed by the disaster prevention risk assessment unit. For example, the product proposal unit proposes furniture fasteners and earthquake-resistant mats as earthquake countermeasures. The product proposal unit can also propose waterproof sheets and drainage pumps as flood countermeasures. The product proposal unit can also propose evacuation equipment in the event of a disaster. In this way, the disaster prevention risk assessment system according to the embodiment can propose specific disaster prevention measures based on the user's living environment and reduce the risk of disaster.

[0060] The structural analysis unit can analyze the color, shape, and age of furniture to perform risk assessment. For example, the generative AI analyzes the color, shape, and age of furniture from a photo, and performs risk assessment based on these detailed attributes. For example, old furniture may be prone to falling over during an earthquake, so the age of the furniture is taken into account when assessing risk. The structural analysis unit can also analyze the color of furniture and assess the risk if a particular color has low visibility during a disaster. The structural analysis unit can also analyze the shape of furniture and assess the risk if a particular shape is dangerous during a disaster. This allows for more precise risk assessment by analyzing the detailed attributes of furniture.

[0061] The structural analysis unit can analyze the exterior of a building and the surrounding environment to perform a risk assessment. For example, the generation AI in the structural analysis unit analyzes the exterior of a building and the surrounding environment to perform a risk assessment that takes into account neighboring buildings and the terrain. For example, if there is a high risk of an adjacent building collapsing, the impact can be evaluated. The structural analysis unit can also analyze the exterior of a building and evaluate the risk if a specific exterior poses a danger in the event of a disaster. The structural analysis unit can also analyze the surrounding environment and evaluate the risk if a specific environment poses a danger in the event of a disaster. This makes it possible to perform a comprehensive risk assessment by analyzing the exterior of a building and the surrounding environment.

[0062] The structural analysis unit can analyze the user's emotions and prioritize analyzing areas where the user feels anxious. For example, the structural analysis unit uses an emotion estimation function to analyze the user's facial expressions and voice when submitting photos and identify areas where the user feels anxious. For example, the structural analysis unit prioritizes analyzing rooms or furniture where the user feels anxious. The structural analysis unit can also analyze the user's statements and behavior patterns to identify areas where the user feels anxious. The structural analysis unit can also analyze questionnaire results to identify areas where the user feels anxious. This allows for risk assessment that is more tailored to the user by taking the user's emotions into consideration.

[0063] The photo recognition unit can assess disaster risk from videos provided by users. For example, the photo recognition unit uses photo recognition technology to analyze dynamic information about furniture placement and building materials from videos provided by users and perform risk assessment. For example, it analyzes how furniture moves in the video to assess the risk in the event of an earthquake. The photo recognition unit can also analyze how building materials change in the video to assess the risk in the event of a flood. The photo recognition unit can also analyze how window positions change in the video to assess the risk in the event of a disaster. This makes it possible to assess risk based on dynamic information from videos.

[0064] The structural analysis unit can analyze not only the interior of a building but also external spaces such as gardens and parking lots to propose disaster prevention measures. For example, the generation AI can analyze not only the interior of a building but also external spaces such as gardens and parking lots to propose comprehensive disaster prevention measures. For example, it can evaluate risks based on the plants in the garden and the placement of vehicles in the parking lot. The structural analysis unit can also analyze the area of ​​the garden and the type of plants to evaluate the risk in the event of a disaster. The structural analysis unit can also analyze the placement of vehicles in the parking lot to evaluate the risk in the event of a disaster. This makes it possible to comprehensively analyze the interior and external spaces of a building, enabling more comprehensive disaster prevention measures.

[0065] The structural analysis unit can analyze the user's emotions in real time and provide feedback to elicit positive emotions. For example, the structural analysis unit uses an emotion estimation function to analyze the emotions of the user when submitting a photo in real time and provide feedback to elicit positive emotions. For example, an encouraging message is displayed when the user is feeling anxious. The structural analysis unit can also analyze the user's facial expressions and voice and provide feedback to elicit positive emotions. The structural analysis unit can also analyze the user's behavioral patterns and provide feedback to elicit positive emotions. In this way, the user's emotions can be analyzed in real time and positive emotions can be elicited, thereby increasing the user's sense of security.

[0066] The disaster prevention risk assessment unit can refer to past disaster data and predict risks under similar conditions. For example, the generation AI in the disaster prevention risk assessment unit refers to past disaster data and predicts risks under similar conditions. For example, it evaluates earthquake risk in the same area based on past earthquake data. The disaster prevention risk assessment unit can also refer to past flood data and predict risks under similar conditions. The disaster prevention risk assessment unit can also refer to past fire data and predict risks under similar conditions. In this way, by referring to past disaster data, more accurate risk predictions are possible.

[0067] The disaster prevention risk assessment unit can conduct risk assessments by taking into account the structural characteristics of a building, such as earthquake resistance and fire resistance. For example, the disaster prevention risk assessment unit uses a generation AI to analyze a building's earthquake resistance and fire resistance and reflect this in the risk assessment. For example, a building with earthquake-resistant construction may be assessed as having a low earthquake risk. The disaster prevention risk assessment unit can also assess that a building with high fire resistance has a low fire risk. The disaster prevention risk assessment unit can also analyze the structural characteristics of a building and reflect this in the risk assessment. This allows for more precise risk assessments by taking into account the structural characteristics of a building.

[0068] The disaster prevention risk assessment unit can analyze the user's emotions and provide advice to reduce negative emotions when receiving the risk assessment result. The disaster prevention risk assessment unit can, for example, use an emotion estimation function to analyze the user's emotions when receiving the risk assessment result and provide advice to reduce negative emotions. For example, if the user is feeling anxious, a message that gives a sense of security is displayed. The disaster prevention risk assessment unit can also analyze the user's facial expressions and voice and provide advice to reduce negative emotions. The disaster prevention risk assessment unit can also analyze the user's behavioral patterns and provide advice to reduce negative emotions. In this way, by taking the user's emotions into consideration, it is possible to reduce negative emotions when receiving the risk assessment result.

[0069] The disaster prevention risk assessment unit can analyze not only buildings but also individual items such as furniture and home appliances to conduct detailed risk assessments. For example, the generation AI can analyze not only buildings but also individual items such as furniture and home appliances to conduct detailed risk assessments. For example, it can identify furniture that is likely to fall over during an earthquake or home appliances that are dangerous in the event of a fire. The disaster prevention risk assessment unit can also analyze the type and placement of furniture and reflect this in the risk assessment. The disaster prevention risk assessment unit can also analyze the age and placement of home appliances and reflect this in the risk assessment. This makes it possible to conduct more detailed risk assessments by analyzing not only buildings but also individual items such as furniture and home appliances.

[0070] The disaster prevention risk assessment unit can provide an individually optimized risk assessment by taking into account the user's lifestyle habits and behavioral patterns. For example, the disaster prevention risk assessment unit uses a generation AI to analyze the user's lifestyle habits and behavioral patterns and provide an individually optimized risk assessment. For example, the disaster prevention risk assessment unit can assess risk based on the layout of rooms and furniture frequently used by the user. The disaster prevention risk assessment unit can also analyze the user's wake-up time and meal times and reflect these in the risk assessment. The disaster prevention risk assessment unit can also analyze the user's frequency of going out and travel routes and reflect these in the risk assessment. This makes it possible to provide an individually optimized risk assessment by taking into account the user's lifestyle habits and behavioral patterns.

[0071] The disaster prevention risk assessment unit can monitor the user's emotions in real time and provide feedback to elicit positive emotions. The disaster prevention risk assessment unit can, for example, use an emotion estimation function to monitor the user's emotions when receiving the risk assessment result in real time and provide feedback to elicit positive emotions. For example, a message that makes the user feel reassured is displayed. The disaster prevention risk assessment unit can also analyze the user's facial expressions and voice and provide feedback to elicit positive emotions. The disaster prevention risk assessment unit can also analyze the user's behavioral patterns and provide feedback to elicit positive emotions. In this way, the user's sense of security can be increased by monitoring the user's emotions in real time and eliciting positive emotions.

[0072] The product proposal unit can provide customization options according to the user's budget and preferences. In the product proposal unit, for example, the generation AI takes into account the user's budget and customizes the disaster prevention products it proposes. For example, it proposes cost-effective products to users on a low budget. The product proposal unit can also customize the disaster prevention products it proposes by taking into account the user's preferences. For example, it can propose products with specific colors or designs. The product proposal unit can also customize the disaster prevention products it proposes by taking into account the user's functional preferences. For example, it can propose products with specific functions. This makes it possible to increase user satisfaction by providing customization options according to the user's budget and preferences.

[0073] The product proposal department can propose disaster prevention products that take into account the characteristics of the user's region. For example, the product proposal department uses a generation AI to propose appropriate disaster prevention products by taking into account the climate and topography of the user's region. For example, it can propose waterproof sheets and drainage pumps in areas prone to flooding. The product proposal department can also propose earthquake-resistant goods in areas prone to earthquakes. The product proposal department can also propose fire alarms and fire extinguishers in areas with a high risk of fire. In this way, more appropriate disaster prevention products can be proposed by taking into account the characteristics of the user's region.

[0074] The product proposal unit can analyze the user's emotions and make product proposals that elicit positive emotions toward the proposed disaster prevention products. The product proposal unit can, for example, use an emotion estimation function to analyze the user's emotions toward the proposed disaster prevention products and make product proposals that elicit positive emotions. For example, the product proposal unit can propose products that give the user a sense of security. The product proposal unit can also analyze the user's facial expressions and voice and make product proposals that elicit positive emotions. The product proposal unit can also analyze the user's behavioral patterns and make product proposals that elicit positive emotions. In this way, by taking the user's emotions into consideration, it is possible to elicit positive emotions toward the proposed disaster prevention products.

[0075] The product proposal unit can propose disaster prevention products that meet the individual needs of the user, such as their family composition and whether or not they have pets. For example, the product proposal unit uses a generation AI to consider the user's family composition and propose appropriate disaster prevention products. For example, for households with small children, it can propose disaster prevention products for children. The product proposal unit can also propose disaster prevention products for pets for households with pets. The product proposal unit can also propose disaster prevention products that meet the individual needs of users with special requirements. For example, it can propose special disaster prevention products to users with disabilities. This allows for more appropriate disaster prevention measures to be taken by proposing disaster prevention products that meet the individual needs of the user, such as their family composition and whether or not they have pets.

[0076] The product proposal unit can provide recommendations that take into account the user's past purchase history and usage status. For example, the product proposal unit uses a generation AI to analyze the user's past purchase history and propose appropriate disaster prevention products. For example, it can propose products that are compatible with products purchased in the past. The product proposal unit can also analyze the user's usage status and propose appropriate disaster prevention products. For example, it can propose disaster prevention goods related to frequently used products. The product proposal unit can also provide recommendations based on the user's purchase history and usage status. This makes it possible to propose more appropriate disaster prevention products by taking into account the user's past purchase history and usage status.

[0077] The product proposal unit can monitor the user's emotions in real time and provide feedback to elicit positive emotions toward the proposed disaster prevention product. The product proposal unit can, for example, use an emotion estimation function to monitor the user's emotions toward the proposed disaster prevention product in real time and provide feedback to elicit positive emotions. For example, the product proposal unit can display a message that gives the user a sense of security. The product proposal unit can also analyze the user's facial expressions and voice and provide feedback to elicit positive emotions. The product proposal unit can also analyze the user's behavioral patterns and provide feedback to elicit positive emotions. In this way, the user's emotions can be monitored in real time and positive emotions can be elicited, thereby increasing user satisfaction.

[0078] The report generation unit can add visual elements to make the report easier for users to understand. For example, the report generation unit can add graphs and illustrations to the report generated by the generation AI to make it easier for users to understand. For example, the report generation unit can display risk assessment results in a graph. The report generation unit can also use illustrations to visually explain specific disaster prevention measures. The report generation unit can also use icons to highlight important information. In this way, adding visual elements makes it easier for users to understand the risk assessment results.

[0079] The report generation unit can customize the content of the report according to the user's expertise and level of understanding. For example, the generation AI in the report generation unit customizes the content of the report by taking into account the user's expertise and level of understanding. For example, it provides easy-to-understand explanations without using technical jargon. The report generation unit can also customize the content of the report by taking into account the user's occupation and educational background. For example, it provides detailed technical information for engineers. The report generation unit can also customize the content of the report based on the user's past feedback. This makes it possible to provide more appropriate information by customizing the content of the report according to the user's expertise and level of understanding.

[0080] The report generation unit can analyze the user's emotions and provide feedback to elicit positive emotions when the user receives the report. The report generation unit can, for example, use an emotion estimation function to analyze the user's emotions when the user receives the report and provide feedback to elicit positive emotions. For example, the report generation unit can display a message that makes the user feel reassured. The report generation unit can also analyze the user's facial expressions and voice and provide feedback to elicit positive emotions. The report generation unit can also analyze the user's behavioral patterns and provide feedback to elicit positive emotions. In this way, by taking the user's emotions into consideration, it is possible to elicit positive emotions when the user receives the report.

[0081] The report generation unit can expand the report provision method to include not only paper and digital media, but also audio guide and video formats. For example, the report generation unit provides reports generated by the generation AI not only in paper and digital media, but also in audio guide and video formats. For example, the report generation unit can explain the risk assessment results using audio guide. The report generation unit can also explain specific disaster prevention measures in video format. The report generation unit can also provide information in real time using a streaming service. This diversifies the methods of providing reports, making it possible to provide information that meets user needs.

[0082] The report generation unit can continuously update the contents of the report based on user feedback. In the report generation unit, for example, the generation AI continuously updates the contents of the report based on user feedback. For example, the risk assessment results are revised to reflect the user's opinions. The report generation unit can also periodically update the contents of the report to provide the latest information. The report generation unit can also continuously update the contents of the report using the system's automatic update function. In this way, by continuously updating the contents of the report based on user feedback, it is possible to always provide the latest information.

[0083] The report generation unit can monitor the user's emotions in real time and provide feedback to elicit positive emotions when the user receives a report. The report generation unit can, for example, use an emotion estimation function to monitor the user's emotions in real time when the user receives a report and provide feedback to elicit positive emotions. For example, the report generation unit can display a message that makes the user feel reassured. The report generation unit can also analyze the user's facial expressions and voice and provide feedback to elicit positive emotions. The report generation unit can also analyze the user's behavioral patterns and provide feedback to elicit positive emotions. In this way, the user's sense of security can be increased by monitoring the user's emotions in real time and eliciting positive emotions.

[0084] The Public Disaster Prevention Information Coordination Department can acquire public disaster prevention information in real time and provide it to users. For example, the Public Disaster Prevention Information Coordination Department will build a system in which a generation AI acquires public disaster prevention information in real time and provides it to users. For example, it will immediately notify users of the latest earthquake information and flood warnings. The Public Disaster Prevention Information Coordination Department can also acquire government disaster prevention information and data from the Japan Meteorological Agency in real time and provide it to users. The Public Disaster Prevention Information Coordination Department can also acquire disaster prevention manuals from local governments in real time and provide them to users. In this way, the latest disaster prevention information can be provided by acquiring public disaster prevention information in real time and providing it to users.

[0085] The public disaster prevention information collaboration unit can integrate public disaster prevention information with the user's living environment information and propose specific disaster prevention measures. The public disaster prevention information collaboration unit, for example, builds a system that integrates public disaster prevention information with the user's living environment information and proposes specific disaster prevention measures. For example, it proposes measures based on the earthquake risk of the region and the earthquake resistance of buildings. The public disaster prevention information collaboration unit can also propose measures based on the flood risk of the region and the waterproofing of buildings. The public disaster prevention information collaboration unit can also propose measures based on the fire risk of the region and the fire resistance of buildings. In this way, by integrating public disaster prevention information with the user's living environment information, it is possible to propose more specific disaster prevention measures.

[0086] The official disaster prevention information linking unit can analyze the user's emotions and provide feedback to elicit positive emotions when the user receives official disaster prevention information. The official disaster prevention information linking unit can, for example, use an emotion estimation function to analyze the user's emotions when the user receives official disaster prevention information and provide feedback to elicit positive emotions. For example, it can display a message that gives the user a sense of security. The official disaster prevention information linking unit can also analyze the user's facial expressions and voice and provide feedback to elicit positive emotions. The official disaster prevention information linking unit can also analyze the user's behavioral patterns and provide feedback to elicit positive emotions. In this way, by taking the user's emotions into consideration, it is possible to elicit positive emotions when the user receives official disaster prevention information.

[0087] The public disaster prevention information collaboration department can expand collaboration with public disaster prevention information to include at least one of local community information and volunteer information, and propose comprehensive disaster prevention measures. The public disaster prevention information collaboration department, for example, integrates public disaster prevention information with local community information to build a system that proposes comprehensive disaster prevention measures. For example, it provides information on local evacuation shelters and volunteer activity information. The public disaster prevention information collaboration department can also integrate local disaster prevention plans and community communication networks to propose comprehensive disaster prevention measures. The public disaster prevention information collaboration department can also integrate volunteer recruitment information and activity details to propose comprehensive disaster prevention measures. In this way, by expanding collaboration with public disaster prevention information to include local community information, volunteer information, and the like, more comprehensive disaster prevention measures can be proposed.

[0088] The public disaster prevention information collaboration unit can customize public disaster prevention information according to the user's living environment and provide individually optimized information. The public disaster prevention information collaboration unit, for example, builds a system that customizes public disaster prevention information according to the user's living environment and provides individually optimized information. For example, information is provided based on the earthquake risk of the region and the earthquake resistance of buildings. The public disaster prevention information collaboration unit can also provide information based on the flood risk of the region and the waterproofing of buildings. The public disaster prevention information collaboration unit can also provide information based on the fire risk of the region and the fire resistance of buildings. In this way, by customizing public disaster prevention information according to the user's living environment, it is possible to provide more appropriate information.

[0089] The official disaster prevention information linking unit can monitor the user's emotions in real time and provide feedback to elicit positive emotions when the user receives official disaster prevention information. The official disaster prevention information linking unit can, for example, use an emotion estimation function to monitor the user's emotions in real time when the user receives official disaster prevention information and provide feedback to elicit positive emotions. For example, it can display a message that gives the user a sense of security. The official disaster prevention information linking unit can also analyze the user's facial expressions and voice and provide feedback to elicit positive emotions. The official disaster prevention information linking unit can also analyze the user's behavioral patterns and provide feedback to elicit positive emotions. In this way, the user's sense of security can be increased by monitoring the user's emotions in real time and eliciting positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The disaster prevention risk assessment system can also monitor the user's health condition and reflect it in the risk assessment. For example, if the user has a chronic illness such as high blood pressure or heart disease, the system can suggest evacuation routes and evacuation locations taking that health condition into account. It can also suggest necessary medical supplies and medicines depending on the user's health condition. This makes it possible to plan disaster prevention measures that take the user's health condition into account.

[0092] The disaster prevention risk assessment system can further analyze the user's emotions and provide advice to reduce negative emotions when receiving the risk assessment results. For example, if the user is feeling anxious, a message that provides reassurance can be displayed. The system can also analyze the user's facial expressions and voice to provide advice to reduce negative emotions. In this way, by taking the user's emotions into consideration, it is possible to reduce negative emotions when receiving the risk assessment results.

[0093] The disaster prevention risk assessment system can also provide individually optimized risk assessments by taking into account the user's lifestyle habits and behavioral patterns. For example, risk can be assessed based on the layout of rooms and furniture frequently used by the user. The system can also analyze the user's wake-up time and meal times and reflect these in the risk assessment. It can also analyze the user's frequency of going out and their travel routes and reflect these in the risk assessment. This makes it possible to provide individually optimized risk assessments by taking into account the user's lifestyle habits and behavioral patterns.

[0094] The disaster prevention risk assessment system can also monitor the user's emotions in real time and provide feedback to elicit positive emotions. For example, it can display a message that makes the user feel reassured. It can also analyze the user's facial expressions and voice and provide feedback to elicit positive emotions. It can also analyze the user's behavioral patterns and provide feedback to elicit positive emotions. In this way, the user's emotions can be monitored in real time and elicited, thereby increasing the user's sense of security.

[0095] The disaster prevention risk assessment system can also suggest disaster prevention products according to the individual needs of the user, such as their family structure and whether they have pets. For example, disaster prevention products for children can be suggested for households with small children. Disaster prevention products for pets can also be suggested for households with pets. Disaster prevention products according to the individual needs of users with special requirements can also be suggested. For example, special disaster prevention products can be suggested for users with disabilities. This allows for more appropriate disaster prevention measures to be taken by suggesting disaster prevention products according to the individual needs of the user, such as their family structure and whether they have pets.

[0096] The disaster prevention risk assessment system can further analyze the user's emotions and make product suggestions that elicit positive emotions about the proposed disaster prevention products. For example, it can suggest products that give the user a sense of security. It can also analyze the user's facial expressions and voice to make product suggestions that elicit positive emotions. It can also analyze the user's behavioral patterns to make product suggestions that elicit positive emotions. In this way, by taking the user's emotions into consideration, it is possible to elicit positive emotions about the proposed disaster prevention products.

[0097] The disaster prevention risk assessment system can also provide recommendations that take into account a user's past purchase history and usage status. For example, it can suggest products that are compatible with products previously purchased. It can also analyze a user's usage status and suggest appropriate disaster prevention products. For example, it can suggest disaster prevention goods related to frequently used products. It can also provide recommendations based on a user's purchase history and usage status. This makes it possible to suggest more appropriate disaster prevention products by taking into account a user's past purchase history and usage status.

[0098] The disaster prevention risk assessment system can also monitor the user's emotions in real time and provide feedback to elicit positive emotions toward the proposed disaster prevention products. For example, it can display a message that makes the user feel reassured. It can also analyze the user's facial expressions and voice to provide feedback to elicit positive emotions. It can also analyze the user's behavioral patterns to provide feedback to elicit positive emotions. In this way, it is possible to monitor the user's emotions in real time and elicit positive emotions, thereby increasing user satisfaction.

[0099] The disaster prevention risk assessment system can also suggest disaster prevention products that take into account the characteristics of the user's region. For example, it can suggest waterproof sheets and drainage pumps in areas prone to flooding. It can also suggest earthquake-resistant goods in areas prone to earthquakes. It can also suggest fire alarms and fire extinguishers in areas with a high risk of fire. In this way, it can suggest more appropriate disaster prevention products by taking into account the characteristics of the user's region.

[0100] The disaster prevention risk assessment system can further analyze the user's emotions and provide advice to reduce negative emotions when receiving the risk assessment results. For example, if the user is feeling anxious, a message that provides a sense of security can be displayed. The system can also analyze the user's facial expressions and voice to provide advice to reduce negative emotions. The system can also analyze the user's behavioral patterns to provide advice to reduce negative emotions. In this way, by taking the user's emotions into consideration, it is possible to reduce negative emotions when receiving the risk assessment results.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The photo recognition unit recognizes photos provided by the user. For example, it recognizes indoor and exterior photos and acquires data for analysis. It can also recognize photos taken from specific angles. Step 2: The structural analysis unit analyzes detailed information from the photos recognized by the photo recognition unit, such as furniture placement, types of building materials, and window locations. For example, it analyzes furniture placement to identify locations that are likely to collapse during an earthquake. It can also analyze types of building materials to identify materials that are vulnerable to flooding. It also analyzes window locations to assess risks in the event of a disaster. Step 3: The disaster prevention risk assessment unit evaluates specific risks that may arise in the event of a disaster such as an earthquake or flood based on the detailed information analyzed by the structural analysis unit. For example, if furniture is positioned in a way that makes it likely to collapse during an earthquake, that risk is assessed. If building materials are vulnerable to flooding, that risk can also be assessed. Also, if the location of windows is dangerous during a disaster, that risk is assessed. Step 4: The Product Proposal Department recommends disaster prevention products based on the risks assessed by the Disaster Prevention Risk Assessment Department. For example, they may recommend furniture fixing devices and earthquake-resistant mats as earthquake countermeasures, waterproof sheets and drainage pumps as flood countermeasures, or evacuation equipment for disasters.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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 photo recognition unit that recognizes a photo provided by a user; a structural analysis unit that analyzes detailed information such as furniture layout, types of building materials, and window positions from the photograph recognized by the photograph recognition unit; a disaster prevention risk assessment unit that assesses specific risks that may occur in the event of a disaster such as an earthquake or flood based on the detailed information analyzed by the structural analysis unit; and a product suggestion unit that suggests recommended disaster prevention products based on the risks assessed by the disaster prevention risk assessment unit. A system characterized by:

2. The photo recognition unit The system according to claim 1, characterized in that it evaluates disaster prevention risks from videos provided by the user.

3. The disaster prevention risk assessment unit The system according to claim 1, wherein the system refers to past disaster data and predicts the risk under similar conditions.

4. The product proposal department The system of claim 1 , wherein the system provides customization options based on the user's budget and preferences.

5. The report generator is The system of claim 1 , further comprising adding visual elements to facilitate user understanding.

6. The Public Disaster Prevention Information Coordination Department The system according to claim 1, wherein official disaster prevention information is obtained in real time and provided to the user.

7. The structural analysis unit The system according to claim 1, further comprising: analyzing the user's emotions and giving priority to analyzing areas where the user feels anxious.

8. The disaster prevention risk assessment unit The system according to claim 1, further comprising: analyzing the user's emotions and providing advice to reduce negative emotions upon receiving a risk assessment result.

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