system

Abstract

The system according to the embodiment aims to select the most suitable disaster prevention goods based on user information and notify the user when it is time to replace them. [Solution] A system according to an embodiment includes a collection unit, a selection unit, and a notification unit. The collection unit collects user information. The selection unit analyzes the information collected by the collection unit and selects disaster prevention goods. The notification unit notifies the user of the replacement time for the disaster prevention goods selected by the selection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately select the most suitable disaster prevention goods for each user or manage the timing of replacement, so there is room for improvement.

[0005] The system according to the embodiment aims to select the most suitable disaster prevention goods based on user information and notify the user when it is time to replace them. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a selection unit, and a notification unit. The collection unit collects user information. The selection unit analyzes the information collected by the collection unit and selects disaster prevention goods. The notification unit notifies the user of the replacement timing of the disaster prevention goods selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can select the most suitable disaster prevention goods based on the user's information and notify the user when it is time to replace them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A disaster preparedness supply management system according to an embodiment of the present invention uses AI to select necessary disaster preparedness supplies tailored to individual circumstances, notify the user of the optimal replacement time, manage storage locations, and propose initial response methods in the event of a disaster. In this disaster preparedness supply management system, users input information about their home and living area, and the AI ​​analyzes that information to select disaster preparedness supplies tailored to their individual circumstances. The AI ​​then calculates the optimal replacement time for disaster preparedness supplies and notifies the user. It also manages storage locations and informs the user of where the supplies are stored. In the event of a disaster, the AI ​​proposes a response method based on the information about the home and living area. Furthermore, the registered information is shared with companies and local governments for use in confirming the safety of users in the event of an emergency. For example, a user inputs information about their home and living area, such as family composition, type of residence, and geographical information about the living area. This information is then input into the AI. The AI ​​then analyzes the input information and selects the necessary disaster preparedness supplies tailored to the individual situation. For example, if the family has infants, the AI ​​suggests infant-specific disaster preparedness supplies. Furthermore, if the residence is a high-rise apartment, the AI ​​suggests specific disaster preparedness supplies, such as evacuation ropes. Furthermore, the AI ​​calculates the optimal time to replace disaster preparedness supplies and notifies the user. For example, if a preserved food is nearing its expiration date, the AI ​​will notify the user and encourage them to replace it. It also manages storage locations and informs the user where the supplies are stored. In the event of a disaster, the AI ​​will suggest a response based on individual information about the home and surrounding area. For example, in the event of an earthquake, it will suggest evacuation routes and evacuation locations. In the event of a fire, it will suggest how to use a fire extinguisher and how to evacuate. Furthermore, the registered information can be shared with companies and local governments and used to check the safety of users in the event of an emergency. For example, in the event of a disaster, the system can automatically send the user's safety information to the company or local government, supporting a rapid response. This system is expected to enable disaster preparedness measures tailored to individual situations and enable a swift and appropriate initial response when a disaster occurs. As a result, the disaster preparedness supply management system can provide disaster preparedness measures tailored to individual situations and enable a swift and appropriate initial response when a disaster occurs.

[0029] A disaster prevention goods management system according to an embodiment includes a collection unit, a selection unit, and a notification unit. The collection unit collects user information. The user information includes, but is not limited to, personal information, location information, and past behavioral history. The collection unit collects, for example, information about the user's residence and living area. For example, the collection unit may collect information such as address, nearby evacuation shelters, and disaster risks. The selection unit analyzes the information collected by the collection unit and selects disaster prevention goods. For example, data mining, statistical analysis, machine learning algorithms, and the like may be used for the analysis, but are not limited to these examples. The selection unit selects necessary disaster prevention goods based on, for example, family composition and health status. For example, if there are infants in the family, the selection unit may suggest disaster prevention goods for infants. Furthermore, if the residence is a high-rise apartment building, the selection unit may suggest specific disaster prevention goods such as evacuation ropes. The notification unit calculates when to replace the disaster prevention goods selected by the selection unit and notifies the user. For example, expiration dates, deterioration levels, and seasonal changes may be taken into consideration in the calculation, but are not limited to these examples. For example, the notification unit may notify the user when the expiration date of preserved food is approaching, and prompt the user to replace the food. The notification unit may also estimate the user's emotions and adjust the timing of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification may be sent during a time when the user is relaxed. As a result, the disaster preparedness goods management system according to the embodiment collects and analyzes user information to select disaster preparedness goods and notify the user when it is time to replace the goods, enabling disaster preparedness measures tailored to individual situations.

[0030] The system includes a unit for managing storage locations of disaster preparedness supplies. Storage locations include, but are not limited to, specific locations in a home, a warehouse, or a vehicle. The management unit, for example, manages storage locations of disaster preparedness supplies and informs the user of where the supplies are stored. For example, the management unit may register the storage locations in a database so that the user can search for them when necessary. The management unit may also periodically update storage location information to maintain the latest information. For example, the management unit may update the information when the user moves the disaster preparedness supplies. This allows for management of storage locations of disaster preparedness supplies and informing the user of where they are stored, enabling a prompt response. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit may input storage location information into AI and have the AI ​​manage the storage locations.

[0031] The system includes a unit for proposing a response method in the event of a disaster. The proposal unit proposes a response method based on individual information, for example, based on information about the user's residence and living area when a disaster occurs. For example, the proposal unit proposes evacuation routes and evacuation locations when an earthquake occurs. The proposal unit can also propose how to use a fire extinguisher and evacuation methods when a fire occurs. The proposal unit, for example, analyzes information about the user's residence and living area and proposes an optimal response method. For example, the proposal unit can propose an evacuation route taking into account the type of residence of the user and surrounding geographic information. This enables a prompt and appropriate initial response by proposing a response method based on individual information, based on information about the user's residence and living area when a disaster occurs. Some or all of the above-mentioned processing by the proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the proposal unit can input information about the occurrence of a disaster into AI and have the AI ​​execute the response method proposal.

[0032] The device includes a unit that shares the registered information with a company or a local government. The sharing unit, for example, shares the registered information with a company or a local government and uses it to check the user's safety in the event of an emergency. For example, the sharing unit automatically sends the user's safety information to a company or a local government in the event of a disaster, supporting a rapid response. The sharing unit, for example, registers the user's personal information and emergency contact information in a database and can share them when necessary. The sharing unit can also share the user's health information and disaster response history. This allows the registered information to be shared with a company or a local government and used to check the user's safety in the event of an emergency, enabling a rapid response. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without AI. For example, the sharing unit may input the registered information into AI and have the AI ​​share the information.

[0033] The collection unit can collect information about the user's home or living area. The collection unit, for example, collects information about the user's home or living area. For example, the collection unit can collect information such as the user's address, nearby evacuation shelters, and disaster risks. The collection unit can collect necessary information based on the user's type of residence and family composition. For example, if the user lives in a high-rise apartment, the collection unit can collect information taking into account the need for evacuation ropes. By collecting information about the user's home or living area, disaster prevention measures tailored to individual situations are possible. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information about the user's home or living area into AI and have the AI ​​collect the information.

[0034] The selection unit can analyze the collected information and select disaster prevention supplies tailored to each individual situation. The selection unit, for example, analyzes the collected information and selects disaster prevention supplies tailored to each individual situation. For example, data mining, statistical analysis, machine learning algorithms, and the like may be used for the analysis, but are not limited to these examples. The selection unit selects necessary disaster prevention supplies based on, for example, family composition and health status. For example, if there are infants in the family, disaster prevention supplies for infants may be suggested. Furthermore, if the residence is a high-rise apartment, specific disaster prevention supplies such as evacuation ropes may be suggested. This enables appropriate disaster prevention measures by analyzing the collected information and selecting disaster prevention supplies tailored to each individual situation. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit may input the collected information into AI and have the AI ​​select the disaster prevention supplies.

[0035] The notification unit can calculate when to replace disaster preparedness supplies and notify the user. For example, the notification unit calculates when to replace disaster preparedness supplies and notifies the user. The calculation takes into account, for example, expiration dates, deterioration status, seasonal changes, etc., but is not limited to these examples. For example, the notification unit can notify the user when the expiration date of preserved food is approaching and encourage the user to replace the supplies. The notification unit can also estimate the user's emotions and adjust the timing of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification can be sent during a time when the user is relaxed. This allows the optimal replacement time for disaster preparedness supplies to be calculated and notified to the user, enabling replacement at the appropriate time. Some or all of the above-mentioned processing by the notification unit may be performed using, for example, AI, or without AI. For example, the notification unit can input the replacement time for disaster preparedness supplies into AI and have the AI ​​execute the notification timing.

[0036] The management unit manages the storage locations of disaster preparedness supplies and can inform the user where they are stored. For example, the management unit manages the storage locations of disaster preparedness supplies and informs the user where they are stored. Storage locations include, but are not limited to, specific locations in the home, warehouses, and inside cars. For example, the management unit registers the storage locations in a database so that the user can search for them when needed. The management unit can also periodically update the storage location information to maintain the latest information. For example, the management unit updates the information when the user moves the disaster preparedness supplies. This allows for the management of the storage locations of disaster preparedness supplies and the user to be informed of where they are stored, enabling a prompt response. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input the storage location information into AI and have the AI ​​manage the storage locations.

[0037] The suggestion unit can propose a response method based on individual information about the user's home or living area when a disaster occurs. For example, the suggestion unit proposes a response method based on individual information about the user's home or living area when a disaster occurs. For example, the suggestion unit proposes evacuation routes and evacuation locations when an earthquake occurs. Furthermore, the suggestion unit can also propose how to use a fire extinguisher and how to evacuate when a fire occurs. For example, the suggestion unit analyzes information about the user's home or living area to propose an optimal response method. For example, the suggestion unit can propose an evacuation route taking into account the type of the user's home and surrounding geographic information. This enables a prompt and appropriate initial response by proposing a response method based on individual information about the user's home or living area when a disaster occurs. Some or all of the above-described processing by the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit can input disaster information into an AI and have the AI ​​execute the response method proposal.

[0038] The sharing unit can share the registered information with a company or local government and use it to check the safety of users in the event of an emergency. The sharing unit, for example, shares the registered information with a company or local government and use it to check the safety of users in the event of an emergency. For example, the sharing unit can automatically send information about the user's safety to a company or local government in the event of a disaster, supporting a rapid response. The sharing unit can, for example, register the user's personal information and emergency contact information in a database and share it when necessary. The sharing unit can also share the user's health information and disaster response history. This allows the registered information to be shared with a company or local government and used to check the safety of users in the event of an emergency, enabling a rapid response. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without AI. For example, the sharing unit can input the registered information into AI and have the AI ​​share the information.

[0039] The collection unit can analyze the user's past information provision history and select the optimal collection method. The collection unit, for example, analyzes the user's past information provision history and selects the optimal collection method. For example, the collection unit determines the optimal collection frequency based on the frequency of information provided by the user in the past. The collection unit can also select the optimal collection method based on the type of information provided by the user in the past. Furthermore, the collection unit can select the optimal collection method based on the accuracy of the information provided by the user in the past. In this way, the optimal collection method can be selected by analyzing the user's past information provision history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past information provision history into AI and have the AI ​​execute the optimal collection method.

[0040] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting information. For example, the collection unit performs filtering based on the user's current living situation and areas of interest when collecting information. For example, the collection unit prioritizes collecting highly relevant information based on the user's current living situation. The collection unit can also filter and collect relevant information based on the user's areas of interest. Furthermore, the collection unit can eliminate and collect unnecessary information based on the user's living situation and areas of interest. In this way, highly relevant information can be collected by filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's living situation and areas of interest into AI and have the AI ​​perform information filtering.

[0041] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting nearby disaster prevention information based on the user's current location. The collection unit can also collect region-specific disaster prevention information based on the user's geographical location information. Furthermore, the collection unit can also collect highly relevant information by taking into account the user's movement history. This enables more appropriate information collection by preferentially collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect information.

[0042] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media posts and collect disaster prevention information of interest. The collection unit can also analyze posts from the user's followers and friends and collect related information. Furthermore, the collection unit can collect trend information from the user's social media activities. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​collect information.

[0043] The selection unit can select the optimal selection method by referring to the user's past disaster prevention product usage history during selection. For example, the selection unit selects the optimal selection method by referring to the user's past disaster prevention product usage history during selection. For example, the selection unit suggests the optimal selection method based on the user's past history of disaster prevention product use. The selection unit can also select effective disaster prevention products from the user's past usage history. Furthermore, the selection unit can analyze the user's past usage history and provide optimal selection criteria. This makes it possible to select the optimal selection method by referring to the user's past disaster prevention product usage history. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past usage history data into AI and have the AI ​​select the selection method.

[0044] The selection unit can customize the selection of disaster prevention goods based on the user's current living situation at the time of selection. The selection unit, for example, customizes the selection of disaster prevention goods based on the user's current living situation at the time of selection. For example, the selection unit selects necessary disaster prevention goods based on the user's family composition. The selection unit can also select specific disaster prevention goods based on the user's type of residence. Furthermore, the selection unit can select optimal disaster prevention goods based on the user's living situation. This enables more appropriate disaster prevention goods to be selected by customizing the selection of disaster prevention goods based on the user's current living situation. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's living situation data into AI and have the AI ​​customize the selection.

[0045] The selection unit may analyze the user's social media activity to select disaster preparedness supplies. For example, the selection unit may analyze the user's social media activity to select disaster preparedness supplies. For example, the selection unit may analyze the user's social media posts to select disaster preparedness supplies of interest. The selection unit may also analyze posts from the user's followers and friends to select related disaster preparedness supplies. Furthermore, the selection unit may select trending disaster preparedness supplies from the user's social media activity. In this way, disaster preparedness supplies of interest can be selected by analyzing the user's social media activity. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may input the user's social media data into AI and have the AI ​​select the disaster preparedness supplies.

[0046] The notification unit can select the optimal notification method by referring to the user's past notification history when notifying the user. For example, the notification unit can select the optimal notification method by referring to the user's past notification history when notifying the user. For example, the notification unit can suggest the optimal notification method based on the history of notifications the user has received in the past. The notification unit can also select an effective notification method from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and provide optimal notification criteria. This allows the optimal notification method to be selected by referring to the user's past notification history. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's past notification history data into AI and have the AI ​​select the notification method.

[0047] The notification unit can customize the notification content based on the user's current living situation at the time of notification. The notification unit, for example, customizes the notification content based on the user's current living situation at the time of notification. For example, the notification unit customizes the necessary notification content based on the user's family composition. The notification unit can also customize specific notification content based on the user's type of residence. Furthermore, the notification unit can customize optimal notification content based on the user's living situation. This enables more appropriate notifications by customizing the notification content based on the user's current living situation. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's living situation data into AI and have the AI ​​customize the notification content.

[0048] The notification unit can select the optimal notification method by taking into account the user's geographical location information when providing a notification. For example, the notification unit selects the optimal notification method by taking into account the user's geographical location information when providing a notification. For example, the notification unit prioritizes region-specific notifications based on the user's current location. The notification unit can also select the optimal notification method based on the user's geographical location information. Furthermore, the notification unit can provide highly relevant notifications by taking into account the user's movement history. This enables more appropriate notifications by selecting the optimal notification method by taking into account the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's geographical location information into AI and have the AI ​​select the notification method.

[0049] The notification unit can adjust the notification content by analyzing the user's social media activity at the time of notification. For example, the notification unit can adjust the notification content by analyzing the user's social media activity at the time of notification. For example, the notification unit can analyze the user's social media posts and provide notification content of interest. The notification unit can also analyze posts from the user's followers and friends and provide related notification content. Furthermore, the notification unit can provide trending notification content from the user's social media activity. In this way, notification content of interest can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the notification unit can be performed using, or without, AI, for example. For example, the notification unit can input the user's social media data into AI and have the AI ​​adjust the notification content.

[0050] The management unit can select the optimal management method by referring to the user's past storage location history during management. For example, the management unit selects the optimal management method by referring to the user's past storage location history during management. For example, the management unit suggests the optimal management method based on the history of storage locations used by the user in the past. The management unit can also select an effective management method from the user's past storage location history. Furthermore, the management unit can analyze the user's past storage location history and provide optimal management standards. This makes it possible to select the optimal management method by referring to the user's past storage location history. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's past storage location history data into AI and have the AI ​​select the management method.

[0051] The management unit can customize the management of storage locations based on the user's current living situation during management. The management unit, for example, customizes the management of storage locations based on the user's current living situation during management. For example, the management unit customizes the necessary storage locations based on the user's family composition. The management unit can also customize specific storage locations based on the user's type of residence. Furthermore, the management unit can customize the optimal storage location based on the user's living situation. This enables more appropriate management by customizing the management of storage locations based on the user's current living situation. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's living situation data into AI and have the AI ​​customize the management.

[0052] The management unit can select the optimal storage location during management by taking into account the user's geographical location information. For example, the management unit selects the optimal storage location by taking into account the user's geographical location information during management. For example, the management unit prioritizes management of storage locations specific to a region based on the user's current location. The management unit can also select the optimal storage location based on the user's geographical location information. Furthermore, the management unit can select a highly relevant storage location by taking into account the user's movement history. This enables more appropriate management by selecting the optimal storage location by taking into account the user's geographical location information. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit can input the user's geographical location information into AI and have the AI ​​select the storage location.

[0053] The management unit may, during management, analyze the user's social media activity and manage the storage locations. For example, during management, the management unit may analyze the user's social media activity and manage the storage locations. For example, the management unit may analyze the user's social media posts and manage storage locations of interest. The management unit may also analyze posts from the user's followers and friends and manage related storage locations. Furthermore, the management unit may manage trending storage locations from the user's social media activity. In this way, the storage locations of interest can be managed by analyzing the user's social media activity. Some or all of the above-described processing in the management unit may be performed using, or without, AI, for example. For example, the management unit may input the user's social media data into AI and have the AI ​​manage the storage locations.

[0054] The suggestion unit can select the optimal suggestion method by referring to the user's past response history when making a suggestion. For example, the suggestion unit selects the optimal suggestion method by referring to the user's past response history when making a suggestion. For example, the suggestion unit suggests the optimal suggestion method based on the history of response methods previously performed by the user. The suggestion unit can also select an effective suggestion method from the user's past response history. Furthermore, the suggestion unit can analyze the user's past response history and provide optimal suggestion criteria. This makes it possible to select the optimal suggestion method by referring to the user's past response history. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past response history data into AI and have the AI ​​select the suggestion method.

[0055] The suggestion unit can customize the proposed response method based on the user's current living situation when making the suggestion. The suggestion unit, for example, customizes the proposed response method based on the user's current living situation when making the suggestion. For example, the suggestion unit suggests a necessary response method based on the user's family composition. The suggestion unit can also suggest a specific response method based on the user's type of residence. Furthermore, the suggestion unit can also suggest an optimal response method based on the user's living situation. This enables a more appropriate response by customizing the proposed response method based on the user's current living situation. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's living situation data into AI and have the AI ​​customize the proposal.

[0056] The suggestion unit can propose an optimal response method by taking into account the user's geographical location information when making a suggestion. For example, the suggestion unit proposes an optimal response method by taking into account the user's geographical location information when making a suggestion. For example, the suggestion unit proposes a region-specific response method based on the user's current location. The suggestion unit can also propose an optimal response method based on the user's geographical location information. Furthermore, the suggestion unit can also propose a highly relevant response method by taking into account the user's movement history. This enables a more appropriate response by proposing an optimal response method by taking into account the user's geographical location information. Some or all of the above-described processing by the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit can input the user's geographical location information into an AI and have the AI ​​execute the response method proposal.

[0057] The suggestion unit, at the time of making a suggestion, can analyze the user's social media activity and suggest a response method. For example, at the time of making a suggestion, the suggestion unit analyzes the user's social media activity and suggests a response method of interest. For example, the suggestion unit analyzes the user's social media posts and suggests a response method of interest. The suggestion unit can also analyze posts from the user's followers and friends and suggest related response methods. Furthermore, the suggestion unit can suggest trending response methods from the user's social media activity. In this way, response methods of interest can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's social media data into AI and have the AI ​​execute the response method suggestion.

[0058] The sharing unit can select the optimal sharing method by referring to the user's past sharing history when sharing. For example, the sharing unit selects the optimal sharing method by referring to the user's past sharing history when sharing. For example, the sharing unit suggests the optimal sharing method based on the history of information the user has previously shared. The sharing unit can also select an effective sharing method from the user's past sharing history. Furthermore, the sharing unit can analyze the user's past sharing history and provide optimal sharing criteria. In this way, the optimal sharing method can be selected by referring to the user's past sharing history. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's past sharing history data into AI and have the AI ​​select the sharing method.

[0059] The sharing unit can customize information sharing based on the user's current living situation at the time of sharing. The sharing unit customizes information sharing based on the user's current living situation, for example, at the time of sharing. For example, the sharing unit customizes necessary information sharing based on the user's family composition. The sharing unit can also customize specific information sharing based on the user's type of residence. Furthermore, the sharing unit can customize optimal information sharing based on the user's living situation. This enables more appropriate information sharing by customizing information sharing based on the user's current living situation. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's living situation data into AI and have the AI ​​customize the information sharing.

[0060] The sharing unit can select the optimal sharing method by taking into account the user's geographical location information when sharing. For example, the sharing unit selects the optimal sharing method by taking into account the user's geographical location information when sharing. For example, the sharing unit prioritizes sharing of region-specific information based on the user's current location. The sharing unit can also select the optimal sharing method based on the user's geographical location information. Furthermore, the sharing unit can share highly relevant information by taking into account the user's movement history. This enables more appropriate information sharing by selecting the optimal sharing method by taking into account the user's geographical location information. Some or all of the above-described processing in the sharing unit may be performed using, or without, AI, for example. For example, the sharing unit can input the user's geographical location information into AI and have the AI ​​select the sharing method.

[0061] The sharing unit can adjust the content of information sharing by analyzing the user's social media activity at the time of sharing. For example, the sharing unit can adjust the content of information sharing by analyzing the user's social media activity at the time of sharing. For example, the sharing unit can analyze the user's social media posts and share information of interest. The sharing unit can also analyze posts from the user's followers and friends and share related information. Furthermore, the sharing unit can share trending information from the user's social media activity. In this way, information of interest can be shared by analyzing the user's social media activity. Some or all of the above-mentioned processing in the sharing unit can be performed using, for example, AI, or can be performed without using AI. For example, the sharing unit can input the user's social media data into AI and have the AI ​​execute the content of information sharing.

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

[0063] The disaster prevention goods management system can further include a health management unit that monitors the user's health condition. By collecting and analyzing the user's health data, the health management unit can select disaster prevention goods and suggest countermeasures based on the user's health condition. For example, if the user has high blood pressure, it can suggest a blood pressure monitor and stockpiles of medication. Also, if the user has allergies, it can suggest emergency food suitable for allergies. Furthermore, the health management unit can suggest precautions to take during evacuation and the preparation of necessary medical equipment based on the user's health condition. This makes it possible to take disaster prevention measures based on the user's health condition, and more appropriate responses can be expected.

[0064] The disaster prevention goods management system can further include a pet management unit that manages information about the user's pets. The pet management unit can collect and analyze information such as the type and number of the user's pets and their health conditions to suggest disaster prevention goods and response methods necessary for pets. For example, if the user has a dog, it can suggest emergency food and evacuation cages for the pet. It can also suggest necessary medicines and medical supplies based on the pet's health condition. Furthermore, the pet management unit can suggest evacuation methods and evacuation locations for pets in the event of a disaster. This enables disaster prevention measures for the entire family, including pets, and allows for more peace of mind when preparing for disasters.

[0065] The disaster prevention goods management system can further include a vehicle management unit that manages the user's vehicle information. The vehicle management unit can propose disaster prevention measures using the vehicle by collecting and analyzing the type, condition, and location information of the user's vehicle. For example, it can monitor the vehicle's fuel status and propose stockpiles of fuel needed in the event of a disaster. It can also propose optimal evacuation routes and evacuation locations based on the vehicle's location information. Furthermore, the vehicle management unit can propose disaster prevention goods to be stockpiled in the vehicle and evacuation methods using the vehicle. This makes it possible to implement disaster prevention measures using the vehicle, and can expect quick and safe evacuation.

[0066] The disaster prevention goods management system can also be equipped with a community management unit that manages user community information. The community management unit can propose disaster prevention measures for the entire community by collecting and analyzing information about the local community to which the user belongs. For example, it can provide information about local evacuation shelters and evacuation routes and propose evacuation plans for the entire community. It can also provide information about local disaster prevention drills and disaster prevention events and encourage participation. Furthermore, the community management unit can check the safety of local residents and coordinate support activities in the event of a disaster. This will strengthen disaster prevention measures for the entire local community, and is expected to enable a quick and effective response in the event of a disaster.

[0067] The disaster prevention goods management system can further include a travel management unit that manages the user's travel information. The travel management unit can suggest disaster prevention measures during the trip by collecting and analyzing information on the user's travel plans and travel destinations. For example, it can evaluate the disaster risk at the travel destination and suggest necessary disaster prevention goods. It can also provide information on evacuation sites and evacuation routes at the travel destination and suggest how to respond in an emergency. Furthermore, the travel management unit can suggest health management and safety measures during the trip. This strengthens disaster prevention measures during the trip, allowing the user to enjoy the trip with peace of mind.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects user information. The user information includes, but is not limited to, personal information, location information, and past behavioral history. The collection unit collects, for example, information about the user's residence and living area. For example, information such as address, nearby evacuation shelters, and disaster risks can be collected. Step 2: The selection unit analyzes the information collected by the collection unit and selects disaster prevention goods. Examples of the analysis that may be used include, but are not limited to, data mining, statistical analysis, and machine learning algorithms. The selection unit selects necessary disaster prevention goods based on, for example, family structure and health status. For example, if there are infants in the family, the selection unit may suggest disaster prevention goods for infants. Furthermore, if the residence is a high-rise apartment building, the selection unit may suggest specific disaster prevention goods such as evacuation ropes. Step 3: The notification unit calculates when to replace the disaster prevention goods selected by the selection unit and notifies the user. The calculation takes into consideration, for example, expiration dates, deterioration status, seasonal changes, etc., but is not limited to these examples. For example, the notification unit notifies the user when the expiration date of preserved food is approaching, encouraging the user to replace the goods. The notification unit can also estimate the user's emotions and adjust the timing of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification can be sent during a relaxing time.

[0070] (Example 2) A disaster preparedness supply management system according to an embodiment of the present invention uses AI to select necessary disaster preparedness supplies tailored to individual circumstances, notify the user of the optimal replacement time, manage storage locations, and propose initial response methods in the event of a disaster. In this disaster preparedness supply management system, users input information about their home and living area, and the AI ​​analyzes that information to select disaster preparedness supplies tailored to their individual circumstances. The AI ​​then calculates the optimal replacement time for disaster preparedness supplies and notifies the user. It also manages storage locations and informs the user of where the supplies are stored. In the event of a disaster, the AI ​​proposes a response method based on the information about the home and living area. Furthermore, the registered information is shared with companies and local governments for use in confirming the safety of users in the event of an emergency. For example, a user inputs information about their home and living area, such as family composition, type of residence, and geographical information about the living area. This information is then input into the AI. The AI ​​then analyzes the input information and selects the necessary disaster preparedness supplies tailored to the individual situation. For example, if the family has infants, the AI ​​suggests infant-specific disaster preparedness supplies. Furthermore, if the residence is a high-rise apartment, the AI ​​suggests specific disaster preparedness supplies, such as evacuation ropes. Furthermore, the AI ​​calculates the optimal time to replace disaster preparedness supplies and notifies the user. For example, if a preserved food is nearing its expiration date, the AI ​​will notify the user and encourage them to replace it. It also manages storage locations and informs the user where the supplies are stored. In the event of a disaster, the AI ​​will suggest a response based on individual information about the home and surrounding area. For example, in the event of an earthquake, it will suggest evacuation routes and evacuation locations. In the event of a fire, it will suggest how to use a fire extinguisher and how to evacuate. Furthermore, the registered information can be shared with companies and local governments and used to check the safety of users in the event of an emergency. For example, in the event of a disaster, the system can automatically send the user's safety information to the company or local government, supporting a rapid response. This system is expected to enable disaster preparedness measures tailored to individual situations and enable a swift and appropriate initial response when a disaster occurs. As a result, the disaster preparedness supply management system can provide disaster preparedness measures tailored to individual situations and enable a swift and appropriate initial response when a disaster occurs.

[0071] A disaster prevention goods management system according to an embodiment includes a collection unit, a selection unit, and a notification unit. The collection unit collects user information. The user information includes, but is not limited to, personal information, location information, and past behavioral history. The collection unit collects, for example, information about the user's residence and living area. For example, the collection unit may collect information such as address, nearby evacuation shelters, and disaster risks. The selection unit analyzes the information collected by the collection unit and selects disaster prevention goods. For example, data mining, statistical analysis, machine learning algorithms, and the like may be used for the analysis, but are not limited to these examples. The selection unit selects necessary disaster prevention goods based on, for example, family composition and health status. For example, if there are infants in the family, the selection unit may suggest disaster prevention goods for infants. Furthermore, if the residence is a high-rise apartment building, the selection unit may suggest specific disaster prevention goods such as evacuation ropes. The notification unit calculates when to replace the disaster prevention goods selected by the selection unit and notifies the user. For example, expiration dates, deterioration levels, and seasonal changes may be taken into consideration in the calculation, but are not limited to these examples. For example, the notification unit may notify the user when the expiration date of preserved food is approaching, and prompt the user to replace the food. The notification unit may also estimate the user's emotions and adjust the timing of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification may be sent during a time when the user is relaxed. As a result, the disaster preparedness goods management system according to the embodiment collects and analyzes user information to select disaster preparedness goods and notify the user when it is time to replace the goods, enabling disaster preparedness measures tailored to individual situations.

[0072] The system includes a unit for managing storage locations of disaster preparedness supplies. Storage locations include, but are not limited to, specific locations in a home, a warehouse, or a vehicle. The management unit, for example, manages storage locations of disaster preparedness supplies and informs the user of where the supplies are stored. For example, the management unit may register the storage locations in a database so that the user can search for them when necessary. The management unit may also periodically update storage location information to maintain the latest information. For example, the management unit may update the information when the user moves the disaster preparedness supplies. This allows for management of storage locations of disaster preparedness supplies and informing the user of where they are stored, enabling a prompt response. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit may input storage location information into AI and have the AI ​​manage the storage locations.

[0073] The system includes a unit for proposing a response method in the event of a disaster. The proposal unit proposes a response method based on individual information, for example, based on information about the user's residence and living area when a disaster occurs. For example, the proposal unit proposes evacuation routes and evacuation locations when an earthquake occurs. The proposal unit can also propose how to use a fire extinguisher and evacuation methods when a fire occurs. The proposal unit, for example, analyzes information about the user's residence and living area and proposes an optimal response method. For example, the proposal unit can propose an evacuation route taking into account the type of residence of the user and surrounding geographic information. This enables a prompt and appropriate initial response by proposing a response method based on individual information, based on information about the user's residence and living area when a disaster occurs. Some or all of the above-mentioned processing by the proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the proposal unit can input information about the occurrence of a disaster into AI and have the AI ​​execute the response method proposal.

[0074] The device includes a unit that shares the registered information with a company or a local government. The sharing unit, for example, shares the registered information with a company or a local government and uses it to check the user's safety in the event of an emergency. For example, the sharing unit automatically sends the user's safety information to a company or a local government in the event of a disaster, supporting a rapid response. The sharing unit, for example, registers the user's personal information and emergency contact information in a database and can share them when necessary. The sharing unit can also share the user's health information and disaster response history. This allows the registered information to be shared with a company or a local government and used to check the user's safety in the event of an emergency, enabling a rapid response. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without AI. For example, the sharing unit may input the registered information into AI and have the AI ​​share the information.

[0075] The collection unit can collect information about the user's home or living area. The collection unit, for example, collects information about the user's home or living area. For example, the collection unit can collect information such as the user's address, nearby evacuation shelters, and disaster risks. The collection unit can collect necessary information based on the user's type of residence and family composition. For example, if the user lives in a high-rise apartment, the collection unit can collect information taking into account the need for evacuation ropes. By collecting information about the user's home or living area, disaster prevention measures tailored to individual situations are possible. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information about the user's home or living area into AI and have the AI ​​collect the information.

[0076] The selection unit can analyze the collected information and select disaster prevention supplies tailored to each individual situation. The selection unit, for example, analyzes the collected information and selects disaster prevention supplies tailored to each individual situation. For example, data mining, statistical analysis, machine learning algorithms, and the like may be used for the analysis, but are not limited to these examples. The selection unit selects necessary disaster prevention supplies based on, for example, family composition and health status. For example, if there are infants in the family, disaster prevention supplies for infants may be suggested. Furthermore, if the residence is a high-rise apartment, specific disaster prevention supplies such as evacuation ropes may be suggested. This enables appropriate disaster prevention measures by analyzing the collected information and selecting disaster prevention supplies tailored to each individual situation. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit may input the collected information into AI and have the AI ​​select the disaster prevention supplies.

[0077] The notification unit can calculate when to replace disaster preparedness supplies and notify the user. For example, the notification unit calculates when to replace disaster preparedness supplies and notifies the user. The calculation takes into account, for example, expiration dates, deterioration status, seasonal changes, etc., but is not limited to these examples. For example, the notification unit can notify the user when the expiration date of preserved food is approaching and encourage the user to replace the supplies. The notification unit can also estimate the user's emotions and adjust the timing of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification can be sent during a time when the user is relaxed. This allows the optimal replacement time for disaster preparedness supplies to be calculated and notified to the user, enabling replacement at the appropriate time. Some or all of the above-mentioned processing by the notification unit may be performed using, for example, AI, or without AI. For example, the notification unit can input the replacement time for disaster preparedness supplies into AI and have the AI ​​execute the notification timing.

[0078] The management unit manages the storage locations of disaster preparedness supplies and can inform the user where they are stored. For example, the management unit manages the storage locations of disaster preparedness supplies and informs the user where they are stored. Storage locations include, but are not limited to, specific locations in the home, warehouses, and inside cars. For example, the management unit registers the storage locations in a database so that the user can search for them when needed. The management unit can also periodically update the storage location information to maintain the latest information. For example, the management unit updates the information when the user moves the disaster preparedness supplies. This allows for the management of the storage locations of disaster preparedness supplies and the user to be informed of where they are stored, enabling a prompt response. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input the storage location information into AI and have the AI ​​manage the storage locations.

[0079] The suggestion unit can propose a response method based on individual information about the user's home or living area when a disaster occurs. For example, the suggestion unit proposes a response method based on individual information about the user's home or living area when a disaster occurs. For example, the suggestion unit proposes evacuation routes and evacuation locations when an earthquake occurs. Furthermore, the suggestion unit can also propose how to use a fire extinguisher and how to evacuate when a fire occurs. For example, the suggestion unit analyzes information about the user's home or living area to propose an optimal response method. For example, the suggestion unit can propose an evacuation route taking into account the type of the user's home and surrounding geographic information. This enables a prompt and appropriate initial response by proposing a response method based on individual information about the user's home or living area when a disaster occurs. Some or all of the above-described processing by the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit can input disaster information into an AI and have the AI ​​execute the response method proposal.

[0080] The sharing unit can share the registered information with a company or local government and use it to check the safety of users in the event of an emergency. The sharing unit, for example, shares the registered information with a company or local government and use it to check the safety of users in the event of an emergency. For example, the sharing unit can automatically send information about the user's safety to a company or local government in the event of a disaster, supporting a rapid response. The sharing unit can, for example, register the user's personal information and emergency contact information in a database and share it when necessary. The sharing unit can also share the user's health information and disaster response history. This allows the registered information to be shared with a company or local government and used to check the safety of users in the event of an emergency, enabling a rapid response. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without AI. For example, the sharing unit can input the registered information into AI and have the AI ​​share the information.

[0081] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects information during a relaxed time period. Also, if the user is busy, the collection unit can collect information during a free time period. Furthermore, if the user is relaxed, the collection unit can collect information immediately. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data into AI and have the AI ​​execute the timing of information collection.

[0082] The collection unit can analyze the user's past information provision history and select the optimal collection method. The collection unit, for example, analyzes the user's past information provision history and selects the optimal collection method. For example, the collection unit determines the optimal collection frequency based on the frequency of information provided by the user in the past. The collection unit can also select the optimal collection method based on the type of information provided by the user in the past. Furthermore, the collection unit can select the optimal collection method based on the accuracy of the information provided by the user in the past. In this way, the optimal collection method can be selected by analyzing the user's past information provision history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past information provision history into AI and have the AI ​​execute the optimal collection method.

[0083] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting information. For example, the collection unit performs filtering based on the user's current living situation and areas of interest when collecting information. For example, the collection unit prioritizes collecting highly relevant information based on the user's current living situation. The collection unit can also filter and collect relevant information based on the user's areas of interest. Furthermore, the collection unit can eliminate and collect unnecessary information based on the user's living situation and areas of interest. In this way, highly relevant information can be collected by filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's living situation and areas of interest into AI and have the AI ​​perform information filtering.

[0084] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, when the user is nervous, the collection unit can prioritize collecting important information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. This enables more appropriate information collection by determining the priority of information to be collected according to the user's emotions. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's emotion data into AI and have the AI ​​execute the information prioritization.

[0085] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting nearby disaster prevention information based on the user's current location. The collection unit can also collect region-specific disaster prevention information based on the user's geographical location information. Furthermore, the collection unit can also collect highly relevant information by taking into account the user's movement history. This enables more appropriate information collection by preferentially collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect information.

[0086] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media posts and collect disaster prevention information of interest. The collection unit can also analyze posts from the user's followers and friends and collect related information. Furthermore, the collection unit can collect trend information from the user's social media activities. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​collect information.

[0087] The selection unit can estimate the user's emotions and adjust the selection criteria for disaster prevention goods based on the estimated user emotions. For example, the selection unit can estimate the user's emotions and adjust the selection criteria for disaster prevention goods based on the estimated user emotions. For example, if the user is feeling anxious, the selection unit can prioritize selecting disaster prevention goods that provide a sense of security. The selection unit can also provide detailed selection criteria when the user is relaxed. Furthermore, the selection unit can provide easy-to-use selection criteria when the user is in a hurry. This allows for the selection of more appropriate disaster prevention goods by adjusting the selection criteria for disaster prevention goods according to the user's emotions. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input user emotion data into AI and have the AI ​​adjust the selection criteria.

[0088] The selection unit can select the optimal selection method by referring to the user's past disaster prevention product usage history during selection. For example, the selection unit selects the optimal selection method by referring to the user's past disaster prevention product usage history during selection. For example, the selection unit suggests the optimal selection method based on the user's past history of disaster prevention product use. The selection unit can also select effective disaster prevention products from the user's past usage history. Furthermore, the selection unit can analyze the user's past usage history and provide optimal selection criteria. This makes it possible to select the optimal selection method by referring to the user's past disaster prevention product usage history. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past usage history data into AI and have the AI ​​select the selection method.

[0089] The selection unit can customize the selection of disaster prevention goods based on the user's current living situation at the time of selection. The selection unit, for example, customizes the selection of disaster prevention goods based on the user's current living situation at the time of selection. For example, the selection unit selects necessary disaster prevention goods based on the user's family composition. The selection unit can also select specific disaster prevention goods based on the user's type of residence. Furthermore, the selection unit can select optimal disaster prevention goods based on the user's living situation. This enables more appropriate disaster prevention goods to be selected by customizing the selection of disaster prevention goods based on the user's current living situation. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's living situation data into AI and have the AI ​​customize the selection.

[0090] The selection unit can estimate the user's emotions and determine the priority of disaster prevention goods to be selected based on the estimated user emotions. For example, the selection unit can estimate the user's emotions and determine the priority of disaster prevention goods to be selected based on the estimated user emotions. For example, if the user is feeling anxious, the selection unit can prioritize disaster prevention goods that provide a sense of security. The selection unit can also provide detailed selection criteria when the user is relaxed. Furthermore, the selection unit can provide criteria that make selection easier when the user is in a hurry. This enables more appropriate disaster prevention goods to be selected by determining the priority of disaster prevention goods based on the user's emotions. Some or all of the above-described processing by the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user emotion data into AI and have the AI ​​perform the prioritization of disaster prevention goods.

[0091] The selection unit may analyze the user's social media activity to select disaster preparedness supplies. For example, the selection unit may analyze the user's social media activity to select disaster preparedness supplies. For example, the selection unit may analyze the user's social media posts to select disaster preparedness supplies of interest. The selection unit may also analyze posts from the user's followers and friends to select related disaster preparedness supplies. Furthermore, the selection unit may select trending disaster preparedness supplies from the user's social media activity. In this way, disaster preparedness supplies of interest can be selected by analyzing the user's social media activity. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may input the user's social media data into AI and have the AI ​​select the disaster preparedness supplies.

[0092] The notification unit can estimate the user's emotions and adjust the timing of the notification based on the estimated user emotions. The notification unit, for example, estimates the user's emotions and adjusts the timing of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can notify the user during a relaxed time period. Furthermore, if the user is busy, the notification unit can notify the user during a free time period. Furthermore, if the user is relaxed, the notification unit can notify the user immediately. This allows for more appropriate notification by adjusting the timing of the notification according to the user's emotions. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's emotion data into AI and have the AI ​​execute the timing of the notification.

[0093] The notification unit can select the optimal notification method by referring to the user's past notification history when notifying the user. For example, the notification unit can select the optimal notification method by referring to the user's past notification history when notifying the user. For example, the notification unit can suggest the optimal notification method based on the history of notifications the user has received in the past. The notification unit can also select an effective notification method from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and provide optimal notification criteria. This allows the optimal notification method to be selected by referring to the user's past notification history. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's past notification history data into AI and have the AI ​​select the notification method.

[0094] The notification unit can customize the notification content based on the user's current living situation at the time of notification. The notification unit, for example, customizes the notification content based on the user's current living situation at the time of notification. For example, the notification unit customizes the necessary notification content based on the user's family composition. The notification unit can also customize specific notification content based on the user's type of residence. Furthermore, the notification unit can customize optimal notification content based on the user's living situation. This enables more appropriate notifications by customizing the notification content based on the user's current living situation. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's living situation data into AI and have the AI ​​customize the notification content.

[0095] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. The notification unit, for example, estimates the user's emotions and determines the priority of notifications based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit can prioritize important notifications. Furthermore, if the user is relaxed, the notification unit can prioritize detailed notifications. Furthermore, if the user is in a hurry, the notification unit can prioritize content that can be notified quickly. This enables more appropriate notifications by determining the priority of notifications according to the user's emotions. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input user emotion data into AI and have the AI ​​determine the priority of notifications.

[0096] The notification unit can select the optimal notification method by taking into account the user's geographical location information when providing a notification. For example, the notification unit selects the optimal notification method by taking into account the user's geographical location information when providing a notification. For example, the notification unit prioritizes region-specific notifications based on the user's current location. The notification unit can also select the optimal notification method based on the user's geographical location information. Furthermore, the notification unit can provide highly relevant notifications by taking into account the user's movement history. This enables more appropriate notifications by selecting the optimal notification method by taking into account the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's geographical location information into AI and have the AI ​​select the notification method.

[0097] The notification unit can adjust the notification content by analyzing the user's social media activity at the time of notification. For example, the notification unit can adjust the notification content by analyzing the user's social media activity at the time of notification. For example, the notification unit can analyze the user's social media posts and provide notification content of interest. The notification unit can also analyze posts from the user's followers and friends and provide related notification content. Furthermore, the notification unit can provide trending notification content from the user's social media activity. In this way, notification content of interest can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the notification unit can be performed using, or without, AI, for example. For example, the notification unit can input the user's social media data into AI and have the AI ​​adjust the notification content.

[0098] The management unit can estimate the user's emotions and adjust the storage location management method based on the estimated user emotions. The management unit, for example, estimates the user's emotions and adjusts the storage location management method based on the estimated user emotions. For example, if the user is feeling anxious, the management unit prioritizes managing storage locations that provide a sense of security. The management unit can also provide a detailed management method when the user is relaxed. Furthermore, the management unit can provide a simple management method when the user is in a hurry. This enables more appropriate management by adjusting the storage location management method according to the user's emotions. Some or all of the above-described processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input user emotion data into AI and have the AI ​​adjust the management method.

[0099] The management unit can select the optimal management method by referring to the user's past storage location history during management. For example, the management unit selects the optimal management method by referring to the user's past storage location history during management. For example, the management unit suggests the optimal management method based on the history of storage locations used by the user in the past. The management unit can also select an effective management method from the user's past storage location history. Furthermore, the management unit can analyze the user's past storage location history and provide optimal management standards. This makes it possible to select the optimal management method by referring to the user's past storage location history. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's past storage location history data into AI and have the AI ​​select the management method.

[0100] The management unit can customize the management of storage locations based on the user's current living situation during management. The management unit, for example, customizes the management of storage locations based on the user's current living situation during management. For example, the management unit customizes the necessary storage locations based on the user's family composition. The management unit can also customize specific storage locations based on the user's type of residence. Furthermore, the management unit can customize the optimal storage location based on the user's living situation. This enables more appropriate management by customizing the management of storage locations based on the user's current living situation. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's living situation data into AI and have the AI ​​customize the management.

[0101] The management unit can estimate the user's emotions and prioritize storage locations based on the estimated user emotions. For example, the management unit can estimate the user's emotions and prioritize storage locations based on the estimated user emotions. For example, if the user is feeling anxious, the management unit can prioritize storage locations that provide a sense of security. The management unit can also provide a detailed management method when the user is relaxed. Furthermore, the management unit can provide an easy management method when the user is in a hurry. This enables more appropriate management by prioritizing storage locations according to the user's emotions. Some or all of the above-described processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input user emotion data into AI and have the AI ​​prioritize storage locations.

[0102] The management unit can select the optimal storage location during management by taking into account the user's geographical location information. For example, the management unit selects the optimal storage location by taking into account the user's geographical location information during management. For example, the management unit prioritizes management of storage locations specific to a region based on the user's current location. The management unit can also select the optimal storage location based on the user's geographical location information. Furthermore, the management unit can select a highly relevant storage location by taking into account the user's movement history. This enables more appropriate management by selecting the optimal storage location by taking into account the user's geographical location information. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit can input the user's geographical location information into AI and have the AI ​​select the storage location.

[0103] The management unit may, during management, analyze the user's social media activity and manage the storage locations. For example, during management, the management unit may analyze the user's social media activity and manage the storage locations. For example, the management unit may analyze the user's social media posts and manage storage locations of interest. The management unit may also analyze posts from the user's followers and friends and manage related storage locations. Furthermore, the management unit may manage trending storage locations from the user's social media activity. In this way, the storage locations of interest can be managed by analyzing the user's social media activity. Some or all of the above-described processing in the management unit may be performed using, or without, AI, for example. For example, the management unit may input the user's social media data into AI and have the AI ​​manage the storage locations.

[0104] The suggestion unit can estimate the user's emotions and adjust the suggested response methods based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the suggested response methods based on the estimated user emotions. For example, if the user is feeling anxious, the suggestion unit can prioritize suggesting response methods that provide a sense of security. Furthermore, if the user is relaxed, the suggestion unit can provide detailed response methods. Furthermore, if the user is in a hurry, the suggestion unit can provide easy-to-execute response methods. This allows for more appropriate responses by adjusting the suggested response methods according to the user's emotions. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's emotion data into AI and have the AI ​​execute the suggested response methods.

[0105] The suggestion unit can select the optimal suggestion method by referring to the user's past response history when making a suggestion. For example, the suggestion unit selects the optimal suggestion method by referring to the user's past response history when making a suggestion. For example, the suggestion unit suggests the optimal suggestion method based on the history of response methods previously performed by the user. The suggestion unit can also select an effective suggestion method from the user's past response history. Furthermore, the suggestion unit can analyze the user's past response history and provide optimal suggestion criteria. This makes it possible to select the optimal suggestion method by referring to the user's past response history. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past response history data into AI and have the AI ​​select the suggestion method.

[0106] The suggestion unit can customize the proposed response method based on the user's current living situation when making the suggestion. The suggestion unit, for example, customizes the proposed response method based on the user's current living situation when making the suggestion. For example, the suggestion unit suggests a necessary response method based on the user's family composition. The suggestion unit can also suggest a specific response method based on the user's type of residence. Furthermore, the suggestion unit can also suggest an optimal response method based on the user's living situation. This enables a more appropriate response by customizing the proposed response method based on the user's current living situation. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's living situation data into AI and have the AI ​​customize the proposal.

[0107] The suggestion unit can estimate the user's emotions and prioritize response methods based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and prioritizes response methods based on the estimated user emotions. For example, if the user feels anxious, the suggestion unit prioritizes response methods that provide a sense of security. The suggestion unit can also provide detailed response methods when the user feels relaxed. Furthermore, the suggestion unit can also provide easy-to-execute response methods when the user is in a hurry. This enables more appropriate responses by prioritizing response methods according to the user's emotions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input user emotion data into AI and have the AI ​​execute the prioritized response methods.

[0108] The suggestion unit can propose an optimal response method by taking into account the user's geographical location information when making a suggestion. For example, the suggestion unit proposes an optimal response method by taking into account the user's geographical location information when making a suggestion. For example, the suggestion unit proposes a region-specific response method based on the user's current location. The suggestion unit can also propose an optimal response method based on the user's geographical location information. Furthermore, the suggestion unit can also propose a highly relevant response method by taking into account the user's movement history. This enables a more appropriate response by proposing an optimal response method by taking into account the user's geographical location information. Some or all of the above-described processing by the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit can input the user's geographical location information into an AI and have the AI ​​execute the response method proposal.

[0109] The suggestion unit, at the time of making a suggestion, can analyze the user's social media activity and suggest a response method. For example, at the time of making a suggestion, the suggestion unit analyzes the user's social media activity and suggests a response method of interest. For example, the suggestion unit analyzes the user's social media posts and suggests a response method of interest. The suggestion unit can also analyze posts from the user's followers and friends and suggest related response methods. Furthermore, the suggestion unit can suggest trending response methods from the user's social media activity. In this way, response methods of interest can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's social media data into AI and have the AI ​​execute the response method suggestion.

[0110] The sharing unit can estimate the user's emotions and adjust the timing of information sharing based on the estimated user emotions. The sharing unit, for example, estimates the user's emotions and adjusts the timing of information sharing based on the estimated user emotions. For example, if the user is feeling stressed, the sharing unit can share information during a relaxed time. Also, if the user is busy, the sharing unit can share information during a free time. Furthermore, if the user is relaxed, the sharing unit can share information immediately. This enables more appropriate information sharing by adjusting the timing of information sharing according to the user's emotions. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's emotion data into AI and have the AI ​​execute the timing of information sharing.

[0111] The sharing unit can select the optimal sharing method by referring to the user's past sharing history when sharing. For example, the sharing unit selects the optimal sharing method by referring to the user's past sharing history when sharing. For example, the sharing unit suggests the optimal sharing method based on the history of information the user has previously shared. The sharing unit can also select an effective sharing method from the user's past sharing history. Furthermore, the sharing unit can analyze the user's past sharing history and provide optimal sharing criteria. In this way, the optimal sharing method can be selected by referring to the user's past sharing history. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's past sharing history data into AI and have the AI ​​select the sharing method.

[0112] The sharing unit can customize information sharing based on the user's current living situation at the time of sharing. The sharing unit customizes information sharing based on the user's current living situation, for example, at the time of sharing. For example, the sharing unit customizes necessary information sharing based on the user's family composition. The sharing unit can also customize specific information sharing based on the user's type of residence. Furthermore, the sharing unit can customize optimal information sharing based on the user's living situation. This enables more appropriate information sharing by customizing information sharing based on the user's current living situation. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's living situation data into AI and have the AI ​​customize the information sharing.

[0113] The sharing unit can estimate the user's emotions and determine the priority of information to be shared based on the estimated user emotions. The sharing unit, for example, estimates the user's emotions and determines the priority of information to be shared based on the estimated user emotions. For example, if the user is feeling anxious, the sharing unit can prioritize sharing important information. Furthermore, if the user is relaxed, the sharing unit can prioritize sharing detailed information. Furthermore, if the user is in a hurry, the sharing unit can prioritize sharing information that can be shared quickly. This enables more appropriate information sharing by determining the priority of information to be shared based on the user's emotions. Some or all of the above-described processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the user's emotional data into AI and have the AI ​​prioritize the information.

[0114] The sharing unit can select the optimal sharing method by taking into account the user's geographical location information when sharing. For example, the sharing unit selects the optimal sharing method by taking into account the user's geographical location information when sharing. For example, the sharing unit prioritizes sharing of region-specific information based on the user's current location. The sharing unit can also select the optimal sharing method based on the user's geographical location information. Furthermore, the sharing unit can share highly relevant information by taking into account the user's movement history. This enables more appropriate information sharing by selecting the optimal sharing method by taking into account the user's geographical location information. Some or all of the above-described processing in the sharing unit may be performed using, or without, AI, for example. For example, the sharing unit can input the user's geographical location information into AI and have the AI ​​select the sharing method.

[0115] The sharing unit can adjust the content of information sharing by analyzing the user's social media activity at the time of sharing. For example, the sharing unit can adjust the content of information sharing by analyzing the user's social media activity at the time of sharing. For example, the sharing unit can analyze the user's social media posts and share information of interest. The sharing unit can also analyze posts from the user's followers and friends and share related information. Furthermore, the sharing unit can share trending information from the user's social media activity. In this way, information of interest can be shared by analyzing the user's social media activity. Some or all of the above-mentioned processing in the sharing unit can be performed using, for example, AI, or can be performed without using AI. For example, the sharing unit can input the user's social media data into AI and have the AI ​​execute the content of information sharing. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, selection unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to select disaster prevention goods. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the user when it is time to replace the selected disaster prevention goods. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, selection unit, and notification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12 via the control unit 46A. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to select disaster prevention goods. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the user when it is time to replace the selected disaster prevention goods. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, selection unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the information to the data processing device 12 by the control unit 46A. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to select disaster prevention goods. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the user when it is time to replace the selected disaster prevention goods. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, selection unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits the information to the data processing device 12 by the control unit 46A. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to select disaster prevention goods. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies the user when it is time to replace the selected disaster prevention goods.

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

[0117] The disaster prevention goods management system can further include a health management unit that monitors the user's health condition. By collecting and analyzing the user's health data, the health management unit can select disaster prevention goods and suggest countermeasures based on the user's health condition. For example, if the user has high blood pressure, it can suggest a blood pressure monitor and stockpiles of medication. Also, if the user has allergies, it can suggest emergency food suitable for allergies. Furthermore, the health management unit can suggest precautions to take during evacuation and the preparation of necessary medical equipment based on the user's health condition. This makes it possible to take disaster prevention measures based on the user's health condition, and more appropriate responses can be expected.

[0118] The disaster prevention goods management system can further include a pet management unit that manages information about the user's pets. The pet management unit can collect and analyze information such as the type and number of the user's pets and their health conditions to suggest disaster prevention goods and response methods necessary for pets. For example, if the user has a dog, it can suggest emergency food and evacuation cages for the pet. It can also suggest necessary medicines and medical supplies based on the pet's health condition. Furthermore, the pet management unit can suggest evacuation methods and evacuation locations for pets in the event of a disaster. This enables disaster prevention measures for the entire family, including pets, and allows for more peace of mind when preparing for disasters.

[0119] The disaster prevention goods management system can further include a vehicle management unit that manages the user's vehicle information. The vehicle management unit can propose disaster prevention measures using the vehicle by collecting and analyzing the type, condition, and location information of the user's vehicle. For example, it can monitor the vehicle's fuel status and propose stockpiles of fuel needed in the event of a disaster. It can also propose optimal evacuation routes and evacuation locations based on the vehicle's location information. Furthermore, the vehicle management unit can propose disaster prevention goods to be stockpiled in the vehicle and evacuation methods using the vehicle. This makes it possible to implement disaster prevention measures using the vehicle, and can expect quick and safe evacuation.

[0120] The disaster prevention goods management system can also be equipped with a community management unit that manages user community information. The community management unit can propose disaster prevention measures for the entire community by collecting and analyzing information about the local community to which the user belongs. For example, it can provide information about local evacuation shelters and evacuation routes and propose evacuation plans for the entire community. It can also provide information about local disaster prevention drills and disaster prevention events and encourage participation. Furthermore, the community management unit can check the safety of local residents and coordinate support activities in the event of a disaster. This will strengthen disaster prevention measures for the entire local community, and is expected to enable a quick and effective response in the event of a disaster.

[0121] The disaster prevention goods management system can further include a travel management unit that manages the user's travel information. The travel management unit can suggest disaster prevention measures during the trip by collecting and analyzing information on the user's travel plans and travel destinations. For example, it can evaluate the disaster risk at the travel destination and suggest necessary disaster prevention goods. It can also provide information on evacuation sites and evacuation routes at the travel destination and suggest how to respond in an emergency. Furthermore, the travel management unit can suggest health management and safety measures during the trip. This strengthens disaster prevention measures during the trip, allowing the user to enjoy the trip with peace of mind.

[0122] The disaster prevention goods management system can further include an emotion selection unit that estimates the user's emotion and selects disaster prevention goods based on the estimated emotion. The emotion selection unit can select disaster prevention goods according to the user's emotion by collecting and analyzing the user's emotion data. For example, if the user is feeling anxious, the emotion selection unit can prioritize the selection of disaster prevention goods that give a sense of security. Also, if the user is relaxed, detailed selection criteria can be provided. Furthermore, if the user is in a hurry, criteria that allow for easy selection can be provided. This makes it possible to select disaster prevention goods according to the user's emotion, and more appropriate disaster prevention measures can be expected.

[0123] The disaster prevention goods management system can further include an emotion notification unit that estimates the user's emotion and adjusts the notification content based on the estimated emotion. The emotion notification unit can provide notification content that corresponds to the user's emotion by collecting and analyzing the user's emotion data. For example, if the user is feeling stressed, the notification unit can provide relaxing content. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, the notification can be brief. This makes it possible to adjust the notification content according to the user's emotion, and more appropriate information can be provided.

[0124] The disaster prevention goods management system can further include an emotion management unit that estimates the user's emotions and manages storage locations based on the estimated emotions. The emotion management unit can manage storage locations according to the user's emotions by collecting and analyzing the user's emotion data. For example, if the user is feeling anxious, it can prioritize management of storage locations that give the user a sense of security. It can also provide a detailed management method if the user is relaxed. It can also provide an easy management method if the user is in a hurry. This makes it possible to manage storage locations according to the user's emotions, and more appropriate management can be expected.

[0125] The disaster prevention goods management system can further include an emotion suggestion unit that estimates the user's emotions and suggests a response method based on the estimated emotions. The emotion suggestion unit can suggest a response method according to the user's emotions by collecting and analyzing the user's emotion data. For example, if the user is feeling anxious, the unit can prioritize suggesting a response method that gives the user a sense of security. Also, if the user is relaxed, the unit can provide detailed response methods. Furthermore, if the user is in a hurry, the unit can provide easy-to-implement response methods. This makes it possible to suggest response methods according to the user's emotions, and more appropriate responses can be expected.

[0126] The disaster prevention goods management system can further include an emotion sharing unit that estimates the user's emotions and adjusts the timing of information sharing based on the estimated emotions. The emotion sharing unit can adjust the timing of information sharing according to the user's emotions by collecting and analyzing the user's emotion data. For example, if the user is feeling stressed, information sharing can be performed during a relaxed time. Also, if the user is relaxed, information sharing can be performed immediately. Furthermore, if the user is in a hurry, information sharing can be performed quickly. This makes it possible to adjust the timing of information sharing according to the user's emotions, and more appropriate information sharing can be expected.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The collection unit collects user information. The user information includes, but is not limited to, personal information, location information, and past behavioral history. The collection unit collects, for example, information about the user's residence and living area. For example, information such as address, nearby evacuation shelters, and disaster risks can be collected. Step 2: The selection unit analyzes the information collected by the collection unit and selects disaster prevention goods. Examples of the analysis that may be used include, but are not limited to, data mining, statistical analysis, and machine learning algorithms. The selection unit selects necessary disaster prevention goods based on, for example, family structure and health status. For example, if there are infants in the family, the selection unit may suggest disaster prevention goods for infants. Furthermore, if the residence is a high-rise apartment building, the selection unit may suggest specific disaster prevention goods such as evacuation ropes. Step 3: The notification unit calculates when to replace the disaster prevention goods selected by the selection unit and notifies the user. The calculation takes into consideration, for example, expiration dates, deterioration status, seasonal changes, etc., but is not limited to these examples. For example, the notification unit notifies the user when the expiration date of preserved food is approaching, encouraging the user to replace the goods. The notification unit can also estimate the user's emotions and adjust the timing of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification can be sent during a relaxing time.

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

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

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

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0173] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0183] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

[0189] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

[0191] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0192] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0193] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0195] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0200] [Explanation of symbols]

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

Claims

1. a collection unit that collects user information; A selection unit that analyzes the information collected by the collection unit and selects disaster prevention goods; A notification unit that notifies the replacement time of the disaster prevention goods selected by the selection unit; Equipped with A system characterized by:

2. Equipped with a management department that manages storage locations for disaster prevention goods 2. The system of claim 1.

3. Equipped with a proposal department that proposes response methods in the event of a disaster 2. The system of claim 1.

4. Equipped with a sharing section to share registered information with companies or local governments 2. The system of claim 1.

5. The collecting unit Collect information about the user's home or living area 2. The system of claim 1.

6. The selection unit A department will be established to analyze the collected information and select disaster prevention goods suited to each individual situation.

2. The system of claim 1.

7. The notification unit Calculates when to replace disaster prevention goods and notifies users 2. The system of claim 1.

8. The management unit Equipped with a section that manages the storage location of disaster prevention goods and tells the user where they are stored 3. The system of claim 2.

9. The proposal unit In the event of a disaster, we will have a department that will propose response methods based on individual information from within the home or living area.

4. The system of claim 3.

10. The common part is The registered information will be shared with the company or local government, and a department will be established to use it to check the safety of employees in the event of an emergency.

5. The system of claim 4.

11. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

Images