system

Abstract

The system according to the embodiment aims to enable residents to take prompt and appropriate action in the event of a disaster. [Solution] A system according to an embodiment includes a notification unit, an education unit, a community unit, and a data collection unit. The notification unit provides disaster information in real time. The education unit provides education for residents to learn based on the information provided by the notification unit. The community unit provides a function for residents to share information and help each other based on the knowledge provided by the education unit. The data collection unit collects resident behavior data based on the information shared by the units.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide enough information or education to residents to act quickly and appropriately in the event of a disaster, and there is room for improvement.

[0005] The system according to the embodiment aims to enable residents to take prompt and appropriate action in the event of a disaster. [Means for solving the problem]

[0006] The system according to the embodiment includes a notification unit, an education unit, a community unit, and a data collection unit. The notification unit provides disaster information in real time. The education unit provides education for residents to learn based on the information provided by the notification unit. The community unit provides a function for residents to share information and help each other based on the knowledge provided by the education unit. The data collection unit collects behavioral data of residents based on the information shared by the units. [Effects of the Invention]

[0007] The system according to the embodiment can enable residents to act quickly and appropriately in the event of a disaster. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A disaster prevention system according to an embodiment of the present invention is a tool for promoting cooperation and improving awareness among residents. This disaster prevention system includes a notification system for quickly providing information to residents when a disaster occurs. This notification system transmits disaster information to residents' smartphones or computers in real time. For example, when a disaster such as an earthquake or typhoon occurs, it provides residents with evacuation instructions and information on safe evacuation sites. Next, educational content is provided to teach residents how to act in the event of a disaster. This educational content allows residents to acquire knowledge for taking appropriate actions in the event of a disaster through videos and simulations. For example, residents can learn how to evacuate in the event of an earthquake or what to do when a typhoon approaches. Furthermore, a community function is provided to enable residents to cooperate and respond to disasters. This community function provides a platform for residents to share information and help each other. For example, residents can share information about evacuation shelters and the availability of relief supplies. Finally, the system includes a data collection function for recording residents' behavior when a disaster occurs and analyzing it later. This data collection function can collect data on residents' behavior and use it to improve disaster prevention measures. For example, it can record evacuation route selection and behavior at evacuation shelters, which can be used in the event of the next disaster. Thus, the present invention aims to provide tools to promote cooperation and awareness among residents and minimize damage during disasters. For example, for the notification system, a mechanism for providing information in real time through a smartphone app or website will be added. For educational content, an online learning platform using videos and simulations will be provided. For community functions, chat and bulletin board functions will be added to enable residents to share information and help each other. For data collection functions, a mechanism will be added to anonymously collect resident behavioral data and use it to improve disaster prevention measures. As a result, the disaster prevention system will be able to promote cooperation and awareness among residents and minimize damage during disasters.

[0029] A disaster prevention system according to an embodiment includes a notification unit, an education unit, a community unit, and a data collection unit. The notification unit provides disaster information in real time. For example, when a disaster such as an earthquake or typhoon occurs, the notification unit provides residents with evacuation instructions and information on safe evacuation locations. The notification unit can provide information in real time via a smartphone app or a website. For example, the notification unit sends disaster information to residents' smartphones via push notifications. The notification unit can also provide disaster information via a website. The education unit provides educational content for residents to learn how to act in the event of a disaster. For example, the education unit can provide residents with knowledge to take appropriate action in the event of a disaster through videos and simulations. The education unit provides an online learning platform to allow residents to study at home. For example, the education unit provides videos to teach residents how to evacuate in the event of an earthquake or what measures to take when a typhoon approaches. The education unit can also conduct simulations to train residents to take appropriate action in the event of a disaster. The community unit provides functions to enable residents to cooperate with each other in responding to disasters. For example, the community unit has a chat function and a bulletin board function that allow residents to share information and help each other. The community unit provides a platform for residents to share information about evacuation shelters and the status of relief supply distribution. For example, the community unit allows residents to share information about evacuation shelters via chat. The community unit can also share the status of relief supply distribution through a bulletin board. The data collection unit has a function for recording residents' behavior when a disaster occurs and analyzing it later. For example, the data collection unit collects behavioral data of residents and can use it to improve disaster prevention measures. The data collection unit collects behavioral data of residents anonymously to protect privacy. For example, the data collection unit records residents' evacuation route selection, behavior at evacuation shelters, etc., and can use the data in the next disaster. As a result, the disaster prevention system according to the embodiment can improve residents' cooperation and awareness and minimize damage during a disaster.

[0030] The notification unit can provide information in real time through a smartphone app or a website. The notification unit can provide disaster information in real time using, for example, a smartphone app. For example, when an earthquake occurs, the notification unit can send a push notification to residents' smartphones to provide evacuation instructions and information on safe evacuation locations. Furthermore, when a typhoon is approaching, the notification unit can send an alert to residents' smartphones to encourage them to take measures. Furthermore, the notification unit can provide disaster information through a website. For example, the notification unit can post disaster information on a website so that residents can access and check the information. Furthermore, the notification unit can provide information on evacuation centers and the availability of relief supplies through the website. This allows residents to receive disaster information quickly. Some or all of the above-described processing in the notification unit can be performed, for example, using AI, or can be performed without AI. For example, the notification unit can input disaster information into AI, which can analyze the information and generate an appropriate notification.

[0031] The Ministry of Education may provide an online learning platform using videos or simulations. For example, the Ministry of Education may provide content using videos to help residents learn how to act in the event of a disaster. For example, the Ministry of Education may provide a video explaining evacuation methods in the event of an earthquake. The Ministry of Education may also provide a video explaining measures to take when a typhoon approaches. Furthermore, the Ministry of Education may use simulations to train residents to take appropriate actions in the event of a disaster. For example, the Ministry of Education may use simulations to conduct evacuation drills in the event of an earthquake. The Ministry of Education may also use simulations to learn measures to take when a typhoon approaches. This allows residents to acquire the knowledge to take appropriate actions in the event of a disaster. Some or all of the above-mentioned processing by the Ministry of Education may be performed using, for example, AI, or may be performed without using AI. For example, the Ministry of Education may input video or simulation content into AI, which may analyze the content and provide an appropriate learning plan.

[0032] The community unit may have a chat function or a bulletin board function that allows residents to share information and help each other. The community unit, for example, can use the chat function to allow residents to share information and help each other. For example, the community unit can allow residents to share information about evacuation shelters via chat. The community unit can also share the status of relief supply delivery via chat. Furthermore, the community unit can use the bulletin board function to allow residents to share information and help each other. For example, the community unit can post information about evacuation shelters on a bulletin board so that residents can check the information. The community unit can also post the status of relief supply delivery on a bulletin board so that residents can check the information. This allows residents to cooperate with each other to respond to disasters. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI, or may be performed without using AI. For example, the community unit can input information from chats or bulletin boards into AI, which can analyze the information and provide appropriate feedback.

[0033] The data collection unit collects resident behavior data and can use it to improve disaster prevention measures. The data collection unit can record, for example, residents' evacuation route selection and behavior at evacuation shelters, and use it to help in the next disaster. For example, the data collection unit records residents' evacuation routes using GPS data. The data collection unit can also record behavior at evacuation shelters using sensors. Furthermore, the data collection unit collects resident behavior data anonymously to protect privacy. For example, the data collection unit anonymizes and stores the resident behavior data and uses it for analysis. This allows the resident behavior data to be analyzed and used to help in the next disaster. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit inputs resident behavior data into AI, which analyzes the data and can use it to help improve disaster prevention measures.

[0034] When sending a notification, the notification unit can analyze the resident's past behavioral history and select an appropriate notification method. For example, the notification unit analyzes the resident's past behavioral history and selects the optimal notification method. For example, if the resident has previously preferred email notifications, the notification unit can send notifications by email. Furthermore, if the resident has previously frequently checked app notifications, the notification unit can prioritize app notifications. Furthermore, if the resident has previously preferred SMS notifications, the notification unit can send notifications by SMS. This allows the optimal notification method to be selected based on the resident's past behavioral history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input the resident's behavioral history data into a generation AI, which can analyze the data and select the optimal notification method.

[0035] At the time of notification, the notification unit can customize information about evacuation routes or evacuation locations based on the resident's current location information. The notification unit customizes information about evacuation routes or evacuation locations based on, for example, the resident's current location information. For example, the notification unit notifies the resident of the evacuation location closest to their current location. The notification unit can also suggest an optimal evacuation route based on the resident's current location. Furthermore, the notification unit can notify the resident of the congestion status of evacuation locations based on the resident's current location. This makes it possible to provide information about optimal evacuation routes and evacuation locations based on the resident's current location information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the resident's location information data to a generation AI, which can analyze the data and provide information about optimal evacuation routes and evacuation locations.

[0036] When notifying, the notification unit can select an appropriate notification format depending on the type of device and usage status of the resident. The notification unit selects the optimal notification format depending on, for example, the type of device and usage status of the resident. For example, if the resident is using a smartphone, the notification unit can send a push notification. Also, if the resident is using a PC, the notification unit can send a browser notification. Furthermore, if the resident is using a tablet, the notification unit can send an in-app notification. This makes it possible to select the optimal notification format depending on the type of device and usage status of the resident. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the resident's device information into a generation AI, which can analyze the data and select the optimal notification format.

[0037] The notification unit can analyze the resident's social media activity at the time of notification and notify the resident of relevant information. The notification unit, for example, analyzes the resident's social media activity and notifies the resident of relevant information. For example, if the resident frequently shares disaster information on social media, the notification unit can send relevant notifications. In addition, if the resident is searching for information on evacuation sites on social media, the notification unit can also notify the resident of the optimal evacuation site. Furthermore, if the resident is searching for information on relief supplies on social media, the notification unit can also notify the resident of the availability status of relief supplies. This makes it possible to notify the resident of relevant information based on the resident's social media activity. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the resident's social media data into the generation AI, which can analyze the data and notify the resident of relevant information.

[0038] When providing educational content, the Ministry of Education can propose an appropriate study plan by referring to the resident's past learning history. The Ministry of Education, for example, can propose an optimal study plan by referring to the resident's past learning history. For example, the Ministry of Education can propose the next content to be studied based on what the resident has previously studied. The Ministry of Education can also provide content that complements insufficient understanding based on the resident's past learning history. Furthermore, the Ministry of Education can analyze the resident's learning history and propose an optimal study order. This makes it possible to propose an optimal study plan based on the resident's past learning history. Some or all of the above-mentioned processing in the Ministry of Education may be performed using, for example, AI, or may be performed without using AI. For example, the Ministry of Education can input the resident's learning history data into a generation AI, which can analyze the data and propose an optimal study plan.

[0039] When providing educational content, the Ministry of Education can provide personalized content based on residents' areas of interest. The Ministry of Education, for example, provides content customized based on residents' areas of interest. For example, if residents are interested in earthquake countermeasures, the Ministry of Education can provide content specialized in earthquake countermeasures. Furthermore, if residents are interested in typhoon countermeasures, the Ministry of Education can provide content specialized in typhoon countermeasures. Furthermore, if residents are interested in fire countermeasures, the Ministry of Education can provide content specialized in fire countermeasures. This makes it possible to provide customized content based on residents' areas of interest. Some or all of the above-mentioned processing by the Ministry of Education may be performed using, for example, AI, or may be performed without using AI. For example, the Ministry of Education can input residents' areas of interest data into a generation AI, which can analyze the data and provide customized content.

[0040] When providing educational content, the education department can suggest appropriate study timing based on the resident's living situation and time of day. For example, the education department can suggest optimal study timing based on the resident's living situation and time of day. For example, the education department can suggest studying during the resident's relaxation time after work. The education department can also provide short learning content to residents during their morning commute. Furthermore, the education department can provide longer learning content to residents if they have a block of time on the weekend. This makes it possible to suggest optimal study timing based on the resident's living situation and time of day. Some or all of the above-mentioned processing in the education department may be performed using, or without, AI. For example, the education department can input resident's living situation data into a generation AI, which can analyze the data and suggest optimal study timing.

[0041] When providing educational content, the Ministry of Education can provide content that allows residents to learn about disaster prevention measures specific to their region based on their geographical background. For example, the Ministry of Education can provide content that allows residents to learn about disaster prevention measures specific to their region based on their geographical background. For example, if residents live in an earthquake-prone area, the Ministry of Education can provide content specialized in earthquake prevention measures. Furthermore, if residents live in an area prone to typhoons, the Ministry of Education can provide content specialized in typhoon prevention measures. Furthermore, if residents live in an area prone to flooding, the Ministry of Education can provide content specialized in flood prevention measures. This makes it possible to provide content that allows residents to learn about disaster prevention measures specific to their region based on their geographical background. Some or all of the above-described processing by the Ministry of Education may be performed using, or without, AI, for example. For example, the Ministry of Education can input residents' geographical background data into a generation AI, which can analyze the data to provide content that allows residents to learn about disaster prevention measures specific to their region.

[0042] When providing the community function, the community unit can suggest an appropriate cooperation method by referring to the residents' past cooperation history. For example, the community unit can suggest the optimal cooperation method by referring to the residents' past cooperation history. For example, the community unit can suggest the next cooperation content based on the residents' past cooperation content. The community unit can also identify and suggest areas where cooperation is needed from the residents' past cooperation history. Furthermore, the community unit can analyze the residents' cooperation history and suggest the optimal cooperation method. This makes it possible to suggest the optimal cooperation method based on the residents' past cooperation history. Some or all of the above-mentioned processing in the community unit may be performed using, for example, AI, or may be performed without using AI. For example, the community unit can input the residents' cooperation history data into the generation AI, which can analyze the data and suggest the optimal cooperation method.

[0043] When providing the community function, the community unit can perform appropriate grouping based on the attribute information of residents. The community unit, for example, performs optimal grouping based on the attribute information of residents. For example, the community unit performs grouping based on the age group of residents. The community unit can also perform grouping based on the occupation of residents. Furthermore, the community unit can perform grouping based on the residential area of ​​residents. This allows optimal grouping based on the attribute information of residents. Some or all of the above-mentioned processing in the community unit may be performed using, for example, AI, or may be performed without using AI. For example, the community unit can input attribute information data of residents into a generation AI, which can analyze the data and perform optimal grouping.

[0044] When providing the community function, the community unit can select an appropriate information sharing method taking into account the geographic distribution of residents. The community unit selects the optimal information sharing method, for example, taking into account the geographic distribution of residents. For example, if residents live nearby, the community unit can suggest a method of sharing information by meeting in person. In addition, if residents live far away, the community unit can also suggest a method of sharing information online. Furthermore, if residents are concentrated in a specific area, the community unit can also suggest an information sharing method specialized for that area. This makes it possible to select the optimal information sharing method based on the geographic distribution of residents. Some or all of the above-mentioned processing in the community unit may be performed using, for example, AI, or may be performed without using AI. For example, the community unit can input residents' geographic distribution data into a generation AI, which can analyze the data and select the optimal information sharing method.

[0045] When providing the community function, the community department can analyze residents' social media activities and share relevant information. For example, the community department can analyze residents' social media activities and share relevant information. For example, if residents frequently share disaster information on social media, the community department can share relevant information. Also, if residents are searching for information on evacuation locations on social media, the community department can share the optimal evacuation locations. Furthermore, if residents are searching for information on relief supplies on social media, the community department can share the status of relief supply distribution. This makes it possible to share relevant information based on residents' social media activities. Some or all of the above-mentioned processing in the community department may be performed using, for example, AI, or may be performed without using AI. For example, the community department can input residents' social media data into a generation AI, which can analyze the data and share relevant information.

[0046] When collecting data, the data collection unit can select an appropriate data collection method by referring to the resident's past behavioral data. The data collection unit, for example, selects the optimal data collection method by referring to the resident's past behavioral data. For example, the data collection unit suggests the next data to be collected based on data previously provided by the resident. The data collection unit can also select the necessary data collection method from the resident's past behavioral data. Furthermore, the data collection unit can analyze the resident's behavioral data and suggest the optimal timing for data collection. This makes it possible to select the optimal data collection method based on the resident's past behavioral data. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI or without AI. For example, the data collection unit can input the resident's behavioral data into a generation AI, which analyzes the data and selects the optimal data collection method.

[0047] The data collection unit can classify or analyze the data based on the residents' attribute information when collecting data. The data collection unit classifies and analyzes the data based on the residents' attribute information, for example. For example, the data collection unit classifies and analyzes the data based on the residents' age groups. The data collection unit can also classify and analyze the data based on the residents' occupations. Furthermore, the data collection unit can classify and analyze the data based on the residents' residential areas. This allows data to be classified and analyzed based on the residents' attribute information. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the residents' attribute information data into a generation AI, and the generation AI can analyze the data to classify and analyze the data.

[0048] When collecting data, the data collection unit can select an appropriate data collection method taking into account the geographic distribution of residents. The data collection unit selects the optimal data collection method, for example, taking into account the geographic distribution of residents. For example, if residents live nearby, the data collection unit can suggest a method of conducting in-person interviews. Furthermore, if residents live far away, the data collection unit can also suggest a method of conducting an online survey. Furthermore, if residents are concentrated in a specific area, the data collection unit can also suggest a data collection method specialized for that area. This makes it possible to select the optimal data collection method based on the geographic distribution of residents. Some or all of the above-described processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input geographic distribution data of residents into a generation AI, which can analyze the data and select the optimal data collection method.

[0049] The data collection unit can analyze residents' social media activities during data collection and collect related data. For example, the data collection unit analyzes residents' social media activities and collects related data. For example, if residents frequently share disaster information on social media, the data collection unit collects related data. Furthermore, if residents search for information on evacuation sites on social media, the data collection unit can also collect data on optimal evacuation sites. Furthermore, if residents search for information on relief supplies on social media, the data collection unit can also collect data on the status of relief supply distribution. This makes it possible to collect related data based on residents' social media activities. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input residents' social media data into a generation AI, which then analyzes the data and collects related data.

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

[0051] The notification unit can monitor the health status of residents and encourage appropriate responses in the event of a disaster. For example, the notification unit can monitor residents' heart rate and blood pressure in real time and send emergency notifications if any abnormalities are detected. The notification unit can also provide special evacuation instructions based on information about residents with chronic illnesses. Furthermore, the notification unit can collect residents' health data and use it to provide medical support after a disaster. This allows appropriate responses to be made according to the residents' health status.

[0052] The Ministry of Education can provide educational content customized to each resident's learning style. For example, for residents who prefer visual learning, they can provide content using infographics and animations. For residents who prefer auditory learning, they can provide podcasts and audio guides. Furthermore, for residents who prefer hands-on learning, they can provide interactive simulations and hands-on training. This allows for effective education that caters to each resident's learning style.

[0053] The Community Affairs Department can assign roles based on the skills and expertise of residents. For example, a resident who is a medical professional can be assigned the role of providing first aid and medical support. A resident who works in the construction industry can be assigned the role of setting up and repairing evacuation shelters. Furthermore, a resident with IT skills can be assigned the role of managing information systems and collecting data. This allows for effective disaster response that makes the most of the skills and expertise of residents.

[0054] The data collection unit analyzes resident behavior data in real time, which can be used to improve disaster prevention measures. For example, the data collection unit can track residents' evacuation routes in real time and suggest the optimal evacuation route. The data collection unit can also monitor the congestion status of evacuation shelters in real time and provide guidance to appropriate evacuation locations. Furthermore, the data collection unit can analyze resident behavior data and evaluate the effectiveness of disaster prevention measures. This enables rapid response through real-time data analysis.

[0055] The notification unit can analyze residents' past behavioral history and select the appropriate notification method in the event of a disaster. For example, if a resident has previously preferred email notifications, the notification unit can send notifications by email. The notification unit can also prioritize app notifications if a resident has previously frequently checked app notifications. Furthermore, if a resident has previously preferred SMS notifications, the notification unit can send notifications by SMS. This makes it possible to select the optimal notification method based on the resident's past behavioral history.

[0056] The Ministry of Education can provide personalized educational content based on residents' areas of interest. For example, if residents are interested in earthquake countermeasures, content specialized in earthquake countermeasures can be provided. If residents are interested in typhoon countermeasures, content specialized in typhoon countermeasures can be provided. Furthermore, if residents are interested in fire countermeasures, content specialized in fire countermeasures can be provided. This enables effective education that is customized based on residents' areas of interest.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The notification unit provides disaster information in real time. For example, when a disaster such as an earthquake or typhoon occurs, the notification unit provides residents with evacuation instructions and information on safe evacuation locations. The notification unit can provide information in real time through a smartphone app or website. For example, the notification unit sends disaster information to residents' smartphones via push notifications. The notification unit can also provide disaster information through a website. Step 2: The Ministry of Education provides educational content to help residents learn how to act in the event of a disaster. For example, the Ministry of Education can provide videos and simulations to help residents acquire the knowledge they need to take appropriate action in the event of a disaster. The Ministry of Education provides an online learning platform so residents can study at home. For example, the Ministry of Education can provide videos to teach residents how to evacuate in the event of an earthquake or what measures to take when a typhoon is approaching. The Ministry of Education can also use simulations to train residents on how to act appropriately in the event of a disaster. Step 3: The Community Department provides functions that allow residents to cooperate with each other in dealing with disasters. For example, the Community Department provides chat and bulletin board functions that allow residents to share information and help each other. The Community Department provides a platform for residents to share information about evacuation shelters and the status of relief supply distribution. For example, the Community Department allows residents to share information about evacuation shelters via chat. The Community Department can also share the status of relief supply distribution through the bulletin board. Step 4: The data collection unit has the function of recording residents' behavior when a disaster occurs and analyzing it later. For example, the data collection unit can collect data on residents' behavior and use it to improve disaster prevention measures. The data collection unit collects residents' behavior data anonymously to protect privacy. For example, the data collection unit can record residents' evacuation route choices and behavior at evacuation shelters, which can be used to help in the next disaster.

[0059] (Example 2) A disaster prevention system according to an embodiment of the present invention is a tool for promoting cooperation and improving awareness among residents. This disaster prevention system includes a notification system for quickly providing information to residents when a disaster occurs. This notification system transmits disaster information to residents' smartphones or computers in real time. For example, when a disaster such as an earthquake or typhoon occurs, it provides residents with evacuation instructions and information on safe evacuation sites. Next, educational content is provided to teach residents how to act in the event of a disaster. This educational content allows residents to acquire knowledge for taking appropriate actions in the event of a disaster through videos and simulations. For example, residents can learn how to evacuate in the event of an earthquake or what to do when a typhoon approaches. Furthermore, a community function is provided to enable residents to cooperate and respond to disasters. This community function provides a platform for residents to share information and help each other. For example, residents can share information about evacuation shelters and the availability of relief supplies. Finally, the system includes a data collection function for recording residents' behavior when a disaster occurs and analyzing it later. This data collection function can collect data on residents' behavior and use it to improve disaster prevention measures. For example, it can record evacuation route selection and behavior at evacuation shelters, which can be used in the event of the next disaster. Thus, the present invention aims to provide tools to promote cooperation and awareness among residents and minimize damage during disasters. For example, for the notification system, a mechanism for providing information in real time through a smartphone app or website will be added. For educational content, an online learning platform using videos and simulations will be provided. For community functions, chat and bulletin board functions will be added to enable residents to share information and help each other. For data collection functions, a mechanism will be added to anonymously collect resident behavioral data and use it to improve disaster prevention measures. As a result, the disaster prevention system will be able to promote cooperation and awareness among residents and minimize damage during disasters.

[0060] A disaster prevention system according to an embodiment includes a notification unit, an education unit, a community unit, and a data collection unit. The notification unit provides disaster information in real time. For example, when a disaster such as an earthquake or typhoon occurs, the notification unit provides residents with evacuation instructions and information on safe evacuation locations. The notification unit can provide information in real time via a smartphone app or a website. For example, the notification unit sends disaster information to residents' smartphones via push notifications. The notification unit can also provide disaster information via a website. The education unit provides educational content for residents to learn how to act in the event of a disaster. For example, the education unit can provide residents with knowledge to take appropriate action in the event of a disaster through videos and simulations. The education unit provides an online learning platform to allow residents to study at home. For example, the education unit provides videos to teach residents how to evacuate in the event of an earthquake or what measures to take when a typhoon approaches. The education unit can also conduct simulations to train residents to take appropriate action in the event of a disaster. The community unit provides functions to enable residents to cooperate with each other in responding to disasters. For example, the community unit has a chat function and a bulletin board function that allow residents to share information and help each other. The community unit provides a platform for residents to share information about evacuation shelters and the status of relief supply distribution. For example, the community unit allows residents to share information about evacuation shelters via chat. The community unit can also share the status of relief supply distribution through a bulletin board. The data collection unit has a function for recording residents' behavior when a disaster occurs and analyzing it later. For example, the data collection unit collects behavioral data of residents and can use it to improve disaster prevention measures. The data collection unit collects behavioral data of residents anonymously to protect privacy. For example, the data collection unit records residents' evacuation route selection, behavior at evacuation shelters, etc., and can use the data in the next disaster. As a result, the disaster prevention system according to the embodiment can improve residents' cooperation and awareness and minimize damage during a disaster.

[0061] The notification unit can provide information in real time through a smartphone app or a website. The notification unit can provide disaster information in real time using, for example, a smartphone app. For example, when an earthquake occurs, the notification unit can send a push notification to residents' smartphones to provide evacuation instructions and information on safe evacuation locations. Furthermore, when a typhoon is approaching, the notification unit can send an alert to residents' smartphones to encourage them to take measures. Furthermore, the notification unit can provide disaster information through a website. For example, the notification unit can post disaster information on a website so that residents can access and check the information. Furthermore, the notification unit can provide information on evacuation centers and the availability of relief supplies through the website. This allows residents to receive disaster information quickly. Some or all of the above-described processing in the notification unit can be performed, for example, using AI, or can be performed without AI. For example, the notification unit can input disaster information into AI, which can analyze the information and generate an appropriate notification.

[0062] The Ministry of Education may provide an online learning platform using videos or simulations. For example, the Ministry of Education may provide content using videos to help residents learn how to act in the event of a disaster. For example, the Ministry of Education may provide a video explaining evacuation methods in the event of an earthquake. The Ministry of Education may also provide a video explaining measures to take when a typhoon approaches. Furthermore, the Ministry of Education may use simulations to train residents to take appropriate actions in the event of a disaster. For example, the Ministry of Education may use simulations to conduct evacuation drills in the event of an earthquake. The Ministry of Education may also use simulations to learn measures to take when a typhoon approaches. This allows residents to acquire the knowledge to take appropriate actions in the event of a disaster. Some or all of the above-mentioned processing by the Ministry of Education may be performed using, for example, AI, or may be performed without using AI. For example, the Ministry of Education may input video or simulation content into AI, which may analyze the content and provide an appropriate learning plan.

[0063] The community unit may have a chat function or a bulletin board function that allows residents to share information and help each other. The community unit, for example, can use the chat function to allow residents to share information and help each other. For example, the community unit can allow residents to share information about evacuation shelters via chat. The community unit can also share the status of relief supply delivery via chat. Furthermore, the community unit can use the bulletin board function to allow residents to share information and help each other. For example, the community unit can post information about evacuation shelters on a bulletin board so that residents can check the information. The community unit can also post the status of relief supply delivery on a bulletin board so that residents can check the information. This allows residents to cooperate with each other to respond to disasters. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI, or may be performed without using AI. For example, the community unit can input information from chats or bulletin boards into AI, which can analyze the information and provide appropriate feedback.

[0064] The data collection unit collects resident behavior data and can use it to improve disaster prevention measures. The data collection unit can record, for example, residents' evacuation route selection and behavior at evacuation shelters, and use it to help in the next disaster. For example, the data collection unit records residents' evacuation routes using GPS data. The data collection unit can also record behavior at evacuation shelters using sensors. Furthermore, the data collection unit collects resident behavior data anonymously to protect privacy. For example, the data collection unit anonymizes and stores the resident behavior data and uses it for analysis. This allows the resident behavior data to be analyzed and used to help in the next disaster. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit inputs resident behavior data into AI, which analyzes the data and can use it to help improve disaster prevention measures.

[0065] The notification unit can estimate the resident's emotion and adjust the content and timing of the notification based on the estimated emotion. For example, the notification unit can estimate the resident's emotion and adjust the content and timing of the notification based on the estimated emotion. For example, if the resident is feeling anxious, the notification unit can prioritize notification that provides a sense of security. If the resident is calm, the notification unit can also send a notification containing detailed information. Furthermore, if the resident is in a panicked state, the notification unit can also send a notification containing concise and clear instructions. This allows for appropriate notification based on the resident's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the resident's emotion data into the generation AI, which can analyze the emotion and generate an appropriate notification.

[0066] When sending a notification, the notification unit can analyze the resident's past behavioral history and select an appropriate notification method. For example, the notification unit analyzes the resident's past behavioral history and selects the optimal notification method. For example, if the resident has previously preferred email notifications, the notification unit can send notifications by email. Furthermore, if the resident has previously frequently checked app notifications, the notification unit can prioritize app notifications. Furthermore, if the resident has previously preferred SMS notifications, the notification unit can send notifications by SMS. This allows the optimal notification method to be selected based on the resident's past behavioral history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input the resident's behavioral history data into a generation AI, which can analyze the data and select the optimal notification method.

[0067] At the time of notification, the notification unit can customize information about evacuation routes or evacuation locations based on the resident's current location information. The notification unit customizes information about evacuation routes or evacuation locations based on, for example, the resident's current location information. For example, the notification unit notifies the resident of the evacuation location closest to their current location. The notification unit can also suggest an optimal evacuation route based on the resident's current location. Furthermore, the notification unit can notify the resident of the congestion status of evacuation locations based on the resident's current location. This makes it possible to provide information about optimal evacuation routes and evacuation locations based on the resident's current location information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the resident's location information data to a generation AI, which can analyze the data and provide information about optimal evacuation routes and evacuation locations.

[0068] The notification unit can estimate emotions and determine the priority of notifications based on the estimated emotions. The notification unit, for example, estimates the emotions of residents and determines the priority of notifications based on the estimated emotions. For example, if a resident is feeling anxious, the notification unit can prioritize sending important notifications. The notification unit can also send normal notifications if the resident is calm. Furthermore, if a resident is in a panic, the notification unit can prioritize sending emergency notifications. This makes it possible to determine the priority of notifications based on the emotions of the resident. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input resident emotion data into a generation AI, which can analyze the data and determine the priority of notifications.

[0069] When notifying, the notification unit can select an appropriate notification format depending on the type of device and usage status of the resident. The notification unit selects the optimal notification format depending on, for example, the type of device and usage status of the resident. For example, if the resident is using a smartphone, the notification unit can send a push notification. Also, if the resident is using a PC, the notification unit can send a browser notification. Furthermore, if the resident is using a tablet, the notification unit can send an in-app notification. This makes it possible to select the optimal notification format depending on the type of device and usage status of the resident. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the resident's device information into a generation AI, which can analyze the data and select the optimal notification format.

[0070] The notification unit can analyze the resident's social media activity at the time of notification and notify the resident of relevant information. The notification unit, for example, analyzes the resident's social media activity and notifies the resident of relevant information. For example, if the resident frequently shares disaster information on social media, the notification unit can send relevant notifications. In addition, if the resident is searching for information on evacuation sites on social media, the notification unit can also notify the resident of the optimal evacuation site. Furthermore, if the resident is searching for information on relief supplies on social media, the notification unit can also notify the resident of the availability status of relief supplies. This makes it possible to notify the resident of relevant information based on the resident's social media activity. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the resident's social media data into the generation AI, which can analyze the data and notify the resident of relevant information.

[0071] The Ministry of Education can estimate emotions and adjust the difficulty level and expression method of educational content based on the estimated emotions. For example, the Ministry of Education can estimate residents' emotions and adjust the difficulty level and expression method of educational content based on the estimated emotions. For example, if a resident is feeling anxious, the Ministry of Education can provide simple and easy-to-understand content. If a resident is calm, the Ministry of Education can provide content containing detailed information. Furthermore, if a resident is excited, the Ministry of Education can provide visually stimulating content. This allows the difficulty level and expression method of educational content to be adjusted according to the residents' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the Ministry of Education can be performed using AI, for example, or without AI. For example, the Ministry of Education can input residents' emotion data into a generation AI, which can analyze the data and adjust the difficulty level and expression method of educational content.

[0072] When providing educational content, the Ministry of Education can propose an appropriate study plan by referring to the resident's past learning history. The Ministry of Education, for example, can propose an optimal study plan by referring to the resident's past learning history. For example, the Ministry of Education can propose the next content to be studied based on what the resident has previously studied. The Ministry of Education can also provide content that complements insufficient understanding based on the resident's past learning history. Furthermore, the Ministry of Education can analyze the resident's learning history and propose an optimal study order. This makes it possible to propose an optimal study plan based on the resident's past learning history. Some or all of the above-mentioned processing in the Ministry of Education may be performed using, for example, AI, or may be performed without using AI. For example, the Ministry of Education can input the resident's learning history data into a generation AI, which can analyze the data and propose an optimal study plan.

[0073] When providing educational content, the Ministry of Education can provide personalized content based on residents' areas of interest. The Ministry of Education, for example, provides content customized based on residents' areas of interest. For example, if residents are interested in earthquake countermeasures, the Ministry of Education can provide content specialized in earthquake countermeasures. Furthermore, if residents are interested in typhoon countermeasures, the Ministry of Education can provide content specialized in typhoon countermeasures. Furthermore, if residents are interested in fire countermeasures, the Ministry of Education can provide content specialized in fire countermeasures. This makes it possible to provide customized content based on residents' areas of interest. Some or all of the above-mentioned processing by the Ministry of Education may be performed using, for example, AI, or may be performed without using AI. For example, the Ministry of Education can input residents' areas of interest data into a generation AI, which can analyze the data and provide customized content.

[0074] The Ministry of Education can estimate emotions and adjust the length and format of educational content based on the estimated emotions. For example, the Ministry of Education can estimate residents' emotions and adjust the length and format of educational content based on the estimated emotions. For example, if a resident is feeling anxious, the Ministry of Education can provide short, to-the-point content. If a resident is feeling relaxed, the Ministry of Education can provide longer content with detailed explanations. If a resident is feeling excited, the Ministry of Education can provide content with visually stimulating effects. This allows the length and format of educational content to be adjusted according to the resident's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the Ministry of Education can be performed using AI, or without AI. For example, the Ministry of Education can input residents' emotion data into a generation AI, which can analyze the data and adjust the length and format of the educational content.

[0075] When providing educational content, the education department can suggest appropriate study timing based on the resident's living situation and time of day. For example, the education department can suggest optimal study timing based on the resident's living situation and time of day. For example, the education department can suggest studying during the resident's relaxation time after work. The education department can also provide short learning content to residents during their morning commute. Furthermore, the education department can provide longer learning content to residents if they have a block of time on the weekend. This makes it possible to suggest optimal study timing based on the resident's living situation and time of day. Some or all of the above-mentioned processing in the education department may be performed using, or without, AI. For example, the education department can input resident's living situation data into a generation AI, which can analyze the data and suggest optimal study timing.

[0076] When providing educational content, the Ministry of Education can provide content that allows residents to learn about disaster prevention measures specific to their region based on their geographical background. For example, the Ministry of Education can provide content that allows residents to learn about disaster prevention measures specific to their region based on their geographical background. For example, if residents live in an earthquake-prone area, the Ministry of Education can provide content specialized in earthquake prevention measures. Furthermore, if residents live in an area prone to typhoons, the Ministry of Education can provide content specialized in typhoon prevention measures. Furthermore, if residents live in an area prone to flooding, the Ministry of Education can provide content specialized in flood prevention measures. This makes it possible to provide content that allows residents to learn about disaster prevention measures specific to their region based on their geographical background. Some or all of the above-described processing by the Ministry of Education may be performed using, or without, AI, for example. For example, the Ministry of Education can input residents' geographical background data into a generation AI, which can analyze the data to provide content that allows residents to learn about disaster prevention measures specific to their region.

[0077] The community unit can estimate emotions and adjust the method of information sharing within the community based on the estimated emotions. For example, the community unit can estimate the emotions of residents and adjust the method of information sharing within the community based on the estimated emotions. For example, if residents are feeling anxious, the community unit can prioritize sharing information that provides a sense of security. Furthermore, if residents are calm, the community unit can share information that includes detailed information. Furthermore, if residents are in a panic, the community unit can share information that includes concise and clear instructions. This allows the method of information sharing within the community to be adjusted according to residents' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the community unit can be performed using, for example, AI, or without AI. For example, the community unit can input residents' emotion data into a generation AI, which can analyze the data and adjust the method of information sharing.

[0078] When providing the community function, the community unit can suggest an appropriate cooperation method by referring to the residents' past cooperation history. For example, the community unit can suggest the optimal cooperation method by referring to the residents' past cooperation history. For example, the community unit can suggest the next cooperation content based on the residents' past cooperation content. The community unit can also identify and suggest areas where cooperation is needed from the residents' past cooperation history. Furthermore, the community unit can analyze the residents' cooperation history and suggest the optimal cooperation method. This makes it possible to suggest the optimal cooperation method based on the residents' past cooperation history. Some or all of the above-mentioned processing in the community unit may be performed using, for example, AI, or may be performed without using AI. For example, the community unit can input the residents' cooperation history data into the generation AI, which can analyze the data and suggest the optimal cooperation method.

[0079] When providing the community function, the community unit can perform appropriate grouping based on the attribute information of residents. The community unit, for example, performs optimal grouping based on the attribute information of residents. For example, the community unit performs grouping based on the age group of residents. The community unit can also perform grouping based on the occupation of residents. Furthermore, the community unit can perform grouping based on the residential area of ​​residents. This allows optimal grouping based on the attribute information of residents. Some or all of the above-mentioned processing in the community unit may be performed using, for example, AI, or may be performed without using AI. For example, the community unit can input attribute information data of residents into a generation AI, which can analyze the data and perform optimal grouping.

[0080] The community unit can estimate emotions and adjust the display order of information within the community based on the estimated emotions. For example, the community unit can estimate the emotions of residents and adjust the display order of information within the community based on the estimated emotions. For example, if a resident is feeling anxious, the community unit can prioritize displaying information that provides a sense of security. Furthermore, if a resident is calm, the community unit can display information including detailed information. Furthermore, if a resident is in a panic, the community unit can prioritize displaying information including concise and clear instructions. This allows the display order of information within the community to be adjusted according to the emotions of the residents. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the community unit can be performed using, for example, AI, or without AI. For example, the community unit can input residents' emotion data into the generation AI, which can analyze the data and adjust the display order of information.

[0081] When providing the community function, the community unit can select an appropriate information sharing method taking into account the geographic distribution of residents. The community unit selects the optimal information sharing method, for example, taking into account the geographic distribution of residents. For example, if residents live nearby, the community unit can suggest a method of sharing information by meeting in person. In addition, if residents live far away, the community unit can also suggest a method of sharing information online. Furthermore, if residents are concentrated in a specific area, the community unit can also suggest an information sharing method specialized for that area. This makes it possible to select the optimal information sharing method based on the geographic distribution of residents. Some or all of the above-mentioned processing in the community unit may be performed using, for example, AI, or may be performed without using AI. For example, the community unit can input residents' geographic distribution data into a generation AI, which can analyze the data and select the optimal information sharing method.

[0082] When providing the community function, the community department can analyze residents' social media activities and share relevant information. For example, the community department can analyze residents' social media activities and share relevant information. For example, if residents frequently share disaster information on social media, the community department can share relevant information. Also, if residents are searching for information on evacuation locations on social media, the community department can share the optimal evacuation locations. Furthermore, if residents are searching for information on relief supplies on social media, the community department can share the status of relief supply distribution. This makes it possible to share relevant information based on residents' social media activities. Some or all of the above-mentioned processing in the community department may be performed using, for example, AI, or may be performed without using AI. For example, the community department can input residents' social media data into a generation AI, which can analyze the data and share relevant information.

[0083] The data collection unit can estimate emotions and adjust the method and timing of data collection based on the estimated emotions. For example, the data collection unit estimates the emotions of residents and adjusts the method and timing of data collection based on the estimated emotions. For example, the data collection unit may collect data sparingly if the residents are feeling anxious. Furthermore, the data collection unit may collect detailed data if the residents are calm. Furthermore, the data collection unit may collect brief, minimal data if the residents are in a panic. This allows the method and timing of data collection to be adjusted according to the residents' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit may input residents' emotion data into a generation AI, which then analyzes the data and adjusts the method and timing of data collection.

[0084] When collecting data, the data collection unit can select an appropriate data collection method by referring to the resident's past behavioral data. The data collection unit, for example, selects the optimal data collection method by referring to the resident's past behavioral data. For example, the data collection unit suggests the next data to be collected based on data previously provided by the resident. The data collection unit can also select the necessary data collection method from the resident's past behavioral data. Furthermore, the data collection unit can analyze the resident's behavioral data and suggest the optimal timing for data collection. This makes it possible to select the optimal data collection method based on the resident's past behavioral data. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI or without AI. For example, the data collection unit can input the resident's behavioral data into a generation AI, which analyzes the data and selects the optimal data collection method.

[0085] The data collection unit can classify or analyze the data based on the residents' attribute information when collecting data. The data collection unit classifies and analyzes the data based on the residents' attribute information, for example. For example, the data collection unit classifies and analyzes the data based on the residents' age groups. The data collection unit can also classify and analyze the data based on the residents' occupations. Furthermore, the data collection unit can classify and analyze the data based on the residents' residential areas. This allows data to be classified and analyzed based on the residents' attribute information. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the residents' attribute information data into a generation AI, and the generation AI can analyze the data to classify and analyze the data.

[0086] The data collection unit can estimate emotions and determine the priority of data collection based on the estimated emotions. The data collection unit, for example, estimates the emotions of residents and determines the priority of data collection based on the estimated emotions. For example, if a resident is feeling anxious, the data collection unit prioritizes collecting important data. The data collection unit can also perform normal data collection if the resident is calm. Furthermore, if a resident is in a panic, the data collection unit can prioritize collecting urgent data. This makes it possible to determine the priority of data collection based on the emotions of the residents. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input resident emotion data into a generation AI, which then analyzes the data and determines the priority of data collection.

[0087] When collecting data, the data collection unit can select an appropriate data collection method taking into account the geographic distribution of residents. The data collection unit selects the optimal data collection method, for example, taking into account the geographic distribution of residents. For example, if residents live nearby, the data collection unit can suggest a method of conducting in-person interviews. Furthermore, if residents live far away, the data collection unit can also suggest a method of conducting an online survey. Furthermore, if residents are concentrated in a specific area, the data collection unit can also suggest a data collection method specialized for that area. This makes it possible to select the optimal data collection method based on the geographic distribution of residents. Some or all of the above-described processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input geographic distribution data of residents into a generation AI, which can analyze the data and select the optimal data collection method.

[0088] The data collection unit can analyze residents' social media activities during data collection and collect related data. For example, the data collection unit analyzes residents' social media activities and collects related data. For example, if residents frequently share disaster information on social media, the data collection unit collects related data. Furthermore, if residents search for information on evacuation sites on social media, the data collection unit can also collect data on optimal evacuation sites. Furthermore, if residents search for information on relief supplies on social media, the data collection unit can also collect data on the status of relief supply distribution. This makes it possible to collect related data based on residents' social media activities. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input residents' social media data into a generation AI, which then analyzes the data and collects related data. === Hard Collateral 1-1 === Each of the multiple elements, including the notification unit, education unit, community unit, and data collection unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the notification unit is implemented by the control unit 46A of the smart device 14 and sends disaster information to residents' smartphones via push notifications. The education unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides educational content through videos and simulations. The community unit is implemented, for example, by the control unit 46A of the smart device 14 and provides chat and bulletin board functions for residents to share information with each other. The data collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and anonymously collects behavioral data of residents to help improve disaster prevention measures. === Hard Collateral 1-2 === Each of the multiple elements, including the notification unit, education unit, community unit, and data collection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the notification unit is realized by the control unit 46A of the smart glasses 214 and sends disaster information to residents' smartphones via push notifications. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides educational content through videos and simulations. The community unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides chat and bulletin board functions for residents to share information with each other. The data collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymously collects residents' behavioral data to help improve disaster prevention measures. === Hard Collateral 1-3 === Each of the multiple elements, including the notification unit, education unit, community unit, and data collection unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the notification unit is implemented by the control unit 46A of the headset terminal 314 and sends disaster information to residents' smartphones via push notifications. The education unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides educational content through videos and simulations. The community unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides chat and bulletin board functions that allow residents to share information with each other. The data collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and anonymously collects behavioral data of residents to help improve disaster prevention measures. === Hard Collateral 1-4 === Each of the multiple elements, including the notification unit, education unit, community unit, and data collection unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the notification unit is realized by the control unit 46A of the robot 414 and sends disaster information to residents' smartphones via push notifications. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides educational content through videos and simulations. The community unit is realized, for example, by the control unit 46A of the robot 414 and provides chat and bulletin board functions that allow residents to share information with each other. The data collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and anonymously collects behavioral data of residents to help improve disaster prevention measures.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The notification unit can monitor the health status of residents and encourage appropriate responses in the event of a disaster. For example, the notification unit can monitor residents' heart rate and blood pressure in real time and send emergency notifications if any abnormalities are detected. The notification unit can also provide special evacuation instructions based on information about residents with chronic illnesses. Furthermore, the notification unit can collect residents' health data and use it to provide medical support after a disaster. This allows appropriate responses to be made according to the residents' health status.

[0091] The Ministry of Education can provide educational content customized to each resident's learning style. For example, for residents who prefer visual learning, they can provide content using infographics and animations. For residents who prefer auditory learning, they can provide podcasts and audio guides. Furthermore, for residents who prefer hands-on learning, they can provide interactive simulations and hands-on training. This allows for effective education that caters to each resident's learning style.

[0092] The Community Affairs Department can assign roles based on the skills and expertise of residents. For example, a resident who is a medical professional can be assigned the role of providing first aid and medical support. A resident who works in the construction industry can be assigned the role of setting up and repairing evacuation shelters. Furthermore, a resident with IT skills can be assigned the role of managing information systems and collecting data. This allows for effective disaster response that makes the most of the skills and expertise of residents.

[0093] The data collection unit analyzes resident behavior data in real time, which can be used to improve disaster prevention measures. For example, the data collection unit can track residents' evacuation routes in real time and suggest the optimal evacuation route. The data collection unit can also monitor the congestion status of evacuation shelters in real time and provide guidance to appropriate evacuation locations. Furthermore, the data collection unit can analyze resident behavior data and evaluate the effectiveness of disaster prevention measures. This enables rapid response through real-time data analysis.

[0094] The notification unit can estimate the resident's emotions and adjust the content and timing of notifications based on the estimated emotions. For example, if the resident is feeling anxious, the notification unit can prioritize notifications that provide a sense of security. If the resident is calm, the notification unit can also send notifications that include detailed information. Furthermore, if the resident is in a panic, the notification unit can send notifications that include concise and clear instructions. This makes it possible to provide appropriate notifications according to the resident's emotions.

[0095] The Ministry of Education can estimate residents' emotions and adjust the difficulty level and presentation of educational content based on the estimated emotions. For example, if residents are feeling anxious, the Ministry of Education can provide simple, easy-to-understand content. If residents are calm, the Ministry of Education can provide content with detailed information. Furthermore, if residents are excited, the Ministry of Education can provide visually stimulating content. This enables effective education tailored to residents' emotions.

[0096] The community department can estimate the emotions of residents and adjust the method of information sharing within the community based on the estimated emotions. For example, if residents are feeling anxious, the community department will prioritize sharing information that gives them a sense of security. If residents are calm, the community department can also share information that includes detailed information. Furthermore, if residents are in a state of panic, the community department can share information that includes concise and clear instructions. This enables effective information sharing according to residents' emotions.

[0097] The data collection unit can estimate the emotions of residents and adjust the method and timing of data collection based on the estimated emotions. For example, if residents are feeling anxious, the data collection unit will collect data sparingly. If residents are calm, the data collection unit can also collect detailed data. Furthermore, if residents are in a state of panic, the data collection unit can collect simple, minimal data. This makes it possible to collect appropriate data according to residents' emotions.

[0098] The notification unit can analyze residents' past behavioral history and select the appropriate notification method in the event of a disaster. For example, if a resident has previously preferred email notifications, the notification unit can send notifications by email. The notification unit can also prioritize app notifications if a resident has previously frequently checked app notifications. Furthermore, if a resident has previously preferred SMS notifications, the notification unit can send notifications by SMS. This makes it possible to select the optimal notification method based on the resident's past behavioral history.

[0099] The Ministry of Education can provide personalized educational content based on residents' areas of interest. For example, if residents are interested in earthquake countermeasures, content specialized in earthquake countermeasures can be provided. If residents are interested in typhoon countermeasures, content specialized in typhoon countermeasures can be provided. Furthermore, if residents are interested in fire countermeasures, content specialized in fire countermeasures can be provided. This enables effective education that is customized based on residents' areas of interest.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The notification unit provides disaster information in real time. For example, when a disaster such as an earthquake or typhoon occurs, the notification unit provides residents with evacuation instructions and information on safe evacuation locations. The notification unit can provide information in real time through a smartphone app or website. For example, the notification unit sends disaster information to residents' smartphones via push notifications. The notification unit can also provide disaster information through a website. Step 2: The Ministry of Education provides educational content to help residents learn how to act in the event of a disaster. For example, the Ministry of Education can provide videos and simulations to help residents acquire the knowledge they need to take appropriate action in the event of a disaster. The Ministry of Education provides an online learning platform so residents can study at home. For example, the Ministry of Education can provide videos to teach residents how to evacuate in the event of an earthquake or what measures to take when a typhoon is approaching. The Ministry of Education can also use simulations to train residents on how to act appropriately in the event of a disaster. Step 3: The Community Department provides functions that allow residents to cooperate with each other in dealing with disasters. For example, the Community Department provides chat and bulletin board functions that allow residents to share information and help each other. The Community Department provides a platform for residents to share information about evacuation shelters and the status of relief supply distribution. For example, the Community Department allows residents to share information about evacuation shelters via chat. The Community Department can also share the status of relief supply distribution through the bulletin board. Step 4: The data collection unit has the function of recording residents' behavior when a disaster occurs and analyzing it later. For example, the data collection unit can collect data on residents' behavior and use it to improve disaster prevention measures. The data collection unit collects residents' behavior data anonymously to protect privacy. For example, the data collection unit can record residents' evacuation route choices and behavior at evacuation shelters, which can be used to help in the next disaster.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A notification section that provides disaster information in real time, an education department for residents to learn based on the information provided by the notification department; a community department for residents to share information and help each other based on the knowledge provided by the education department; a data collection unit that collects behavioral data of residents based on the information shared by the unit; A system characterized by:

2. The notification unit Providing real-time information via a smartphone app or website 2. The system of claim 1.

3. The Ministry of Education, Providing an online learning platform using videos or simulations 2. The system of claim 1.

4. The community section Equipped with a chat function or bulletin board function for residents to share information and help each other 2. The system of claim 1.

5. The data collection unit Collecting data on residents' behavior to help improve disaster prevention measures 2. The system of claim 1.

6. The notification unit Estimate emotions and adjust the content and timing of notifications based on the estimated emotions 2. The system of claim 1.

7. The notification unit When notifying residents, analyze their past behavioral history and select the appropriate notification method.

2. The system of claim 1.

8. The notification unit Customize evacuation route or location information based on the resident's current location during notifications 2. The system of claim 1.

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