system

Abstract

The system according to this embodiment aims to efficiently manage the stockpiling status of each household and the entire community, and to propose the optimal support routes and evacuation routes in the event of a disaster. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a management unit, a visualization unit, and a proposal unit. The collection unit collects information on the members of each household. The generation unit generates a list of stockpiled items based on the information collected by the collection unit. The management unit manages the inventory of stockpiled items. The visualization unit visualizes the stockpiling status of the entire region. The proposal unit proposes the optimal support routes and evacuation routes in the event of a disaster.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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, there is a problem that it is difficult to efficiently manage the stockpiling situation of each household and the entire region, and to propose an optimal support route and evacuation route in case of disasters.

[0005] The system according to the embodiment aims to efficiently manage the stockpiling situation of each household and the entire region, and to propose an optimal support route and evacuation route in case of disasters.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a management unit, a visualization unit, and a proposal unit. The collection unit collects information on the members of each household. The generation unit generates a list of stockpiled items based on the information collected by the collection unit. The management unit manages the inventory of stockpiled items. The visualization unit visualizes the stockpiling status of the entire region. The proposal unit proposes the optimal support routes and evacuation routes in the event of a disaster. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the stockpiling status of each household and the entire community, and can propose the optimal support routes and evacuation routes 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 platform according to an embodiment of the present invention is an innovative stockpiling management and sharing system that dramatically improves disaster preparedness from an individual level to that of an entire community. This disaster prevention platform is centered around AI that automatically generates an optimal list of stockpiled items, taking into account the age, health status, and special needs (such as allergies and chronic illnesses) of each household member. Furthermore, it analyzes local disaster risks (earthquakes, floods, tsunamis, etc.) and past disaster experiences to suggest stockpiled items tailored to local characteristics. Users can easily update their stockpiling inventory via voice input, and the AI ​​manages expiration dates and suggests replenishment or replacement at the appropriate time. In addition, this system visualizes the stockpiling status of the entire community in real time and has a function to adjust for surpluses and shortages. For example, if a household has a surplus of water, it matches it with nearby households that are experiencing water shortages, promoting efficient resource allocation. In the event of a disaster, the AI ​​compares the disaster situation with the stockpiling status of each household and suggests the optimal support routes and evacuation routes. It functions as a comprehensive disaster prevention platform that goes beyond a mere stockpiling management tool, strengthening the bonds of local communities and contributing to the construction of a resilient society that is strong against disasters. This allows the disaster prevention platform to generate an optimal list of emergency supplies based on information about each household member, manage inventory, visualize the overall stockpiling situation in the region, and suggest support routes and evacuation routes in the event of a disaster.

[0029] The disaster prevention platform according to this embodiment comprises a collection unit, a generation unit, a management unit, a visualization unit, and a proposal unit. The collection unit collects information on the members of each household. For example, the collection unit can collect the age, gender, health status, and special needs (such as allergies or chronic illnesses) of each household member. For example, the collection unit can collect information through questionnaire surveys. The collection unit can also use sensors to monitor the health status of each household member in real time and collect data. The generation unit generates a list of emergency supplies based on the information collected by the collection unit. For example, the generation unit can automatically generate an optimal list of emergency supplies for each household based on the collected information. For example, the generation unit can use AI to generate a list of emergency supplies tailored to the age and health status of each household member. The generation unit can also analyze regional disaster risks and past disaster experiences and propose emergency supplies that are appropriate to regional characteristics. The management unit manages the inventory of emergency supplies. For example, the management unit can monitor the inventory status of emergency supplies in real time and update the inventory information. The management unit can, for example, update the inventory of stockpiled supplies using voice input. The management unit can also manage expiration dates and suggest replenishment or replacement at appropriate times. The visualization unit visualizes the stockpiling status of the entire region. For example, the visualization unit can display the stockpiling status of the entire region in real time and adjust for surpluses or shortages. The visualization unit can visually display the stockpiling status of the entire region using, for example, graphs or maps. The proposal unit proposes optimal support routes and evacuation routes in the event of a disaster. For example, the proposal unit can compare the disaster situation with the stockpiling status of each household and propose optimal support routes and evacuation routes. For example, the proposal unit can grasp the situation in the disaster area in real time and propose the optimal evacuation route for households that need to evacuate. The proposal unit can also propose the optimal support route for households that need relief supplies. As a result, the disaster prevention platform according to this embodiment can generate an optimal list of stockpiled items based on information of each household member, manage inventory, visualize the stockpiling status of the entire region, and propose support routes and evacuation routes in the event of a disaster.

[0030] The data collection unit collects information about each household member. For example, it can collect the age, gender, health status, and special needs (such as allergies or chronic illnesses) of each household member. The unit can collect information through surveys, for example. It can also use sensors to monitor the health status of each household member in real time and collect data. Specifically, surveys are conducted using online forms or paper-based questionnaires, with each household member entering their own information. This allows the data collection unit to obtain detailed personal information and store it in a database. Furthermore, the data collection unit utilizes wearable devices and home health monitoring systems to collect health data such as heart rate, blood pressure, and body temperature of each household member in real time. This data is transmitted wirelessly to a central server and managed centrally by the data collection unit. The data collection unit regularly updates this data to maintain up-to-date information. The data collection unit also implements data encryption and access control to protect privacy and prevent the leakage of personal information. This allows the data collection unit to efficiently and securely collect detailed information about each household member, improving the accuracy and reliability of the entire system.

[0031] The generation unit generates a list of emergency supplies based on the information collected by the collection unit. For example, the generation unit can automatically generate an optimal list of emergency supplies for each household based on the collected information. For example, the generation unit can use AI to generate an emergency supply list tailored to the age and health status of each household member. Specifically, the AI ​​analyzes the collected data and lists the necessary emergency supplies based on the age, gender, health status, and special needs of each household member. For example, it generates an emergency supply list that includes formula and diapers for households with infants, and specific foods and medicines for households with members who have allergies. The generation unit can also analyze regional disaster risks and past disaster experiences to suggest emergency supplies tailored to regional characteristics. For example, it recommends emergency supplies such as tarpaulins, pumps, and batteries for areas with a high risk of flooding. Based on this information, the generation unit can provide each household with an optimal list of emergency supplies, strengthening their preparedness for disasters. Furthermore, the generation unit can update and add to the emergency supply list to respond to changes in household circumstances and needs. This allows the generator to provide each household with an optimal list of emergency supplies, thereby strengthening their preparedness for disasters.

[0032] The management department manages the inventory of stockpiled supplies. For example, the management department can monitor the inventory status of stockpiled supplies in real time and update inventory information. For example, the management department can update the inventory of stockpiled supplies using voice input. In addition, the management department can manage expiration dates and suggest replenishment or replacement at the appropriate time. Specifically, the management department will attach RFID tags or barcodes to the stockpiled supplies in each household and grasp the inventory status in real time by scanning these. Furthermore, a voice input system will be introduced so that users can report the inventory status by voice. For example, by voice inputting "We're running low on rice," the system will automatically update the inventory information and notify the user of the need for replenishment. In addition, the management department will register the expiration date of each stockpiled item in a database to manage expiration dates and issue alerts when the expiration date is approaching. This will allow users to replenish or replace stockpiled supplies at the appropriate time. Furthermore, the management department can analyze the inventory status and make suggestions to adjust for surpluses or shortages. For example, if a particular stockpiled item is in excess, it may suggest sharing it with other households or the community. This allows the management department to efficiently manage stockpiles, reduce waste, and optimize disaster preparedness.

[0033] The visualization unit visualizes the stockpiling status of the entire region. For example, the visualization unit can display the stockpiling status of the entire region in real time and adjust for surpluses and shortages. For example, the visualization unit can visually display the stockpiling status of the entire region using graphs and maps. Specifically, the visualization unit aggregates inventory data of stockpiles collected from each household and displays the stockpiling status of the entire region in real time. This allows for an at-a-glance understanding of the surplus or shortage of stockpiles in the entire region. For example, using a map display, the stockpiling status of each household can be color-coded to visually show which areas are lacking in stockpiles. In addition, using a graph display, the inventory level of each stockpile can be displayed over time and compared with past data. Furthermore, the visualization unit can analyze the stockpiling status of the entire region and make suggestions for adjusting for surpluses and shortages. For example, in areas where a particular stockpile is lacking, it can suggest support from other regions and encourage the sharing of excess stockpiles. In this way, the visualization unit can efficiently manage the stockpiling status of the entire region and optimize disaster preparedness. Furthermore, the visualization unit provides an interface for users to check their own stockpiling status and take necessary measures. This allows the visualization unit to grasp the stockpiling status of the entire region in real time and support efficient stockpiling management.

[0034] The proposal unit proposes optimal support routes and evacuation routes in the event of a disaster. For example, the proposal unit can compare the damage situation with the stockpiling status of each household and propose the optimal support routes and evacuation routes. For example, the proposal unit can grasp the situation in the disaster area in real time and propose the optimal evacuation route for households that need to evacuate. The proposal unit can also propose the optimal support route for households that need relief supplies. Specifically, based on data provided by the collection and visualization units, the proposal unit analyzes the situation in the disaster area in real time and calculates the optimal evacuation and support routes, taking into account the stockpiling status and health status of each household. For example, in the event of a flood, the proposal unit analyzes the flooding situation and road passability and proposes a safe evacuation route. It can also calculate the optimal support route for households that need relief supplies, enabling efficient delivery of those supplies. Furthermore, the proposal unit can use AI to simulate multiple scenarios and identify the most effective support routes and evacuation routes. This allows the proposal unit to provide rapid and appropriate support in the event of a disaster and ensure the safety of disaster victims. Furthermore, the proposal department can collect feedback from users and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to efficiently provide support and evacuate during disasters, ensuring the safety and security of those affected.

[0035] The management department can manage expiration dates and suggest replenishment or replacement at the appropriate time. For example, the management department can monitor the expiration dates of stockpiled items and send notifications when they are approaching their expiration date. For example, the management department can list stockpiled items that are nearing their expiration date and suggest replenishment or replacement to the user. The management department can also automatically remove stockpiled items that have expired from the list and add new stockpiled items. In this way, the quality of stockpiled items can be maintained by managing expiration dates and suggesting replenishment or replacement at the appropriate time. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input information on stockpiled items that are nearing their expiration date into the AI, and the AI ​​can suggest the appropriate timing for replenishment or replacement.

[0036] The data collection unit can analyze regional disaster risks and past disaster experiences to propose stockpiled supplies tailored to regional characteristics. For example, the data collection unit collects and analyzes regional geographical conditions and past disaster data. For example, in areas with a high earthquake risk, the data collection unit can propose stockpiling earthquake-resistant goods and emergency food. In areas with a high flood risk, the data collection unit can also propose stockpiling waterproof sheets and pumps. In areas with a high tsunami risk, the data collection unit can also propose stockpiling evacuation equipment and life jackets. This makes it possible to propose stockpiled supplies that take into account regional disaster risks and past disaster experiences. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input regional disaster risk data into AI, and the AI ​​can propose stockpiled supplies tailored to regional characteristics.

[0037] The visualization unit can visualize the overall stockpiling situation of a region in real time and adjust for surpluses and shortages. For example, the visualization unit can display the overall stockpiling situation of a region using graphs and maps, making it easy to understand visually. For example, the visualization unit can update the inventory status of each household's stockpiles in real time and display the overall stockpiling situation of the region. In addition, the visualization unit can also propose efficient resource allocation when surpluses or shortages occur. For example, if a household has a surplus of water, it can match it with nearby households that are experiencing water shortages, thereby promoting efficient resource allocation. This makes it possible to grasp the overall stockpiling situation of the region in real time and adjust for surpluses and shortages, enabling efficient resource allocation. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input inventory data of each household's stockpiles into AI, which can then visualize the overall stockpiling situation of the region in real time.

[0038] The proposal unit can compare the disaster situation with the stockpiling status of each household and propose the optimal support routes and evacuation routes. For example, the proposal unit can grasp the situation in the disaster area in real time and propose the optimal evacuation route for households that need to evacuate. For example, the proposal unit can propose the optimal evacuation route considering the traffic situation in the disaster area and the location of evacuation shelters. The proposal unit can also propose the optimal support route for households that need relief supplies. For example, the proposal unit can analyze the damage situation in the disaster area and optimize the distribution route of relief supplies. This enables quick and appropriate support by proposing the optimal support routes and evacuation routes based on the disaster situation and the stockpiling status of each household. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input disaster situation data into AI, and the AI ​​can propose the optimal support routes and evacuation routes.

[0039] The management department can update the stock inventory using voice input. For example, the management department can allow users to update stock inventory information using voice input. The management department can update inventory information by voice input such as, "There are 2 bottles of water left." The management department can also use voice recognition technology to convert the user's voice input into text data and automatically update the inventory information. This allows users to easily manage stocks by updating inventory using voice input. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's voice input data into AI, and the AI ​​can update the inventory information.

[0040] The data collection unit can analyze the lifestyle patterns of each household member and select the optimal timing for information collection. For example, the unit can analyze each household's morning routine and collect information after breakfast. For example, the unit can analyze each household's evening routine and collect information before bedtime. The unit can also analyze how each household spends their weekends and collect information at specific times on weekends. This allows for efficient information collection by selecting the timing of information collection based on each household's lifestyle patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each household's lifestyle pattern data into AI, which can then select the optimal timing for information collection.

[0041] The data collection unit can collect information by integrating data from IoT devices within the home. For example, the data collection unit can collect inventory information from a smart refrigerator and reflect it in a list of emergency supplies. For example, the data collection unit can collect health data from a smart scale and suggest emergency supplies based on the user's health status. The data collection unit can also collect data from a smart home security system and integrate information related to home safety. This allows for more accurate information collection by integrating data from IoT devices within the home. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from IoT devices within the home into an AI, which can then integrate and collect the information.

[0042] The collection unit can collect information while taking local event information into consideration. For example, the collection unit can consider the schedule of local disaster prevention drills and collect information on stockpiled supplies before and after those events. For example, the collection unit can consider the schedule of local festivals and events and collect information on stockpiled supplies related to those events. The collection unit can also consider school holidays in the area and collect information on stockpiled supplies for children at home. This allows for more appropriate information collection by taking local event information into consideration. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input local event information into AI, and the AI ​​can collect the information.

[0043] The data collection unit can supplement the information it collects by analyzing information from social media. For example, the data collection unit can collect local disaster prevention information from social media and reflect it in the stockpiling list. For example, the data collection unit can analyze user posts on social media and suggest stockpiling items based on local needs. The data collection unit can also collect disaster information from social media and update the stockpiling list in real time. This allows for more accurate information collection by supplementing information from social media. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input information from social media into AI, which can then analyze and collect the information.

[0044] The generation unit can generate an optimal list of stockpiled items by referring to each household's past consumption data. For example, the generation unit can generate an optimal list of food stockpiled items based on each household's past food consumption data. For example, the generation unit can generate an optimal list of medicine stockpiled items based on each household's past medicine consumption data. The generation unit can also analyze each household's past consumption patterns and generate an optimal list of stockpiled items. This allows for the generation of a more appropriate list of stockpiled items by referring to past consumption data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input each household's past consumption data into AI, and the AI ​​can generate an optimal list of stockpiled items.

[0045] The generation unit can adjust the stockpile list to account for seasonal and weather variations. For example, in winter, the generation unit can add heating appliances and warm clothing to the stockpile list. In summer, for example, the generation unit can add cooling products and hydration supplies to the stockpile list. In the rainy season, the generation unit can also add tarpaulins and ponchos to the stockpile list. This allows for the generation of a more appropriate stockpile list by taking into account seasonal and weather variations. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input seasonal and weather data into the AI, which can then adjust the stockpile list.

[0046] The generation unit can generate a list of emergency supplies by referring to a regional disaster risk map. For example, in areas with a high earthquake risk, the generation unit can add earthquake-resistant goods to the list of emergency supplies. For example, in areas with a high flood risk, the generation unit can add waterproof sheets and pumps to the list of emergency supplies. Furthermore, in areas with a high tsunami risk, the generation unit can add evacuation equipment to the list of emergency supplies. This allows for the generation of a more appropriate list of emergency supplies by referring to a regional disaster risk map. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input regional disaster risk map data into AI, and the AI ​​can generate the list of emergency supplies.

[0047] The generation unit can adjust the stockpile list to take into account the needs of pets in the household. For example, the generation unit can add pet food and water to the stockpile list. For example, the generation unit can add pet medicines and first-aid kits to the stockpile list. The generation unit can also add pet evacuation equipment and cages to the stockpile list. This allows for the generation of a more appropriate stockpile list by taking pet needs into account. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input pet needs data into the AI, and the AI ​​can adjust the stockpile list.

[0048] The management department can analyze each household's consumption patterns and select the optimal inventory management method. For example, the management department can select the optimal inventory management method based on each household's past consumption data. For example, the management department can analyze each household's consumption patterns and adjust the frequency and method of inventory management. Furthermore, the management department can automate inventory management based on each household's consumption patterns. This enables efficient inventory management by selecting the optimal inventory management method based on each household's consumption patterns. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input each household's consumption pattern data into AI, and the AI ​​can select the optimal inventory management method.

[0049] The management department can integrate data from IoT devices within the home to manage inventory. For example, the management department can integrate inventory information from a smart refrigerator and manage inventory. For example, the management department can integrate health data from a smart scale and manage inventory based on health status. Furthermore, the management department can integrate data from a smart home security system and manage inventory related to home safety. This allows for more accurate inventory management by integrating data from IoT devices within the home. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input data from IoT devices within the home into an AI, which can then perform inventory management.

[0050] The management department can manage inventory while taking local event information into consideration. For example, the management department can consider the dates of local disaster prevention drills and manage inventory before and after them. For example, the management department can consider the schedules of local festivals and events and manage the inventory of stockpiled supplies related to those events. Furthermore, the management department can consider school holidays in the area and manage the inventory of stockpiled supplies for children at home. This allows for more appropriate inventory management by taking local event information into consideration. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input local event information into AI, and the AI ​​can perform inventory management.

[0051] The management department can supplement inventory management by analyzing information from social media. For example, the management department can collect local disaster prevention information from social media and reflect it in inventory management. For example, the management department can analyze user posts on social media and perform inventory management based on local needs. The management department can also collect disaster information from social media and update inventory management in real time. This allows for more accurate inventory management by supplementing information from social media. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input information from social media into AI, and the AI ​​can analyze the information to supplement inventory management.

[0052] The visualization unit can integrate and display regional disaster risk maps. For example, the visualization unit can display a map of areas with high earthquake risk and optimize the placement of stockpiled supplies. For example, the visualization unit can display a map of areas with high flood risk and visualize evacuation routes. The visualization unit can also display a map of areas with high tsunami risk and visualize evacuation sites. By integrating regional disaster risk maps, more appropriate information can be displayed. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input regional disaster risk map data into AI, and the AI ​​can integrate and display the information.

[0053] The visualization unit can integrate and display data from IoT devices within the home. For example, the visualization unit can visualize inventory information from a smart refrigerator and display the status of stored goods. For example, the visualization unit can visualize health data from a smart scale and display suggestions for stored goods based on the user's health status. The visualization unit can also visualize data from a smart home security system and display information related to home safety. By integrating data from IoT devices within the home, more accurate information can be displayed. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data from IoT devices within the home into an AI, which can then integrate and display the information.

[0054] The visualization unit can display information while taking local event information into consideration. For example, the visualization unit can display the schedule of local disaster prevention drills and information on stockpiled supplies before and after those drills. For example, the visualization unit can display the schedule of local festivals and events and information on stockpiled supplies related to those events. The visualization unit can also display school holidays in the local area and information on stockpiled supplies for children at home. This allows for more appropriate information to be displayed by taking local event information into consideration. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input local event information into AI, and the AI ​​can display the information.

[0055] The visualization unit can analyze information from social media to supplement its display. For example, the visualization unit can display local disaster prevention information from social media and reflect it in the stockpiling list. For example, the visualization unit can analyze user posts on social media and display stockpiling items based on local needs. The visualization unit can also display disaster information from social media and update the stockpiling list in real time. This allows for more accurate information display by supplementing information from social media. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input information from social media into AI, which can then analyze the information and supplement the display.

[0056] The suggestion unit can make optimal suggestions by referring to each household's past evacuation behavior data. For example, the suggestion unit can suggest the optimal evacuation route based on each household's past evacuation behavior data. For example, the suggestion unit can analyze each household's past evacuation behavior data and suggest points to be aware of during evacuation. The suggestion unit can also refer to each household's past evacuation behavior data and suggest the preparation of evacuation equipment. This makes it possible to make more appropriate suggestions by referring to past evacuation behavior data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input each household's past evacuation behavior data into AI, and the AI ​​can make optimal suggestions.

[0057] The proposal department can integrate regional disaster risk maps and make recommendations. For example, in areas with a high earthquake risk, the proposal department can suggest preparing earthquake-resistant goods. In areas with a high flood risk, the proposal department can suggest preparing waterproof sheets and pumps. In areas with a high tsunami risk, the proposal department can also suggest preparing evacuation equipment. By integrating regional disaster risk maps, more appropriate recommendations can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input regional disaster risk map data into AI, and the AI ​​can make recommendations.

[0058] The proposal department can make suggestions while taking local event information into consideration. For example, the proposal department can consider the schedule of local disaster prevention drills and suggest stockpiling supplies before and after those events. For example, the proposal department can consider the schedule of local festivals and events and suggest stockpiling supplies related to those events. Furthermore, the proposal department can consider school holidays in the area and suggest stockpiling supplies for children at home. This allows for more appropriate suggestions by taking local event information into consideration. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input local event information into AI, and the AI ​​can make suggestions.

[0059] The proposal unit can analyze information from social media to supplement its proposals. For example, the proposal unit can analyze local disaster prevention information on social media and reflect it in its suggestions for emergency supplies. For example, the proposal unit can analyze user posts on social media and make suggestions for emergency supplies based on local needs. The proposal unit can also analyze disaster information on social media and update its suggestions for emergency supplies in real time. This allows for more appropriate suggestions by supplementing information from social media. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input information from social media into AI, which can then analyze the information and supplement its suggestions.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze the lifestyle patterns of each household member and select the optimal timing for information collection. For example, the unit can analyze each household's morning routine and collect information after breakfast. For example, the unit can analyze each household's evening routine and collect information before bedtime. The unit can also analyze how each household spends their weekends and collect information at specific times on weekends. This allows for efficient information collection by selecting the timing of information collection based on each household's lifestyle patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each household's lifestyle pattern data into AI, which can then select the optimal timing for information collection.

[0062] The data collection unit can collect information by integrating data from IoT devices within the home. For example, the data collection unit can collect inventory information from a smart refrigerator and reflect it in a list of emergency supplies. For example, the data collection unit can collect health data from a smart scale and suggest emergency supplies based on the user's health status. The data collection unit can also collect data from a smart home security system and integrate information related to home safety. This allows for more accurate information collection by integrating data from IoT devices within the home. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from IoT devices within the home into an AI, which can then integrate and collect the information.

[0063] The collection unit can collect information while taking local event information into consideration. For example, the collection unit can consider the schedule of local disaster prevention drills and collect information on stockpiled supplies before and after those events. For example, the collection unit can consider the schedule of local festivals and events and collect information on stockpiled supplies related to those events. The collection unit can also consider school holidays in the area and collect information on stockpiled supplies for children at home. This allows for more appropriate information collection by taking local event information into consideration. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input local event information into AI, and the AI ​​can collect the information.

[0064] The data collection unit can supplement the information it collects by analyzing information from social media. For example, the data collection unit can collect local disaster prevention information from social media and reflect it in the stockpiling list. For example, the data collection unit can analyze user posts on social media and suggest stockpiling items based on local needs. The data collection unit can also collect disaster information from social media and update the stockpiling list in real time. This allows for more accurate information collection by supplementing information from social media. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input information from social media into AI, which can then analyze and collect the information.

[0065] The generation unit can generate an optimal list of stockpiled items by referring to each household's past consumption data. For example, the generation unit can generate an optimal list of food stockpiled items based on each household's past food consumption data. For example, the generation unit can generate an optimal list of medicine stockpiled items based on each household's past medicine consumption data. The generation unit can also analyze each household's past consumption patterns and generate an optimal list of stockpiled items. This allows for the generation of a more appropriate list of stockpiled items by referring to past consumption data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input each household's past consumption data into AI, and the AI ​​can generate an optimal list of stockpiled items.

[0066] The generation unit can adjust the stockpile list to account for seasonal and weather variations. For example, in winter, the generation unit can add heating appliances and warm clothing to the stockpile list. In summer, for example, the generation unit can add cooling products and hydration supplies to the stockpile list. In the rainy season, the generation unit can also add tarpaulins and ponchos to the stockpile list. This allows for the generation of a more appropriate stockpile list by taking into account seasonal and weather variations. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input seasonal and weather data into the AI, which can then adjust the stockpile list.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects information about each household member. For example, the data collection unit can collect information such as the age, gender, health status, and special needs (allergies, chronic illnesses, etc.) of each household member. The data collection unit can collect information, for example, through questionnaire surveys. The data collection unit can also use sensors to monitor the health status of each household member in real time and collect data. Step 2: The generation unit generates a list of emergency supplies based on the information collected by the collection unit. For example, the generation unit can automatically generate an optimal list of emergency supplies for each household based on the collected information. For example, the generation unit can use AI to generate an emergency supply list tailored to the age and health status of each household member. The generation unit can also analyze local disaster risks and past disaster experiences to suggest emergency supplies that are appropriate for the local characteristics. Step 3: The management department manages the stockpiling inventory. The management department can, for example, monitor the stockpiling inventory status in real time and update the inventory information. The management department can, for example, update the stockpiling inventory using voice input. The management department can also manage expiration dates and suggest replenishment or replacement at the appropriate time. Step 4: The visualization unit visualizes the stockpiling status of the entire region. The visualization unit can, for example, display the stockpiling status of the entire region in real time and adjust for surpluses or shortages. The visualization unit can, for example, visually display the stockpiling status of the entire region using graphs or maps. Step 5: The proposal department proposes the optimal support routes and evacuation routes in the event of a disaster. For example, the proposal department can compare the disaster situation with the stockpiling status of each household and propose the optimal support routes and evacuation routes. For example, the proposal department can grasp the situation in the disaster area in real time and propose the optimal evacuation route for households that need to evacuate. The proposal department can also propose the optimal support route for households that need relief supplies.

[0069] (Example of form 2) The disaster prevention platform according to an embodiment of the present invention is an innovative stockpiling management and sharing system that dramatically improves disaster preparedness from an individual level to that of an entire community. This disaster prevention platform is centered around AI that automatically generates an optimal list of stockpiled items, taking into account the age, health status, and special needs (such as allergies and chronic illnesses) of each household member. Furthermore, it analyzes local disaster risks (earthquakes, floods, tsunamis, etc.) and past disaster experiences to suggest stockpiled items tailored to local characteristics. Users can easily update their stockpiling inventory via voice input, and the AI ​​manages expiration dates and suggests replenishment or replacement at the appropriate time. In addition, this system visualizes the stockpiling status of the entire community in real time and has a function to adjust for surpluses and shortages. For example, if a household has a surplus of water, it matches it with nearby households that are experiencing water shortages, promoting efficient resource allocation. In the event of a disaster, the AI ​​compares the disaster situation with the stockpiling status of each household and suggests the optimal support routes and evacuation routes. It functions as a comprehensive disaster prevention platform that goes beyond a mere stockpiling management tool, strengthening the bonds of local communities and contributing to the construction of a resilient society that is strong against disasters. This allows the disaster prevention platform to generate an optimal list of emergency supplies based on information about each household member, manage inventory, visualize the overall stockpiling situation in the region, and suggest support routes and evacuation routes in the event of a disaster.

[0070] The disaster prevention platform according to this embodiment comprises a collection unit, a generation unit, a management unit, a visualization unit, and a proposal unit. The collection unit collects information on the members of each household. For example, the collection unit can collect the age, gender, health status, and special needs (such as allergies or chronic illnesses) of each household member. For example, the collection unit can collect information through questionnaire surveys. The collection unit can also use sensors to monitor the health status of each household member in real time and collect data. The generation unit generates a list of emergency supplies based on the information collected by the collection unit. For example, the generation unit can automatically generate an optimal list of emergency supplies for each household based on the collected information. For example, the generation unit can use AI to generate a list of emergency supplies tailored to the age and health status of each household member. The generation unit can also analyze regional disaster risks and past disaster experiences and propose emergency supplies that are appropriate to regional characteristics. The management unit manages the inventory of emergency supplies. For example, the management unit can monitor the inventory status of emergency supplies in real time and update the inventory information. The management unit can, for example, update the inventory of stockpiled supplies using voice input. The management unit can also manage expiration dates and suggest replenishment or replacement at appropriate times. The visualization unit visualizes the stockpiling status of the entire region. For example, the visualization unit can display the stockpiling status of the entire region in real time and adjust for surpluses or shortages. The visualization unit can visually display the stockpiling status of the entire region using, for example, graphs or maps. The proposal unit proposes optimal support routes and evacuation routes in the event of a disaster. For example, the proposal unit can compare the disaster situation with the stockpiling status of each household and propose optimal support routes and evacuation routes. For example, the proposal unit can grasp the situation in the disaster area in real time and propose the optimal evacuation route for households that need to evacuate. The proposal unit can also propose the optimal support route for households that need relief supplies. As a result, the disaster prevention platform according to this embodiment can generate an optimal list of stockpiled items based on information of each household member, manage inventory, visualize the stockpiling status of the entire region, and propose support routes and evacuation routes in the event of a disaster.

[0071] The data collection unit collects information about each household member. For example, it can collect the age, gender, health status, and special needs (such as allergies or chronic illnesses) of each household member. The unit can collect information through surveys, for example. It can also use sensors to monitor the health status of each household member in real time and collect data. Specifically, surveys are conducted using online forms or paper-based questionnaires, with each household member entering their own information. This allows the data collection unit to obtain detailed personal information and store it in a database. Furthermore, the data collection unit utilizes wearable devices and home health monitoring systems to collect health data such as heart rate, blood pressure, and body temperature of each household member in real time. This data is transmitted wirelessly to a central server and managed centrally by the data collection unit. The data collection unit regularly updates this data to maintain up-to-date information. The data collection unit also implements data encryption and access control to protect privacy and prevent the leakage of personal information. This allows the data collection unit to efficiently and securely collect detailed information about each household member, improving the accuracy and reliability of the entire system.

[0072] The generation unit generates a list of emergency supplies based on the information collected by the collection unit. For example, the generation unit can automatically generate an optimal list of emergency supplies for each household based on the collected information. For example, the generation unit can use AI to generate an emergency supply list tailored to the age and health status of each household member. Specifically, the AI ​​analyzes the collected data and lists the necessary emergency supplies based on the age, gender, health status, and special needs of each household member. For example, it generates an emergency supply list that includes formula and diapers for households with infants, and specific foods and medicines for households with members who have allergies. The generation unit can also analyze regional disaster risks and past disaster experiences to suggest emergency supplies tailored to regional characteristics. For example, it recommends emergency supplies such as tarpaulins, pumps, and batteries for areas with a high risk of flooding. Based on this information, the generation unit can provide each household with an optimal list of emergency supplies, strengthening their preparedness for disasters. Furthermore, the generation unit can update and add to the emergency supply list to respond to changes in household circumstances and needs. This allows the generator to provide each household with an optimal list of emergency supplies, thereby strengthening their preparedness for disasters.

[0073] The management department manages the inventory of stockpiled supplies. For example, the management department can monitor the inventory status of stockpiled supplies in real time and update inventory information. For example, the management department can update the inventory of stockpiled supplies using voice input. In addition, the management department can manage expiration dates and suggest replenishment or replacement at the appropriate time. Specifically, the management department will attach RFID tags or barcodes to the stockpiled supplies in each household and grasp the inventory status in real time by scanning these. Furthermore, a voice input system will be introduced so that users can report the inventory status by voice. For example, by voice inputting "We're running low on rice," the system will automatically update the inventory information and notify the user of the need for replenishment. In addition, the management department will register the expiration date of each stockpiled item in a database to manage expiration dates and issue alerts when the expiration date is approaching. This will allow users to replenish or replace stockpiled supplies at the appropriate time. Furthermore, the management department can analyze the inventory status and make suggestions to adjust for surpluses or shortages. For example, if a particular stockpiled item is in excess, it may suggest sharing it with other households or the community. This allows the management department to efficiently manage stockpiles, reduce waste, and optimize disaster preparedness.

[0074] The visualization unit visualizes the stockpiling status of the entire region. For example, the visualization unit can display the stockpiling status of the entire region in real time and adjust for surpluses and shortages. For example, the visualization unit can visually display the stockpiling status of the entire region using graphs and maps. Specifically, the visualization unit aggregates inventory data of stockpiles collected from each household and displays the stockpiling status of the entire region in real time. This allows for an at-a-glance understanding of the surplus or shortage of stockpiles in the entire region. For example, using a map display, the stockpiling status of each household can be color-coded to visually show which areas are lacking in stockpiles. In addition, using a graph display, the inventory level of each stockpile can be displayed over time and compared with past data. Furthermore, the visualization unit can analyze the stockpiling status of the entire region and make suggestions for adjusting for surpluses and shortages. For example, in areas where a particular stockpile is lacking, it can suggest support from other regions and encourage the sharing of excess stockpiles. In this way, the visualization unit can efficiently manage the stockpiling status of the entire region and optimize disaster preparedness. Furthermore, the visualization unit provides an interface for users to check their own stockpiling status and take necessary measures. This allows the visualization unit to grasp the stockpiling status of the entire region in real time and support efficient stockpiling management.

[0075] The proposal unit proposes optimal support routes and evacuation routes in the event of a disaster. For example, the proposal unit can compare the damage situation with the stockpiling status of each household and propose the optimal support routes and evacuation routes. For example, the proposal unit can grasp the situation in the disaster area in real time and propose the optimal evacuation route for households that need to evacuate. The proposal unit can also propose the optimal support route for households that need relief supplies. Specifically, based on data provided by the collection and visualization units, the proposal unit analyzes the situation in the disaster area in real time and calculates the optimal evacuation and support routes, taking into account the stockpiling status and health status of each household. For example, in the event of a flood, the proposal unit analyzes the flooding situation and road passability and proposes a safe evacuation route. It can also calculate the optimal support route for households that need relief supplies, enabling efficient delivery of those supplies. Furthermore, the proposal unit can use AI to simulate multiple scenarios and identify the most effective support routes and evacuation routes. This allows the proposal unit to provide rapid and appropriate support in the event of a disaster and ensure the safety of disaster victims. Furthermore, the proposal department can collect feedback from users and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to efficiently provide support and evacuate during disasters, ensuring the safety and security of disaster victims.

[0076] The management department can manage expiration dates and suggest replenishment or replacement at the appropriate time. For example, the management department can monitor the expiration dates of stockpiled items and send notifications when they are approaching their expiration date. For example, the management department can list stockpiled items that are nearing their expiration date and suggest replenishment or replacement to the user. The management department can also automatically remove stockpiled items that have expired from the list and add new stockpiled items. In this way, the quality of stockpiled items can be maintained by managing expiration dates and suggesting replenishment or replacement at the appropriate time. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input information on stockpiled items that are nearing their expiration date into the AI, and the AI ​​can suggest the appropriate timing for replenishment or replacement.

[0077] The data collection unit can analyze regional disaster risks and past disaster experiences to propose stockpiled supplies tailored to regional characteristics. For example, the data collection unit collects and analyzes regional geographical conditions and past disaster data. For example, in areas with a high earthquake risk, the data collection unit can propose stockpiling earthquake-resistant goods and emergency food. In areas with a high flood risk, the data collection unit can also propose stockpiling waterproof sheets and pumps. In areas with a high tsunami risk, the data collection unit can also propose stockpiling evacuation equipment and life jackets. This makes it possible to propose stockpiled supplies that take into account regional disaster risks and past disaster experiences. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input regional disaster risk data into AI, and the AI ​​can propose stockpiled supplies tailored to regional characteristics.

[0078] The visualization unit can visualize the overall stockpiling situation of a region in real time and adjust for surpluses and shortages. For example, the visualization unit can display the overall stockpiling situation of a region using graphs and maps, making it easy to understand visually. For example, the visualization unit can update the inventory status of each household's stockpiles in real time and display the overall stockpiling situation of the region. In addition, the visualization unit can also propose efficient resource allocation when surpluses or shortages occur. For example, if a household has a surplus of water, it can match it with nearby households that are experiencing water shortages, thereby promoting efficient resource allocation. This makes it possible to grasp the overall stockpiling situation of the region in real time and adjust for surpluses and shortages, enabling efficient resource allocation. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input inventory data of each household's stockpiles into AI, which can then visualize the overall stockpiling situation of the region in real time.

[0079] The proposal unit can compare the disaster situation with the stockpiling status of each household and propose the optimal support routes and evacuation routes. For example, the proposal unit can grasp the situation in the disaster area in real time and propose the optimal evacuation route for households that need to evacuate. For example, the proposal unit can propose the optimal evacuation route considering the traffic situation in the disaster area and the location of evacuation shelters. The proposal unit can also propose the optimal support route for households that need relief supplies. For example, the proposal unit can analyze the damage situation in the disaster area and optimize the distribution route of relief supplies. This enables quick and appropriate support by proposing the optimal support routes and evacuation routes based on the disaster situation and the stockpiling status of each household. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input disaster situation data into AI, and the AI ​​can propose the optimal support routes and evacuation routes.

[0080] The management department can update the stock inventory using voice input. For example, the management department can allow users to update stock inventory information using voice input. The management department can update inventory information by voice input such as, "There are 2 bottles of water left." The management department can also use voice recognition technology to convert the user's voice input into text data and automatically update the inventory information. This allows users to easily manage stocks by updating inventory using voice input. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's voice input data into AI, and the AI ​​can update the inventory information.

[0081] The data collection 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 data collection unit may prioritize collecting information about their health status and special needs. If the user is relaxed, the data collection unit may prioritize collecting information about their age and lifestyle patterns. If the user is in a hurry, the data collection unit may quickly collect only the minimum necessary information. By adjusting the priority of information according to the user's emotions, more appropriate information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can then determine the priority of the information.

[0082] The data collection unit can analyze the lifestyle patterns of each household member and select the optimal timing for information collection. For example, the unit can analyze each household's morning routine and collect information after breakfast. For example, the unit can analyze each household's evening routine and collect information before bedtime. The unit can also analyze how each household spends their weekends and collect information at specific times on weekends. This allows for efficient information collection by selecting the timing of information collection based on each household's lifestyle patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each household's lifestyle pattern data into AI, which can then select the optimal timing for information collection.

[0083] The data collection unit can collect information by integrating data from IoT devices within the home. For example, the data collection unit can collect inventory information from a smart refrigerator and reflect it in a list of emergency supplies. For example, the data collection unit can collect health data from a smart scale and suggest emergency supplies based on the user's health status. The data collection unit can also collect data from a smart home security system and integrate information related to home safety. This allows for more accurate information collection by integrating data from IoT devices within the home. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from IoT devices within the home into an AI, which can then integrate and collect the information.

[0084] The data collection unit can estimate the user's emotions and adjust the level of detail of the information collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will only collect basic information and not detailed information. For example, if the user is relaxed, the data collection unit can collect detailed information and create a more accurate list of supplies. Also, if the user is in a hurry, the data collection unit can collect only the minimum necessary information and process it quickly. This allows for more appropriate information collection by adjusting the level of detail of the information 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 processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI, which can then adjust the level of detail of the information.

[0085] The collection unit can collect information while taking local event information into consideration. For example, the collection unit can consider the schedule of local disaster prevention drills and collect information on stockpiled supplies before and after those events. For example, the collection unit can consider the schedule of local festivals and events and collect information on stockpiled supplies related to those events. The collection unit can also consider school holidays in the area and collect information on stockpiled supplies for children at home. This allows for more appropriate information collection by taking local event information into consideration. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input local event information into AI, and the AI ​​can collect the information.

[0086] The data collection unit can supplement the information it collects by analyzing information from social media. For example, the data collection unit can collect local disaster prevention information from social media and reflect it in the stockpiling list. For example, the data collection unit can analyze user posts on social media and suggest stockpiling items based on local needs. The data collection unit can also collect disaster information from social media and update the stockpiling list in real time. This allows for more accurate information collection by supplementing information from social media. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input information from social media into AI, which can then analyze and collect the information.

[0087] The generation unit can estimate the user's emotions and adjust the contents of the emergency supply list it generates based on the estimated emotions. For example, if the user is feeling anxious, the generation unit can add emergency supplies to the list to provide a sense of security. For example, if the user is relaxed, the generation unit can generate a normal emergency supply list. Also, if the user is in a hurry, the generation unit can include only the minimum necessary emergency supplies in the list. In this way, by adjusting the contents of the emergency supply list according to the user's emotions, a more appropriate emergency supply list can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, and the generation AI can adjust the contents of the emergency supply list.

[0088] The generation unit can generate an optimal list of stockpiled items by referring to each household's past consumption data. For example, the generation unit can generate an optimal list of food stockpiled items based on each household's past food consumption data. For example, the generation unit can generate an optimal list of medicine stockpiled items based on each household's past medicine consumption data. The generation unit can also analyze each household's past consumption patterns and generate an optimal list of stockpiled items. This allows for the generation of a more appropriate list of stockpiled items by referring to past consumption data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input each household's past consumption data into AI, and the AI ​​can generate an optimal list of stockpiled items.

[0089] The generation unit can adjust the stockpile list to account for seasonal and weather variations. For example, in winter, the generation unit can add heating appliances and warm clothing to the stockpile list. In summer, for example, the generation unit can add cooling products and hydration supplies to the stockpile list. In the rainy season, the generation unit can also add tarpaulins and ponchos to the stockpile list. This allows for the generation of a more appropriate stockpile list by taking into account seasonal and weather variations. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input seasonal and weather data into the AI, which can then adjust the stockpile list.

[0090] The generation unit can estimate the user's emotions and determine the priority of the emergency supply list based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will prioritize including emergency supplies that provide a sense of security in the list. If the user is relaxed, the generation unit can generate an emergency supply list with normal priorities. Also, if the user is in a hurry, the generation unit can prioritize including the minimum necessary emergency supplies in the list. This allows for the generation of a more appropriate emergency supply list by determining the priority of the emergency supply list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI, which can then determine the priority of the emergency supply list.

[0091] The generation unit can generate a list of emergency supplies by referring to a regional disaster risk map. For example, in areas with a high earthquake risk, the generation unit can add earthquake-resistant goods to the list of emergency supplies. For example, in areas with a high flood risk, the generation unit can add waterproof sheets and pumps to the list of emergency supplies. Furthermore, in areas with a high tsunami risk, the generation unit can add evacuation equipment to the list of emergency supplies. This allows for the generation of a more appropriate list of emergency supplies by referring to a regional disaster risk map. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input regional disaster risk map data into AI, and the AI ​​can generate the list of emergency supplies.

[0092] The generation unit can adjust the stockpile list to take into account the needs of pets in the household. For example, the generation unit can add pet food and water to the stockpile list. For example, the generation unit can add pet medicines and first-aid kits to the stockpile list. The generation unit can also add pet evacuation equipment and cages to the stockpile list. This allows for the generation of a more appropriate stockpile list by taking pet needs into account. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input pet needs data into the AI, and the AI ​​can adjust the stockpile list.

[0093] The management department can estimate the user's emotions and adjust inventory management methods based on those estimated emotions. For example, if the user is feeling anxious, the management department can increase the frequency of inventory management to provide reassurance. If the user is relaxed, the management department can apply the normal inventory management method. Furthermore, if the user is in a hurry, the management department can perform only the minimum necessary inventory management. This allows for more appropriate inventory management by adjusting inventory management methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input user emotion data into a generative AI, which can then adjust inventory management methods.

[0094] The management department can analyze each household's consumption patterns and select the optimal inventory management method. For example, the management department can select the optimal inventory management method based on each household's past consumption data. For example, the management department can analyze each household's consumption patterns and adjust the frequency and method of inventory management. Furthermore, the management department can automate inventory management based on each household's consumption patterns. This enables efficient inventory management by selecting the optimal inventory management method based on each household's consumption patterns. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input each household's consumption pattern data into AI, and the AI ​​can select the optimal inventory management method.

[0095] The management department can integrate data from IoT devices within the home to manage inventory. For example, the management department can integrate inventory information from a smart refrigerator and manage inventory. For example, the management department can integrate health data from a smart scale and manage inventory based on health status. Furthermore, the management department can integrate data from a smart home security system and manage inventory related to home safety. This allows for more accurate inventory management by integrating data from IoT devices within the home. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input data from IoT devices within the home into an AI, which can then perform inventory management.

[0096] The management department can estimate the user's emotions and determine inventory management priorities based on those estimated emotions. For example, if the user is feeling anxious, the management department will prioritize stockpiling items that provide a sense of security. If the user is relaxed, the management department can manage inventory with normal priorities. If the user is in a hurry, the management department can prioritize stockpiling only the minimum necessary items. This allows for more appropriate inventory management by determining inventory management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input user emotion data into a generative AI, which can then determine inventory management priorities.

[0097] The management department can manage inventory while taking local event information into consideration. For example, the management department can consider the dates of local disaster prevention drills and manage inventory before and after them. For example, the management department can consider the schedules of local festivals and events and manage the inventory of stockpiled supplies related to those events. Furthermore, the management department can consider school holidays in the area and manage the inventory of stockpiled supplies for children at home. This allows for more appropriate inventory management by taking local event information into consideration. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input local event information into AI, and the AI ​​can perform inventory management.

[0098] The management department can supplement inventory management by analyzing information from social media. For example, the management department can collect local disaster prevention information from social media and reflect it in inventory management. For example, the management department can analyze user posts on social media and perform inventory management based on local needs. The management department can also collect disaster information from social media and update inventory management in real time. This allows for more accurate inventory management by supplementing information from social media. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input information from social media into AI, and the AI ​​can analyze the information to supplement inventory management.

[0099] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is feeling anxious, the visualization unit can provide a simple and highly visible display method. For example, if the user is relaxed, the visualization unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the visualization unit can provide a display method that gets straight to the point. By adjusting the display method of the visualization according to the user's emotions, more appropriate information can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into the generative AI, and the generative AI can adjust the display method of the visualization.

[0100] The visualization unit can integrate and display regional disaster risk maps. For example, the visualization unit can display a map of areas with high earthquake risk and optimize the placement of stockpiled supplies. For example, the visualization unit can display a map of areas with high flood risk and visualize evacuation routes. The visualization unit can also display a map of areas with high tsunami risk and visualize evacuation sites. By integrating regional disaster risk maps, more appropriate information can be displayed. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input regional disaster risk map data into AI, and the AI ​​can integrate and display the information.

[0101] The visualization unit can integrate and display data from IoT devices within the home. For example, the visualization unit can visualize inventory information from a smart refrigerator and display the status of stored goods. For example, the visualization unit can visualize health data from a smart scale and display suggestions for stored goods based on the user's health status. The visualization unit can also visualize data from a smart home security system and display information related to home safety. By integrating data from IoT devices within the home, more accurate information can be displayed. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data from IoT devices within the home into an AI, which can then integrate and display the information.

[0102] The visualization unit can estimate the user's emotions and determine the visualization priority based on the estimated emotions. For example, if the user is feeling anxious, the visualization unit will prioritize displaying information that provides a sense of security. If the user is relaxed, the visualization unit can display information with normal priority. Furthermore, if the user is in a hurry, the visualization unit can prioritize displaying only the essential information. This allows for more appropriate information display by determining the visualization priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, or not using AI. For example, the visualization unit can input user emotion data into a generative AI, which can then determine the visualization priority.

[0103] The visualization unit can display information while taking local event information into consideration. For example, the visualization unit can display the schedule of local disaster prevention drills and information on stockpiled supplies before and after those drills. For example, the visualization unit can display the schedule of local festivals and events and information on stockpiled supplies related to those events. The visualization unit can also display school holidays in the local area and information on stockpiled supplies for children at home. This allows for more appropriate information to be displayed by taking local event information into consideration. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input local event information into AI, and the AI ​​can display the information.

[0104] The visualization unit can analyze information from social media to supplement its display. For example, the visualization unit can display local disaster prevention information from social media and reflect it in the stockpiling list. For example, the visualization unit can analyze user posts on social media and display stockpiling items based on local needs. The visualization unit can also display disaster information from social media and update the stockpiling list in real time. This allows for more accurate information display by supplementing information from social media. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input information from social media into AI, which can then analyze the information and supplement the display.

[0105] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion unit can offer reassuring suggestions. If the user is relaxed, the suggestion unit can offer standard suggestions. If the user is in a hurry, the suggestion unit can offer only the essential suggestions. By adjusting the content of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 generative AI, which can then adjust the content of its suggestions.

[0106] The suggestion unit can make optimal suggestions by referring to each household's past evacuation behavior data. For example, the suggestion unit can suggest the optimal evacuation route based on each household's past evacuation behavior data. For example, the suggestion unit can analyze each household's past evacuation behavior data and suggest points to be aware of during evacuation. The suggestion unit can also refer to each household's past evacuation behavior data and suggest the preparation of evacuation equipment. This makes it possible to make more appropriate suggestions by referring to past evacuation behavior data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input each household's past evacuation behavior data into AI, and the AI ​​can make optimal suggestions.

[0107] The proposal department can integrate regional disaster risk maps and make recommendations. For example, in areas with a high earthquake risk, the proposal department can suggest preparing earthquake-resistant goods. In areas with a high flood risk, the proposal department can suggest preparing waterproof sheets and pumps. In areas with a high tsunami risk, the proposal department can also suggest preparing evacuation equipment. By integrating regional disaster risk maps, more appropriate recommendations can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input regional disaster risk map data into AI, and the AI ​​can make recommendations.

[0108] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion unit will prioritize suggestions that provide reassurance. If the user is relaxed, the suggestion unit can provide suggestions with normal priority. If the user is in a hurry, the suggestion unit can prioritize only the essential suggestions. This allows for more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 generative AI, which can then determine the priority of suggestions.

[0109] The proposal department can make suggestions while taking local event information into consideration. For example, the proposal department can consider the schedule of local disaster prevention drills and suggest stockpiling supplies before and after those events. For example, the proposal department can consider the schedule of local festivals and events and suggest stockpiling supplies related to those events. Furthermore, the proposal department can consider school holidays in the area and suggest stockpiling supplies for children at home. This allows for more appropriate suggestions by taking local event information into consideration. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input local event information into AI, and the AI ​​can make suggestions.

[0110] The proposal unit can analyze information from social media to supplement its proposals. For example, the proposal unit can analyze local disaster prevention information on social media and reflect it in its suggestions for emergency supplies. For example, the proposal unit can analyze user posts on social media and make suggestions for emergency supplies based on local needs. The proposal unit can also analyze disaster information on social media and update its suggestions for emergency supplies in real time. This allows for more appropriate suggestions by supplementing information from social media. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input information from social media into AI, which can then analyze the information and supplement its suggestions.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The data collection 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 data collection unit may prioritize collecting information about their health status and special needs. If the user is relaxed, the data collection unit may prioritize collecting information about their age and lifestyle patterns. If the user is in a hurry, the data collection unit may quickly collect only the minimum necessary information. By adjusting the priority of information according to the user's emotions, more appropriate information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can then determine the priority of the information.

[0113] The data collection unit can analyze the lifestyle patterns of each household member and select the optimal timing for information collection. For example, the unit can analyze each household's morning routine and collect information after breakfast. For example, the unit can analyze each household's evening routine and collect information before bedtime. The unit can also analyze how each household spends their weekends and collect information at specific times on weekends. This allows for efficient information collection by selecting the timing of information collection based on each household's lifestyle patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each household's lifestyle pattern data into AI, which can then select the optimal timing for information collection.

[0114] The data collection unit can collect information by integrating data from IoT devices within the home. For example, the data collection unit can collect inventory information from a smart refrigerator and reflect it in a list of emergency supplies. For example, the data collection unit can collect health data from a smart scale and suggest emergency supplies based on the user's health status. The data collection unit can also collect data from a smart home security system and integrate information related to home safety. This allows for more accurate information collection by integrating data from IoT devices within the home. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from IoT devices within the home into an AI, which can then integrate and collect the information.

[0115] The data collection unit can estimate the user's emotions and adjust the level of detail of the information collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will only collect basic information and not detailed information. For example, if the user is relaxed, the data collection unit can collect detailed information and create a more accurate list of supplies. Also, if the user is in a hurry, the data collection unit can collect only the minimum necessary information and process it quickly. This allows for more appropriate information collection by adjusting the level of detail of the information 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 processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI, which can then adjust the level of detail of the information.

[0116] The collection unit can collect information while taking local event information into consideration. For example, the collection unit can consider the schedule of local disaster prevention drills and collect information on stockpiled supplies before and after those events. For example, the collection unit can consider the schedule of local festivals and events and collect information on stockpiled supplies related to those events. The collection unit can also consider school holidays in the area and collect information on stockpiled supplies for children at home. This allows for more appropriate information collection by taking local event information into consideration. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input local event information into AI, and the AI ​​can collect the information.

[0117] The data collection unit can supplement the information it collects by analyzing information from social media. For example, the data collection unit can collect local disaster prevention information from social media and reflect it in the stockpiling list. For example, the data collection unit can analyze user posts on social media and suggest stockpiling items based on local needs. The data collection unit can also collect disaster information from social media and update the stockpiling list in real time. This allows for more accurate information collection by supplementing information from social media. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input information from social media into AI, which can then analyze and collect the information.

[0118] The generation unit can estimate the user's emotions and adjust the contents of the emergency supply list it generates based on the estimated emotions. For example, if the user is feeling anxious, the generation unit can add emergency supplies to the list to provide a sense of security. For example, if the user is relaxed, the generation unit can generate a normal emergency supply list. Also, if the user is in a hurry, the generation unit can include only the minimum necessary emergency supplies in the list. In this way, by adjusting the contents of the emergency supply list according to the user's emotions, a more appropriate emergency supply list can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, and the generation AI can adjust the contents of the emergency supply list.

[0119] The generation unit can generate an optimal list of stockpiled items by referring to each household's past consumption data. For example, the generation unit can generate an optimal list of food stockpiled items based on each household's past food consumption data. For example, the generation unit can generate an optimal list of medicine stockpiled items based on each household's past medicine consumption data. The generation unit can also analyze each household's past consumption patterns and generate an optimal list of stockpiled items. This allows for the generation of a more appropriate list of stockpiled items by referring to past consumption data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input each household's past consumption data into AI, and the AI ​​can generate an optimal list of stockpiled items.

[0120] The generation unit can adjust the stockpile list to account for seasonal and weather variations. For example, in winter, the generation unit can add heating appliances and warm clothing to the stockpile list. In summer, for example, the generation unit can add cooling products and hydration supplies to the stockpile list. In the rainy season, the generation unit can also add tarpaulins and ponchos to the stockpile list. This allows for the generation of a more appropriate stockpile list by taking into account seasonal and weather variations. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input seasonal and weather data into the AI, which can then adjust the stockpile list.

[0121] The generation unit can estimate the user's emotions and determine the priority of the emergency supply list based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will prioritize including emergency supplies that provide a sense of security in the list. If the user is relaxed, the generation unit can generate an emergency supply list with normal priorities. Also, if the user is in a hurry, the generation unit can prioritize including the minimum necessary emergency supplies in the list. This allows for the generation of a more appropriate emergency supply list by determining the priority of the emergency supply list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI, which can then determine the priority of the emergency supply list.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects information about each household member. For example, the data collection unit can collect information such as the age, gender, health status, and special needs (allergies, chronic illnesses, etc.) of each household member. The data collection unit can collect information, for example, through questionnaire surveys. The data collection unit can also use sensors to monitor the health status of each household member in real time and collect data. Step 2: The generation unit generates a list of emergency supplies based on the information collected by the collection unit. For example, the generation unit can automatically generate an optimal list of emergency supplies for each household based on the collected information. For example, the generation unit can use AI to generate an emergency supply list tailored to the age and health status of each household member. The generation unit can also analyze local disaster risks and past disaster experiences to suggest emergency supplies that are appropriate for the local characteristics. Step 3: The management department manages the stockpiling inventory. The management department can, for example, monitor the stockpiling inventory status in real time and update the inventory information. The management department can, for example, update the stockpiling inventory using voice input. The management department can also manage expiration dates and suggest replenishment or replacement at the appropriate time. Step 4: The visualization unit visualizes the stockpiling status of the entire region. The visualization unit can, for example, display the stockpiling status of the entire region in real time and adjust for surpluses or shortages. The visualization unit can, for example, visually display the stockpiling status of the entire region using graphs or maps. Step 5: The proposal department proposes the optimal support routes and evacuation routes in the event of a disaster. For example, the proposal department can compare the disaster situation with the stockpiling status of each household and propose the optimal support routes and evacuation routes. For example, the proposal department can grasp the situation in the disaster area in real time and propose the optimal evacuation route for households that need to evacuate. The proposal department can also propose the optimal support route for households that need relief supplies.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, generation unit, management unit, visualization unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information on the members of each household using the sensors and survey functions of the smart device 14. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimal list of stockpiled items based on the collected information. The management unit is implemented, for example, by the control unit 46A of the smart device 14, and updates the stockpiled item inventory and manages expiration dates using voice input. The visualization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and displays the stockpiling status of the entire region in real time. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes the optimal support route and evacuation route in the event of a disaster. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, generation unit, management unit, visualization unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information on the members of each household using the sensors and survey functions of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimal list of stockpiled items based on the collected information. The management unit is implemented, for example, by the control unit 46A of the smart glasses 214, and updates the stockpiled item inventory and manages expiration dates using voice input. The visualization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and displays the stockpiling status of the entire region in real time. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes the optimal support routes and evacuation routes in the event of a disaster. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, generation unit, management unit, visualization unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information on the members of each household using the sensors and survey functions of the headset terminal 314. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal list of stockpiled items based on the collected information. The management unit is implemented by, for example, the control unit 46A of the headset terminal 314 and updates the stockpiled item inventory and manages expiration dates using voice input. The visualization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and displays the stockpiling status of the entire region in real time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal support route and evacuation route in the event of a disaster. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, generation unit, management unit, visualization unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information on the members of each household using the sensors and survey functions of the robot 414. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal list of stockpiled items based on the collected information. The management unit is implemented by, for example, the control unit 46A of the robot 414 and updates the stockpiled item inventory and manages expiration dates using voice input. The visualization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and displays the stockpiling status of the entire region in real time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal support routes and evacuation routes in the event of a disaster. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A collection department that collects information on the members of each household, A generation unit generates a list of stockpiled items based on the information collected by the collection unit, The management department manages the inventory of stockpiled supplies, A visualization unit that visualizes the stockpiling status of the entire region, It includes a proposal unit that suggests the optimal support routes and evacuation routes in the event of a disaster. A system characterized by the following features. (Note 2) The aforementioned management department, We manage expiration dates and suggest replenishment or replacement at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We analyze local disaster risks and past disaster experiences to propose emergency supplies tailored to regional characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned visualization unit, Visualize the overall stockpiling situation in the region in real time and adjust for surpluses and shortages. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We will compare the extent of the damage with the stockpiling situation in each household and propose the most suitable support routes and evacuation routes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Update stockpiled supplies inventory using voice input. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is 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 8) The aforementioned collection unit is Analyze the lifestyle patterns of each household member and select the optimal timing for information gathering. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Integrate data from IoT devices within the home to collect information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the level of detail of the information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Gather information while taking local event information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We analyze information from social media to supplement the information we collect. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the contents of the emergency supplies list generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system generates an optimal list of emergency supplies by referencing each household's past consumption data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is Adjust the list of emergency supplies to take into account seasonal and weather variations. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the user's emotions and prioritizes the emergency supplies list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Generate a list of emergency supplies by referring to the local disaster risk map. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is Adjust your emergency supplies list to take into account the needs of your pets at home. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, It estimates user sentiment and adjusts inventory management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, Analyze each household's consumption patterns to select the optimal inventory management method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, Integrate data from IoT devices within the home for inventory management. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, The system estimates user sentiment and prioritizes inventory management based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, Inventory management is carried out while taking local event information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, Analyze information from social media to supplement inventory management. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned visualization unit, Display integrated regional disaster risk maps. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned visualization unit, Integrate and display data from IoT devices within the home. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned visualization unit, It estimates the user's emotions and determines the visualization priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned visualization unit, Display information while taking local event information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned visualization unit, Analyze information from social media to enhance the display. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the content of the suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, We make optimal suggestions by referring to each household's past evacuation behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, We will integrate regional disaster risk maps and make proposals. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, We will make proposals while taking into account local event information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, Analyze information from social media to complement your proposals. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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. A collection department that collects information on the members of each household, A generation unit generates a list of stockpiled items based on the information collected by the collection unit, The management department manages the inventory of stockpiled supplies, A visualization unit that visualizes the stockpiling status of the entire region, It includes a proposal unit that suggests the optimal support routes and evacuation routes in the event of a disaster. A system characterized by the following features.

2. The aforementioned management department, We manage expiration dates and suggest replenishment or replacement at the appropriate time. The system according to feature 1.

3. The aforementioned collection unit is We analyze local disaster risks and past disaster experiences to propose stockpiles tailored to regional characteristics. The system according to feature 1.

4. The aforementioned visualization unit, Visualize the overall stockpiling situation in the region in real time and adjust for surpluses and shortages. The system according to feature 1.

5. The aforementioned proposal section is, We will compare the extent of the damage with the stockpiling situation in each household and propose the most suitable support routes and evacuation routes. The system according to feature 1.

6. The aforementioned management department, Update stockpiled supplies inventory using voice input. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the lifestyle patterns of each household member and select the optimal timing for information gathering. The system according to feature 1.

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