System

Abstract

An object of a system according to an embodiment is to efficiently formulate a disaster prevention guideline for a building complex and enable support for other languages.SOLUTION: A system includes an information collection unit, an analysis unit, a guideline formulation unit, and a translation unit. The information collection unit receives a prompt from a user and collects information about a disaster prevention guideline for a building complex. The analysis unit analyzes the information collected by the information collection unit. The guideline formulation unit formulates a disaster prevention guideline suitable for each facility based on the information analyzed by the analysis unit. The translation unit translates the disaster prevention guideline formulated by the guideline formulation unit into another language.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] With conventional technology, it was difficult to collect information needed to formulate disaster prevention guidelines for complex buildings, and there was a particular problem of a lack of support for other languages.

[0005] The system according to the embodiment aims to efficiently formulate disaster prevention guidelines for a complex building and to enable support in multiple languages. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a guideline formulation unit, and a translation unit. The information collection unit receives prompts from a user and collects information related to disaster prevention guidelines for a complex building. The analysis unit analyzes the information collected by the information collection unit. The guideline formulation unit formulates disaster prevention guidelines appropriate for each facility based on the information analyzed by the analysis unit. The translation unit translates the disaster prevention guidelines formulated by the guideline formulation unit into other languages. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently formulate disaster prevention guidelines for a complex building and can support multiple languages. [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) The disaster prevention guideline formulation system according to the embodiment of the present invention is a system that efficiently formulates disaster prevention guidelines for a complex building and realizes comprehensive disaster prevention measures, including measures to accommodate foreign visitors. As a result, the disaster prevention guideline formulation system can efficiently formulate disaster prevention guidelines for a complex building and realize comprehensive disaster prevention measures, including measures to accommodate foreign visitors.

[0029] A disaster prevention guideline formulation system according to an embodiment includes an information collection unit, an analysis unit, a guideline formulation unit, and a translation unit. The information collection unit receives a prompt from a user and collects information related to disaster prevention guidelines for a complex building. For example, the information collection unit performs an internet search to search relevant databases and literature to collect necessary information. The information collection unit can also conduct on-site investigations to confirm the actual status of the facility. For example, the information collection unit uses an internet search engine to collect information related to the latest disaster prevention guidelines. A database reference function is used to search academic papers and government guidelines. A field investigation function is used to confirm the current status of the facility and collect necessary information. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit uses data mining technology to analyze the collected information and extract important data. The analysis unit can also use statistical analysis technology to analyze the collected information and find trends and patterns. The analysis unit can also use machine learning technology to analyze the collected information and create a predictive model. For example, data mining technology is used to extract important data from the collected information. Statistical analysis technology is used to find trends and patterns in the collected information. Using machine learning technology, the system creates a predictive model based on the collected information. The guideline formulation department formulates disaster prevention guidelines appropriate for each facility based on the information analyzed by the analysis department. For example, the guideline formulation department creates disaster prevention guidelines tailored to the characteristics and risks of different facilities, such as office buildings, commercial facilities, and residential buildings. The guideline formulation department can also formulate specific disaster prevention guidelines, including evacuation route settings, emergency equipment placement, and emergency communication procedures. For example, for office buildings, the department can propose evacuation drills and emergency equipment placement to ensure employee safety. For commercial facilities, the department can propose rapid evacuation guidance for large numbers of visitors. For residential buildings, the department can propose individual measures to protect the safety of residents. The translation department translates the disaster prevention guidelines formulated by the guideline formulation department into other languages. For example, the translation department translates disaster prevention guidelines created in Japanese into English, Chinese, Korean, etc. The translation department can also use generative AI to create other language versions with accurate and natural expression.For example, the translation unit uses a generation AI to translate Japanese disaster prevention guidelines into English. The generation AI is also used to translate into multiple languages, such as Chinese and Korean. The generation AI is also used to create other language versions with accurate and natural expressions. This allows the disaster prevention guideline formulation system according to the embodiment to efficiently formulate disaster prevention guidelines for complex buildings and realize comprehensive disaster prevention measures, including those for foreign residents. For example, the output unit provides the formulated disaster prevention guidelines to users via a web application or mobile application. If users wish to receive the guidelines in paper form, the output unit uses a printer to print them. Sending the disaster prevention guidelines via email allows for quick provision by sending them directly to users.

[0030] The information gathering unit can diversify sources of information and collect information from at least one of academic papers, government guidelines, and industry best practices. For example, SmartAi-Chat searches academic paper databases to collect the latest research results. For example, it analyzes the latest academic papers on disaster prevention and provides specific guidelines. The information gathering unit also automatically collects government guidelines, which SmartAi-Chat analyzes. For example, it compares disaster prevention guidelines from various countries and formulates optimal disaster prevention guidelines. The information gathering unit also collects industry best practices, which SmartAi-Chat analyzes. For example, it refers to disaster prevention measures in the construction and real estate industries and provides specific guidelines. In this way, by collecting information from a variety of sources, more comprehensive disaster prevention guidelines can be formulated.

[0031] The information collecting unit can customize information based on the user's past search history or prompts and provide more appropriate information. For example, the information collecting unit analyzes the user's past search history and provides related information preferentially. For example, more specific guidelines are provided based on disaster prevention-related information searched in the past. The information collecting unit also customizes information based on the user's prompts. For example, in response to a prompt such as "I would like to formulate disaster prevention guidelines for office buildings," information specialized for office buildings is provided. The information collecting unit also combines the user's past search history and prompts to provide optimal information. For example, disaster prevention guidelines customized based on information searched in the past and the current prompt are provided. In this way, more appropriate disaster prevention guidelines can be formulated by providing information customized based on the user's past search history and prompts.

[0032] The information collection unit can include multimedia content such as images or videos in the information it collects and provide it in a visually easy-to-understand format. For example, the information collection unit automatically searches for images or videos related to the information collected by SmartAi-Chat and provides it in a visually easy-to-understand format. For example, it provides evacuation route diagrams and videos of disaster prevention drills. The information collection unit also uses multimedia content to deepen understanding of the information. For example, it uses videos to explain evacuation procedures in the event of a fire, making it visually easy to understand. The information collection unit also builds a system that provides the collected information as multimedia content. For example, it uses images and videos to visually explain disaster prevention guidelines. In this way, providing information in a visually easy-to-understand format deepens the user's understanding.

[0033] The information collection unit can compare disaster prevention guidelines from different industries or regions to identify similarities and differences. For example, SmartAi-Chat collects disaster prevention guidelines from different industries and performs a comparative analysis. For example, it compares disaster prevention guidelines from the construction industry and the medical industry to identify similarities and differences. The information collection unit also collects disaster prevention guidelines from different regions and SmartAi-Chat analyzes them. For example, it compares disaster prevention guidelines from Japan and the United States to identify the characteristics of each region. The information collection unit also compares disaster prevention guidelines from different industries or regions to formulate optimal guidelines. For example, it provides standard disaster prevention guidelines based on commonalities. This makes it possible to formulate more comprehensive disaster prevention guidelines by comparing disaster prevention guidelines from different industries or regions.

[0034] The guideline formulation department can incorporate specific facility layout or equipment information to provide more realistic and feasible guidelines. For example, the guideline formulation department uses SmartAi-Chat to analyze a facility layout diagram and set up evacuation routes based on that. For example, it proposes evacuation routes that take into account the location of emergency exits and the placement of stairs. The guideline formulation department also incorporates facility equipment information to formulate specific disaster prevention guidelines. For example, it provides guidelines that take into account the placement of fire extinguishers and AEDs. The guideline formulation department also builds a system that provides feasible disaster prevention guidelines based on facility layout and equipment information. For example, it simulates evacuation routes and formulates optimal guidelines. In this way, by incorporating specific facility layout and equipment information, it is possible to provide more realistic and feasible disaster prevention guidelines.

[0035] The guideline formulation unit can provide more accurate guidelines, including risk assessments based on past disaster data or simulation results. For example, the guideline formulation unit uses SmartAi-Chat to collect past disaster data and perform risk assessments based on that data. For example, it identifies high-risk areas based on data from past fires and earthquakes. The guideline formulation unit also performs disaster simulations and formulates disaster prevention guidelines based on the results. For example, it performs earthquake simulations to optimize evacuation routes. The guideline formulation unit also integrates past disaster data and simulation results to build a system that performs highly accurate risk assessments. For example, it integrates multiple data sources to perform risk assessments. This makes it possible to provide more accurate disaster prevention guidelines by including risk assessments based on past disaster data and simulation results.

[0036] The guideline formulation unit can provide guidelines for multiple facilities to cooperate in evacuation or rescue, taking into account cooperation between different facilities. For example, the guideline formulation unit uses SmartAi-Chat to collect information from multiple facilities and formulate guidelines for cooperative evacuation or rescue. For example, it provides an evacuation plan that takes into account cooperation with adjacent buildings. The guideline formulation unit also considers cooperation between different facilities and sets evacuation routes and rescue procedures. For example, it provides guidelines for multiple facilities to cooperate in evacuation guidance. The guideline formulation unit also builds a system for multiple facilities to cooperate in disaster prevention measures. For example, it sets common evacuation routes and communication procedures and strengthens cooperation. In this way, by considering cooperation between different facilities, it is possible to provide guidelines for multiple facilities to cooperate in evacuation or rescue.

[0037] The guideline formulation unit can provide flexible guidelines according to the situation, taking into account fluctuations in risk due to the season or time of day. For example, the guideline formulation unit uses SmartAi-Chat to analyze fluctuations in risk due to the season or time of day and formulate disaster prevention guidelines based on this. For example, it provides guidelines that take into account the risk of fires in winter. The guideline formulation unit also performs risk assessments according to the season and time of day and provides flexible disaster prevention guidelines. For example, it sets evacuation routes for nighttime and provides guidelines that are different from those for daytime. The guideline formulation unit also builds a system that automatically updates disaster prevention guidelines that take into account fluctuations in risk due to the season and time of day. For example, it performs risk assessments for each season and adjusts the guidelines. This makes it possible to provide flexible disaster prevention guidelines according to the situation by taking into account fluctuations in risk due to the season and time of day.

[0038] The translation department can provide accurate and appropriate translations by taking into account technical terms or expressions specific to the region. For example, SmartAi-Chat can refer to a technical terminology database to provide accurate translations. For example, it can accurately translate technical terms related to disaster prevention. The translation department can also provide appropriate translations by taking into account technical terms specific to the region. For example, it can use expressions that are in line with the culture and customs of each region. The translation department can also develop translation algorithms that take into account technical terms and expressions specific to the region. For example, it can provide translations that are specialized for specific industries or regions. This allows for accurate and appropriate translations by taking into account technical terms and expressions specific to the region.

[0039] The translation unit can customize the translation based on the user's past translation history or prompts to provide a more appropriate translation. For example, the translation unit analyzes the user's past translation history and provides related information preferentially. For example, a more appropriate translation is provided based on disaster prevention-related documents that have been translated in the past. The translation unit also customizes the translation based on the user's prompts. For example, in response to the prompt "I would like to translate disaster prevention guidelines for office buildings," a translation specialized for office buildings is provided. The translation unit also combines the user's past translation history and prompts to provide an optimal translation. For example, a customized translation is provided based on information translated in the past and the current prompt. This makes it possible to achieve a more appropriate translation by providing a customized translation based on the user's past translation history and prompts.

[0040] The translation unit can refer to disaster prevention guidelines from different cultural spheres and provide translations that take cultural backgrounds into consideration. For example, SmartAi-Chat collects disaster prevention guidelines from different cultural spheres and provides translations that take cultural backgrounds into consideration. For example, it compares disaster prevention guidelines from Japan and the United States and provides translations that are tailored to the culture. The translation unit also refers to disaster prevention guidelines from different cultural spheres and provides appropriate translations. For example, it uses expressions that take cultural differences into consideration. The translation unit also develops translation algorithms that take cultural backgrounds into consideration. For example, it provides translations that are specialized for specific cultural spheres. In this way, by referring to disaster prevention guidelines from different cultural spheres, it is possible to provide translations that take cultural backgrounds into consideration.

[0041] The translation unit can support voice input or voice output, making it possible to accommodate the visually impaired or elderly. For example, the translation unit allows SmartAi-Chat to support voice input, making it possible to accommodate the visually impaired or elderly. For example, disaster prevention guidelines are input by voice and translated. The translation unit also supports voice output, making it possible to accommodate the visually impaired or elderly. For example, the translated disaster prevention guidelines are output by voice. The translation unit also integrates voice input and voice output to build a system that can accommodate the visually impaired or elderly. For example, disaster prevention guidelines are input by voice and the translation results are output by voice. In this way, by supporting voice input and voice output, it can accommodate the visually impaired or elderly.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The disaster prevention guideline formulation system can further include a health management unit that monitors the user's health condition. For example, the health management unit can monitor the user's heart rate and blood pressure in real time and immediately issue an alert if any abnormalities are detected. The health management unit can also analyze users' health data and identify users who require special consideration during evacuation. For example, it can suggest the shortest and safest evacuation route for users with heart disease. The health management unit can also customize the frequency and content of evacuation drills based on the user's health condition. This allows for safer and more effective evacuations by providing disaster prevention guidelines that take the user's health condition into consideration.

[0044] The disaster prevention guideline formulation system may further include a behavior analysis unit that analyzes the user's behavior history. For example, the behavior analysis unit collects data on the user's past evacuation drills and analyzes evacuation behavior patterns. The behavior analysis unit can also predict evacuation behavior based on the user's behavior history and suggest optimal evacuation routes. For example, the behavior analysis unit can predict what actions the user will take during evacuation based on behavior data from past evacuation drills and suggest an evacuation route accordingly. The behavior analysis unit can also customize the content of evacuation drills based on the user's behavior history. This allows for more effective evacuation by providing disaster prevention guidelines that take the user's behavior history into consideration.

[0045] The information collection unit can customize information based on the user's past search history or prompts to provide more appropriate information. For example, the information collection unit can analyze the user's past search history and provide related information preferentially. For example, the information collection unit can provide more specific guidelines based on disaster prevention-related information searched in the past. The information collection unit also customizes information based on the user's prompts. For example, in response to a prompt such as "I would like to formulate disaster prevention guidelines for office buildings," the information collection unit can provide information specialized for office buildings. The information collection unit also combines the user's past search history and prompts to provide optimal information. For example, the information collection unit can provide customized disaster prevention guidelines based on information searched in the past and the current prompt. This allows the user to formulate more appropriate disaster prevention guidelines by providing customized information based on the user's past search history and prompts.

[0046] The disaster prevention guideline formulation system may further include a location information acquisition unit that acquires the user's location information. For example, the location information acquisition unit may acquire the user's current location in real time and propose an optimal evacuation route. The location information acquisition unit may also analyze the congestion status of evacuation routes in real time based on the user's location information. For example, if an evacuation route is congested, it may propose an alternative evacuation route. The location information acquisition unit may also customize the content of evacuation drills based on the user's location information. This allows for more effective evacuation by providing disaster prevention guidelines that take the user's location information into consideration.

[0047] The information gathering unit can compare disaster prevention guidelines from different industries or regions to identify similarities and differences. For example, SmartAi-Chat collects disaster prevention guidelines from different industries and performs a comparative analysis. For example, it compares disaster prevention guidelines from the construction industry and the medical industry to identify similarities and differences. The information gathering unit also collects disaster prevention guidelines from different regions and SmartAi-Chat analyzes them. For example, it compares disaster prevention guidelines from Japan and the United States to identify the characteristics of each region. The information gathering unit also compares disaster prevention guidelines from different industries and regions to formulate optimal guidelines. For example, it provides standard disaster prevention guidelines based on commonalities. This makes it possible to formulate more comprehensive disaster prevention guidelines by comparing disaster prevention guidelines from different industries and regions.

[0048] The disaster prevention guideline formulation system may further include a communication support unit that supports user communication. For example, the communication support unit provides a means for users to contact other users during evacuation. For example, it provides a chat function or voice call function that can be used in an emergency. The communication support unit also enables users to quickly share information necessary during evacuation. For example, it shares information on evacuation routes and evacuation locations in real time. The communication support unit also builds a system to support user communication. For example, it provides a function that automatically notifies contact information during evacuation. By supporting user communication in this way, confusion during evacuation can be prevented and a rapid evacuation can be achieved.

[0049] The guideline formulation department can provide more accurate guidelines, including risk assessments based on past disaster data or simulation results. For example, SmartAi-Chat collects past disaster data and performs risk assessments based on that data. For example, it identifies high-risk areas based on data from past fires and earthquakes. The guideline formulation department also performs disaster simulations and formulates disaster prevention guidelines based on the results. For example, it performs earthquake simulations to optimize evacuation routes. The guideline formulation department also integrates past disaster data with simulation results to build a system that performs highly accurate risk assessments. For example, it integrates multiple data sources to perform risk assessments. This allows for more accurate disaster prevention guidelines to be provided by including risk assessments based on past disaster data and simulation results.

[0050] The disaster prevention guideline formulation system may further include a learning support unit that supports the user's learning. For example, the learning support unit may provide learning materials for the user to acquire knowledge about disaster prevention. For example, the learning support unit may provide online courses and video tutorials about disaster prevention. The learning support unit may also monitor the user's learning progress and provide appropriate feedback. For example, the learning support unit may provide additional learning materials according to the user's learning progress. The learning support unit may also customize the learning content based on the user's learning data. In this way, by supporting the user's learning, it is possible to deepen knowledge about disaster prevention and realize more effective disaster prevention measures.

[0051] The disaster prevention guideline formulation system may further include a feedback collection unit that collects user feedback. For example, the feedback collection unit collects feedback provided by users on the disaster prevention guideline and uses it to improve the system. For example, feedback provided by users after an evacuation drill is collected and reflected in the next drill. The feedback collection unit may also analyze user feedback and identify areas for improvement in the system. For example, the feedback collection unit may identify areas for improvement in evacuation routes based on user feedback. The feedback collection unit may also customize system functions based on user feedback. In this way, by collecting user feedback and using it to improve the system, more effective disaster prevention guidelines may be provided.

[0052] The translation department can provide accurate and appropriate translations by taking into account technical terms or expressions specific to the region. For example, SmartAi-Chat references a database of technical terms to provide accurate translations. For example, it accurately translates technical terms related to disaster prevention. The translation department also takes into account technical terms specific to the region to provide appropriate translations. For example, it uses expressions that are in line with the culture and customs of each region. The translation department also develops translation algorithms that take into account technical terms and expressions specific to the region. For example, it provides translations that are specialized for specific industries or regions. This allows for accurate and appropriate translations by taking into account technical terms and expressions specific to the region.

[0053] The translation unit can customize the translation based on the user's past translation history or prompts to provide a more appropriate translation. For example, the translation unit can analyze the user's past translation history and provide relevant information preferentially. For example, the translation unit can provide a more appropriate translation based on disaster prevention-related documents that have been translated in the past. The translation unit also customizes the translation based on the user's prompts. For example, in response to the prompt "I would like to translate disaster prevention guidelines for office buildings," the translation unit can provide a translation specialized for office buildings. The translation unit also combines the user's past translation history and prompts to provide an optimal translation. For example, the translation unit provides a customized translation based on information translated in the past and the current prompt. This allows for more appropriate translations to be provided by providing a customized translation based on the user's past translation history and prompts.

[0054] The translation department can refer to disaster prevention guidelines from different cultural spheres and provide translations that take cultural backgrounds into account. For example, SmartAi-Chat collects disaster prevention guidelines from different cultural spheres and provides translations that take cultural backgrounds into account. For example, it compares disaster prevention guidelines from Japan and the United States and provides translations that are tailored to the culture. The translation department also refers to disaster prevention guidelines from different cultural spheres and provides appropriate translations. For example, it uses expressions that take cultural differences into account. The translation department also develops translation algorithms that take cultural backgrounds into account. For example, it provides translations that are specialized for specific cultural spheres. In this way, by referring to disaster prevention guidelines from different cultural spheres, it is possible to provide translations that take cultural backgrounds into account.

[0055] The translation unit can support voice input or voice output, making it possible to accommodate the visually impaired or elderly. For example, SmartAi-Chat supports voice input, making it possible to accommodate the visually impaired or elderly. For example, disaster prevention guidelines are input by voice and translated. The translation unit also supports voice output, making it possible to accommodate the visually impaired or elderly. For example, the translated disaster prevention guidelines are output by voice. The translation unit also integrates voice input and voice output to build a system that can accommodate the visually impaired or elderly. For example, disaster prevention guidelines are input by voice and the translation results are output by voice. In this way, by supporting voice input and voice output, it can accommodate the visually impaired or elderly.

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

[0057] Step 1: The information gathering unit receives a prompt from the user and collects information related to the disaster prevention guidelines for the complex building. For example, the information gathering unit may conduct an internet search, search relevant databases and literature, and collect the necessary information. It may also conduct an on-site survey to confirm the actual situation at the facility. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it uses data mining techniques to extract important data and statistical analysis techniques to find trends and patterns. It can also use machine learning techniques to create predictive models. Step 3: The guideline formulation department formulates disaster prevention guidelines appropriate for each facility based on the information analyzed by the analysis department. For example, disaster prevention guidelines are created for different types of facilities, such as office buildings, commercial facilities, and residential buildings, according to their respective characteristics and risks. Specific disaster prevention guidelines are also formulated, including the establishment of evacuation routes, the placement of emergency equipment, and emergency contact procedures. Step 4: The translation department translates the disaster prevention guidelines formulated by the guideline formulation department into other languages. For example, disaster prevention guidelines written in Japanese can be translated into English, Chinese, Korean, etc., and generative AI can be used to create other language versions with accurate and natural expression.

[0058] (Example 2) The disaster prevention guideline formulation system according to the embodiment of the present invention is a system that efficiently formulates disaster prevention guidelines for a complex building and realizes comprehensive disaster prevention measures, including measures to accommodate foreign visitors. As a result, the disaster prevention guideline formulation system can efficiently formulate disaster prevention guidelines for a complex building and realize comprehensive disaster prevention measures, including measures to accommodate foreign visitors.

[0059] A disaster prevention guideline formulation system according to an embodiment includes an information collection unit, an analysis unit, a guideline formulation unit, and a translation unit. The information collection unit receives a prompt from a user and collects information related to disaster prevention guidelines for a complex building. For example, the information collection unit performs an internet search to search relevant databases and literature to collect necessary information. The information collection unit can also conduct on-site investigations to confirm the actual status of the facility. For example, the information collection unit uses an internet search engine to collect information related to the latest disaster prevention guidelines. A database reference function is used to search academic papers and government guidelines. A field investigation function is used to confirm the current status of the facility and collect necessary information. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit uses data mining technology to analyze the collected information and extract important data. The analysis unit can also use statistical analysis technology to analyze the collected information and find trends and patterns. The analysis unit can also use machine learning technology to analyze the collected information and create a predictive model. For example, data mining technology is used to extract important data from the collected information. Statistical analysis technology is used to find trends and patterns in the collected information. Using machine learning technology, the system creates a predictive model based on the collected information. The guideline formulation department formulates disaster prevention guidelines appropriate for each facility based on the information analyzed by the analysis department. For example, the guideline formulation department creates disaster prevention guidelines tailored to the characteristics and risks of different facilities, such as office buildings, commercial facilities, and residential buildings. The guideline formulation department can also formulate specific disaster prevention guidelines, including evacuation route settings, emergency equipment placement, and emergency communication procedures. For example, for office buildings, the department can propose evacuation drills and emergency equipment placement to ensure employee safety. For commercial facilities, the department can propose rapid evacuation guidance for large numbers of visitors. For residential buildings, the department can propose individual measures to protect the safety of residents. The translation department translates the disaster prevention guidelines formulated by the guideline formulation department into other languages. For example, the translation department translates disaster prevention guidelines created in Japanese into English, Chinese, Korean, etc. The translation department can also use generative AI to create other language versions with accurate and natural expression.For example, the translation unit uses a generation AI to translate Japanese disaster prevention guidelines into English. The generation AI is also used to translate into multiple languages, such as Chinese and Korean. The generation AI is also used to create other language versions with accurate and natural expressions. This allows the disaster prevention guideline formulation system according to the embodiment to efficiently formulate disaster prevention guidelines for complex buildings and realize comprehensive disaster prevention measures, including those for foreign residents. For example, the output unit provides the formulated disaster prevention guidelines to users via a web application or mobile application. If users wish to receive the guidelines in paper form, the output unit uses a printer to print them. Sending the disaster prevention guidelines via email allows for quick provision by sending them directly to users.

[0060] The information collection unit uses the emotion estimation function to evaluate the reliability and importance of information and prioritizes analysis of highly reliable information. For example, the information collection unit uses the emotion estimation function on information collected by SmartAi-Chat to analyze the emotional state of the information provider. For example, if the information provider is calm, it determines that the information is highly reliable and prioritizes analysis of it. The information collection unit also uses the emotion estimation function to develop an algorithm for evaluating the reliability of information. For example, if the information provider's emotion score is high, it analyzes the information as highly reliable. The information collection unit also filters the information collected by SmartAi-Chat using the emotion estimation function to analyze only highly reliable information. For example, if the information provider has positive emotions, it prioritizes analysis of that information. This prioritizes analysis of highly reliable information, enabling the formulation of more accurate disaster prevention guidelines.

[0061] The information gathering unit can diversify sources of information and collect information from at least one of academic papers, government guidelines, and industry best practices. For example, SmartAi-Chat searches academic paper databases to collect the latest research results. For example, it analyzes the latest academic papers on disaster prevention and provides specific guidelines. The information gathering unit also automatically collects government guidelines, which SmartAi-Chat analyzes. For example, it compares disaster prevention guidelines from various countries and formulates optimal disaster prevention guidelines. The information gathering unit also collects industry best practices, which SmartAi-Chat analyzes. For example, it refers to disaster prevention measures in the construction and real estate industries and provides specific guidelines. In this way, by collecting information from a variety of sources, more comprehensive disaster prevention guidelines can be formulated.

[0062] The information collecting unit can customize information based on the user's past search history or prompts and provide more appropriate information. For example, the information collecting unit analyzes the user's past search history and provides related information preferentially. For example, more specific guidelines are provided based on disaster prevention-related information searched in the past. The information collecting unit also customizes information based on the user's prompts. For example, in response to a prompt such as "I would like to formulate disaster prevention guidelines for office buildings," information specialized for office buildings is provided. The information collecting unit also combines the user's past search history and prompts to provide optimal information. For example, disaster prevention guidelines customized based on information searched in the past and the current prompt are provided. In this way, more appropriate disaster prevention guidelines can be formulated by providing information customized based on the user's past search history and prompts.

[0063] The information collection unit can include multimedia content such as images or videos in the information it collects and provide it in a visually easy-to-understand format. For example, the information collection unit automatically searches for images or videos related to the information collected by SmartAi-Chat and provides it in a visually easy-to-understand format. For example, it provides evacuation route diagrams and videos of disaster prevention drills. The information collection unit also uses multimedia content to deepen understanding of the information. For example, it uses videos to explain evacuation procedures in the event of a fire, making it visually easy to understand. The information collection unit also builds a system that provides the collected information as multimedia content. For example, it uses images and videos to visually explain disaster prevention guidelines. In this way, providing information in a visually easy-to-understand format deepens the user's understanding.

[0064] The information collection unit can compare disaster prevention guidelines from different industries or regions to identify similarities and differences. For example, SmartAi-Chat collects disaster prevention guidelines from different industries and performs a comparative analysis. For example, it compares disaster prevention guidelines from the construction industry and the medical industry to identify similarities and differences. The information collection unit also collects disaster prevention guidelines from different regions and SmartAi-Chat analyzes them. For example, it compares disaster prevention guidelines from Japan and the United States to identify the characteristics of each region. The information collection unit also compares disaster prevention guidelines from different industries or regions to formulate optimal guidelines. For example, it provides standard disaster prevention guidelines based on commonalities. This makes it possible to formulate more comprehensive disaster prevention guidelines by comparing disaster prevention guidelines from different industries or regions.

[0065] The information collection unit can use the emotion estimation function to analyze the user's emotional response and provide information that elicits a positive response preferentially. The information collection unit, for example, uses the emotion estimation function on information collected by SmartAi-Chat to analyze the user's emotional response in real time. For example, it provides information that elicits a positive response preferentially. The information collection unit also uses the emotion estimation function to customize information based on the user's emotional response. For example, it provides information that gives the user a sense of security preferentially. The information collection unit also builds a system that selects information that elicits a positive response based on the user's emotional response data. For example, it provides information with a high emotion score preferentially. In this way, the user's emotional response is analyzed and information that elicits a positive response is provided, thereby improving user satisfaction.

[0066] The guideline formulation unit can use the emotion estimation function to consider the user's emotions and propose evacuation routes or procedures that reduce stress. For example, the guideline formulation unit uses the emotion estimation function in SmartAi-Chat to analyze the user's emotional state and propose evacuation routes that reduce stress. For example, it prioritizes proposing routes that avoid crowds. The guideline formulation unit also uses the emotion estimation function to measure the user's stress level and propose evacuation procedures accordingly. For example, it proposes simple procedures when stress is high. The guideline formulation unit also formulates disaster prevention guidelines to reduce stress based on the user's emotion data. For example, it proposes evacuation routes and procedures that give a sense of security. In this way, by considering the user's emotions and proposing evacuation routes and procedures that reduce stress, confusion during evacuation can be prevented.

[0067] The guideline formulation department can incorporate specific facility layout or equipment information to provide more realistic and feasible guidelines. For example, the guideline formulation department uses SmartAi-Chat to analyze a facility layout diagram and set up evacuation routes based on that. For example, it proposes evacuation routes that take into account the location of emergency exits and the placement of stairs. The guideline formulation department also incorporates facility equipment information to formulate specific disaster prevention guidelines. For example, it provides guidelines that take into account the placement of fire extinguishers and AEDs. The guideline formulation department also builds a system that provides feasible disaster prevention guidelines based on facility layout and equipment information. For example, it simulates evacuation routes and formulates optimal guidelines. In this way, by incorporating specific facility layout and equipment information, it is possible to provide more realistic and feasible disaster prevention guidelines.

[0068] The guideline formulation unit can provide more accurate guidelines, including risk assessments based on past disaster data or simulation results. For example, the guideline formulation unit uses SmartAi-Chat to collect past disaster data and perform risk assessments based on that data. For example, it identifies high-risk areas based on data from past fires and earthquakes. The guideline formulation unit also performs disaster simulations and formulates disaster prevention guidelines based on the results. For example, it performs earthquake simulations to optimize evacuation routes. The guideline formulation unit also integrates past disaster data and simulation results to build a system that performs highly accurate risk assessments. For example, it integrates multiple data sources to perform risk assessments. This makes it possible to provide more accurate disaster prevention guidelines by including risk assessments based on past disaster data and simulation results.

[0069] The guideline formulation unit can provide guidelines for multiple facilities to cooperate in evacuation or rescue, taking into account cooperation between different facilities. For example, the guideline formulation unit uses SmartAi-Chat to collect information from multiple facilities and formulate guidelines for cooperative evacuation or rescue. For example, it provides an evacuation plan that takes into account cooperation with adjacent buildings. The guideline formulation unit also considers cooperation between different facilities and sets evacuation routes and rescue procedures. For example, it provides guidelines for multiple facilities to cooperate in evacuation guidance. The guideline formulation unit also builds a system for multiple facilities to cooperate in disaster prevention measures. For example, it sets common evacuation routes and communication procedures and strengthens cooperation. In this way, by considering cooperation between different facilities, it is possible to provide guidelines for multiple facilities to cooperate in evacuation or rescue.

[0070] The guideline formulation unit can provide flexible guidelines according to the situation, taking into account fluctuations in risk due to the season or time of day. For example, the guideline formulation unit uses SmartAi-Chat to analyze fluctuations in risk due to the season or time of day and formulate disaster prevention guidelines based on this. For example, it provides guidelines that take into account the risk of fires in winter. The guideline formulation unit also performs risk assessments according to the season and time of day and provides flexible disaster prevention guidelines. For example, it sets evacuation routes for nighttime and provides guidelines that are different from those for daytime. The guideline formulation unit also builds a system that automatically updates disaster prevention guidelines that take into account fluctuations in risk due to the season and time of day. For example, it performs risk assessments for each season and adjusts the guidelines. This makes it possible to provide flexible disaster prevention guidelines according to the situation by taking into account fluctuations in risk due to the season and time of day.

[0071] The guideline formulation unit can use the emotion estimation function to analyze the user's emotions and provide guidelines that elicit positive emotions. For example, the guideline formulation unit, such as SmartAi-Chat, uses the emotion estimation function to analyze the user's emotional state and provide disaster prevention guidelines that elicit positive emotions. For example, it suggests evacuation routes that provide a sense of security. The guideline formulation unit also uses the emotion estimation function to customize disaster prevention guidelines based on the user's emotional response. For example, it provides specific steps for eliciting positive emotions. The guideline formulation unit also builds a system that formulates disaster prevention guidelines that elicit positive emotions based on the user's emotional data. For example, it provides guidelines with a high emotion score preferentially. In this way, the user's emotions are analyzed and disaster prevention guidelines that elicit positive emotions are provided, thereby increasing the user's sense of security.

[0072] The translation unit uses the emotion estimation function to preserve the emotional nuances of the translated text and provide natural, empathetic expressions. For example, SmartAi-Chat uses the emotion estimation function to analyze the emotional nuances of the translated text and provide natural, empathetic expressions. For example, expressions with positive emotions are used preferentially. The translation unit also uses the emotion estimation function to develop an algorithm that preserves the emotional nuances of the translated text. For example, expressions with high emotion scores are used preferentially. The translation unit also customizes the translation based on the emotion estimation data to preserve the emotional nuances of the translated text. For example, the translation is adjusted based on the user's emotional response. This allows the emotional nuances of the translated text to be preserved and natural, empathetic expressions to be provided.

[0073] The translation department can provide accurate and appropriate translations by taking into account technical terms or expressions specific to the region. For example, SmartAi-Chat can refer to a technical terminology database to provide accurate translations. For example, it can accurately translate technical terms related to disaster prevention. The translation department can also provide appropriate translations by taking into account technical terms specific to the region. For example, it can use expressions that are in line with the culture and customs of each region. The translation department can also develop translation algorithms that take into account technical terms and expressions specific to the region. For example, it can provide translations that are specialized for specific industries or regions. This allows for accurate and appropriate translations by taking into account technical terms and expressions specific to the region.

[0074] The translation unit can customize the translation based on the user's past translation history or prompts to provide a more appropriate translation. For example, the translation unit analyzes the user's past translation history and provides related information preferentially. For example, a more appropriate translation is provided based on disaster prevention-related documents that have been translated in the past. The translation unit also customizes the translation based on the user's prompts. For example, in response to the prompt "I would like to translate disaster prevention guidelines for office buildings," a translation specialized for office buildings is provided. The translation unit also combines the user's past translation history and prompts to provide an optimal translation. For example, a customized translation is provided based on information translated in the past and the current prompt. This makes it possible to achieve a more appropriate translation by providing a customized translation based on the user's past translation history and prompts.

[0075] The translation unit can refer to disaster prevention guidelines from different cultural spheres and provide translations that take cultural backgrounds into consideration. For example, SmartAi-Chat collects disaster prevention guidelines from different cultural spheres and provides translations that take cultural backgrounds into consideration. For example, it compares disaster prevention guidelines from Japan and the United States and provides translations that are tailored to the culture. The translation unit also refers to disaster prevention guidelines from different cultural spheres and provides appropriate translations. For example, it uses expressions that take cultural differences into consideration. The translation unit also develops translation algorithms that take cultural backgrounds into consideration. For example, it provides translations that are specialized for specific cultural spheres. In this way, by referring to disaster prevention guidelines from different cultural spheres, it is possible to provide translations that take cultural backgrounds into consideration.

[0076] The translation unit can support voice input or voice output, making it possible to accommodate the visually impaired or elderly. For example, the translation unit allows SmartAi-Chat to support voice input, making it possible to accommodate the visually impaired or elderly. For example, disaster prevention guidelines are input by voice and translated. The translation unit also supports voice output, making it possible to accommodate the visually impaired or elderly. For example, the translated disaster prevention guidelines are output by voice. The translation unit also integrates voice input and voice output to build a system that can accommodate the visually impaired or elderly. For example, disaster prevention guidelines are input by voice and the translation results are output by voice. In this way, by supporting voice input and voice output, it can accommodate the visually impaired or elderly.

[0077] The translation unit can use the emotion estimation function to analyze the user's emotional response and provide a translation that elicits positive emotions. For example, SmartAi-Chat uses the emotion estimation function to analyze the user's emotional response in real time and provide a translation that elicits positive emotions. For example, it uses expressions that give a sense of security. The translation unit also uses the emotion estimation function to customize the translation based on the user's emotional response. For example, it uses specific expressions that elicit positive emotions. The translation unit also builds a system that provides translations that elicit positive emotions based on the user's emotional data. For example, it prioritizes the use of expressions with high emotion scores. In this way, the translation unit analyzes the user's emotional response and provides a translation that elicits positive emotions, thereby improving user satisfaction.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The disaster prevention guideline formulation system can further include a health management unit that monitors the user's health condition. For example, the health management unit can monitor the user's heart rate and blood pressure in real time and immediately issue an alert if any abnormalities are detected. The health management unit can also analyze users' health data and identify users who require special consideration during evacuation. For example, it can suggest the shortest and safest evacuation route for users with heart disease. The health management unit can also customize the frequency and content of evacuation drills based on the user's health condition. This allows for safer and more effective evacuations by providing disaster prevention guidelines that take the user's health condition into consideration.

[0080] The information collection unit uses the emotion estimation function to evaluate the reliability and importance of information and prioritizes analysis of highly reliable information. For example, if the information provider appears calm, it is determined that the information is highly reliable and is prioritized for analysis. The information collection unit also uses the emotion estimation function to develop an algorithm for evaluating the reliability of information. For example, if the information provider's emotion score is high, it is analyzed as being highly reliable. The information collection unit also filters the information collected by SmartAi-Chat using the emotion estimation function and analyzes only highly reliable information. For example, if the information provider has positive emotions, it is prioritized for analysis. This allows for the prioritization of highly reliable information, enabling the formulation of more accurate disaster prevention guidelines.

[0081] The disaster prevention guideline formulation system may further include a behavior analysis unit that analyzes the user's behavior history. For example, the behavior analysis unit collects data on the user's past evacuation drills and analyzes evacuation behavior patterns. The behavior analysis unit can also predict evacuation behavior based on the user's behavior history and suggest optimal evacuation routes. For example, the behavior analysis unit can predict what actions the user will take during evacuation based on behavior data from past evacuation drills and suggest an evacuation route accordingly. The behavior analysis unit can also customize the content of evacuation drills based on the user's behavior history. This allows for more effective evacuation by providing disaster prevention guidelines that take the user's behavior history into consideration.

[0082] The information collection unit can customize information based on the user's past search history or prompts to provide more appropriate information. For example, the information collection unit can analyze the user's past search history and provide related information preferentially. For example, the information collection unit can provide more specific guidelines based on disaster prevention-related information searched in the past. The information collection unit also customizes information based on the user's prompts. For example, in response to a prompt such as "I would like to formulate disaster prevention guidelines for office buildings," the information collection unit can provide information specialized for office buildings. The information collection unit also combines the user's past search history and prompts to provide optimal information. For example, the information collection unit can provide customized disaster prevention guidelines based on information searched in the past and the current prompt. This allows the user to formulate more appropriate disaster prevention guidelines by providing customized information based on the user's past search history and prompts.

[0083] The disaster prevention guideline formulation system may further include a location information acquisition unit that acquires the user's location information. For example, the location information acquisition unit may acquire the user's current location in real time and propose an optimal evacuation route. The location information acquisition unit may also analyze the congestion status of evacuation routes in real time based on the user's location information. For example, if an evacuation route is congested, it may propose an alternative evacuation route. The location information acquisition unit may also customize the content of evacuation drills based on the user's location information. This allows for more effective evacuation by providing disaster prevention guidelines that take the user's location information into consideration.

[0084] The information gathering unit can compare disaster prevention guidelines from different industries or regions to identify similarities and differences. For example, SmartAi-Chat collects disaster prevention guidelines from different industries and performs a comparative analysis. For example, it compares disaster prevention guidelines from the construction industry and the medical industry to identify similarities and differences. The information gathering unit also collects disaster prevention guidelines from different regions and SmartAi-Chat analyzes them. For example, it compares disaster prevention guidelines from Japan and the United States to identify the characteristics of each region. The information gathering unit also compares disaster prevention guidelines from different industries and regions to formulate optimal guidelines. For example, it provides standard disaster prevention guidelines based on commonalities. This makes it possible to formulate more comprehensive disaster prevention guidelines by comparing disaster prevention guidelines from different industries and regions.

[0085] The information collection unit can use the emotion estimation function to analyze the user's emotional response and prioritize providing information that elicits a positive response. For example, the emotion estimation function is used on information collected by SmartAi-Chat to analyze the user's emotional response in real time. For example, information that elicits a positive response is prioritized. The information collection unit also uses the emotion estimation function to customize information based on the user's emotional response. For example, information that gives the user a sense of security is prioritized. The information collection unit also builds a system that selects information that elicits a positive response based on the user's emotional response data. For example, information with a high emotion score is prioritized. In this way, the user's emotional response is analyzed and information that elicits a positive response is provided, thereby improving user satisfaction.

[0086] The guideline formulation unit can use the emotion estimation function to consider the user's emotions and suggest evacuation routes or procedures that reduce stress. For example, SmartAi-Chat uses the emotion estimation function to analyze the user's emotional state and suggest evacuation routes that reduce stress. For example, it may prioritize suggesting routes that avoid crowds. The guideline formulation unit also uses the emotion estimation function to measure the user's stress level and suggest evacuation procedures accordingly. For example, it may suggest simple procedures if stress is high. The guideline formulation unit also formulates disaster prevention guidelines to reduce stress based on the user's emotion data. For example, it may suggest evacuation routes and procedures that give a sense of security. In this way, by considering the user's emotions and suggesting evacuation routes and procedures that reduce stress, confusion during evacuation can be prevented.

[0087] The disaster prevention guideline formulation system may further include a communication support unit that supports user communication. For example, the communication support unit provides a means for users to contact other users during evacuation. For example, it provides a chat function or voice call function that can be used in an emergency. The communication support unit also enables users to quickly share information necessary during evacuation. For example, it shares information on evacuation routes and evacuation locations in real time. The communication support unit also builds a system to support user communication. For example, it provides a function that automatically notifies contact information during evacuation. By supporting user communication in this way, confusion during evacuation can be prevented and a rapid evacuation can be achieved.

[0088] The guideline formulation department can provide more accurate guidelines, including risk assessments based on past disaster data or simulation results. For example, SmartAi-Chat collects past disaster data and performs risk assessments based on that data. For example, it identifies high-risk areas based on data from past fires and earthquakes. The guideline formulation department also performs disaster simulations and formulates disaster prevention guidelines based on the results. For example, it performs earthquake simulations to optimize evacuation routes. The guideline formulation department also integrates past disaster data with simulation results to build a system that performs highly accurate risk assessments. For example, it integrates multiple data sources to perform risk assessments. This allows for more accurate disaster prevention guidelines to be provided by including risk assessments based on past disaster data and simulation results.

[0089] The guideline formulation unit can use the emotion estimation function to analyze the user's emotions and provide guidelines that elicit positive emotions. For example, SmartAi-Chat uses the emotion estimation function to analyze the user's emotional state and provide disaster prevention guidelines that elicit positive emotions. For example, it can suggest evacuation routes that provide a sense of security. The guideline formulation unit also uses the emotion estimation function to customize disaster prevention guidelines based on the user's emotional response. For example, it can provide specific steps for eliciting positive emotions. The guideline formulation unit also builds a system that formulates disaster prevention guidelines that elicit positive emotions based on the user's emotional data. For example, it can prioritize providing guidelines with a high emotion score. In this way, the user's emotions are analyzed and disaster prevention guidelines that elicit positive emotions are provided, thereby increasing the user's sense of security.

[0090] The disaster prevention guideline formulation system may further include a learning support unit that supports the user's learning. For example, the learning support unit may provide learning materials for the user to acquire knowledge about disaster prevention. For example, the learning support unit may provide online courses and video tutorials about disaster prevention. The learning support unit may also monitor the user's learning progress and provide appropriate feedback. For example, the learning support unit may provide additional learning materials according to the user's learning progress. The learning support unit may also customize the learning content based on the user's learning data. In this way, by supporting the user's learning, it is possible to deepen knowledge about disaster prevention and realize more effective disaster prevention measures.

[0091] The translation unit uses the emotion estimation function to preserve the emotional nuances of the translated text and provide natural, empathetic expressions. For example, SmartAi-Chat uses the emotion estimation function to analyze the emotional nuances of the translated text and provide natural, empathetic expressions. For example, it prioritizes the use of expressions with positive emotions. The translation unit also uses the emotion estimation function to develop an algorithm that preserves the emotional nuances of the translated text. For example, it prioritizes the use of expressions with high emotion scores. The translation unit also customizes the translation based on the emotion estimation data to preserve the emotional nuances of the translated text. For example, it adjusts the translation based on the user's emotional response. This allows the emotional nuances of the translated text to be preserved, providing natural, empathetic expressions.

[0092] The disaster prevention guideline formulation system may further include a feedback collection unit that collects user feedback. For example, the feedback collection unit collects feedback provided by users on the disaster prevention guideline and uses it to improve the system. For example, feedback provided by users after an evacuation drill is collected and reflected in the next drill. The feedback collection unit may also analyze user feedback and identify areas for improvement in the system. For example, the feedback collection unit may identify areas for improvement in evacuation routes based on user feedback. The feedback collection unit may also customize system functions based on user feedback. In this way, by collecting user feedback and using it to improve the system, more effective disaster prevention guidelines may be provided.

[0093] The translation department can provide accurate and appropriate translations by taking into account technical terms or expressions specific to the region. For example, SmartAi-Chat references a database of technical terms to provide accurate translations. For example, it accurately translates technical terms related to disaster prevention. The translation department also takes into account technical terms specific to the region to provide appropriate translations. For example, it uses expressions that are in line with the culture and customs of each region. The translation department also develops translation algorithms that take into account technical terms and expressions specific to the region. For example, it provides translations that are specialized for specific industries or regions. This allows for accurate and appropriate translations by taking into account technical terms and expressions specific to the region.

[0094] The translation unit can customize the translation based on the user's past translation history or prompts to provide a more appropriate translation. For example, the translation unit can analyze the user's past translation history and provide relevant information preferentially. For example, the translation unit can provide a more appropriate translation based on disaster prevention-related documents that have been translated in the past. The translation unit also customizes the translation based on the user's prompts. For example, in response to the prompt "I would like to translate disaster prevention guidelines for office buildings," the translation unit can provide a translation specialized for office buildings. The translation unit also combines the user's past translation history and prompts to provide an optimal translation. For example, the translation unit provides a customized translation based on information translated in the past and the current prompt. This allows for more appropriate translations to be provided by providing a customized translation based on the user's past translation history and prompts.

[0095] The translation department can refer to disaster prevention guidelines from different cultural spheres and provide translations that take cultural backgrounds into account. For example, SmartAi-Chat collects disaster prevention guidelines from different cultural spheres and provides translations that take cultural backgrounds into account. For example, it compares disaster prevention guidelines from Japan and the United States and provides translations that are tailored to the culture. The translation department also refers to disaster prevention guidelines from different cultural spheres and provides appropriate translations. For example, it uses expressions that take cultural differences into account. The translation department also develops translation algorithms that take cultural backgrounds into account. For example, it provides translations that are specialized for specific cultural spheres. In this way, by referring to disaster prevention guidelines from different cultural spheres, it is possible to provide translations that take cultural backgrounds into account.

[0096] The translation unit can support voice input or voice output, making it possible to accommodate the visually impaired or elderly. For example, SmartAi-Chat supports voice input, making it possible to accommodate the visually impaired or elderly. For example, disaster prevention guidelines are input by voice and translated. The translation unit also supports voice output, making it possible to accommodate the visually impaired or elderly. For example, the translated disaster prevention guidelines are output by voice. The translation unit also integrates voice input and voice output to build a system that can accommodate the visually impaired or elderly. For example, disaster prevention guidelines are input by voice and the translation results are output by voice. In this way, by supporting voice input and voice output, it can accommodate the visually impaired or elderly.

[0097] The translation unit can use the emotion estimation function to analyze the user's emotional response and provide a translation that elicits positive emotions. For example, SmartAi-Chat uses the emotion estimation function to analyze the user's emotional response in real time and provide a translation that elicits positive emotions. For example, it uses expressions that give a sense of security. The translation unit also uses the emotion estimation function to customize the translation based on the user's emotional response. For example, it uses specific expressions that elicit positive emotions. The translation unit also builds a system that provides translations that elicit positive emotions based on the user's emotional data. For example, it prioritizes the use of expressions with high emotion scores. In this way, the user's emotional response is analyzed and a translation that elicits positive emotions is provided, thereby improving user satisfaction.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The information gathering unit receives a prompt from the user and collects information related to the disaster prevention guidelines for the complex building. For example, the information gathering unit may conduct an internet search, search relevant databases and literature, and collect the necessary information. It may also conduct an on-site survey to confirm the actual situation at the facility. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it uses data mining techniques to extract important data and statistical analysis techniques to find trends and patterns. It can also use machine learning techniques to create predictive models. Step 3: The guideline formulation department formulates disaster prevention guidelines appropriate for each facility based on the information analyzed by the analysis department. For example, disaster prevention guidelines are created for different types of facilities, such as office buildings, commercial facilities, and residential buildings, according to their respective characteristics and risks. Specific disaster prevention guidelines are also formulated, including the establishment of evacuation routes, the placement of emergency equipment, and emergency contact procedures. Step 4: The translation department translates the disaster prevention guidelines formulated by the guideline formulation department into other languages. For example, disaster prevention guidelines written in Japanese can be translated into English, Chinese, Korean, etc., and generative AI can be used to create other language versions with accurate and natural expression.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0121] The 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.

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

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

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

[0125] Fig. 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.

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

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] 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.

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

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

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] 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.

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

[0146] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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. an information collection unit that receives a prompt from a user and collects information regarding disaster prevention guidelines for the complex building; an analysis unit that analyzes the information collected by the information collection unit; a guideline formulation unit that formulates disaster prevention guidelines appropriate for each facility based on the information analyzed by the analysis unit; a translation unit that translates the disaster prevention guidelines formulated by the guideline formulation unit into other languages. A system characterized by:

2. The information collecting unit Evaluate the reliability and importance of information, and prioritize analysis of highly reliable information 2. The system of claim 1.

3. The information collecting unit The information collected will include multimedia content such as images or videos, and will be presented in a visually easy-to-understand format.

2. The system of claim 1.

4. The guideline development unit Considering the user's emotions and suggesting evacuation routes or procedures that will reduce stress 2. The system of claim 1.

5. The translation unit 10. The system of claim 1, wherein the system preserves the emotional nuances of the translation and provides natural, relatable expression.

6. The information collecting unit Diversify your sources and gather information from academic papers, government guidelines, and / or industry best practices.

2. The system of claim 1.

7. The guideline development unit Providing more accurate guidance, including risk assessment based on past disaster data or simulation results 2. The system of claim 1.

8. The translation unit The system of claim 1, further comprising: analyzing a user's emotional response; and providing the translation in a manner that elicits positive emotions.

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