System

Abstract

An object of a system according to an embodiment is to simplify management and sharing of stockpiles and to quickly respond to an emergency.SOLUTION: A system includes a stock list creation unit, a stock management unit, an information provision unit, a sharing unit, and an emergency response support unit. The stocklist generator generates a stocklist based on user input. The stockpile management unit manages the stockpile list created by the stockpile list creation unit. The information providing unit provides information and advice related to the stockpile managed by the stockpile management unit. The sharing unit shares the stockpile list. The emergency response support unit supports a response in an emergency.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that managing and sharing stockpiles is complicated, making it difficult to respond quickly in emergencies.

[0005] The system according to the embodiment aims to simplify the management and sharing of stockpiled items and to respond quickly in emergencies. [Means for solving the problem]

[0006] The system according to the embodiment includes a stockpile list creation unit, a stockpile management unit, an information providing unit, a sharing unit, and an emergency response support unit. The stockpile list creation unit creates a stockpile list based on user input. The stockpile management unit manages the stockpile list created by the stockpile list creation unit. The information providing unit provides information and advice about the stockpile items managed by the stockpile management unit. The sharing unit shares the stockpile list. The emergency response support unit supports responses in the event of an emergency. [Effects of the Invention]

[0007] The system according to the embodiment simplifies the management and sharing of stockpiled items, enabling rapid response in emergencies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The emergency stockpile map collaboration service according to an embodiment of the present invention is a platform that enables individuals and families to efficiently manage and share necessary emergency stockpiles. This platform uses generative AI to allow users to easily create lists of emergency stockpiles and provides necessary information and advice. As a result, the emergency stockpile map collaboration service enables individuals and families to efficiently manage and share emergency stockpiles in preparation for natural disasters and emergencies.

[0029] The emergency stockpile map linkage service according to the embodiment includes an emergency stockpile list creation unit, an emergency stockpile management unit, an information provision unit, a sharing unit, and an emergency response support unit. The emergency stockpile list creation unit creates an emergency stockpile list based on user input. For example, when a user inputs, "I want to stockpile a week's worth of food and water for a family of four," the generation AI analyzes the information and creates a specific emergency stockpile list. The generation AI generates the list using a text generation AI (e.g., LLM). The generation AI can also create a list based on prompts containing user instructions. The emergency stockpile management unit manages the emergency stockpile list created by the emergency stockpile list creation unit. For example, the generation AI manages the inventory status of emergency stockpile items registered by the user and suggests updates as necessary. The generation AI notifies the user when the expiration date of an emergency stockpile item is approaching, informing them of the need to update. The generation AI can also suggest replenishing consumed emergency stockpile items. The information provision unit provides information and advice regarding the emergency stockpile items managed by the emergency stockpile management unit. For example, the generation AI provides advice on the types, quantities, and storage methods of emergency supplies needed in the event of a disaster. It can also provide the latest disaster and emergency information. The sharing unit shares the emergency supply list. For example, a function is provided that allows a user to share the emergency supply list with family and friends. All family members can view and manage the same emergency supply list. Furthermore, emergency supply information can be shared with friends and neighbors, allowing for cooperative preparations. The emergency response support unit supports responses in emergencies. For example, the generation AI supports the user in responding quickly when an emergency occurs. The generation AI provides information on evacuation sites, emergency contact information, and confirmation of necessary emergency supplies. As a result, the emergency supply map collaboration service according to the embodiment allows users to efficiently manage and share emergency supplies and respond quickly in the event of an emergency. For example, the generation AI provides information and advice, allowing users to make appropriate preparations based on the latest information. Furthermore, by having all family members view and manage the same emergency supply list, duplication and shortages of emergency supplies can be prevented. Furthermore, sharing emergency supply information with friends and neighbors can strengthen community-wide preparedness.

[0030] The emergency stockpile list creation unit can analyze the user's past purchasing history and lifestyle habits and generate an individually optimized emergency stockpile list. In the emergency stockpile list creation unit, for example, the generation AI analyzes the user's past purchasing history and generates an emergency stockpile list based on frequently purchased foods and daily necessities. For example, canned goods and dried noodles that the user purchases regularly are included in the list. The generation AI also analyzes the user's lifestyle habits and generates an individually optimized emergency stockpile list. For example, if the user has a habit of eating at a specific time, foods suitable for that time can be included in the list. This makes it possible to generate an optimal emergency stockpile list based on the user's past purchasing history and lifestyle habits.

[0031] The stockpile list creation unit can propose a stockpile list specific to a region, taking into account the region's climate and disaster risk. For example, the generation AI can analyze the region's climate data and include cold weather gear and heating appliances in cold regions, and cooling products and hydration supplies in tropical regions in the stockpile list. The generation AI can also analyze the region's disaster risk and include emergency food and disaster prevention supplies in regions where earthquakes occur frequently, and waterproof supplies and life preservers in regions prone to flooding in the list. This makes it possible to propose the optimal stockpile list based on the region's climate and disaster risk.

[0032] The emergency stockpile list creation unit can generate an emergency stockpile list that takes into consideration the user's health condition and allergy information. For example, the generation AI in the emergency stockpile list creation unit analyzes the user's health condition and includes emergency stockpile items to address specific illnesses or health issues in the list. For example, it can suggest low-sugar foods and insulin to a diabetic user. The generation AI can also analyze the user's allergy information and include allergen-free foods and medicines in the list. This makes it possible to generate an emergency stockpile list that takes health into consideration based on the user's health condition and allergy information.

[0033] The emergency stockpile list creation unit can compare the emergency stockpile lists of other users and suggest the most suitable emergency stockpile items. For example, the generation AI analyzes the emergency stockpile lists of other users and suggests the most suitable list based on the common emergency stockpile items. For example, emergency food and medicines that many users have stockpiled are included in the list. The generation AI can also refer to the emergency stockpile lists of other users to suggest emergency stockpile items that the user needs. This makes it possible to suggest the most suitable emergency stockpile items by comparing the emergency stockpile lists of other users.

[0034] The stockpile management unit can learn consumption patterns of stockpiled items and suggest the optimal timing for replenishment. For example, the generation AI analyzes the user's consumption patterns of stockpiled items and suggests the optimal timing for replenishment. For example, it can automatically set replenishment reminders according to the rate of consumption. The generation AI can also learn consumption patterns of stockpiled items and suggest replenishment when inventory falls below a certain level. This allows the generation AI to learn consumption patterns of stockpiled items and suggest the optimal timing for replenishment.

[0035] The stockpile management unit can monitor the storage environment of stockpiled goods and propose appropriate management methods. For example, the generation AI can monitor the storage environment of stockpiled goods and notify if the temperature or humidity is not appropriate. For example, it can make suggestions for maintaining temperatures and humidity levels suitable for storing food. The generation AI can also monitor the storage environment and suggest changing storage locations or using storage containers. This makes it possible to monitor the storage environment of stockpiled goods and propose appropriate management methods.

[0036] The stockpile management unit can compare the stockpile management data of other households and propose the optimal management method. For example, the generation AI analyzes the stockpile management data of other households and proposes the optimal management method based on common management methods. For example, it can include management methods that are practiced by many households in the list. The generation AI can also refer to the stockpile management data of other households to propose the optimal management method for the user. This makes it possible to propose the optimal management method by comparing the stockpile management data of other households.

[0037] The stockpile management unit can visualize the stockpile management status, allowing the user to intuitively understand it. For example, the generation AI can visualize the stockpile management status in graphs and charts, allowing the user to intuitively understand it. For example, it can visually display the inventory status and consumption rate. The generation AI can also display the stockpile management status in dashboard format, allowing the user to grasp the management status at a glance. In this way, the stockpile management status is visualized, allowing the user to intuitively understand it.

[0038] The information provision unit can collect the latest disaster information in real time and provide it to the user. For example, the generation AI can collect various disaster information sites and weather data in real time and provide the latest disaster information to the user. For example, earthquake alerts and typhoon information can be immediately notified. The generation AI can also analyze disaster information and provide information that is important to the user with priority. This allows the latest disaster information to be collected in real time and provided to the user.

[0039] The information provision unit can analyze the user's past behavioral history and provide optimal advice. For example, the generation AI in the information provision unit analyzes the user's past behavioral history and advises on actions to take in the event of a disaster. For example, it can suggest the optimal evacuation route based on past evacuation locations and routes. The generation AI can also analyze the user's behavioral history and provide advice on disaster preparations and responses. This allows the user's past behavioral history to be analyzed and optimal advice to be provided.

[0040] The information provision unit can analyze the advice history of other users and provide optimal advice. For example, the generation AI analyzes the advice history of other users and provides optimal advice based on common advice. For example, it can suggest disaster prevention measures that many users are practicing. The generation AI can also refer to the advice history of other users to provide advice that is useful to the user. This allows the advice history of other users to be analyzed and optimal advice to be provided.

[0041] The information providing unit can visually display advice so that the user can intuitively understand it. For example, the generation AI can visually display advice using graphs or charts so that the user can intuitively understand it. For example, disaster prevention procedures and methods for managing stockpiles can be visually displayed. The generation AI can also display advice using icons or illustrations so that the user can understand it at a glance. In this way, the advice is visually displayed so that the user can intuitively understand it.

[0042] The sharing unit can analyze the shared emergency stockpile list and suggest the optimal sharing method. For example, the generation AI analyzes the shared emergency stockpile list and suggests the optimal sharing method among family and friends. For example, it suggests the division of roles for each member and how to manage the emergency stockpile. The generation AI can also refer to the shared emergency stockpile list and suggest the optimal sharing method for the user. This makes it possible to analyze the shared emergency stockpile list and suggest the optimal sharing method.

[0043] The sharing unit can analyze the usage history of the shared stockpiles and suggest the optimal management method. For example, the generation AI analyzes the usage history of the shared stockpiles and suggests the optimal management method among family and friends. For example, it suggests a management method based on the frequency of use and consumption patterns of each member. The generation AI can also refer to the usage history of the shared stockpiles to suggest the optimal management method for the user. This allows the generation AI to analyze the usage history of the shared stockpiles and suggest the optimal management method.

[0044] The sharing unit can compare the shared data of other households and propose the optimal sharing method. For example, the generation AI analyzes the shared data of other households and proposes the optimal sharing method based on common sharing methods. For example, it includes sharing methods that are practiced by many households in a list. The generation AI can also refer to the shared data of other households to propose the optimal sharing method for the user. This makes it possible to propose the optimal sharing method by comparing the shared data of other households.

[0045] The sharing unit can visualize the management status of the shared stockpiles, allowing the user to intuitively understand. For example, the generating AI can visualize the management status of the shared stockpiles in graphs and charts, allowing the user to intuitively understand. For example, it can visually display the inventory status and consumption rate. The generating AI can also display the management status of the shared stockpiles in dashboard format, allowing the user to grasp the management status at a glance. In this way, the management status of the shared stockpiles is visualized, allowing the user to intuitively understand.

[0046] The emergency response support unit can simulate actions to be taken in an emergency and propose the optimal response method. In the emergency response support unit, for example, the generation AI simulates actions to be taken in an emergency and proposes the optimal evacuation route to the user. For example, it simulates evacuation routes and evacuation locations in the event of an earthquake and presents the optimal route. The generation AI can also propose the optimal response method for the user based on the emergency behavior simulation. This makes it possible to simulate actions to be taken in an emergency and propose the optimal response method.

[0047] The emergency response support unit can analyze the user's location information and propose the optimal evacuation route. For example, the generation AI analyzes the user's current location and proposes the optimal evacuation route. For example, in the event of an earthquake, the safest evacuation route can be presented in real time. The generation AI can also propose the location of evacuation shelters and evacuation routes based on the user's location information. This allows the user's location information to be analyzed and the optimal evacuation route to be proposed.

[0048] The emergency response support unit can analyze other users' emergency response data and propose the optimal response method. For example, the generation AI analyzes other users' emergency response data and proposes the optimal response method based on common response methods. For example, it includes evacuation methods that many users practice in a list. The generation AI can also refer to other users' emergency response data and propose the optimal response method for the user. This makes it possible to analyze other users' emergency response data and propose the optimal response method.

[0049] The emergency response support unit can visualize the response status in an emergency, allowing the user to intuitively understand it. For example, the generation AI visualizes the response status in an emergency using graphs and charts, allowing the user to intuitively understand it. For example, it visually displays evacuation routes and evacuation locations. The generation AI can also display the response status in an emergency in dashboard format, allowing the user to grasp the response status at a glance. This visualizes the response status in an emergency, allowing the user to intuitively understand it.

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

[0051] The emergency supplies list creation unit can also generate a list that takes into account the user's hobbies and preferences, allowing the user to enjoy selecting emergency supplies. For example, if the user enjoys outdoor activities, camping supplies and outdoor gear can be included in the list. Also, if the user enjoys cooking, emergency food items that are fun to cook can be suggested. Furthermore, if the user prefers a particular brand or product, emergency supplies of that brand can be included in the list. This allows the system to generate an emergency supplies list that matches the user's hobbies and preferences, making the process of stockpiling more enjoyable.

[0052] The stockpile management unit can learn consumption patterns of stockpiled items and suggest the optimal timing for replenishment. For example, the generation AI can analyze the user's consumption patterns of stockpiled items and suggest the optimal timing for replenishment. For example, it can automatically set replenishment reminders according to the rate of consumption. The generation AI can also learn consumption patterns of stockpiled items and suggest replenishment when inventory falls below a certain level. This allows it to learn consumption patterns of stockpiled items and suggest the optimal timing for replenishment.

[0053] The emergency supplies management unit can compare the emergency supplies management data of other households and propose the optimal management method. For example, the generation AI can analyze the emergency supplies management data of other households and propose the optimal management method based on common management methods. For example, it can include management methods that are practiced by many households in the list. The generation AI can also refer to the emergency supplies management data of other households to propose the optimal management method for the user. This allows it to propose the optimal management method by comparing the emergency supplies management data of other households.

[0054] The information provision unit can collect the latest disaster information in real time and provide it to users. For example, the generation AI can collect various disaster information sites and weather data in real time and provide the latest disaster information to users. For example, it can instantly notify users of earthquake alerts and typhoon information. The generation AI can also analyze disaster information and prioritize providing information that is important to users. This allows the latest disaster information to be collected in real time and provided to users.

[0055] The information provision unit can analyze the advice history of other users and provide optimal advice. For example, the generation AI can analyze the advice history of other users and provide optimal advice based on common advice. For example, it can suggest disaster prevention measures that many users are practicing. The generation AI can also refer to the advice history of other users to provide advice that is useful to the user. This allows the generation AI to analyze the advice history of other users and provide optimal advice.

[0056] The emergency response support unit can simulate actions to take in an emergency and propose the optimal response method. For example, the generation AI can simulate actions to take in an emergency and propose the optimal evacuation route to the user. For example, it can simulate evacuation routes and evacuation locations in the event of an earthquake and present the optimal route. The generation AI can also propose the optimal response method for the user based on the emergency behavior simulation. This allows it to simulate actions to take in an emergency and propose the optimal response method.

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

[0058] Step 1: The emergency supplies list creation unit creates an emergency supplies list based on user input. For example, if a user inputs, "I want to stockpile a week's worth of food and water for a family of four," the generation AI analyzes that information and creates a specific emergency supplies list. The generation AI generates the list using a text generation AI (e.g., LLM). The generation AI can also generate a list based on prompts containing user instructions. Step 2: The stockpile management unit manages the stockpile list created by the stockpile list creation unit. For example, the generation AI manages the inventory status of stockpile items registered by the user and suggests updates as necessary. The generation AI notifies the user when the expiration date of a stockpile item is approaching, informing them of the need to update. It can also suggest replenishing consumed stockpile items. Step 3: The information provision unit provides information and advice about the stockpiles managed by the stockpile management unit. For example, the generation AI can provide advice on the types and quantities of stockpiles needed in the event of a disaster, as well as storage methods. It can also provide the latest disaster and emergency information. Step 4: The sharing section allows users to share their emergency supplies list. For example, it provides a function that allows users to share their emergency supplies list with family and friends. All family members can view and manage the same emergency supplies list. Users can also share information about emergency supplies with friends and neighbors, allowing them to work together to prepare. Step 5: The emergency response support unit supports responses in the event of an emergency. For example, the generation AI supports the user in responding quickly when an emergency occurs. The generation AI provides information on evacuation sites, emergency contact information, and checks for necessary supplies.

[0059] (Example 2) The emergency stockpile map collaboration service according to an embodiment of the present invention is a platform that enables individuals and families to efficiently manage and share necessary emergency stockpiles. This platform uses generative AI to allow users to easily create lists of emergency stockpiles and provides necessary information and advice. As a result, the emergency stockpile map collaboration service enables individuals and families to efficiently manage and share emergency stockpiles in preparation for natural disasters and emergencies.

[0060] The emergency stockpile map linkage service according to the embodiment includes an emergency stockpile list creation unit, an emergency stockpile management unit, an information provision unit, a sharing unit, and an emergency response support unit. The emergency stockpile list creation unit creates an emergency stockpile list based on user input. For example, when a user inputs, "I want to stockpile a week's worth of food and water for a family of four," the generation AI analyzes the information and creates a specific emergency stockpile list. The generation AI generates the list using a text generation AI (e.g., LLM). The generation AI can also create a list based on prompts containing user instructions. The emergency stockpile management unit manages the emergency stockpile list created by the emergency stockpile list creation unit. For example, the generation AI manages the inventory status of emergency stockpile items registered by the user and suggests updates as necessary. The generation AI notifies the user when the expiration date of an emergency stockpile item is approaching, informing them of the need to update. The generation AI can also suggest replenishing consumed emergency stockpile items. The information provision unit provides information and advice regarding the emergency stockpile items managed by the emergency stockpile management unit. For example, the generation AI provides advice on the types, quantities, and storage methods of emergency supplies needed in the event of a disaster. It can also provide the latest disaster and emergency information. The sharing unit shares the emergency supply list. For example, a function is provided that allows a user to share the emergency supply list with family and friends. All family members can view and manage the same emergency supply list. Furthermore, emergency supply information can be shared with friends and neighbors, allowing for cooperative preparations. The emergency response support unit supports responses in emergencies. For example, the generation AI supports the user in responding quickly when an emergency occurs. The generation AI provides information on evacuation sites, emergency contact information, and confirmation of necessary emergency supplies. As a result, the emergency supply map collaboration service according to the embodiment allows users to efficiently manage and share emergency supplies and respond quickly in the event of an emergency. For example, the generation AI provides information and advice, allowing users to make appropriate preparations based on the latest information. Furthermore, by having all family members view and manage the same emergency supply list, duplication and shortages of emergency supplies can be prevented. Furthermore, sharing emergency supply information with friends and neighbors can strengthen community-wide preparedness.

[0061] The emergency stockpile list creation unit can analyze the user's past purchasing history and lifestyle habits and generate an individually optimized emergency stockpile list. In the emergency stockpile list creation unit, for example, the generation AI analyzes the user's past purchasing history and generates an emergency stockpile list based on frequently purchased foods and daily necessities. For example, canned goods and dried noodles that the user purchases regularly are included in the list. The generation AI also analyzes the user's lifestyle habits and generates an individually optimized emergency stockpile list. For example, if the user has a habit of eating at a specific time, foods suitable for that time can be included in the list. This makes it possible to generate an optimal emergency stockpile list based on the user's past purchasing history and lifestyle habits.

[0062] The stockpile list creation unit can propose a stockpile list specific to a region, taking into account the region's climate and disaster risk. For example, the generation AI can analyze the region's climate data and include cold weather gear and heating appliances in cold regions, and cooling products and hydration supplies in tropical regions in the stockpile list. The generation AI can also analyze the region's disaster risk and include emergency food and disaster prevention supplies in regions where earthquakes occur frequently, and waterproof supplies and life preservers in regions prone to flooding in the list. This makes it possible to propose the optimal stockpile list based on the region's climate and disaster risk.

[0063] The emergency supplies list creation unit can use the emotion estimation function to generate an emergency supplies list to alleviate the user's anxiety and worry. For example, the generation AI analyzes the user's emotions when inputting the list, and if the user's anxiety or worry is strong, the emergency supplies list creation unit includes emergency supplies that will give a sense of security in the list. For example, it may suggest more emergency food and medicine. The generation AI can also analyze the user's emotions and include relaxing items and stress-relieving goods in the list. This makes it possible to generate an emergency supplies list to alleviate the user's anxiety and worry.

[0064] The emergency stockpile list creation unit can generate an emergency stockpile list that takes into consideration the user's health condition and allergy information. For example, the generation AI in the emergency stockpile list creation unit analyzes the user's health condition and includes emergency stockpile items to address specific illnesses or health issues in the list. For example, it can suggest low-sugar foods and insulin to a diabetic user. The generation AI can also analyze the user's allergy information and include allergen-free foods and medicines in the list. This makes it possible to generate an emergency stockpile list that takes health into consideration based on the user's health condition and allergy information.

[0065] The emergency stockpile list creation unit can compare the emergency stockpile lists of other users and suggest the most suitable emergency stockpile items. For example, the generation AI analyzes the emergency stockpile lists of other users and suggests the most suitable list based on the common emergency stockpile items. For example, emergency food and medicines that many users have stockpiled are included in the list. The generation AI can also refer to the emergency stockpile lists of other users to suggest emergency stockpile items that the user needs. This makes it possible to suggest the most suitable emergency stockpile items by comparing the emergency stockpile lists of other users.

[0066] The emergency stockpile list creation unit can use the emotion estimation function to analyze the emotions of the user when creating the emergency stockpile list in real time and make suggestions to elicit positive emotions. The emergency stockpile list creation unit can, for example, use the emotion estimation function to analyze the emotions of the user when creating the emergency stockpile list in real time and make suggestions to elicit positive emotions. For example, it can present encouraging messages or success stories. The generation AI can also analyze the user's emotions and include items in the list that elicit positive emotions. This makes it possible to make suggestions to elicit positive emotions when the user creates the emergency stockpile list.

[0067] The stockpile management unit can learn consumption patterns of stockpiled items and suggest the optimal timing for replenishment. For example, the generation AI analyzes the user's consumption patterns of stockpiled items and suggests the optimal timing for replenishment. For example, it can automatically set replenishment reminders according to the rate of consumption. The generation AI can also learn consumption patterns of stockpiled items and suggest replenishment when inventory falls below a certain level. This allows the generation AI to learn consumption patterns of stockpiled items and suggest the optimal timing for replenishment.

[0068] The stockpile management unit can monitor the storage environment of stockpiled goods and propose appropriate management methods. For example, the generation AI can monitor the storage environment of stockpiled goods and notify if the temperature or humidity is not appropriate. For example, it can make suggestions for maintaining temperatures and humidity levels suitable for storing food. The generation AI can also monitor the storage environment and suggest changing storage locations or using storage containers. This makes it possible to monitor the storage environment of stockpiled goods and propose appropriate management methods.

[0069] The stockpile management unit can use the emotion estimation function to suggest management methods to reduce the user's stress. The stockpile management unit, for example, uses the emotion estimation function to analyze the user's stress level and suggest stockpile management methods to reduce stress. For example, relaxation goods and stress relief items can be included in the list. The generation AI can also analyze the user's emotions and suggest management methods to reduce stress. This makes it possible to suggest management methods to reduce the user's stress.

[0070] The stockpile management unit can compare the stockpile management data of other households and propose the optimal management method. For example, the generation AI analyzes the stockpile management data of other households and proposes the optimal management method based on common management methods. For example, it can include management methods that are practiced by many households in the list. The generation AI can also refer to the stockpile management data of other households to propose the optimal management method for the user. This makes it possible to propose the optimal management method by comparing the stockpile management data of other households.

[0071] The stockpile management unit can visualize the stockpile management status, allowing the user to intuitively understand it. For example, the generation AI can visualize the stockpile management status in graphs and charts, allowing the user to intuitively understand it. For example, it can visually display the inventory status and consumption rate. The generation AI can also display the stockpile management status in dashboard format, allowing the user to grasp the management status at a glance. In this way, the stockpile management status is visualized, allowing the user to intuitively understand it.

[0072] The stockpile management unit can use the emotion estimation function to analyze the user's emotions regarding stockpile management and propose a management method that elicits positive emotions. The stockpile management unit can, for example, use the emotion estimation function to analyze the user's emotions and propose a management method that elicits positive emotions. For example, it can incorporate game elements that allow the user to manage their stockpile while having fun. The generation AI can also analyze the user's emotions and include items that elicit positive emotions in the list. This makes it possible to analyze the user's emotions regarding stockpile management and propose a management method that elicits positive emotions.

[0073] The information provision unit can collect the latest disaster information in real time and provide it to the user. For example, the generation AI can collect various disaster information sites and weather data in real time and provide the latest disaster information to the user. For example, earthquake alerts and typhoon information can be immediately notified. The generation AI can also analyze disaster information and provide information that is important to the user with priority. This allows the latest disaster information to be collected in real time and provided to the user.

[0074] The information provision unit can analyze the user's past behavioral history and provide optimal advice. For example, the generation AI in the information provision unit analyzes the user's past behavioral history and advises on actions to take in the event of a disaster. For example, it can suggest the optimal evacuation route based on past evacuation locations and routes. The generation AI can also analyze the user's behavioral history and provide advice on disaster preparations and responses. This allows the user's past behavioral history to be analyzed and optimal advice to be provided.

[0075] The information provision unit can use the emotion estimation function to provide advice to reduce the user's anxiety. For example, the information provision unit uses the emotion estimation function to analyze the user's anxiety and provide advice to reduce the anxiety. For example, it provides advice on ways to relax or how to relieve stress. The generation AI can also analyze the user's emotions and provide information and advice to give a sense of security. This makes it possible to provide advice to reduce the user's anxiety.

[0076] The information provision unit can analyze the advice history of other users and provide optimal advice. For example, the generation AI analyzes the advice history of other users and provides optimal advice based on common advice. For example, it can suggest disaster prevention measures that many users are practicing. The generation AI can also refer to the advice history of other users to provide advice that is useful to the user. This allows the advice history of other users to be analyzed and optimal advice to be provided.

[0077] The information providing unit can visually display advice so that the user can intuitively understand it. For example, the generation AI can visually display advice using graphs or charts so that the user can intuitively understand it. For example, disaster prevention procedures and methods for managing stockpiles can be visually displayed. The generation AI can also display advice using icons or illustrations so that the user can understand it at a glance. In this way, the advice is visually displayed so that the user can intuitively understand it.

[0078] The information provision unit can use the emotion estimation function to analyze the user's emotions regarding the advice and provide advice that elicits positive emotions. The information provision unit, for example, uses the emotion estimation function to analyze the user's emotions and provide advice that elicits positive emotions. For example, it suggests specific actions that will make the user feel at ease. The generation AI can also analyze the user's emotions and provide information and advice that elicits positive emotions. This makes it possible to analyze the user's emotions regarding the advice and provide advice that elicits positive emotions.

[0079] The sharing unit can analyze the shared emergency stockpile list and suggest the optimal sharing method. For example, the generation AI analyzes the shared emergency stockpile list and suggests the optimal sharing method among family and friends. For example, it suggests the division of roles for each member and how to manage the emergency stockpile. The generation AI can also refer to the shared emergency stockpile list and suggest the optimal sharing method for the user. This makes it possible to analyze the shared emergency stockpile list and suggest the optimal sharing method.

[0080] The sharing unit can analyze the usage history of the shared stockpiles and suggest the optimal management method. For example, the generation AI analyzes the usage history of the shared stockpiles and suggests the optimal management method among family and friends. For example, it suggests a management method based on the frequency of use and consumption patterns of each member. The generation AI can also refer to the usage history of the shared stockpiles to suggest the optimal management method for the user. This allows the generation AI to analyze the usage history of the shared stockpiles and suggest the optimal management method.

[0081] The sharing unit can use the emotion estimation function to make suggestions to reduce the user's anxiety about sharing. For example, the sharing unit can use the emotion estimation function to analyze the user's anxiety and suggest a sharing method to reduce the anxiety. For example, it can clarify rules and guidelines for sharing. The generation AI can also analyze the user's emotions and provide information and suggestions to give a sense of security. This makes it possible to make suggestions to reduce the user's anxiety about sharing.

[0082] The sharing unit can compare the shared data of other households and propose the optimal sharing method. For example, the generation AI analyzes the shared data of other households and proposes the optimal sharing method based on common sharing methods. For example, it includes sharing methods that are practiced by many households in a list. The generation AI can also refer to the shared data of other households to propose the optimal sharing method for the user. This makes it possible to propose the optimal sharing method by comparing the shared data of other households.

[0083] The sharing unit can visualize the management status of the shared stockpiles, allowing the user to intuitively understand. For example, the generating AI can visualize the management status of the shared stockpiles in graphs and charts, allowing the user to intuitively understand. For example, it can visually display the inventory status and consumption rate. The generating AI can also display the management status of the shared stockpiles in dashboard format, allowing the user to grasp the management status at a glance. In this way, the management status of the shared stockpiles is visualized, allowing the user to intuitively understand.

[0084] The sharing unit can use the emotion estimation function to analyze the user's emotions regarding sharing and propose a sharing method that elicits positive emotions. The sharing unit, for example, uses the emotion estimation function to analyze the user's emotions and propose a sharing method that elicits positive emotions. For example, it can incorporate game elements that allow users to share while having fun. The generation AI can also analyze the user's emotions and provide information and suggestions to elicit positive emotions. This makes it possible to analyze the user's emotions regarding sharing and propose a sharing method that elicits positive emotions.

[0085] The emergency response support unit can simulate actions to be taken in an emergency and propose the optimal response method. In the emergency response support unit, for example, the generation AI simulates actions to be taken in an emergency and proposes the optimal evacuation route to the user. For example, it simulates evacuation routes and evacuation locations in the event of an earthquake and presents the optimal route. The generation AI can also propose the optimal response method for the user based on the emergency behavior simulation. This makes it possible to simulate actions to be taken in an emergency and propose the optimal response method.

[0086] The emergency response support unit can analyze the user's location information and propose the optimal evacuation route. For example, the generation AI analyzes the user's current location and proposes the optimal evacuation route. For example, in the event of an earthquake, the safest evacuation route can be presented in real time. The generation AI can also propose the location of evacuation shelters and evacuation routes based on the user's location information. This allows the user's location information to be analyzed and the optimal evacuation route to be proposed.

[0087] The emergency response support unit can use the emotion estimation function to suggest ways to reduce stress in emergencies. For example, the emergency response support unit uses the emotion estimation function to analyze the user's stress level and suggest ways to reduce stress. For example, it provides advice on ways to relax or relieve stress. The generation AI can also analyze the user's emotions and provide information and advice to give a sense of security. This makes it possible to suggest ways to reduce stress in emergencies.

[0088] The emergency response support unit can analyze other users' emergency response data and propose the optimal response method. For example, the generation AI analyzes other users' emergency response data and proposes the optimal response method based on common response methods. For example, it includes evacuation methods that many users practice in a list. The generation AI can also refer to other users' emergency response data and propose the optimal response method for the user. This makes it possible to analyze other users' emergency response data and propose the optimal response method.

[0089] The emergency response support unit can visualize the response status in an emergency, allowing the user to intuitively understand it. For example, the generation AI visualizes the response status in an emergency using graphs and charts, allowing the user to intuitively understand it. For example, it visually displays evacuation routes and evacuation locations. The generation AI can also display the response status in an emergency in dashboard format, allowing the user to grasp the response status at a glance. This visualizes the response status in an emergency, allowing the user to intuitively understand it.

[0090] The emergency response support unit can use the emotion estimation function to analyze the user's emotions regarding emergency response and propose response methods that will elicit positive emotions. The emergency response support unit can, for example, use the emotion estimation function to analyze the user's emotions and propose response methods that will elicit positive emotions. For example, it can propose specific actions that will make the user feel at ease. The generation AI can also analyze the user's emotions and provide information and advice to elicit positive emotions. This makes it possible to analyze the user's emotions regarding emergency response and propose response methods that will elicit positive emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The emergency supplies list creation unit can also generate a list that takes into account the user's hobbies and preferences, allowing the user to enjoy selecting emergency supplies. For example, if the user enjoys outdoor activities, camping supplies and outdoor gear can be included in the list. Also, if the user enjoys cooking, emergency food items that are fun to cook can be suggested. Furthermore, if the user prefers a particular brand or product, emergency supplies of that brand can be included in the list. This allows the system to generate an emergency supplies list that matches the user's hobbies and preferences, making the process of stockpiling more enjoyable.

[0093] The emergency stockpile list creation unit can use the emotion estimation function to prioritize emergency stockpiles based on the user's emotions. For example, if the user feels anxious, the most important emergency stockpiles can be prioritized in the list. Also, if the user feels safe, the number and types of emergency stockpiles included in the list can be increased. Furthermore, the quantity and types of emergency stockpiles can be adjusted according to the user's emotions. This makes it possible to generate an optimal emergency stockpile list based on the user's emotions.

[0094] The stockpile management unit can learn consumption patterns of stockpiled items and suggest the optimal timing for replenishment. For example, the generation AI can analyze the user's consumption patterns of stockpiled items and suggest the optimal timing for replenishment. For example, it can automatically set replenishment reminders according to the rate of consumption. The generation AI can also learn consumption patterns of stockpiled items and suggest replenishment when inventory falls below a certain level. This allows it to learn consumption patterns of stockpiled items and suggest the optimal timing for replenishment.

[0095] The stockpile management unit can use the emotion estimation function to suggest management methods to reduce the user's stress. For example, the emotion estimation function can be used to analyze the user's stress level and suggest stockpile management methods to reduce stress. For example, relaxation goods and stress relief items can be included in the list. The generation AI can also analyze the user's emotions and suggest management methods to reduce stress. This makes it possible to suggest management methods to reduce the user's stress.

[0096] The emergency supplies management unit can compare the emergency supplies management data of other households and propose the optimal management method. For example, the generation AI can analyze the emergency supplies management data of other households and propose the optimal management method based on common management methods. For example, it can include management methods that are practiced by many households in the list. The generation AI can also refer to the emergency supplies management data of other households to propose the optimal management method for the user. This allows it to propose the optimal management method by comparing the emergency supplies management data of other households.

[0097] The information provision unit can collect the latest disaster information in real time and provide it to users. For example, the generation AI can collect various disaster information sites and weather data in real time and provide the latest disaster information to users. For example, it can instantly notify users of earthquake alerts and typhoon information. The generation AI can also analyze disaster information and prioritize providing information that is important to users. This allows the latest disaster information to be collected in real time and provided to users.

[0098] The information provision unit can use the emotion estimation function to provide advice to reduce the user's anxiety. For example, the emotion estimation function can be used to analyze the user's anxiety and provide advice to reduce the anxiety. For example, advice on how to relax or relieve stress can be provided. The generation AI can also analyze the user's emotions and provide information and advice to give a sense of security. This makes it possible to provide advice to reduce the user's anxiety.

[0099] The information provision unit can analyze the advice history of other users and provide optimal advice. For example, the generation AI can analyze the advice history of other users and provide optimal advice based on common advice. For example, it can suggest disaster prevention measures that many users are practicing. The generation AI can also refer to the advice history of other users to provide advice that is useful to the user. This allows the generation AI to analyze the advice history of other users and provide optimal advice.

[0100] The emergency response support unit can use the emotion estimation function to suggest ways to reduce stress in emergencies. For example, the emotion estimation function can be used to analyze the user's stress level and suggest ways to reduce stress. For example, it can provide advice on how to relax or relieve stress. The generation AI can also analyze the user's emotions and provide information and advice to give a sense of security. This makes it possible to suggest ways to reduce stress in emergencies.

[0101] The emergency response support unit can simulate actions to take in an emergency and propose the optimal response method. For example, the generation AI can simulate actions to take in an emergency and propose the optimal evacuation route to the user. For example, it can simulate evacuation routes and evacuation locations in the event of an earthquake and present the optimal route. The generation AI can also propose the optimal response method for the user based on the emergency behavior simulation. This allows it to simulate actions to take in an emergency and propose the optimal response method.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The emergency supplies list creation unit creates an emergency supplies list based on user input. For example, if a user inputs, "I want to stockpile a week's worth of food and water for a family of four," the generation AI analyzes that information and creates a specific emergency supplies list. The generation AI generates the list using a text generation AI (e.g., LLM). The generation AI can also generate a list based on prompts containing user instructions. Step 2: The stockpile management unit manages the stockpile list created by the stockpile list creation unit. For example, the generation AI manages the inventory status of stockpile items registered by the user and suggests updates as necessary. The generation AI notifies the user when the expiration date of a stockpile item is approaching, informing them of the need to update. It can also suggest replenishing consumed stockpile items. Step 3: The information provision unit provides information and advice about the stockpiles managed by the stockpile management unit. For example, the generation AI can provide advice on the types and quantities of stockpiles needed in the event of a disaster, as well as storage methods. It can also provide the latest disaster and emergency information. Step 4: The sharing section allows users to share their emergency supplies list. For example, it provides a function that allows users to share their emergency supplies list with family and friends. All family members can view and manage the same emergency supplies list. Users can also share information about emergency supplies with friends and neighbors, allowing them to work together to prepare. Step 5: The emergency response support unit supports responses in the event of an emergency. For example, the generation AI supports the user in responding quickly when an emergency occurs. The generation AI provides information on evacuation sites, emergency contact information, and checks for necessary supplies.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

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

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 stockpile list creation unit that creates a stockpile list based on user input; a stockpile management unit that manages the stockpile list created by the stockpile list creation unit; an information providing unit that provides information and advice regarding the stockpiles managed by the stockpiles management unit; a sharing unit that shares the stockpile list; An emergency response support department that supports responses in emergencies. A system characterized by:

2. The stockpile item list creation unit Considering the local climate and disaster risks, propose a list of stockpiles specific to the region.

2. The system of claim 1.

3. The stockpile management unit Learn consumption patterns of stockpiled items and suggest optimal replenishment timing 2. The system of claim 1.

4. The information providing unit Collect the latest disaster information in real time and provide it to the users.

2. The system of claim 1.

5. The common part is Analyze the shared stockpile list and propose the best sharing method 2. The system of claim 1.

6. The emergency response support department Conducting emergency response simulations and proposing optimal responses 2. The system of claim 1.

7. The stockpile item list creation unit Using an emotion estimation function, the stockpile list is generated to alleviate the user's anxiety and worry.

2. The system of claim 1.

8. The emergency response support department Using emotion estimation to suggest ways to reduce stress in emergencies 2. The system of claim 1.

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