system

Abstract

The system according to this embodiment aims to help users select the most suitable disaster prevention goods, manage replacement schedules, and propose response methods in the event of a disaster. [Solution] The system according to the embodiment comprises an information collection unit, a list creation unit, a notification unit, a proposal unit, a sharing unit, and an update unit. The information collection unit collects information about the user's residence and living area. The list creation unit creates a list of necessary disaster prevention goods based on the information collected by the information collection unit. The notification unit notifies the user of when to replace the disaster prevention goods listed by the list creation unit. The proposal unit proposes methods for responding to disasters based on the information collected by the information collection unit. The sharing unit shares the registered information with organizations and public institutions. The update unit updates knowledge of response methods based on disaster information that is updated daily and shares it with the user.

Description

Technical Field

[0004] ,

[0006] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the selection of disaster prevention goods optimal for individual users, the management of replacement timing, and the proposal of response methods during disasters have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to select disaster prevention goods optimal for users, manage replacement timing, and propose response methods during disasters.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an information gathering unit, a list creation unit, a notification unit, a proposal unit, a sharing unit, and an update unit. The information gathering unit collects information about the user's residence and living area. The list creation unit creates a list of necessary disaster prevention goods based on the information collected by the information gathering unit. The notification unit notifies the user of when to replace the disaster prevention goods listed by the list creation unit. The proposal unit proposes methods for responding to a disaster based on the information collected by the information gathering unit. The sharing unit shares the registered information with organizations and public institutions. The update unit updates knowledge of response methods based on disaster information that is updated daily and shares it with the user. [Effects of the Invention]

[0007] The system according to this embodiment can help users select the most suitable disaster prevention goods, manage replacement schedules, and propose response methods in the event of a disaster. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The disaster prevention management system according to an embodiment of the present invention is a system that uses AI to manage disaster prevention goods and propose methods for responding in the event of a disaster. Based on information about the user's residence and living area, this disaster prevention management system proposes necessary disaster prevention goods and the optimal timing for their replacement. For example, the disaster prevention management system creates a list of disaster prevention goods such as food, water, and medicine, and notifies the user of when each should be replaced. The disaster prevention management system also tells the user where these disaster prevention goods are stored. Next, the disaster prevention management system proposes methods for responding in the event of a disaster based on information about the user's residence and living area. For example, in the event of a disaster such as an earthquake or flood, the disaster prevention management system provides information on how to evacuate and which evacuation center is the safest. This enables the user to respond quickly and appropriately. Furthermore, the disaster prevention management system shares registered information with the company and local government and uses it for safety confirmation in the event of an emergency. For example, the disaster prevention management system notifies the company and local government of the user's safety status in the event of a disaster to support rapid rescue operations. In addition, the disaster prevention management system updates its knowledge of response methods based on the daily updates of disaster information and shares it with the user. This enables responses based on the latest information at all times. This disaster preparedness management system allows users to improve their knowledge of disaster preparedness supplies and how to respond in the event of a disaster. Furthermore, information sharing with companies and local governments enables rapid safety checks and rescue operations, mitigating risks during disasters. For example, the system suggests the optimal evacuation route based on information about the user's home and living area, and navigates them to evacuation shelters. It also creates a list of necessary disaster preparedness supplies and notifies users of replacement times, ensuring they always have the latest supplies. In this way, the disaster preparedness management system, through AI-powered management of disaster preparedness supplies and suggestions for disaster response, can raise users' disaster preparedness awareness and reduce risks during disasters. Additionally, information sharing with companies and local governments enables rapid safety checks and rescue operations, improving the safety of the entire community. Ultimately, the disaster preparedness management system can raise users' disaster preparedness awareness and reduce risks during disasters.

[0029] The disaster prevention management system according to this embodiment comprises an information collection unit, a list creation unit, a notification unit, a proposal unit, a sharing unit, and an update unit. The information collection unit collects information about the user's residence and living area. For example, the information collection unit can collect geographic information, weather information, and local disaster prevention information. The list creation unit creates a list of necessary disaster prevention goods based on the information collected by the information collection unit. For example, the list creation unit creates a list of disaster prevention goods such as food, water, and medicine. The list creation unit can use AI to analyze information about the user's residence and living area and create a list of necessary disaster prevention goods. The notification unit notifies the user of when it is time to replace the disaster prevention goods listed by the list creation unit. For example, the notification unit can notify the user of when it is time to replace disaster prevention goods based on expiration dates, expiration dates, deterioration status, etc. The proposal unit proposes methods for responding to a disaster based on the information collected by the information collection unit. For example, the proposal unit can propose the optimal evacuation route and shelter in the event of a disaster such as an earthquake or flood. The sharing unit shares the registered information with organizations and public institutions. The sharing unit can, for example, notify organizations and public institutions of the user's safety status in the event of a disaster. The update unit updates knowledge of response methods based on the disaster information which is updated daily, and shares it with users. The update unit can, for example, update earthquake information, weather information, evacuation information, etc., on a daily basis and share it with users. As a result, the disaster prevention management system according to the embodiment can manage disaster prevention goods and propose response methods in the event of a disaster based on information about the user's home and living area, and by sharing this information, it enables a rapid response and risk reduction.

[0030] The Information Gathering Department collects information about the user's residence and living area. Specifically, it can collect geographical information, meteorological information, and local disaster prevention information. Geographical information includes the location of the user's residence, surrounding topography, road network, and the location of evacuation shelters. Meteorological information includes data such as current weather, rainfall, wind speed, and temperature, which are obtained in real time from the Japan Meteorological Agency and private weather services. Local disaster prevention information includes disaster prevention maps and evacuation shelter information provided by local governments, as well as past disaster history. The Information Gathering Department automatically acquires this information from the internet and dedicated databases and updates it regularly. Furthermore, the Information Gathering Department can also collect information manually entered by the user. For example, by having the user enter information such as the structure of their home, family composition, and special medical needs, more personalized disaster prevention measures become possible. In this way, the Information Gathering Department comprehensively collects detailed information about the user's residence and living area and makes it available to other departments.

[0031] The list creation unit creates a list of necessary disaster preparedness items based on information collected by the information gathering unit. Specifically, it creates a list of disaster preparedness items such as food, water, and medicine. The list creation unit can use AI to analyze information about the user's home and living area and create a list of necessary disaster preparedness items. For example, if the user has a family that includes elderly people or infants, special medical supplies and baby food will be added to the list. Also, if the user's home is in an earthquake-prone area, earthquake-resistant goods and emergency water tanks will be included in the list. Based on past disaster data and statistical information, the AI ​​suggests the most suitable disaster preparedness items for specific regions and situations. Furthermore, the list creation unit can customize the list according to the user's lifestyle and individual needs. For example, for users who own pets, emergency food for pets and evacuation supplies will be added to the list. In this way, the list creation unit provides a list of disaster preparedness items optimized for each user, ensuring that necessary supplies are prepared in the event of a disaster.

[0032] The notification unit notifies users of when it is time to replace disaster preparedness supplies listed by the list creation unit. Specifically, it can notify users of when it is time to replace disaster preparedness supplies based on factors such as expiration dates, best-before dates, and deterioration status. For example, if the expiration dates of preserved food or drinking water are approaching, the notification unit will inform the user of the need to replace them. It also notifies users at the appropriate time before the best-before dates of medicines, batteries, etc., expire. The notification unit can send notifications to users via smartphone apps, email, SMS, etc. Furthermore, the notification unit can be linked with the user's calendar and reminders to ensure that important replacement dates are not forgotten. In this way, the notification unit ensures that users keep the status of their disaster preparedness supplies up to date and that they have all the necessary supplies on hand in case of a disaster.

[0033] The proposal department proposes response methods in the event of a disaster based on information collected by the information gathering department. Specifically, it can propose the optimal evacuation routes and shelters in the event of disasters such as earthquakes and floods. The proposal department uses AI to analyze collected geographic and meteorological information and calculate the optimal evacuation route in real time. For example, in the event of an earthquake, the proposal department proposes the safest and fastest evacuation route based on the user's current location and the location of shelters. In the event of a flood, the proposal department proposes routes with a low risk of flooding based on river water level information and rainfall. Furthermore, the proposal department can also propose response methods tailored to the user's specific needs. For example, for a user who requires medical attention, it will propose an evacuation route to the nearest medical facility. In this way, the proposal department helps users evacuate quickly and safely in the event of a disaster.

[0034] The shared information section shares registered information with organizations and public institutions. Specifically, it can notify organizations and public institutions of users' safety status in the event of a disaster. Based on the emergency contact information and organizational affiliation information that users have registered in advance, the shared information section automatically sends safety information in the event of a disaster. For example, when a user arrives at an evacuation center, their safety information is automatically updated based on their smartphone's location information, and a notification is sent to their registered contacts. The shared information section can also provide public institutions with information on the user's evacuation status and necessary support, supporting rapid support activities. In this way, the shared information section enables information sharing to smoothly confirm users' safety and support activities in the event of a disaster.

[0035] The update unit updates and shares knowledge of response methods with users based on daily updates to disaster information. Specifically, it can update and share earthquake information, weather information, evacuation information, etc., on a daily basis. The update unit obtains the latest disaster information from the internet and dedicated databases and automatically reflects it in the system. For example, if a new earthquake occurs, it immediately notifies users of the information and proposes necessary response methods. Also, if weather information is updated, it updates information on evacuation routes and shelters according to changes in rainfall and wind speed. Furthermore, the update unit can perform future risk assessments and review countermeasures based on past disaster data and statistical information. In this way, the update unit always provides users with response methods based on the latest information and supports quick and appropriate responses.

[0036] The list creation unit can create a list of disaster preparedness items such as food, water, and medicine. For example, the list creation unit can include items such as emergency food, drinking water, and first-aid kits in the list. The list creation unit can use AI to analyze information about the user's residence and living area and create a list of necessary disaster preparedness items. For example, the list creation unit can create a list of necessary disaster preparedness items based on geographical and weather information of the user's residence. Furthermore, the list creation unit can also create a list of disaster preparedness items based on disaster preparedness information for the area within the user's living area. This makes it easier to understand what items are needed by creating a list of disaster preparedness items. Some or all of the above-described processes in the list creation unit may be performed using AI, or they may not. For example, the list creation unit can input information about the user's residence and living area into a generating AI, and have the generating AI create a list of necessary disaster preparedness items.

[0037] The notification unit can notify users of the replacement timing for listed disaster preparedness supplies. The notification unit notifies users of the replacement timing for disaster preparedness supplies based on, for example, expiration dates, expiration dates, and deterioration status. The notification unit can use AI to analyze the replacement timing of listed disaster preparedness supplies and notify users accordingly. For example, the notification unit notifies the user when the expiration date of emergency food supplies is approaching. The notification unit can also notify the user before the expiration date of medicines. Furthermore, the notification unit can monitor the deterioration status of disaster preparedness supplies and notify users of the replacement timing. This ensures that users always have the latest disaster preparedness supplies by notifying them of the replacement timing. Some or all of the above-described processes in the notification unit may be performed using AI or not using AI. For example, the notification unit can input the replacement timing of listed disaster preparedness supplies into a generating AI and have the generating AI execute a notification of the replacement timing.

[0038] The suggestion unit can propose the optimal evacuation routes and shelters in the event of disasters such as earthquakes and floods. For example, in the event of an earthquake, the suggestion unit can propose the optimal evacuation route. The suggestion unit can use AI to analyze information about the user's residence and living area and propose the optimal evacuation routes and shelters. For example, the suggestion unit can propose the optimal evacuation route based on geographical information and weather information of the user's residence. The suggestion unit can also propose the optimal shelters based on disaster prevention information for the area within the user's living area. Furthermore, the suggestion unit can propose the optimal evacuation routes and shelters in the event of a flood. This enables a quick and appropriate response by proposing the optimal evacuation routes and shelters in the event of a disaster. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input information about the user's residence and living area into a generating AI and have the generating AI propose the optimal evacuation routes and shelters.

[0039] The shared unit can notify organizations and public institutions of users' safety status in the event of a disaster. For example, the shared unit can notify users of their current location and health status in the event of a disaster. The shared unit can use AI to analyze users' safety status information and notify organizations and public institutions. For example, the shared unit can notify organizations and public institutions of safety status information based on users' current location information. The shared unit can also monitor users' health status and notify organizations and public institutions if an abnormality is detected. Furthermore, the shared unit can register users' emergency contact information and notify them quickly in the event of a disaster. This allows for rapid rescue operations by notifying users of their safety status in the event of a disaster. Some or all of the above processes in the shared unit may be performed using AI or not using AI. For example, the shared unit can input user safety status information into a generating AI and have the generating AI execute the notification of safety status information.

[0040] The update unit can update its knowledge of response methods based on the disaster information that is updated daily and share it with users. For example, the update unit can update earthquake information, weather information, evacuation information, etc., on a daily basis and share it with users. The update unit can use AI to analyze the disaster information that is updated daily and update its knowledge of response methods. For example, the update unit can update its knowledge of response methods based on the latest earthquake information. The update unit can also update its knowledge of evacuation methods based on the latest weather information. Furthermore, the update unit can update information on evacuation routes and shelters based on the latest evacuation information. In this way, by updating knowledge of response methods based on the latest disaster information and sharing it with users, the system can always provide the most up-to-date information. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input the disaster information that is updated daily into a generating AI and have the generating AI perform the updating of knowledge of response methods.

[0041] The information gathering unit can analyze the user's past behavioral history and select the optimal information gathering method. For example, the information gathering unit can prioritize collecting information sources that the user has frequently accessed in the past. The information gathering unit can use AI to analyze the user's past behavioral history and select the optimal information gathering method. For example, the information gathering unit can analyze the user's past evacuation behavior and select the optimal information gathering method. The information gathering unit can also analyze the user's purchase history and select the optimal information gathering method. Furthermore, the information gathering unit can analyze the user's movement history and select the optimal information gathering method. In this way, the optimal information gathering method can be selected by analyzing the user's past behavioral history. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's past behavioral history data into a generating AI and have the generating AI select the optimal information gathering method.

[0042] The information gathering unit can filter information based on the user's current living situation and areas of interest during the information gathering process. For example, the information gathering unit can prioritize the collection of information necessary for the user based on their current living situation. The information gathering unit can use AI to analyze the user's current living situation and areas of interest and filter the information. For example, the information gathering unit can filter necessary information based on the user's family structure and occupation. The information gathering unit can also filter relevant information based on the user's hobbies and areas of interest. Furthermore, the information gathering unit can evaluate the importance of information and determine its priority based on the user's living situation and areas of interest. This allows for the priority collection of necessary information by filtering information based on the user's living situation and areas of interest. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0043] The information gathering unit can prioritize the collection of highly relevant information by considering the user's geographical location information during information gathering. For example, the information gathering unit can prioritize the collection of nearby disaster information based on the user's current location. The information gathering unit can use AI to analyze the user's geographical location information and prioritize the collection of highly relevant information. For example, the information gathering unit can collect nearby disaster information based on the user's GPS data. The information gathering unit can also collect information on evacuation shelters and relief supplies based on the user's address information. Furthermore, the information gathering unit can collect information on local disaster prevention plans and evacuation routes based on the user's movement history. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0044] The information gathering unit can analyze a user's social media activity and collect relevant information during information gathering. For example, the information gathering unit can analyze a user's social media posts and collect disaster prevention information of interest. The information gathering unit can use AI to analyze a user's social media activity and collect relevant information. For example, the information gathering unit can collect disaster prevention information of interest based on the content of a user's posts. The information gathering unit can also analyze posts from a user's followers and friends and collect relevant information. Furthermore, the information gathering unit can suggest the optimal information gathering method based on the user's social media activity. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0045] The list creation unit can adjust the level of detail in the list based on the importance of the disaster preparedness items when creating the list. For example, the list creation unit can prioritize displaying disaster preparedness items with high importance in the list. The list creation unit can use AI to analyze the importance of disaster preparedness items and adjust the level of detail in the list. For example, the list creation unit can evaluate the importance of disaster preparedness items based on frequency of use or necessity and adjust the level of detail in the list. The list creation unit can also evaluate the importance of disaster preparedness items based on storage period and adjust the level of detail in the list. Furthermore, the list creation unit can evaluate the importance of disaster preparedness items based on their intended use or usage scenario and adjust the level of detail in the list. By adjusting the level of detail in the list based on the importance of disaster preparedness items, necessary information can be provided preferentially. Some or all of the above processing in the list creation unit may be performed using AI or not. For example, the list creation unit can input disaster preparedness item importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the list.

[0046] The list creation unit can apply different list creation algorithms depending on the category of disaster preparedness goods when creating a list. For example, the list creation unit can create a list of consumables such as food and water based on their expiration dates. The list creation unit can use AI to analyze the categories of disaster preparedness goods and apply different list creation algorithms. For example, the list creation unit can create a list of medicines and first-aid supplies based on their frequency of use and importance. The list creation unit can also create a list of non-consumable items such as clothing and bedding based on their storage location and usage. Furthermore, the list creation unit can apply the most suitable list creation algorithm depending on the category of disaster preparedness goods. This allows for the provision of more appropriate lists by applying the list creation algorithm according to the category of disaster preparedness goods. Some or all of the above processes in the list creation unit may be performed using AI or not. For example, the list creation unit can input disaster preparedness goods category data into a generating AI and have the generating AI execute the application of the list creation algorithm.

[0047] The list creation unit can determine the priority of disaster preparedness items based on when they were acquired. For example, the list creation unit can prioritize displaying recently acquired disaster preparedness items in the list. The list creation unit can use AI to analyze when disaster preparedness items were acquired and determine the priority of the list. For example, the list creation unit can evaluate when disaster preparedness items were acquired based on the purchase date or start date of use and determine the priority of the list. The list creation unit can also evaluate when disaster preparedness items were acquired based on their expiration date or best-before date and determine the priority of the list. Furthermore, the list creation unit can evaluate the importance of disaster preparedness items according to when they were acquired and determine the priority of the list. This allows for the priority provision of necessary information by determining the priority of the list based on when disaster preparedness items were acquired. Some or all of the above processing in the list creation unit may be performed using AI or not. For example, the list creation unit can input data on when disaster preparedness items were acquired into a generating AI and have the generating AI perform the determination of the list priority.

[0048] The list creation unit can adjust the order of disaster preparedness items based on their relevance during list creation. For example, the list creation unit can group highly relevant disaster preparedness items and display them in the list. The list creation unit can use AI to analyze the relevance of disaster preparedness items and adjust the order of the list. For example, the list creation unit can evaluate the relevance of disaster preparedness items based on their intended use or usage scenario and adjust the order of the list. The list creation unit can also evaluate the relevance of disaster preparedness items based on their functional relevance and adjust the order of the list. Furthermore, the list creation unit can evaluate the relevance of disaster preparedness items based on their frequency of use and adjust the order of the list. By adjusting the order of the list based on the relevance of disaster preparedness items, necessary information can be provided preferentially. Some or all of the above processing in the list creation unit may be performed using AI or not. For example, the list creation unit can input data on the relevance of disaster preparedness items into a generating AI and have the generating AI perform the adjustment of the list order.

[0049] The notification unit can adjust the level of detail in notifications based on the importance of the disaster preparedness items. For example, the notification unit will provide notifications with detailed information for disaster preparedness items of high importance. The notification unit can use AI to analyze the importance of disaster preparedness items and adjust the level of detail in notifications. For example, the notification unit can evaluate the importance of disaster preparedness items based on frequency of use or necessity and adjust the level of detail in notifications. The notification unit can also evaluate the importance of disaster preparedness items based on storage period and adjust the level of detail in notifications. Furthermore, the notification unit can evaluate the importance of disaster preparedness items based on their intended use or usage scenario and adjust the level of detail in notifications. This allows for the priority provision of necessary information by adjusting the level of detail in notifications based on the importance of disaster preparedness items. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input disaster preparedness item importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in notifications.

[0050] The notification unit can apply different notification algorithms depending on the category of disaster preparedness goods when sending a notification. For example, for consumables such as food and water, the notification unit will send a notification based on the expiration date. The notification unit can use AI to analyze the category of disaster preparedness goods and apply different notification algorithms. For example, for medicines and first-aid supplies, the notification unit will send a notification based on the frequency of use and importance. The notification unit can also send notifications for non-consumable items such as clothing and bedding based on the storage location and usage status. Furthermore, the notification unit can apply the most appropriate notification algorithm depending on the category of disaster preparedness goods. This makes it possible to send more appropriate notifications by applying a notification algorithm according to the category of disaster preparedness goods. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input disaster preparedness goods category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0051] The notification unit can adjust the order of notifications based on when the disaster preparedness goods were acquired. For example, the notification unit may prioritize notifications for recently acquired disaster preparedness goods. The notification unit can use AI to analyze when the disaster preparedness goods were acquired and adjust the order of notifications. For example, the notification unit may evaluate when the disaster preparedness goods were acquired based on the purchase date or start date of use and adjust the order of notifications. The notification unit may also evaluate when the disaster preparedness goods were acquired based on their expiration date or best-before date and adjust the order of notifications. Furthermore, the notification unit may evaluate the importance of the disaster preparedness goods according to when they were acquired and adjust the order of notifications. This allows for the priority provision of necessary information by adjusting the order of notifications based on when the disaster preparedness goods were acquired. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may input data on when the disaster preparedness goods were acquired into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0052] The proposal unit can adjust the level of detail of its proposals based on the type of disaster. For example, in the case of an earthquake, the proposal unit will provide detailed information on evacuation routes and shelters. The proposal unit can use AI to analyze the type of disaster and adjust the level of detail of its proposals. For example, in the case of a flood, the proposal unit will provide detailed information on evacuation sites and flood control measures. In the case of a typhoon, the proposal unit can also provide detailed information on wind countermeasures and evacuation sites. Furthermore, the proposal unit can adjust the level of detail of its proposals to the optimal level depending on the type of disaster. By adjusting the level of detail of proposals based on the type of disaster, more appropriate proposals become possible. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input disaster type data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0053] The suggestion unit can apply different suggestion algorithms depending on the disaster category when making suggestions. For example, in the case of a natural disaster, the suggestion unit will prioritize suggesting information on evacuation routes and shelters. The suggestion unit can use AI to analyze the disaster category and apply different suggestion algorithms. For example, in the case of a man-made disaster, the suggestion unit will prioritize suggesting information on emergency contacts and evacuation locations. In addition, in the case of a health-related disaster, the suggestion unit can prioritize suggesting information on medical facilities and emergency supplies. Furthermore, the suggestion unit can apply the most suitable suggestion algorithm depending on the disaster category. This allows for more appropriate suggestions by applying the suggestion algorithm according to the disaster category. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input disaster category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0054] The proposal unit can determine the priority of proposals based on the timing of disaster occurrences. For example, the proposal unit may prioritize proposals related to the most recent disaster. The proposal unit can use AI to analyze the timing of disaster occurrences and determine the priority of proposals. For example, the proposal unit may evaluate the timing of disaster occurrences based on past frequency and seasonal factors and determine the priority of proposals. The proposal unit can also prioritize the proposals of the most urgent information based on the timing of disaster occurrences. Furthermore, the proposal unit can evaluate importance according to the timing of disaster occurrences and determine the priority of proposals. This allows for the provision of more appropriate information by prioritizing proposals based on the timing of disaster occurrences. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input disaster occurrence timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0055] The proposal unit can adjust the order of proposals based on the relevance of the disasters during the proposal process. For example, the proposal unit can prioritize proposing highly relevant disaster information. The proposal unit can use AI to analyze the relevance of disasters and adjust the order of proposals. For example, the proposal unit can evaluate the relevance of disasters based on geographical relevance and impact area and adjust the order of proposals. The proposal unit can also prioritize proposing the most important information based on the relevance of the disasters. Furthermore, the proposal unit can display less relevant disaster information later in the proposals. This allows for the provision of more appropriate information by adjusting the order of proposals based on the relevance of the disasters. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input disaster relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0056] The sharing unit can adjust the level of detail shared based on the importance of the information. For example, the sharing unit will share more detailed information for information of high importance. The sharing unit can use AI to analyze the importance of information and adjust the level of detail of sharing. For example, the sharing unit can evaluate the importance of information based on urgency and scope of impact and adjust the level of detail of sharing. The sharing unit can also evaluate the importance of information based on necessity and adjust the level of detail of sharing. Furthermore, the sharing unit can adjust the optimal level of detail of sharing according to the importance of the information. This allows necessary information to be provided preferentially by adjusting the level of detail of sharing based on the importance of the information. Some or all of the above processes in the sharing unit may be performed using AI or not. For example, the sharing unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of sharing.

[0057] The sharing unit can apply different sharing algorithms depending on the category of information during sharing. For example, for disaster preparedness supplies, the sharing unit can share information such as expiration dates and storage locations. The sharing unit can use AI to analyze the category of information and apply different sharing algorithms. For example, for disaster response methods, the sharing unit can share information such as evacuation routes and shelters. The sharing unit can also share emergency contact information and relief supplies for safety confirmation information. Furthermore, the sharing unit can apply the most appropriate sharing algorithm depending on the category of information. This allows for the provision of more appropriate information by applying a sharing algorithm according to the category of information. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input information category data into a generating AI and have the generating AI execute the application of the sharing algorithm.

[0058] The sharing unit can adjust the order of sharing based on when the information was created. For example, the sharing unit prioritizes sharing the most recently created information. The sharing unit can use AI to analyze when the information was created and adjust the order of sharing. For example, the sharing unit evaluates when the information was created based on the date and time of creation or update date and adjusts the order of sharing. The sharing unit can also prioritize sharing the most urgent information based on when it was created. Furthermore, the sharing unit can evaluate the importance of the information according to when it was created and adjust the order of sharing. This allows necessary information to be provided preferentially by adjusting the order of sharing based on when the information was created. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input information creation data into a generating AI and have the generating AI perform the adjustment of the sharing order.

[0059] The update unit can adjust the level of detail of updates based on the importance of the information during the update process. For example, the update unit will perform updates that include detailed information for information of high importance. The update unit can use AI to analyze the importance of information and adjust the level of detail of updates. For example, the update unit can evaluate the importance of information based on urgency and scope of impact and adjust the level of detail of updates. The update unit can also evaluate the importance of information based on necessity and adjust the level of detail of updates. Furthermore, the update unit can adjust the optimal level of detail of updates according to the importance of the information. This allows for the priority provision of necessary information by adjusting the level of detail of updates based on the importance of the information. Some or all of the above processes in the update unit may be performed using AI or not. For example, the update unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of updates.

[0060] The update unit can apply different update algorithms depending on the information category during the update process. For example, for disaster preparedness goods, the update unit updates information such as expiration dates and storage locations. The update unit can use AI to analyze information categories and apply different update algorithms. For example, for disaster response methods, the update unit updates information such as evacuation routes and shelters. The update unit can also update information such as emergency contact information and relief supplies for safety confirmation information. Furthermore, the update unit can apply the most appropriate update algorithm depending on the information category. This allows for the provision of more appropriate information by applying update algorithms according to the information category. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input information category data into a generating AI and have the generating AI execute the application of the update algorithm.

[0061] The update unit can adjust the update order based on the timing of information generation during the update process. For example, the update unit prioritizes updating the most recently generated information. The update unit can use AI to analyze the timing of information generation and adjust the update order. For example, the update unit evaluates the timing of information generation based on the generation date and update date and adjusts the update order. The update unit can also prioritize updating the most urgent information based on the timing of information generation. Furthermore, the update unit can evaluate the importance of information according to its generation timing and adjust the update order accordingly. By adjusting the update order based on the timing of information generation, necessary information can be provided preferentially. Some or all of the above-described processes in the update unit may be performed using AI or not. For example, the update unit can input information generation timing data into a generating AI and have the generating AI perform the adjustment of the update order.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The disaster prevention management system can also include a health management unit that monitors the user's health status. The health management unit periodically checks the user's vital signs and issues a warning to the user via a notification unit if an abnormality is detected. For example, if an abnormality in heart rate or blood pressure is detected, it will notify the user to seek medical attention. The health management unit can also customize the list of disaster preparedness items based on the user's health status. For example, if a user requires a specific medication, that medication will be added to the list. Furthermore, the health management unit can suggest evacuation methods that take into account the user's health status in the event of a disaster. This enables appropriate responses according to the user's health condition.

[0064] The disaster prevention management system can also include a pet management section to manage information about the user's pets. The pet management section lists the type and number of pets the user has, as well as necessary items, and notifies the user when it is time to replace disaster preparedness supplies for pets. For example, it manages the expiration dates and best-before dates of pet food and medicine and notifies the user at the appropriate time. The pet management section can also suggest the best evacuation routes and shelters for evacuating with pets in the event of a disaster. Furthermore, the pet management section can monitor the health status of pets and notify the user if any abnormalities are detected. This allows users to take appropriate action while ensuring the safety of their pets.

[0065] The disaster prevention management system can also include a vehicle management unit that manages user vehicle information. The vehicle management unit tracks the location and status of users' vehicles and proposes the optimal evacuation route in the event of a disaster. For example, it monitors the vehicle's fuel level and maintenance status and notifies the user if necessary. The vehicle management unit can also suggest evacuation methods using the vehicle during a disaster. Furthermore, based on the vehicle's location information, the vehicle management unit can guide users to the nearest evacuation shelter or relief supply distribution point. This enables users to evacuate quickly and safely using their vehicles.

[0066] The disaster prevention management system can also include a family management unit to manage the user's family information. The family management unit registers the user's family structure and contact information, and checks the safety of all family members in the event of a disaster. For example, it can share the family's location information and suggest the optimal evacuation route. The family management unit can also monitor the health status of all family members and notify if any abnormalities are detected. Furthermore, the family management unit can create a list of disaster prevention supplies for all family members and notify them when it is time to replace them. This enables appropriate responses while ensuring the safety of all family members.

[0067] The disaster prevention management system can also include a community management unit that manages user community information. This unit collects information about the local communities to which users belong and supports community-wide responses in the event of a disaster. For example, it can provide information on local evacuation centers and relief supply distribution locations, and facilitate communication among community members. The community management unit can also share information on local disaster prevention drills and events, raising users' disaster preparedness awareness. Furthermore, the community management unit can confirm the safety of the entire community in the event of a disaster, supporting rapid rescue operations. This strengthens cooperation within the community and reduces risks during disasters.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The information gathering unit collects information about the user's home and living area. For example, it collects geographical information, weather information, and local disaster prevention information. Step 2: The list creation unit creates a list of necessary disaster preparedness items based on the information collected by the information gathering unit. For example, it creates a list of disaster preparedness items such as food, water, and medicine. The list creation unit can use AI to analyze information about the user's home and living area and create a list of necessary disaster preparedness items. Step 3: The notification unit notifies the customer of the replacement timing for the disaster preparedness supplies listed by the list creation unit. For example, the replacement timing for disaster preparedness supplies can be notified based on the expiration date, expiration date, deterioration status, etc. Step 4: The proposal department proposes response methods in the event of a disaster based on the information collected by the information gathering department. For example, in the event of a disaster such as an earthquake or flood, they can propose the optimal evacuation routes and shelters. Step 5: The sharing section shares the registered information with organizations and public institutions. For example, in the event of a disaster, it can notify organizations and public institutions of the user's safety status. Step 6: The update department updates and shares knowledge of response methods with users based on the disaster information that is updated daily. For example, it can update and share earthquake information, weather information, evacuation information, etc., with users on a daily basis.

[0070] (Example of form 2) The disaster prevention management system according to an embodiment of the present invention is a system that uses AI to manage disaster prevention goods and propose methods for responding in the event of a disaster. Based on information about the user's residence and living area, this disaster prevention management system proposes necessary disaster prevention goods and the optimal timing for their replacement. For example, the disaster prevention management system creates a list of disaster prevention goods such as food, water, and medicine, and notifies the user of when each should be replaced. The disaster prevention management system also tells the user where these disaster prevention goods are stored. Next, the disaster prevention management system proposes methods for responding in the event of a disaster based on information about the user's residence and living area. For example, in the event of a disaster such as an earthquake or flood, the disaster prevention management system provides information on how to evacuate and which evacuation center is the safest. This enables the user to respond quickly and appropriately. Furthermore, the disaster prevention management system shares registered information with the company and local government and uses it for safety confirmation in the event of an emergency. For example, the disaster prevention management system notifies the company and local government of the user's safety status in the event of a disaster to support rapid rescue operations. In addition, the disaster prevention management system updates its knowledge of response methods based on the daily updates of disaster information and shares it with the user. This enables responses based on the latest information at all times. This disaster preparedness management system allows users to improve their knowledge of disaster preparedness supplies and how to respond in the event of a disaster. Furthermore, information sharing with companies and local governments enables rapid safety checks and rescue operations, mitigating risks during disasters. For example, the system suggests the optimal evacuation route based on information about the user's home and living area, and navigates them to evacuation shelters. It also creates a list of necessary disaster preparedness supplies and notifies users of replacement times, ensuring they always have the latest supplies. In this way, the disaster preparedness management system, through AI-powered management of disaster preparedness supplies and suggestions for disaster response, can raise users' disaster preparedness awareness and reduce risks during disasters. Additionally, information sharing with companies and local governments enables rapid safety checks and rescue operations, improving the safety of the entire community. Ultimately, the disaster preparedness management system can raise users' disaster preparedness awareness and reduce risks during disasters.

[0071] The disaster prevention management system according to this embodiment comprises an information collection unit, a list creation unit, a notification unit, a proposal unit, a sharing unit, and an update unit. The information collection unit collects information about the user's residence and living area. For example, the information collection unit can collect geographic information, weather information, and local disaster prevention information. The list creation unit creates a list of necessary disaster prevention goods based on the information collected by the information collection unit. For example, the list creation unit creates a list of disaster prevention goods such as food, water, and medicine. The list creation unit can use AI to analyze information about the user's residence and living area and create a list of necessary disaster prevention goods. The notification unit notifies the user of when it is time to replace the disaster prevention goods listed by the list creation unit. For example, the notification unit can notify the user of when it is time to replace disaster prevention goods based on expiration dates, expiration dates, deterioration status, etc. The proposal unit proposes methods for responding to a disaster based on the information collected by the information collection unit. For example, the proposal unit can propose the optimal evacuation route and shelter in the event of a disaster such as an earthquake or flood. The sharing unit shares the registered information with organizations and public institutions. The sharing unit can, for example, notify organizations and public institutions of the user's safety status in the event of a disaster. The update unit updates knowledge of response methods based on the disaster information which is updated daily, and shares it with users. The update unit can, for example, update earthquake information, weather information, evacuation information, etc., on a daily basis and share it with users. As a result, the disaster prevention management system according to the embodiment can manage disaster prevention goods and propose response methods in the event of a disaster based on information about the user's home and living area, and by sharing this information, it enables a rapid response and risk reduction.

[0072] The Information Gathering Department collects information about the user's residence and living area. Specifically, it can collect geographical information, meteorological information, and local disaster prevention information. Geographical information includes the location of the user's residence, surrounding topography, road network, and the location of evacuation shelters. Meteorological information includes data such as current weather, rainfall, wind speed, and temperature, which are obtained in real time from the Japan Meteorological Agency and private weather services. Local disaster prevention information includes disaster prevention maps and evacuation shelter information provided by local governments, as well as past disaster history. The Information Gathering Department automatically acquires this information from the internet and dedicated databases and updates it regularly. Furthermore, the Information Gathering Department can also collect information manually entered by the user. For example, by having the user enter information such as the structure of their home, family composition, and special medical needs, more personalized disaster prevention measures become possible. In this way, the Information Gathering Department comprehensively collects detailed information about the user's residence and living area and makes it available to other departments.

[0073] The list creation unit creates a list of necessary disaster preparedness items based on information collected by the information gathering unit. Specifically, it creates a list of disaster preparedness items such as food, water, and medicine. The list creation unit can use AI to analyze information about the user's home and living area and create a list of necessary disaster preparedness items. For example, if the user has a family that includes elderly people or infants, special medical supplies and baby food will be added to the list. Also, if the user's home is in an earthquake-prone area, earthquake-resistant goods and emergency water tanks will be included in the list. Based on past disaster data and statistical information, the AI ​​suggests the most suitable disaster preparedness items for specific regions and situations. Furthermore, the list creation unit can customize the list according to the user's lifestyle and individual needs. For example, for users who own pets, emergency food for pets and evacuation supplies will be added to the list. In this way, the list creation unit provides a list of disaster preparedness items optimized for each user, ensuring that necessary supplies are prepared in the event of a disaster.

[0074] The notification unit notifies users of when it is time to replace disaster preparedness supplies listed by the list creation unit. Specifically, it can notify users of when it is time to replace disaster preparedness supplies based on factors such as expiration dates, best-before dates, and deterioration status. For example, if the expiration dates of preserved food or drinking water are approaching, the notification unit will inform the user of the need to replace them. It also notifies users at the appropriate time before the best-before dates of medicines, batteries, etc., expire. The notification unit can send notifications to users via smartphone apps, email, SMS, etc. Furthermore, the notification unit can be linked with the user's calendar and reminders to ensure that important replacement dates are not forgotten. In this way, the notification unit ensures that users keep the status of their disaster preparedness supplies up to date and that they have all the necessary supplies on hand in case of a disaster.

[0075] The proposal department proposes response methods in the event of a disaster based on information collected by the information gathering department. Specifically, it can propose the optimal evacuation routes and shelters in the event of disasters such as earthquakes and floods. The proposal department uses AI to analyze collected geographic and meteorological information and calculate the optimal evacuation route in real time. For example, in the event of an earthquake, the proposal department proposes the safest and fastest evacuation route based on the user's current location and the location of shelters. In the event of a flood, the proposal department proposes routes with a low risk of flooding based on river water level information and rainfall. Furthermore, the proposal department can also propose response methods tailored to the user's specific needs. For example, for a user who requires medical attention, it will propose an evacuation route to the nearest medical facility. In this way, the proposal department helps users evacuate quickly and safely in the event of a disaster.

[0076] The shared information section shares registered information with organizations and public institutions. Specifically, it can notify organizations and public institutions of users' safety status in the event of a disaster. Based on the emergency contact information and organizational affiliation information that users have registered in advance, the shared information section automatically sends safety information in the event of a disaster. For example, when a user arrives at an evacuation center, their safety information is automatically updated based on their smartphone's location information, and a notification is sent to their registered contacts. The shared information section can also provide public institutions with information on the user's evacuation status and necessary support, supporting rapid support activities. In this way, the shared information section enables information sharing to smoothly confirm users' safety and support activities in the event of a disaster.

[0077] The update unit updates and shares knowledge of response methods with users based on daily updates to disaster information. Specifically, it can update and share earthquake information, weather information, evacuation information, etc., on a daily basis. The update unit obtains the latest disaster information from the internet and dedicated databases and automatically reflects it in the system. For example, if a new earthquake occurs, it immediately notifies users of the information and proposes necessary response methods. Also, if weather information is updated, it updates information on evacuation routes and shelters according to changes in rainfall and wind speed. Furthermore, the update unit can perform future risk assessments and review countermeasures based on past disaster data and statistical information. In this way, the update unit always provides users with response methods based on the latest information and supports quick and appropriate responses.

[0078] The list creation unit can create a list of disaster preparedness items such as food, water, and medicine. For example, the list creation unit can include items such as emergency food, drinking water, and first-aid kits in the list. The list creation unit can use AI to analyze information about the user's residence and living area and create a list of necessary disaster preparedness items. For example, the list creation unit can create a list of necessary disaster preparedness items based on geographical and weather information of the user's residence. Furthermore, the list creation unit can also create a list of disaster preparedness items based on disaster preparedness information for the area within the user's living area. This makes it easier to understand what items are needed by creating a list of disaster preparedness items. Some or all of the above-described processes in the list creation unit may be performed using AI, or they may not. For example, the list creation unit can input information about the user's residence and living area into a generating AI, and have the generating AI create a list of necessary disaster preparedness items.

[0079] The notification unit can notify users of the replacement timing for listed disaster preparedness supplies. The notification unit notifies users of the replacement timing for disaster preparedness supplies based on, for example, expiration dates, expiration dates, and deterioration status. The notification unit can use AI to analyze the replacement timing of listed disaster preparedness supplies and notify users accordingly. For example, the notification unit notifies the user when the expiration date of emergency food supplies is approaching. The notification unit can also notify the user before the expiration date of medicines. Furthermore, the notification unit can monitor the deterioration status of disaster preparedness supplies and notify users of the replacement timing. This ensures that users always have the latest disaster preparedness supplies by notifying them of the replacement timing. Some or all of the above-described processes in the notification unit may be performed using AI or not using AI. For example, the notification unit can input the replacement timing of listed disaster preparedness supplies into a generating AI and have the generating AI execute a notification of the replacement timing.

[0080] The suggestion unit can propose the optimal evacuation routes and shelters in the event of disasters such as earthquakes and floods. For example, in the event of an earthquake, the suggestion unit can propose the optimal evacuation route. The suggestion unit can use AI to analyze information about the user's residence and living area and propose the optimal evacuation routes and shelters. For example, the suggestion unit can propose the optimal evacuation route based on geographical information and weather information of the user's residence. The suggestion unit can also propose the optimal shelters based on disaster prevention information for the area within the user's living area. Furthermore, the suggestion unit can propose the optimal evacuation routes and shelters in the event of a flood. This enables a quick and appropriate response by proposing the optimal evacuation routes and shelters in the event of a disaster. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input information about the user's residence and living area into a generating AI and have the generating AI propose the optimal evacuation routes and shelters.

[0081] The shared unit can notify organizations and public institutions of users' safety status in the event of a disaster. For example, the shared unit can notify users of their current location and health status in the event of a disaster. The shared unit can use AI to analyze users' safety status information and notify organizations and public institutions. For example, the shared unit can notify organizations and public institutions of safety status information based on users' current location information. The shared unit can also monitor users' health status and notify organizations and public institutions if an abnormality is detected. Furthermore, the shared unit can register users' emergency contact information and notify them quickly in the event of a disaster. This allows for rapid rescue operations by notifying users of their safety status in the event of a disaster. Some or all of the above processes in the shared unit may be performed using AI or not using AI. For example, the shared unit can input user safety status information into a generating AI and have the generating AI execute the notification of safety status information.

[0082] The update unit can update its knowledge of response methods based on the disaster information that is updated daily and share it with users. For example, the update unit can update earthquake information, weather information, evacuation information, etc., on a daily basis and share it with users. The update unit can use AI to analyze the disaster information that is updated daily and update its knowledge of response methods. For example, the update unit can update its knowledge of response methods based on the latest earthquake information. The update unit can also update its knowledge of evacuation methods based on the latest weather information. Furthermore, the update unit can update information on evacuation routes and shelters based on the latest evacuation information. In this way, by updating knowledge of response methods based on the latest disaster information and sharing it with users, the system can always provide the most up-to-date information. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input the disaster information that is updated daily into a generating AI and have the generating AI perform the updating of knowledge of response methods.

[0083] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on the estimated emotions. For example, if the user is stressed, the information gathering unit will reduce the frequency of information gathering and collect only important information. The information gathering unit can use AI to estimate the user's emotions and adjust the timing of information gathering. For example, the information gathering unit can use user facial recognition technology to estimate emotions and adjust the timing of information gathering. The information gathering unit can also use user voice analysis technology to estimate emotions and adjust the timing of information gathering. Furthermore, the information gathering unit can analyze the user's behavior patterns, estimate emotions, and adjust the timing of information gathering. This allows for more appropriate information gathering by adjusting the timing of information gathering according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input user emotion data into a generating AI and have the generating AI adjust the timing of information gathering.

[0084] The information gathering unit can analyze the user's past behavioral history and select the optimal information gathering method. For example, the information gathering unit can prioritize collecting information sources that the user has frequently accessed in the past. The information gathering unit can use AI to analyze the user's past behavioral history and select the optimal information gathering method. For example, the information gathering unit can analyze the user's past evacuation behavior and select the optimal information gathering method. The information gathering unit can also analyze the user's purchase history and select the optimal information gathering method. Furthermore, the information gathering unit can analyze the user's movement history and select the optimal information gathering method. In this way, the optimal information gathering method can be selected by analyzing the user's past behavioral history. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's past behavioral history data into a generating AI and have the generating AI select the optimal information gathering method.

[0085] The information gathering unit can filter information based on the user's current living situation and areas of interest during the information gathering process. For example, the information gathering unit can prioritize the collection of information necessary for the user based on their current living situation. The information gathering unit can use AI to analyze the user's current living situation and areas of interest and filter the information. For example, the information gathering unit can filter necessary information based on the user's family structure and occupation. The information gathering unit can also filter relevant information based on the user's hobbies and areas of interest. Furthermore, the information gathering unit can evaluate the importance of information and determine its priority based on the user's living situation and areas of interest. This allows for the priority collection of necessary information by filtering information based on the user's living situation and areas of interest. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0086] The information gathering unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the information gathering unit will prioritize collecting information that provides a sense of security. The information gathering unit can use AI to estimate the user's emotions and determine the priority of information to collect. For example, the information gathering unit can use user facial recognition technology to estimate emotions and determine the priority of information. The information gathering unit can also use user voice analysis technology to estimate emotions and determine the priority of information. Furthermore, the information gathering unit can analyze the user's behavior patterns, estimate emotions, and determine the priority of information. This allows for the provision of more appropriate information by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input user emotion data into a generating AI and have the generating AI determine the priority of the information.

[0087] The information gathering unit can prioritize the collection of highly relevant information by considering the user's geographical location information during information gathering. For example, the information gathering unit can prioritize the collection of nearby disaster information based on the user's current location. The information gathering unit can use AI to analyze the user's geographical location information and prioritize the collection of highly relevant information. For example, the information gathering unit can collect nearby disaster information based on the user's GPS data. The information gathering unit can also collect information on evacuation shelters and relief supplies based on the user's address information. Furthermore, the information gathering unit can collect information on local disaster prevention plans and evacuation routes based on the user's movement history. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0088] The information gathering unit can analyze a user's social media activity and collect relevant information during information gathering. For example, the information gathering unit can analyze a user's social media posts and collect disaster prevention information of interest. The information gathering unit can use AI to analyze a user's social media activity and collect relevant information. For example, the information gathering unit can collect disaster prevention information of interest based on the content of a user's posts. The information gathering unit can also analyze posts from a user's followers and friends and collect relevant information. Furthermore, the information gathering unit can suggest the optimal information gathering method based on the user's social media activity. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0089] The list creation unit can estimate the user's emotions and adjust the way the list is presented based on the estimated emotions. For example, if the user is feeling stressed, the list creation unit will create a simple and highly visual list. The list creation unit can use AI to estimate the user's emotions and adjust the way the list is presented. For example, the list creation unit can use user facial recognition technology to estimate emotions and adjust the way the list is presented. The list creation unit can also use user voice analysis technology to estimate emotions and adjust the way the list is presented. Furthermore, the list creation unit can analyze the user's behavior patterns, estimate emotions, and adjust the way the list is presented. This allows for the provision of more appropriate lists by adjusting the way the list is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the list creation unit may be performed using AI or not. For example, the list creation unit can input user emotion data into a generating AI and have the generating AI adjust how the list is presented.

[0090] The list creation unit can adjust the level of detail in the list based on the importance of the disaster preparedness items when creating the list. For example, the list creation unit can prioritize displaying disaster preparedness items with high importance in the list. The list creation unit can use AI to analyze the importance of disaster preparedness items and adjust the level of detail in the list. For example, the list creation unit can evaluate the importance of disaster preparedness items based on frequency of use or necessity and adjust the level of detail in the list. The list creation unit can also evaluate the importance of disaster preparedness items based on storage period and adjust the level of detail in the list. Furthermore, the list creation unit can evaluate the importance of disaster preparedness items based on their intended use or usage scenario and adjust the level of detail in the list. By adjusting the level of detail in the list based on the importance of disaster preparedness items, necessary information can be provided preferentially. Some or all of the above processing in the list creation unit may be performed using AI or not. For example, the list creation unit can input disaster preparedness item importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the list.

[0091] The list creation unit can apply different list creation algorithms depending on the category of disaster preparedness goods when creating a list. For example, the list creation unit can create a list of consumables such as food and water based on their expiration dates. The list creation unit can use AI to analyze the categories of disaster preparedness goods and apply different list creation algorithms. For example, the list creation unit can create a list of medicines and first-aid supplies based on their frequency of use and importance. The list creation unit can also create a list of non-consumable items such as clothing and bedding based on their storage location and usage. Furthermore, the list creation unit can apply the most suitable list creation algorithm depending on the category of disaster preparedness goods. This allows for the provision of more appropriate lists by applying the list creation algorithm according to the category of disaster preparedness goods. Some or all of the above processes in the list creation unit may be performed using AI or not. For example, the list creation unit can input disaster preparedness goods category data into a generating AI and have the generating AI execute the application of the list creation algorithm.

[0092] The list creation unit can estimate the user's emotions and adjust the length of the list based on the estimated emotions. For example, if the user is feeling stressed, the list creation unit will create a short, concise list. The list creation unit can use AI to estimate the user's emotions and adjust the length of the list. For example, the list creation unit can use user facial recognition technology to estimate emotions and adjust the length of the list. The list creation unit can also use user voice analysis technology to estimate emotions and adjust the length of the list. Furthermore, the list creation unit can analyze the user's behavior patterns, estimate emotions, and adjust the length of the list. This allows for the provision of more appropriate lists by adjusting the length of the list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the list creation unit may be performed using AI or not. For example, the list creation unit can input user sentiment data into a generating AI and have the generating AI adjust the length of the list.

[0093] The list creation unit can determine the priority of disaster preparedness items based on when they were acquired. For example, the list creation unit can prioritize displaying recently acquired disaster preparedness items in the list. The list creation unit can use AI to analyze when disaster preparedness items were acquired and determine the priority of the list. For example, the list creation unit can evaluate when disaster preparedness items were acquired based on the purchase date or start date of use and determine the priority of the list. The list creation unit can also evaluate when disaster preparedness items were acquired based on their expiration date or best-before date and determine the priority of the list. Furthermore, the list creation unit can evaluate the importance of disaster preparedness items according to when they were acquired and determine the priority of the list. This allows for the priority provision of necessary information by determining the priority of the list based on when disaster preparedness items were acquired. Some or all of the above processing in the list creation unit may be performed using AI or not. For example, the list creation unit can input data on when disaster preparedness items were acquired into a generating AI and have the generating AI perform the determination of the list priority.

[0094] The list creation unit can adjust the order of disaster preparedness items based on their relevance during list creation. For example, the list creation unit can group highly relevant disaster preparedness items and display them in the list. The list creation unit can use AI to analyze the relevance of disaster preparedness items and adjust the order of the list. For example, the list creation unit can evaluate the relevance of disaster preparedness items based on their intended use or usage scenario and adjust the order of the list. The list creation unit can also evaluate the relevance of disaster preparedness items based on their functional relevance and adjust the order of the list. Furthermore, the list creation unit can evaluate the relevance of disaster preparedness items based on their frequency of use and adjust the order of the list. By adjusting the order of the list based on the relevance of disaster preparedness items, necessary information can be provided preferentially. Some or all of the above processing in the list creation unit may be performed using AI or not. For example, the list creation unit can input data on the relevance of disaster preparedness items into a generating AI and have the generating AI perform the adjustment of the list order.

[0095] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will reduce the frequency of notifications and only send important information. The notification unit can use AI to estimate the user's emotions and adjust the timing of notifications. For example, the notification unit can use user facial recognition technology to estimate emotions and adjust the timing of notifications. The notification unit can also use user voice analysis technology to estimate emotions and adjust the timing of notifications. Furthermore, the notification unit can analyze the user's behavior patterns, estimate emotions, and adjust the timing of notifications. This allows for more appropriate notifications by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generating AI and have the AI ​​adjust the timing of notifications.

[0096] The notification unit can adjust the level of detail in notifications based on the importance of the disaster preparedness items. For example, the notification unit will provide notifications with detailed information for disaster preparedness items of high importance. The notification unit can use AI to analyze the importance of disaster preparedness items and adjust the level of detail in notifications. For example, the notification unit can evaluate the importance of disaster preparedness items based on frequency of use or necessity and adjust the level of detail in notifications. The notification unit can also evaluate the importance of disaster preparedness items based on storage period and adjust the level of detail in notifications. Furthermore, the notification unit can evaluate the importance of disaster preparedness items based on their intended use or usage scenario and adjust the level of detail in notifications. This allows for the priority provision of necessary information by adjusting the level of detail in notifications based on the importance of disaster preparedness items. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input disaster preparedness item importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in notifications.

[0097] The notification unit can apply different notification algorithms depending on the category of disaster preparedness goods when sending a notification. For example, for consumables such as food and water, the notification unit will send a notification based on the expiration date. The notification unit can use AI to analyze the category of disaster preparedness goods and apply different notification algorithms. For example, for medicines and first-aid supplies, the notification unit will send a notification based on the frequency of use and importance. The notification unit can also send notifications for non-consumable items such as clothing and bedding based on the storage location and usage status. Furthermore, the notification unit can apply the most appropriate notification algorithm depending on the category of disaster preparedness goods. This makes it possible to send more appropriate notifications by applying a notification algorithm according to the category of disaster preparedness goods. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input disaster preparedness goods category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0098] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will prioritize notifications containing reassuring information. The notification unit can use AI to estimate the user's emotions and determine the priority of notifications. For example, the notification unit can use user facial recognition technology to estimate emotions and determine the priority of notifications. The notification unit can also use user voice analysis technology to estimate emotions and determine the priority of notifications. Furthermore, the notification unit can analyze the user's behavior patterns, estimate emotions, and determine the priority of notifications. This allows for the provision of more appropriate information by prioritizing notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generating AI and have the AI ​​determine the priority of notifications.

[0099] The notification unit can adjust the order of notifications based on when the disaster preparedness goods were acquired. For example, the notification unit may prioritize notifications for recently acquired disaster preparedness goods. The notification unit can use AI to analyze when the disaster preparedness goods were acquired and adjust the order of notifications. For example, the notification unit may evaluate when the disaster preparedness goods were acquired based on the purchase date or start date of use and adjust the order of notifications. The notification unit may also evaluate when the disaster preparedness goods were acquired based on their expiration date or best-before date and adjust the order of notifications. Furthermore, the notification unit may evaluate the importance of the disaster preparedness goods according to when they were acquired and adjust the order of notifications. This allows for the priority provision of necessary information by adjusting the order of notifications based on when the disaster preparedness goods were acquired. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may input data on when the disaster preparedness goods were acquired into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0100] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is feeling stressed, the suggestion unit will present simple and highly visible suggestions. The suggestion unit can use AI to estimate the user's emotions and adjust the way suggestions are presented. For example, the suggestion unit can use user facial recognition technology to estimate emotions and adjust the way suggestions are presented. The suggestion unit can also use user voice analysis technology to estimate emotions and adjust the way suggestions are presented. Furthermore, the suggestion unit can analyze the user's behavior patterns, estimate emotions, and adjust the way suggestions are presented. This allows for more appropriate suggestions by adjusting the way suggestions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way the proposal is expressed.

[0101] The proposal unit can adjust the level of detail of its proposals based on the type of disaster. For example, in the case of an earthquake, the proposal unit will provide detailed information on evacuation routes and shelters. The proposal unit can use AI to analyze the type of disaster and adjust the level of detail of its proposals. For example, in the case of a flood, the proposal unit will provide detailed information on evacuation sites and flood control measures. In the case of a typhoon, the proposal unit can also provide detailed information on wind countermeasures and evacuation sites. Furthermore, the proposal unit can adjust the level of detail of its proposals to the optimal level depending on the type of disaster. By adjusting the level of detail of proposals based on the type of disaster, more appropriate proposals become possible. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input disaster type data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0102] The suggestion unit can apply different suggestion algorithms depending on the disaster category when making suggestions. For example, in the case of a natural disaster, the suggestion unit will prioritize suggesting information on evacuation routes and shelters. The suggestion unit can use AI to analyze the disaster category and apply different suggestion algorithms. For example, in the case of a man-made disaster, the suggestion unit will prioritize suggesting information on emergency contacts and evacuation locations. In addition, in the case of a health-related disaster, the suggestion unit can prioritize suggesting information on medical facilities and emergency supplies. Furthermore, the suggestion unit can apply the most suitable suggestion algorithm depending on the disaster category. This allows for more appropriate suggestions by applying the suggestion algorithm according to the disaster category. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input disaster category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0103] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit will make a short, concise suggestion. The suggestion unit can use AI to estimate the user's emotions and adjust the length of the suggestion. For example, the suggestion unit can use user facial recognition technology to estimate emotions and adjust the length of the suggestion. The suggestion unit can also use user voice analysis technology to estimate emotions and adjust the length of the suggestion. Furthermore, the suggestion unit can analyze the user's behavior patterns, estimate emotions, and adjust the length of the suggestion. This allows for more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generating AI and have the AI ​​adjust the length of the suggestion.

[0104] The proposal unit can determine the priority of proposals based on the timing of disaster occurrences. For example, the proposal unit may prioritize proposals related to the most recent disaster. The proposal unit can use AI to analyze the timing of disaster occurrences and determine the priority of proposals. For example, the proposal unit may evaluate the timing of disaster occurrences based on past frequency and seasonal factors and determine the priority of proposals. The proposal unit can also prioritize the proposals of the most urgent information based on the timing of disaster occurrences. Furthermore, the proposal unit can evaluate importance according to the timing of disaster occurrences and determine the priority of proposals. This allows for the provision of more appropriate information by prioritizing proposals based on the timing of disaster occurrences. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input disaster occurrence timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0105] The proposal unit can adjust the order of proposals based on the relevance of the disasters during the proposal process. For example, the proposal unit can prioritize proposing highly relevant disaster information. The proposal unit can use AI to analyze the relevance of disasters and adjust the order of proposals. For example, the proposal unit can evaluate the relevance of disasters based on geographical relevance and impact area and adjust the order of proposals. The proposal unit can also prioritize proposing the most important information based on the relevance of the disasters. Furthermore, the proposal unit can display less relevant disaster information later in the proposals. This allows for the provision of more appropriate information by adjusting the order of proposals based on the relevance of the disasters. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input disaster relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0106] The sharing unit can estimate the user's emotions and determine the priority of information to share based on the estimated emotions. For example, if the user is feeling anxious, the sharing unit will prioritize sharing information that provides reassurance. The sharing unit can use AI to estimate the user's emotions and determine the priority of information to share. For example, the sharing unit can use user facial recognition technology to estimate emotions and determine the priority of information. The sharing unit can also use user voice analysis technology to estimate emotions and determine the priority of information. Furthermore, the sharing unit can analyze the user's behavior patterns, estimate emotions, and determine the priority of information. This allows for the provision of more appropriate information by prioritizing the information to share according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the sharing unit may be performed using AI or not. For example, the shared section can input user emotion data into a generating AI and have the AI ​​determine the priority of the information.

[0107] The sharing unit can adjust the level of detail shared based on the importance of the information. For example, the sharing unit will share more detailed information for information of high importance. The sharing unit can use AI to analyze the importance of information and adjust the level of detail of sharing. For example, the sharing unit can evaluate the importance of information based on urgency and scope of impact and adjust the level of detail of sharing. The sharing unit can also evaluate the importance of information based on necessity and adjust the level of detail of sharing. Furthermore, the sharing unit can adjust the optimal level of detail of sharing according to the importance of the information. This allows necessary information to be provided preferentially by adjusting the level of detail of sharing based on the importance of the information. Some or all of the above processes in the sharing unit may be performed using AI or not. For example, the sharing unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of sharing.

[0108] The sharing unit can apply different sharing algorithms depending on the category of information during sharing. For example, for disaster preparedness supplies, the sharing unit can share information such as expiration dates and storage locations. The sharing unit can use AI to analyze the category of information and apply different sharing algorithms. For example, for disaster response methods, the sharing unit can share information such as evacuation routes and shelters. The sharing unit can also share emergency contact information and relief supplies for safety confirmation information. Furthermore, the sharing unit can apply the most appropriate sharing algorithm depending on the category of information. This allows for the provision of more appropriate information by applying a sharing algorithm according to the category of information. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input information category data into a generating AI and have the generating AI execute the application of the sharing algorithm.

[0109] The sharing unit can estimate the user's emotions and adjust the timing of sharing based on the estimated emotions. For example, if the user is stressed, the sharing unit will reduce the frequency of sharing and share only important information. The sharing unit can use AI to estimate the user's emotions and adjust the timing of sharing. For example, the sharing unit can use user facial recognition technology to estimate emotions and adjust the timing of sharing. The sharing unit can also use user voice analysis technology to estimate emotions and adjust the timing of sharing. Furthermore, the sharing unit can analyze the user's behavior patterns, estimate emotions, and adjust the timing of sharing. This allows for the provision of more appropriate information by adjusting the timing of sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing function can input user emotion data into a generating AI and have the AI ​​adjust the timing of sharing.

[0110] The sharing unit can adjust the order of sharing based on when the information was created. For example, the sharing unit prioritizes sharing the most recently created information. The sharing unit can use AI to analyze when the information was created and adjust the order of sharing. For example, the sharing unit evaluates when the information was created based on the date and time of creation or update date and adjusts the order of sharing. The sharing unit can also prioritize sharing the most urgent information based on when it was created. Furthermore, the sharing unit can evaluate the importance of the information according to when it was created and adjust the order of sharing. This allows necessary information to be provided preferentially by adjusting the order of sharing based on when the information was created. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input information creation data into a generating AI and have the generating AI perform the adjustment of the sharing order.

[0111] The update unit can estimate the user's emotions and determine the priority of information to update based on the estimated emotions. For example, if the user is feeling anxious, the update unit will prioritize updating information that provides a sense of security. The update unit can use AI to estimate the user's emotions and determine the priority of information to update. For example, the update unit can use user facial recognition technology to estimate emotions and determine the priority of information. The update unit can also use user voice analysis technology to estimate emotions and determine the priority of information. Furthermore, the update unit can analyze the user's behavior patterns, estimate emotions, and determine the priority of information. This allows for the provision of more appropriate information by determining the priority of information to update according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the update unit may be performed using AI or not. For example, the update unit can input user emotion data into a generating AI and have the generating AI determine the priority of the information.

[0112] The update unit can adjust the level of detail of updates based on the importance of the information during the update process. For example, the update unit will perform updates that include detailed information for information of high importance. The update unit can use AI to analyze the importance of information and adjust the level of detail of updates. For example, the update unit can evaluate the importance of information based on urgency and scope of impact and adjust the level of detail of updates. The update unit can also evaluate the importance of information based on necessity and adjust the level of detail of updates. Furthermore, the update unit can adjust the optimal level of detail of updates according to the importance of the information. This allows for the priority provision of necessary information by adjusting the level of detail of updates based on the importance of the information. Some or all of the above processes in the update unit may be performed using AI or not. For example, the update unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of updates.

[0113] The update unit can apply different update algorithms depending on the information category during the update process. For example, for disaster preparedness goods, the update unit updates information such as expiration dates and storage locations. The update unit can use AI to analyze information categories and apply different update algorithms. For example, for disaster response methods, the update unit updates information such as evacuation routes and shelters. The update unit can also update information such as emergency contact information and relief supplies for safety confirmation information. Furthermore, the update unit can apply the most appropriate update algorithm depending on the information category. This allows for the provision of more appropriate information by applying update algorithms according to the information category. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input information category data into a generating AI and have the generating AI execute the application of the update algorithm.

[0114] The update unit can estimate the user's emotions and adjust the update timing based on the estimated emotions. For example, if the user is stressed, the update unit will reduce the update frequency and update only important information. The update unit can use AI to estimate the user's emotions and adjust the update timing. For example, the update unit can use user facial recognition technology to estimate emotions and adjust the update timing. The update unit can also use user voice analysis technology to estimate emotions and adjust the update timing. Furthermore, the update unit can analyze the user's behavior patterns, estimate emotions, and adjust the update timing. This allows for the provision of more appropriate information by adjusting the update timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input user emotion data into a generating AI and have the generating AI adjust the timing of the updates.

[0115] The update unit can adjust the update order based on the timing of information generation during the update process. For example, the update unit prioritizes updating the most recently generated information. The update unit can use AI to analyze the timing of information generation and adjust the update order. For example, the update unit evaluates the timing of information generation based on the generation date and update date and adjusts the update order. The update unit can also prioritize updating the most urgent information based on the timing of information generation. Furthermore, the update unit can evaluate the importance of information according to its generation timing and adjust the update order accordingly. By adjusting the update order based on the timing of information generation, necessary information can be provided preferentially. Some or all of the above-described processes in the update unit may be performed using AI or not. For example, the update unit can input information generation timing data into a generating AI and have the generating AI perform the adjustment of the update order.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The disaster prevention management system can also include a health management unit that monitors the user's health status. The health management unit periodically checks the user's vital signs and issues a warning to the user via a notification unit if an abnormality is detected. For example, if an abnormality in heart rate or blood pressure is detected, it will notify the user to seek medical attention. The health management unit can also customize the list of disaster preparedness items based on the user's health status. For example, if a user requires a specific medication, that medication will be added to the list. Furthermore, the health management unit can suggest evacuation methods that take into account the user's health status in the event of a disaster. This enables appropriate responses according to the user's health condition.

[0118] The disaster prevention management system can also include a pet management section to manage information about the user's pets. The pet management section lists the type and number of pets the user has, as well as necessary items, and notifies the user when it is time to replace disaster preparedness supplies for pets. For example, it manages the expiration dates and best-before dates of pet food and medicine and notifies the user at the appropriate time. The pet management section can also suggest the best evacuation routes and shelters for evacuating with pets in the event of a disaster. Furthermore, the pet management section can monitor the health status of pets and notify the user if any abnormalities are detected. This allows users to take appropriate action while ensuring the safety of their pets.

[0119] The disaster prevention management system can also include a vehicle management unit that manages user vehicle information. The vehicle management unit tracks the location and status of users' vehicles and proposes the optimal evacuation route in the event of a disaster. For example, it monitors the vehicle's fuel level and maintenance status and notifies the user if necessary. The vehicle management unit can also suggest evacuation methods using the vehicle during a disaster. Furthermore, based on the vehicle's location information, the vehicle management unit can guide users to the nearest evacuation shelter or relief supply distribution point. This enables users to evacuate quickly and safely using their vehicles.

[0120] The disaster prevention management system can also include a family management unit to manage the user's family information. The family management unit registers the user's family structure and contact information, and checks the safety of all family members in the event of a disaster. For example, it can share the family's location information and suggest the optimal evacuation route. The family management unit can also monitor the health status of all family members and notify if any abnormalities are detected. Furthermore, the family management unit can create a list of disaster prevention supplies for all family members and notify them when it is time to replace them. This enables appropriate responses while ensuring the safety of all family members.

[0121] The disaster prevention management system can also include a community management unit that manages user community information. This unit collects information about the local communities to which users belong and supports community-wide responses in the event of a disaster. For example, it can provide information on local evacuation centers and relief supply distribution locations, and facilitate communication among community members. The community management unit can also share information on local disaster prevention drills and events, raising users' disaster preparedness awareness. Furthermore, the community management unit can confirm the safety of the entire community in the event of a disaster, supporting rapid rescue operations. This strengthens cooperation within the community and reduces risks during disasters.

[0122] The disaster prevention management system can further estimate the user's emotions and adjust the content of disaster prevention training based on those emotions. For example, if a user is feeling anxious, it can suggest disaster prevention training that provides a sense of security. Emotion estimation can be performed using user facial recognition technology and voice analysis technology. It can also analyze the user's behavior patterns, estimate their emotions, and adjust the content of disaster prevention training accordingly. Furthermore, it can adjust the frequency and timing of disaster prevention training according to the user's emotions. By conducting disaster prevention training that takes user emotions into consideration, it is expected that disaster prevention awareness will be more effectively improved.

[0123] The disaster prevention management system can further estimate the user's emotions and adjust the way disaster prevention goods are suggested based on those emotions. For example, if the user is feeling stressed, it will provide simple and highly visible suggestions. Emotion estimation can be performed using facial recognition technology and voice analysis technology. It can also analyze the user's behavior patterns, estimate their emotions, and adjust the suggestion method accordingly. Furthermore, it can adjust the frequency and timing of suggestions according to the user's emotions. This allows for more effective disaster prevention measures by suggesting disaster prevention goods that take the user's emotions into consideration.

[0124] The disaster prevention management system can further estimate the user's emotions and adjust its response methods during a disaster based on those estimated emotions. For example, if a user is feeling anxious, it can suggest a response that provides reassurance. Emotion estimation can be performed using user facial recognition technology and voice analysis technology. It can also analyze the user's behavior patterns, estimate their emotions, and adjust the response methods accordingly. Furthermore, it can adjust the level of detail and timing of the response methods according to the user's emotions. This allows for more effective disaster countermeasures by suggesting response methods that take the user's emotions into consideration.

[0125] The disaster prevention management system can further estimate the user's emotions and adjust the way disaster information is delivered based on those emotions. For example, if a user is feeling stressed, it can provide concise and easy-to-understand information. Emotion estimation can be performed using facial recognition technology and voice analysis technology. It can also analyze the user's behavior patterns to estimate their emotions and adjust the information delivery method. Furthermore, it can adjust the frequency and timing of information delivery according to the user's emotions. This allows for more effective information transmission by providing disaster information that takes the user's emotions into consideration.

[0126] The disaster prevention management system can further estimate the user's emotions and adjust the content of disaster prevention education based on those emotions. For example, if a user is feeling anxious, it can provide disaster prevention education that provides a sense of security. Emotion estimation can be performed using user facial recognition technology and voice analysis technology. It can also analyze the user's behavior patterns, estimate their emotions, and adjust the content of disaster prevention education accordingly. Furthermore, it can adjust the frequency and timing of disaster prevention education according to the user's emotions. By providing disaster prevention education that takes user emotions into consideration, it is expected that disaster prevention awareness will be improved more effectively.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The information gathering unit collects information about the user's home and living area. For example, it collects geographical information, weather information, and local disaster prevention information. Step 2: The list creation unit creates a list of necessary disaster preparedness items based on the information collected by the information gathering unit. For example, it creates a list of disaster preparedness items such as food, water, and medicine. The list creation unit can use AI to analyze information about the user's home and living area and create a list of necessary disaster preparedness items. Step 3: The notification unit notifies the customer of the replacement timing for the disaster preparedness supplies listed by the list creation unit. For example, the replacement timing for disaster preparedness supplies can be notified based on the expiration date, expiration date, deterioration status, etc. Step 4: The proposal department proposes response methods in the event of a disaster based on the information collected by the information gathering department. For example, in the event of a disaster such as an earthquake or flood, they can propose the optimal evacuation routes and shelters. Step 5: The sharing section shares the registered information with organizations and public institutions. For example, in the event of a disaster, it can notify organizations and public institutions of the user's safety status. Step 6: The update department updates and shares knowledge of response methods with users based on the disaster information that is updated daily. For example, it can update and share earthquake information, weather information, evacuation information, etc., with users on a daily basis.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the information gathering unit, list creation unit, notification unit, proposal unit, sharing unit, and update unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the information gathering unit uses the camera 42 and sensors of the smart device 14 to collect information about the user's home and living area. The list creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to create a list of disaster prevention goods based on the collected information. The notification unit is implemented by, for example, the control unit 46A of the smart device 14 to notify the user of when it is time to replace the listed disaster prevention goods. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to propose methods for responding in the event of a disaster. The sharing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to share the registered information with organizations and public institutions. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to update knowledge of response methods based on disaster information that is updated daily and share it with the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the information gathering unit, list creation unit, notification unit, proposal unit, sharing unit, and update unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the information gathering unit uses the camera 42 and sensors of the smart glasses 214 to collect information about the user's home and living area. The list creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and creates a list of disaster prevention goods based on the collected information. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214, and notifies the user of when it is time to replace the listed disaster prevention goods. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes methods for responding in the event of a disaster. The sharing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and shares the registered information with organizations and public institutions. The update unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and updates knowledge of response methods based on disaster information that is updated daily, and shares it with the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the information gathering unit, list creation unit, notification unit, proposal unit, sharing unit, and update unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the information gathering unit uses the camera 42 and sensors of the headset terminal 314 to collect information about the user's home and living area. The list creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to create a list of disaster prevention goods based on the collected information. The notification unit is implemented by, for example, the control unit 46A of the headset terminal 314 to notify the user of when it is time to replace the listed disaster prevention goods. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to propose methods for responding in the event of a disaster. The sharing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to share registered information with organizations and public institutions. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to update knowledge of response methods based on disaster information that is updated daily and share it with the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the information gathering unit, list creation unit, notification unit, proposal unit, sharing unit, and update unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the information gathering unit uses the camera 42 and sensors of the robot 414 to collect information about the user's home and living area. The list creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to create a list of disaster prevention goods based on the collected information. The notification unit is implemented by, for example, the control unit 46A of the robot 414 to notify the user of when it is time to replace the listed disaster prevention goods. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to propose methods for responding in the event of a disaster. The sharing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to share registered information with organizations and public institutions. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to update knowledge of response methods based on disaster information that is updated daily and share it with the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) The information gathering unit collects information about the user's home and living area, A list creation unit that creates a list of necessary disaster prevention goods based on the information collected by the aforementioned information collection unit, A notification unit that notifies the replacement time for disaster prevention goods listed by the aforementioned list creation unit, Based on the information collected by the aforementioned information gathering unit, a proposal unit proposes methods for responding to disasters. The sharing section shares registered information with organizations and public institutions, It includes an update unit that updates knowledge of response methods based on disaster information that is updated daily and shares it with users. A system characterized by the following features. (Note 2) The aforementioned list creation unit, Create a list of disaster preparedness supplies, such as food, water, and medicine. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Notify customers when it's time to replace the listed disaster preparedness supplies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, In the event of a disaster such as an earthquake or flood, we will propose the optimal evacuation route and shelter. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, In the event of a disaster, the system will notify organizations and public institutions of the user's safety status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned update unit is, We update our knowledge of response methods based on the daily updates to disaster information and share it with users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned information gathering unit, Analyze the user's past behavior history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned information gathering unit, When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information gathering unit, It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned information gathering unit, When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information gathering unit, When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned list creation unit, It estimates the user's emotions and adjusts how the list is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned list creation unit, When creating the list, adjust the level of detail based on the importance of the disaster preparedness items. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned list creation unit, When creating the list, different list creation algorithms are applied depending on the category of disaster preparedness goods. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned list creation unit, It estimates the user's sentiment and adjusts the list length based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned list creation unit, When creating the list, prioritize items based on when they were acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned list creation unit, When creating the list, adjust the order of the items based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of the disaster preparedness items. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the category of disaster preparedness goods. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending notifications, the order of notifications will be adjusted based on when the disaster preparedness supplies were acquired. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the type of disaster. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the disaster category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making proposals, prioritize them based on when the disaster occurred. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the disaster. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned shared portion is, It estimates the user's emotions and prioritizes the information to share based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned shared portion is, When sharing information, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned shared portion is, When sharing information, different sharing algorithms are applied depending on the category of the information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned shared portion is, It estimates the user's emotions and adjusts the timing of sharing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned shared portion is, When sharing information, adjust the order of sharing based on when the information was created. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned update unit is, It estimates the user's emotions and determines the priority of information to update based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned update unit is, When updating, adjust the level of detail of the update based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned update unit is, When updating, different update algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned update unit is, It estimates user sentiment and adjusts the timing of updates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned update unit is, When updating, the order of updates will be adjusted based on when the information was created. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information gathering unit collects information about the user's home and living area, A list creation unit that creates a list of necessary disaster prevention goods based on the information collected by the aforementioned information collection unit, A notification unit that notifies the replacement time for disaster prevention goods listed by the aforementioned list creation unit, Based on the information collected by the aforementioned information gathering unit, a proposal unit proposes methods for responding to disasters. The sharing section shares registered information with organizations and public institutions, It includes an update unit that updates knowledge of response methods based on disaster information that is updated daily and shares it with users. A system characterized by the following features.

2. The aforementioned list creation unit, Create a list of disaster preparedness supplies, such as food, water, and medicine. The system according to feature 1.

3. The aforementioned notification unit, Notify customers when it's time to replace the listed disaster preparedness supplies. The system according to feature 1.

4. The aforementioned proposal section is, In the event of a disaster such as an earthquake or flood, we will propose the optimal evacuation route and shelter. The system according to feature 1.

5. The aforementioned shared portion is, In the event of a disaster, the system will notify organizations and public institutions of the user's safety status. The system according to feature 1.

6. The aforementioned update unit is, We update our knowledge of response methods based on the daily updates to disaster information and share it with users. The system according to feature 1.

7. The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned information gathering unit, Analyze the user's past behavior history and select the optimal method for collecting information. The system according to feature 1.

9. The aforementioned information gathering unit, When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned information gathering unit, It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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