system

Abstract

The system according to this embodiment aims to efficiently manage information on disaster relief supplies and provide it quickly to local governments that need it. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a registration unit, a selection unit, and a provision unit. The collection unit collects information on disaster relief supplies. The analysis unit analyzes the information collected by the collection unit. The registration unit registers the information analyzed by the analysis unit into a database. The selection unit selects candidate municipalities for provision based on the information registered by the registration unit. The provision unit provides the information selected by the selection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently manage information on disaster stockpiles and quickly provide it to the necessary local governments.

[0005] The system according to the embodiment aims to efficiently manage information on disaster stockpiles and quickly provide it to the necessary local governments.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a registration unit, a selection unit, and a provision unit. The collection unit collects information on disaster relief supplies. The analysis unit analyzes the information collected by the collection unit. The registration unit registers the information analyzed by the analysis unit into a database. The selection unit selects candidate municipalities for provision based on the information registered by the registration unit. The provision unit provides the information selected by the selection unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage information on disaster relief supplies and provide it quickly to local governments that need it. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The centralized management system according to an embodiment of the present invention is a system for efficiently managing the mutual exchange of disaster relief supplies among local governments in the event of a disaster. This system includes a series of processes for collecting, analyzing, registering information on disaster relief supplies in a database, selecting candidate local governments to provide supplies, and providing the information. First, disaster relief supply information from the national and local governments is collected. In this process, each local government sends images of specified items using an SNS application. For example, information such as the inventory status and expiration date of the supplies is sent as an image. This information is received by the SNS application. Next, the image information received by the SNS application is analyzed. The generating AI analyzes the image information and extracts data such as the inventory status and expiration date of the supplies. For example, text information is extracted from the image and registered in the database. This database is for centralized management of disaster relief supply information nationwide. Requests for supplies from disaster-stricken local governments are also made through the SNS application. Disaster-stricken local governments send the type and quantity of supplies they need through the SNS application. For example, they send a request such as "We need 1000 liters of water." This information is registered in the database. Next, based on the database, a generating AI is used to identify potential municipalities that can provide supplies, taking into account factors such as transportation routes to the disaster area, municipalities with high supply ownership rates, and the expiration dates of the stockpiled supplies. The generating AI analyzes the database information and selects the most suitable candidate municipality. For example, it might select a municipality with a high supply ownership rate and a short transportation route. Finally, the contact information (contact details) and estimated delivery time of the candidate municipalities are communicated to the disaster-stricken municipalities via a social networking application. This allows the disaster-stricken municipalities to quickly receive the necessary supplies. For example, by notifying the contact information of the candidate municipalities and the estimated delivery time, the disaster-stricken municipalities can respond smoothly. This system enables the efficient exchange of disaster supplies and allows for the rapid provision of support to disaster-stricken municipalities. For example, even if there is a shortage of necessary supplies during a disaster, they can be quickly provided by other municipalities. Furthermore, centralized management of the expiration dates and inventory status of stockpiled supplies enables efficient and waste-free management. As a result, the centralized management system can efficiently manage the exchange of disaster supplies among municipalities during disasters.

[0029] The unified management system according to this embodiment comprises a collection unit, an analysis unit, a registration unit, a selection unit, and a provision unit. The collection unit collects information on disaster relief supplies. For example, the collection unit collects information on disaster relief supplies from the national government and local governments. The collection unit can collect images of specified items using an SNS application. For example, it can collect information such as the inventory status and expiration date of supplies as images. The collection unit analyzes the image information received via the SNS application. The analysis unit analyzes the image information and extracts data such as the inventory status and expiration date of supplies. For example, the analysis unit extracts text information from images and registers it in a database. The analysis unit can use a generation AI to analyze image information and extract data such as the inventory status and expiration date of supplies. For example, the generation AI extracts text information from images and registers it in a database. The registration unit registers the information analyzed by the analysis unit in a database. For example, the registration unit registers the extracted data in a database. The registration unit can use a generation AI to register the extracted data in a database. For example, the generation AI registers the extracted data in a database. The selection unit selects candidate municipalities to provide supplies based on the information registered by the registration unit. The selection unit analyzes the database information to select the most suitable candidate municipality. The selection unit can use the generation AI to analyze the database information and select the most suitable candidate municipality. For example, the generation AI analyzes the database information and selects the most suitable candidate municipality. The provision unit provides the information selected by the selection unit. The provision unit contacts the affected municipalities with information such as the contact point of the candidate supplying municipalities and the number of days until provision. The provision unit can use the generation AI to contact the affected municipalities with information such as the contact point of the candidate supplying municipalities and the number of days until provision. For example, the generation AI contacts the affected municipalities with information such as the contact point of the candidate supplying municipalities and the number of days until provision. As a result, the centralized management system according to the embodiment can efficiently collect, analyze, register, select, and provide information on disaster relief supplies.

[0030] The collection unit collects information on disaster relief supplies. Specifically, to collect information on disaster relief supplies from the national and local governments, it obtains information from publicly available databases and official websites of each local government. The collection unit can also collect images of specified items using social media applications. For example, to collect information such as the inventory status and expiration dates of supplies as images, it encourages users of social media applications to post photos of the supplies. This allows the collection unit to efficiently collect real-time updated information on supplies. Furthermore, the collection unit can collect not only image information but also text information and location information. For example, it collects comments, tags, and location information associated with images posted by users to understand detailed information about the supplies. This allows the collection unit to collect comprehensive data from diverse sources and accurately understand the status of disaster relief supplies.

[0031] The analysis unit analyzes the collected image information and extracts data such as the inventory status and expiration dates of stockpiled goods. Specifically, it uses a generative AI to extract text information from images and register it in a database. The generative AI utilizes image recognition technology to analyze text, barcodes, QR codes (registered trademarks), etc., within images and extract the necessary information. For example, it can automatically read the expiration date and inventory quantity written on the labels of stockpiled goods and register them in the database. The generative AI can also perform object recognition within images to identify the type and quantity of stockpiled goods. As a result, the analysis unit can quickly and accurately analyze the collected image information and grasp the status of stockpiled goods in real time. Furthermore, the analysis unit can also use past data and statistical information to predict consumption trends and the need for replenishment of stockpiled goods. For example, based on past consumption data, it can predict the consumption rate of specific stockpiled goods and provide information for replenishment at the appropriate time. As a result, the analysis unit can streamline the management of stockpiled goods and ensure thorough preparation for disasters.

[0032] The registration unit registers the information analyzed by the analysis unit into the database. Specifically, it uses a generation AI to register the extracted data into the database. The generation AI automatically classifies the data provided by the analysis unit and registers it in the database in the appropriate format. For example, it organizes information such as the type of stockpiled goods, inventory quantity, and expiration date for each item and registers it in the database. The registration unit can also perform duplicate data detection and error checking to maintain data integrity and consistency. This allows the registration unit to build an accurate and reliable database and streamline the management of stockpiled goods. Furthermore, the registration unit manages the database update frequency and data retention period, ensuring that the latest information is always available. For example, it optimizes database performance by regularly updating the database and archiving old data. This allows the registration unit to efficiently manage information on stockpiled goods and support a rapid response in the event of a disaster.

[0033] The selection department selects candidate municipalities to provide supplies based on information registered by the registration department. Specifically, it uses a generating AI to analyze the database information and select the most suitable candidate municipalities. The generating AI analyzes the information in the database and comprehensively evaluates each municipality's stockpile status, expiration dates, and demand. For example, it prioritizes selecting municipalities that are short on specific stockpiles or those with many stockpiles nearing their expiration dates. The generating AI can also select the most suitable candidate municipalities by considering past provision history and disaster risk. This allows the selection department to select candidate municipalities for efficient and equitable provision of stockpiles, supporting a rapid response during disasters. Furthermore, the selection department can update the selection results in real time to respond to the latest situation. For example, it can re-select candidate municipalities in response to new disaster information or changes in stockpile inventory status. This allows the selection department to always select the most suitable candidate municipalities and achieve efficient provision of stockpiles during disasters.

[0034] The supply department provides information selected by the selection department. Specifically, it uses a generation AI to contact disaster-stricken municipalities with information such as contact details for potential supply municipalities and the number of days until delivery. Based on the information provided by the selection department, the generation AI automatically generates contact details for potential supply municipalities and the number of days until delivery, and contacts the disaster-stricken municipalities. For example, it automatically calculates the contact information of the person in charge at the potential supply municipality and the number of days required to provide the stockpiled supplies, and notifies the disaster-stricken municipalities. Furthermore, the supply department can reliably transmit information using multiple communication methods. For example, it uses a combination of email, SMS, and voice calls to deliver important information quickly and reliably. This allows the supply department to provide disaster-stricken municipalities with timely and accurate information and support the efficient provision of stockpiled supplies during disasters. In addition, the supply department can collect feedback on the provision status and improve the provision process. For example, it can review the number of days until delivery and communication methods based on feedback from disaster-stricken municipalities. This allows the supply department to always maintain an optimal provision process and support a rapid response during disasters.

[0035] The collection unit can collect images of specified items using a social networking service (SNS) application. For example, the collection unit collects images of specified items using an SNS application. The collection unit analyzes the image information received by the SNS application. The collection unit can also collect images of specified items using an SNS application by using a generation AI. For example, the generation AI collects images of specified items using an SNS application. This makes the collection of disaster preparedness information more efficient by using an SNS application.

[0036] The analysis unit can analyze image information and extract data such as the inventory status and expiration dates of stockpiled goods. For example, the analysis unit can analyze image information and extract data such as the inventory status and expiration dates of stockpiled goods. The analysis unit can use a generation AI to analyze image information and extract data such as the inventory status and expiration dates of stockpiled goods. For example, the generation AI extracts text information from images and registers it in a database. This allows for the efficient extraction of data such as the inventory status and expiration dates of stockpiled goods by analyzing image information.

[0037] The registration unit can register the extracted data into a database. For example, the registration unit registers the extracted data into a database. The registration unit can also register the extracted data into a database using a generation AI. For example, the generation AI registers the extracted data into a database. This allows for centralized management of disaster preparedness information by registering the extracted data into a database.

[0038] The selection unit can analyze the database information and select the most suitable candidate municipality to provide services. For example, the selection unit can analyze the database information and select the most suitable candidate municipality. The selection unit can also use a generation AI to analyze the database information and select the most suitable candidate municipality. For example, the generation AI analyzes the database information and selects the most suitable candidate municipality. Thus, by analyzing the database information, the most suitable candidate municipality can be selected.

[0039] The provision department can contact the disaster-stricken municipalities with information such as the contact points of potential supply municipalities and the number of days until provision. For example, the provision department can contact the disaster-stricken municipalities with information such as the contact points of potential supply municipalities and the number of days until provision. The provision department can use a generation AI to contact the disaster-stricken municipalities with information such as the contact points of potential supply municipalities and the number of days until provision. For example, the generation AI can contact the disaster-stricken municipalities with information such as the contact points of potential supply municipalities and the number of days until provision. By providing information such as the contact points of potential supply municipalities and the number of days until provision, support to the disaster-stricken municipalities can be implemented quickly.

[0040] The data collection unit can analyze the past disaster response history of each local government and select the optimal data collection method. For example, the data collection unit can identify methods that allowed for rapid information collection from past disaster response history and adopt similar methods. The data collection unit can avoid methods that were time-consuming to collect information from past disaster response history and select efficient methods. The data collection unit can analyze past disaster response history and select the most effective means of information collection. In this way, the optimal data collection method can be selected by analyzing past disaster response history. Some or all of the above processes in the data collection unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the data collection unit can input past disaster response history data into a generation AI and have the generation AI select the optimal data collection method.

[0041] The data collection unit can filter information on disaster preparedness supplies based on the local government's current disaster risk assessment. For example, if the local government's disaster risk assessment is high, the data collection unit can prioritize collecting information. If the local government's disaster risk assessment is low, the data collection unit can reduce the frequency of information collection. The data collection unit can adjust the level of detail of the information collected based on the local government's disaster risk assessment. This allows for the priority collection of important information by filtering information based on the local government's disaster risk assessment. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input local government disaster risk assessment data into a generation AI and have the generation AI perform the information filtering.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of local governments when collecting disaster preparedness information. For example, the data collection unit can prioritize the collection of information from neighboring local governments based on the geographical location information of local governments. The data collection unit can prioritize the collection of information from areas with high disaster risk based on the geographical location information of local governments. The data collection unit can prioritize the collection of information from areas with good transportation access based on the geographical location information of local governments. In this way, by considering the geographical location information of local governments, highly relevant information can be prioritized. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the geographical location data of local governments into a generation AI and have the generation AI prioritize the information.

[0043] The data collection unit can analyze the social media activities of local governments and collect relevant information when collecting information on disaster preparedness supplies. For example, the data collection unit can analyze the social media activities of local governments and collect posts related to disaster preparedness supplies. The data collection unit can analyze the social media activities of local governments and collect information related to disaster response. The data collection unit can analyze the social media activities of local governments and collect information related to the needs of residents. This allows for the efficient collection of relevant information by analyzing the social media activities of local governments. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the social media activity data of local governments into a generative AI and have the generative AI collect relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the disaster relief supplies. For example, the analysis unit can perform a detailed analysis on disaster relief supplies of high importance. For disaster relief supplies of low importance, the analysis unit can perform a simplified analysis. The analysis unit can determine the priority of the analysis according to importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the disaster relief supplies. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance data of the disaster relief supplies into a generation AI and have the generation AI perform the analysis with the appropriate level of detail.

[0045] The analysis unit can apply different analysis algorithms depending on the category of disaster relief supplies. For example, for food-related disaster relief supplies, the analysis unit can apply an analysis algorithm that emphasizes the expiration date. For pharmaceutical-related disaster relief supplies, the analysis unit can apply an analysis algorithm that emphasizes the expiration date and inventory status. For clothing and bedding-related disaster relief supplies, the analysis unit can apply an analysis algorithm that emphasizes the quantity and condition. By applying different analysis algorithms depending on the category of disaster relief supplies, highly accurate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input disaster relief supply category data into a generation AI and have the generation AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the submission date of disaster relief supplies. For example, the analysis unit can prioritize the analysis of disaster relief supplies that have been submitted recently. The analysis unit can postpone the analysis of disaster relief supplies that have been submitted recently. The analysis unit can adjust the analysis schedule based on the submission date. This allows for efficient analysis by determining the priority of analysis based on the submission date of disaster relief supplies. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the submission date data of disaster relief supplies into a generation AI and have the generation AI execute the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of disaster relief supplies. For example, the analysis unit can prioritize the analysis of highly relevant disaster relief supplies. The analysis unit can postpone the analysis of less relevant disaster relief supplies. The analysis unit can adjust the analysis schedule based on relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of disaster relief supplies. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the relevance data of disaster relief supplies into a generation AI and have the generation AI execute the analysis in the correct order.

[0048] The registration unit can adjust the level of detail in the registration based on the importance of the disaster relief supplies. For example, the registration unit can register detailed information for disaster relief supplies of high importance, and simpler information for disaster relief supplies of low importance. The registration unit can determine the priority of registration according to importance. This allows for efficient data registration by adjusting the level of detail in the registration based on the importance of the disaster relief supplies. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input the importance data of the disaster relief supplies into a generation AI and have the generation AI perform the level of detail in the registration.

[0049] The registration unit can apply different registration algorithms depending on the category of disaster relief supplies. For example, for food-related disaster relief supplies, the registration unit can apply a registration algorithm that emphasizes the expiration date. For pharmaceutical-related disaster relief supplies, the registration unit can apply a registration algorithm that emphasizes the expiration date and inventory status. For clothing and bedding-related disaster relief supplies, the registration unit can apply a registration algorithm that emphasizes the quantity and condition. By applying different registration algorithms depending on the category of disaster relief supplies, highly accurate data registration can be achieved. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input disaster relief supply category data into a generation AI and have the generation AI execute the application of the registration algorithm.

[0050] The registration unit can determine the priority of registration based on the submission date of disaster relief supplies. For example, the registration unit can prioritize the registration of disaster relief supplies with a more recent submission date. The registration unit can postpone the registration of disaster relief supplies with an older submission date. The registration unit can adjust the registration schedule based on the submission date. This allows for efficient data registration by determining the priority of registration based on the submission date of disaster relief supplies. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input the submission date data of disaster relief supplies into a generation AI and have the generation AI determine the registration priority.

[0051] The registration unit can adjust the registration order based on the relevance of disaster relief supplies. For example, the registration unit can prioritize the registration of highly relevant disaster relief supplies. The registration unit can postpone the registration of less relevant disaster relief supplies. The registration unit can adjust the registration schedule based on relevance. This allows for efficient data registration by adjusting the registration order based on the relevance of disaster relief supplies. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input the relevance data of disaster relief supplies into a generation AI and have the generation AI execute the registration order.

[0052] The selection unit can improve the accuracy of its selection by considering the interrelationships of disaster relief supplies. For example, the selection unit can consider the interrelationships of disaster relief supplies and select municipalities that can provide related supplies in a single package. The selection unit can analyze the interrelationships of disaster relief supplies and select municipalities that can provide them efficiently. Based on the interrelationships of disaster relief supplies, the selection unit can select the most suitable candidate municipalities for provision. This makes it possible to select candidates efficiently and accurately by considering the interrelationships of disaster relief supplies. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the selection unit can input data on the interrelationships of disaster relief supplies into a generation AI and have the generation AI perform the task of improving the accuracy of the selection.

[0053] The selection unit can make selections by considering the attribute information of local governments. For example, the selection unit can select appropriate candidate local governments by considering the population size of the local governments. The selection unit can select local governments that can be provided to by considering the economic situation of the local governments. The selection unit can select local governments that can be provided to quickly by considering the geographical conditions of the local governments. In this way, appropriate candidate local governments can be selected by considering the attribute information of the local governments. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the selection unit can input local government attribute information data into a generation AI and have the generation AI perform the selection.

[0054] The selection unit can perform selections while considering the geographical distribution of municipalities. For example, the selection unit can prioritize selecting neighboring municipalities based on their geographical distribution. The selection unit can prioritize selecting areas with high disaster risk based on their geographical distribution. The selection unit can prioritize selecting areas with good transportation access based on their geographical distribution. This allows for rapid and efficient candidate selection by considering the geographical distribution of municipalities. Some or all of the above-described processes in the selection unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the selection unit can input geographical distribution data of municipalities into a generation AI and have the generation AI perform the selection.

[0055] The selection unit can improve the accuracy of its selection by referring to relevant literature on disaster relief supplies. For example, the selection unit can refer to relevant literature on disaster relief supplies and select the most suitable candidate municipalities for provision. Based on the relevant literature on disaster relief supplies, the selection unit can select municipalities that can provide the supplies efficiently. The selection unit can analyze relevant literature on disaster relief supplies and make highly accurate selections. As a result, by referring to relevant literature on disaster relief supplies, it becomes possible to select candidates with high accuracy. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the selection unit can input data on relevant literature on disaster relief supplies into a generation AI and have the generation AI perform the task of improving the accuracy of the selection.

[0056] The provisioning unit can adjust the level of detail provided based on the importance of the disaster relief supplies. For example, the provisioning unit can provide detailed information for disaster relief supplies of high importance. For disaster relief supplies of low importance, the provisioning unit can provide simplified information. The provisioning unit can determine the priority of provision according to importance. This allows for efficient information provision by adjusting the level of detail provided based on the importance of the disaster relief supplies. Some or all of the above processing in the provisioning unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the provisioning unit can input the importance data of the disaster relief supplies into a generation AI and have the generation AI determine the level of detail provided.

[0057] The supply unit can apply different supply algorithms depending on the category of disaster relief supplies. For example, for food-related disaster relief supplies, the supply unit can apply a supply algorithm that emphasizes the expiration date. For pharmaceutical-related disaster relief supplies, the supply unit can apply a supply algorithm that emphasizes the expiration date and inventory status. For clothing and bedding-related disaster relief supplies, the supply unit can apply a supply algorithm that emphasizes the quantity and condition. By applying different supply algorithms depending on the category of disaster relief supplies, it becomes possible to provide highly accurate information. Some or all of the above processing in the supply unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the supply unit can input disaster relief supply category data into a generation AI and have the generation AI execute the application of the supply algorithm.

[0058] The distribution department can determine the priority of provision based on the submission date of disaster relief supplies. For example, the distribution department can prioritize the provision of disaster relief supplies that have been submitted recently. The distribution department can postpone the provision of disaster relief supplies that have been submitted older. The distribution department can adjust the provision schedule based on the submission date. This allows for efficient provision of information by determining the priority of provision based on the submission date of disaster relief supplies. Some or all of the above processing in the distribution department may be performed using a generation AI, or it may be performed without a generation AI. For example, the distribution department can input data on the submission date of disaster relief supplies into a generation AI and have the generation AI determine the priority of provision.

[0059] The distribution unit can adjust the order of distribution based on the relevance of the disaster relief supplies. For example, the distribution unit can prioritize the distribution of highly relevant disaster relief supplies. The distribution unit can postpone the distribution of less relevant disaster relief supplies. The distribution unit can adjust the distribution schedule based on relevance. This allows for efficient information provision by adjusting the order of distribution based on the relevance of the disaster relief supplies. Some or all of the above processing in the distribution unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the distribution unit can input relevance data of disaster relief supplies into a generation AI and have the generation AI execute the distribution order.

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

[0061] The collection department can adjust its collection methods when gathering information on disaster relief supplies, taking into account the budget situation of local governments. For example, for local governments with limited budgets, the collection department will adopt cost-effective collection methods. For local governments with abundant budgets, the collection department can use advanced technologies to collect detailed information. The collection department can adjust the collection frequency and scope according to the budget situation. This allows for efficient information collection by adjusting the collection method based on the local government's budget.

[0062] The analysis unit can adjust the level of detail in its analysis of disaster preparedness information, taking into account the disaster response capabilities of local governments. For example, for local governments with high disaster response capabilities, the analysis unit can perform detailed analysis and provide highly accurate data. For local governments with low disaster response capabilities, the analysis unit can perform simplified analysis and provide results quickly. The analysis unit can determine the priority of analysis according to the disaster response capabilities. This allows for efficient analysis by adjusting the level of detail based on the disaster response capabilities of local governments.

[0063] The registration unit can adjust the registration method when registering disaster preparedness information, taking into account the population density of the local government. For example, for local governments with high population density, the registration unit can register detailed information and provide highly accurate data. For local governments with low population density, the registration unit can register simplified information and provide data quickly. The registration unit can determine the priority of registration according to population density. This allows for efficient data registration by adjusting the registration method based on the population density of the local government.

[0064] The selection department can adjust its selection criteria when selecting municipalities to receive disaster relief supplies, taking into account the municipalities' disaster history. For example, for municipalities that have experienced large-scale disasters in the past, the selection department will select candidates that can provide supplies quickly. For municipalities with little disaster history, the selection department can adopt detailed selection criteria to select highly accurate candidates. The selection department can determine the priority of selection based on the disaster history. This allows for the rapid and accurate selection of candidates by adjusting the selection criteria based on the disaster history of each municipality.

[0065] The provision department can adjust its provision method when providing information on disaster relief supplies, taking into account the infrastructure status of local governments. For example, it can provide detailed information and highly accurate data to local governments with well-developed infrastructure. For local governments with underdeveloped infrastructure, it can provide simplified information and data quickly. The provision department can determine the priority of provision according to the infrastructure status. This allows for efficient information provision by adjusting the provision method based on the infrastructure status of local governments.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects information on disaster relief supplies. For example, it can collect information on disaster relief supplies from the national and local governments and collect images of specified items using social media applications. Specifically, it collects information such as the inventory status and expiration dates of the supplies as images. Step 2: The analysis unit analyzes the image information collected by the collection unit and extracts data such as the inventory status and expiration dates of stockpiled items. For example, it uses a generation AI to extract text information from images and registers it in a database. Step 3: The registration unit registers the information analyzed by the analysis unit into the database. For example, it registers data extracted using the generation AI into the database. Step 4: The selection department selects candidate municipalities to provide services based on the information registered by the registration department. For example, it uses a generation AI to analyze the database information and select the most suitable candidate municipalities. Step 5: The provision department provides the information selected by the selection department. For example, it will contact the affected municipalities with contact information for potential supply municipalities and the number of days until provision. This information can also be communicated using AI generation.

[0068] (Example of form 2) The centralized management system according to an embodiment of the present invention is a system for efficiently managing the mutual exchange of disaster relief supplies among local governments in the event of a disaster. This system includes a series of processes for collecting, analyzing, registering information on disaster relief supplies in a database, selecting candidate local governments to provide supplies, and providing the information. First, disaster relief supply information from the national and local governments is collected. In this process, each local government sends images of specified items using an SNS application. For example, information such as the inventory status and expiration date of the supplies is sent as an image. This information is received by the SNS application. Next, the image information received by the SNS application is analyzed. The generating AI analyzes the image information and extracts data such as the inventory status and expiration date of the supplies. For example, text information is extracted from the image and registered in the database. This database is for centralized management of disaster relief supply information nationwide. Requests for supplies from disaster-stricken local governments are also made through the SNS application. Disaster-stricken local governments send the type and quantity of supplies they need through the SNS application. For example, they send a request such as "We need 1000 liters of water." This information is registered in the database. Next, based on the database, a generating AI is used to identify potential municipalities that can provide supplies, taking into account factors such as transportation routes to the disaster area, municipalities with high supply ownership rates, and the expiration dates of the stockpiled supplies. The generating AI analyzes the database information and selects the most suitable candidate municipality. For example, it might select a municipality with a high supply ownership rate and a short transportation route. Finally, the contact information (contact details) and estimated delivery time of the candidate municipalities are communicated to the disaster-stricken municipalities via a social networking application. This allows the disaster-stricken municipalities to quickly receive the necessary supplies. For example, by notifying the contact information of the candidate municipalities and the estimated delivery time, the disaster-stricken municipalities can respond smoothly. This system enables the efficient exchange of disaster supplies and allows for the rapid provision of support to disaster-stricken municipalities. For example, even if there is a shortage of necessary supplies during a disaster, they can be quickly provided by other municipalities. Furthermore, centralized management of the expiration dates and inventory status of stockpiled supplies enables efficient and waste-free management. As a result, the centralized management system can efficiently manage the exchange of disaster supplies among municipalities during disasters.

[0069] The unified management system according to this embodiment comprises a collection unit, an analysis unit, a registration unit, a selection unit, and a provision unit. The collection unit collects information on disaster relief supplies. For example, the collection unit collects information on disaster relief supplies from the national government and local governments. The collection unit can collect images of specified items using an SNS application. For example, it can collect information such as the inventory status and expiration date of supplies as images. The collection unit analyzes the image information received via the SNS application. The analysis unit analyzes the image information and extracts data such as the inventory status and expiration date of supplies. For example, the analysis unit extracts text information from images and registers it in a database. The analysis unit can use a generation AI to analyze image information and extract data such as the inventory status and expiration date of supplies. For example, the generation AI extracts text information from images and registers it in a database. The registration unit registers the information analyzed by the analysis unit in a database. For example, the registration unit registers the extracted data in a database. The registration unit can use a generation AI to register the extracted data in a database. For example, the generation AI registers the extracted data in a database. The selection unit selects candidate municipalities to provide supplies based on the information registered by the registration unit. The selection unit analyzes the database information to select the most suitable candidate municipality. The selection unit can use the generation AI to analyze the database information and select the most suitable candidate municipality. For example, the generation AI analyzes the database information and selects the most suitable candidate municipality. The provision unit provides the information selected by the selection unit. The provision unit contacts the affected municipalities with information such as the contact point of the candidate supplying municipalities and the number of days until provision. The provision unit can use the generation AI to contact the affected municipalities with information such as the contact point of the candidate supplying municipalities and the number of days until provision. For example, the generation AI contacts the affected municipalities with information such as the contact point of the candidate supplying municipalities and the number of days until provision. As a result, the centralized management system according to the embodiment can efficiently collect, analyze, register, select, and provide information on disaster relief supplies.

[0070] The collection unit collects information on disaster relief supplies. Specifically, to collect information on disaster relief supplies from the national and local governments, it obtains information from publicly available databases and official websites of each local government. The collection unit can also collect images of specified items using social media applications. For example, to collect information such as the inventory status and expiration dates of supplies as images, it encourages users of social media applications to post photos of the supplies. This allows the collection unit to efficiently collect real-time updated information on supplies. Furthermore, the collection unit can collect not only image information but also text information and location information. For example, it collects comments, tags, and location information associated with images posted by users to understand detailed information about the supplies. This allows the collection unit to collect comprehensive data from diverse sources and accurately understand the status of disaster relief supplies.

[0071] The analysis unit analyzes the collected image information and extracts data such as the inventory status and expiration dates of stockpiled goods. Specifically, it uses a generative AI to extract text information from images and register it in a database. The generative AI utilizes image recognition technology to analyze text, barcodes, QR codes, etc., within images and extract the necessary information. For example, it can automatically read the expiration date and inventory quantity written on the labels of stockpiled goods and register them in the database. The generative AI can also perform object recognition within images to identify the type and quantity of stockpiled goods. As a result, the analysis unit can quickly and accurately analyze the collected image information and grasp the status of stockpiled goods in real time. Furthermore, the analysis unit can also use past data and statistical information to predict consumption trends and the need for replenishment of stockpiled goods. For example, based on past consumption data, it can predict the consumption rate of specific stockpiled goods and provide information for replenishment at the appropriate time. This allows the analysis unit to streamline the management of stockpiled goods and ensure thorough preparation for disasters.

[0072] The registration unit registers the information analyzed by the analysis unit into the database. Specifically, it uses a generation AI to register the extracted data into the database. The generation AI automatically classifies the data provided by the analysis unit and registers it in the database in the appropriate format. For example, it organizes information such as the type of stockpiled goods, inventory quantity, and expiration date for each item and registers it in the database. The registration unit can also perform duplicate data detection and error checking to maintain data integrity and consistency. This allows the registration unit to build an accurate and reliable database and streamline the management of stockpiled goods. Furthermore, the registration unit manages the database update frequency and data retention period, ensuring that the latest information is always available. For example, it optimizes database performance by regularly updating the database and archiving old data. This allows the registration unit to efficiently manage information on stockpiled goods and support a rapid response in the event of a disaster.

[0073] The selection department selects candidate municipalities to provide supplies based on information registered by the registration department. Specifically, it uses a generating AI to analyze the database information and select the most suitable candidate municipalities. The generating AI analyzes the information in the database and comprehensively evaluates each municipality's stockpile status, expiration dates, and demand. For example, it prioritizes selecting municipalities that are short on specific stockpiles or those with many stockpiles nearing their expiration dates. The generating AI can also select the most suitable candidate municipalities by considering past provision history and disaster risk. This allows the selection department to select candidate municipalities for efficient and equitable provision of stockpiles, supporting a rapid response during disasters. Furthermore, the selection department can update the selection results in real time to respond to the latest situation. For example, it can re-select candidate municipalities in response to new disaster information or changes in stockpile inventory status. This allows the selection department to always select the most suitable candidate municipalities and achieve efficient provision of stockpiles during disasters.

[0074] The supply department provides information selected by the selection department. Specifically, it uses a generation AI to contact disaster-stricken municipalities with information such as contact details for potential supply municipalities and the number of days until delivery. Based on the information provided by the selection department, the generation AI automatically generates contact details for potential supply municipalities and the number of days until delivery, and contacts the disaster-stricken municipalities. For example, it automatically calculates the contact information of the person in charge at the potential supply municipality and the number of days required to provide the stockpiled supplies, and notifies the disaster-stricken municipalities. Furthermore, the supply department can reliably transmit information using multiple communication methods. For example, it uses a combination of email, SMS, and voice calls to deliver important information quickly and reliably. This allows the supply department to provide disaster-stricken municipalities with timely and accurate information and support the efficient provision of stockpiled supplies during disasters. In addition, the supply department can collect feedback on the provision status and improve the provision process. For example, it can review the number of days until delivery and communication methods based on feedback from disaster-stricken municipalities. This allows the supply department to always maintain an optimal provision process and support a rapid response during disasters.

[0075] The collection unit can collect images of specified items using a social networking service (SNS) application. For example, the collection unit collects images of specified items using an SNS application. The collection unit analyzes the image information received by the SNS application. The collection unit can also collect images of specified items using an SNS application by using a generation AI. For example, the generation AI collects images of specified items using an SNS application. This makes the collection of disaster preparedness information more efficient by using an SNS application.

[0076] The analysis unit can analyze image information and extract data such as the inventory status and expiration dates of stockpiled goods. For example, the analysis unit can analyze image information and extract data such as the inventory status and expiration dates of stockpiled goods. The analysis unit can use a generation AI to analyze image information and extract data such as the inventory status and expiration dates of stockpiled goods. For example, the generation AI extracts text information from images and registers it in a database. This allows for the efficient extraction of data such as the inventory status and expiration dates of stockpiled goods by analyzing image information.

[0077] The registration unit can register the extracted data into a database. For example, the registration unit registers the extracted data into a database. The registration unit can also register the extracted data into a database using a generation AI. For example, the generation AI registers the extracted data into a database. This allows for centralized management of disaster preparedness information by registering the extracted data into a database.

[0078] The selection unit can analyze the database information and select the most suitable candidate municipality to provide services. For example, the selection unit can analyze the database information and select the most suitable candidate municipality. The selection unit can also use a generation AI to analyze the database information and select the most suitable candidate municipality. For example, the generation AI analyzes the database information and selects the most suitable candidate municipality. Thus, by analyzing the database information, the most suitable candidate municipality can be selected.

[0079] The provision department can contact the disaster-stricken municipalities with information such as the contact points of potential supply municipalities and the number of days until provision. For example, the provision department can contact the disaster-stricken municipalities with information such as the contact points of potential supply municipalities and the number of days until provision. The provision department can use a generation AI to contact the disaster-stricken municipalities with information such as the contact points of potential supply municipalities and the number of days until provision. For example, the generation AI can contact the disaster-stricken municipalities with information such as the contact points of potential supply municipalities and the number of days until provision. By providing information such as the contact points of potential supply municipalities and the number of days until provision, support to the disaster-stricken municipalities can be implemented quickly.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of collecting disaster preparedness information based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. If the user is relaxed, the data collection unit can accelerate the collection timing to quickly collect information. If the user is facing an emergency, the data collection unit can collect information immediately. In this way, by adjusting the collection timing based on the user's emotions, the user's burden is reduced and information can be collected efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection unit can analyze the past disaster response history of each local government and select the optimal data collection method. For example, the data collection unit can identify methods that allowed for rapid information collection from past disaster response history and adopt similar methods. The data collection unit can avoid methods that were time-consuming to collect information from past disaster response history and select efficient methods. The data collection unit can analyze past disaster response history and select the most effective means of information collection. In this way, the optimal data collection method can be selected by analyzing past disaster response history. Some or all of the above processes in the data collection unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the data collection unit can input past disaster response history data into a generation AI and have the generation AI select the optimal data collection method.

[0082] The data collection unit can filter information on disaster preparedness supplies based on the local government's current disaster risk assessment. For example, if the local government's disaster risk assessment is high, the data collection unit can prioritize collecting information. If the local government's disaster risk assessment is low, the data collection unit can reduce the frequency of information collection. The data collection unit can adjust the level of detail of the information collected based on the local government's disaster risk assessment. This allows for the priority collection of important information by filtering information based on the local government's disaster risk assessment. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input local government disaster risk assessment data into a generation AI and have the generation AI perform the information filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of disaster preparedness information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important preparedness information. If the user is feeling at ease, the data collection unit can collect detailed preparedness information. If the user is facing an emergency, the data collection unit can quickly collect the most necessary preparedness information. This allows for the priority collection of important information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of local governments when collecting disaster preparedness information. For example, the data collection unit can prioritize the collection of information from neighboring local governments based on the geographical location information of local governments. The data collection unit can prioritize the collection of information from areas with high disaster risk based on the geographical location information of local governments. The data collection unit can prioritize the collection of information from areas with good transportation access based on the geographical location information of local governments. In this way, by considering the geographical location information of local governments, highly relevant information can be prioritized. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the geographical location data of local governments into a generation AI and have the generation AI prioritize the information.

[0085] The data collection unit can analyze the social media activities of local governments and collect relevant information when collecting information on disaster preparedness supplies. For example, the data collection unit can analyze the social media activities of local governments and collect posts related to disaster preparedness supplies. The data collection unit can analyze the social media activities of local governments and collect information related to disaster response. The data collection unit can analyze the social media activities of local governments and collect information related to the needs of residents. This allows for the efficient collection of relevant information by analyzing the social media activities of local governments. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the social media activity data of local governments into a generative AI and have the generative AI collect relevant information.

[0086] The analysis unit can estimate the user's emotions and adjust the image information analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can employ a simple analysis method and provide results quickly. If the user is relaxed, the analysis unit can employ a detailed analysis method and provide highly accurate results. If the user is facing an emergency, the analysis unit can perform an immediate analysis and provide results quickly. By adjusting the analysis method based on the user's emotions, the burden on the user is reduced and analysis can be performed efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the disaster relief supplies. For example, the analysis unit can perform a detailed analysis on disaster relief supplies of high importance. For disaster relief supplies of low importance, the analysis unit can perform a simplified analysis. The analysis unit can determine the priority of the analysis according to importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the disaster relief supplies. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance data of the disaster relief supplies into a generation AI and have the generation AI perform the analysis with the appropriate level of detail.

[0088] The analysis unit can apply different analysis algorithms depending on the category of disaster relief supplies. For example, for food-related disaster relief supplies, the analysis unit can apply an analysis algorithm that emphasizes the expiration date. For pharmaceutical-related disaster relief supplies, the analysis unit can apply an analysis algorithm that emphasizes the expiration date and inventory status. For clothing and bedding-related disaster relief supplies, the analysis unit can apply an analysis algorithm that emphasizes the quantity and condition. By applying different analysis algorithms depending on the category of disaster relief supplies, highly accurate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input disaster relief supply category data into a generation AI and have the generation AI execute the application of the analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The analysis unit can determine the priority of analysis based on the submission date of disaster relief supplies. For example, the analysis unit can prioritize the analysis of disaster relief supplies that have been submitted recently. The analysis unit can postpone the analysis of disaster relief supplies that have been submitted recently. The analysis unit can adjust the analysis schedule based on the submission date. This allows for efficient analysis by determining the priority of analysis based on the submission date of disaster relief supplies. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the submission date data of disaster relief supplies into a generation AI and have the generation AI execute the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of disaster relief supplies. For example, the analysis unit can prioritize the analysis of highly relevant disaster relief supplies. The analysis unit can postpone the analysis of less relevant disaster relief supplies. The analysis unit can adjust the analysis schedule based on relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of disaster relief supplies. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the relevance data of disaster relief supplies into a generation AI and have the generation AI execute the analysis in the correct order.

[0092] The registration unit can estimate the user's emotions and adjust the data registration method based on the estimated emotions. For example, if the user is stressed, the registration unit can provide a simple registration method and register data quickly. If the user is relaxed, the registration unit can provide a detailed registration method and register data with high accuracy. If the user is facing an emergency, the registration unit can register data immediately. This reduces the burden on the user and allows for efficient data registration by adjusting the registration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The registration unit can adjust the level of detail in the registration based on the importance of the disaster relief supplies. For example, the registration unit can register detailed information for disaster relief supplies of high importance, and simpler information for disaster relief supplies of low importance. The registration unit can determine the priority of registration according to importance. This allows for efficient data registration by adjusting the level of detail in the registration based on the importance of the disaster relief supplies. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input the importance data of the disaster relief supplies into a generation AI and have the generation AI perform the level of detail in the registration.

[0094] The registration unit can apply different registration algorithms depending on the category of disaster relief supplies. For example, for food-related disaster relief supplies, the registration unit can apply a registration algorithm that emphasizes the expiration date. For pharmaceutical-related disaster relief supplies, the registration unit can apply a registration algorithm that emphasizes the expiration date and inventory status. For clothing and bedding-related disaster relief supplies, the registration unit can apply a registration algorithm that emphasizes the quantity and condition. By applying different registration algorithms depending on the category of disaster relief supplies, highly accurate data registration can be achieved. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input disaster relief supply category data into a generation AI and have the generation AI execute the application of the registration algorithm.

[0095] The registration unit can estimate the user's emotions and adjust how the registration data is displayed based on the estimated emotions. For example, if the user is nervous, the registration unit can provide a simple and highly visible display. If the user is relaxed, the registration unit can provide a display that includes detailed information. If the user is in a hurry, the registration unit can provide a display that gets straight to the point. By adjusting the display method based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The registration unit can determine the priority of registration based on the submission date of disaster relief supplies. For example, the registration unit can prioritize the registration of disaster relief supplies with a more recent submission date. The registration unit can postpone the registration of disaster relief supplies with an older submission date. The registration unit can adjust the registration schedule based on the submission date. This allows for efficient data registration by determining the priority of registration based on the submission date of disaster relief supplies. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input the submission date data of disaster relief supplies into a generation AI and have the generation AI determine the registration priority.

[0097] The registration unit can adjust the registration order based on the relevance of disaster relief supplies. For example, the registration unit can prioritize the registration of highly relevant disaster relief supplies. The registration unit can postpone the registration of less relevant disaster relief supplies. The registration unit can adjust the registration schedule based on relevance. This allows for efficient data registration by adjusting the registration order based on the relevance of disaster relief supplies. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input the relevance data of disaster relief supplies into a generation AI and have the generation AI execute the registration order.

[0098] The selection unit can estimate the user's emotions and adjust the selection criteria for candidate municipalities based on the estimated emotions. For example, if the user is stressed, the selection unit can use simple selection criteria to quickly select candidates. If the user is relaxed, the selection unit can use detailed selection criteria to select highly accurate candidates. If the user is facing an emergency, the selection unit can immediately select candidates. This enables rapid and highly accurate candidate selection by adjusting the selection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The selection unit can improve the accuracy of its selection by considering the interrelationships of disaster relief supplies. For example, the selection unit can consider the interrelationships of disaster relief supplies and select municipalities that can provide related supplies in a single package. The selection unit can analyze the interrelationships of disaster relief supplies and select municipalities that can provide them efficiently. Based on the interrelationships of disaster relief supplies, the selection unit can select the most suitable candidate municipalities for provision. This makes it possible to select candidates efficiently and accurately by considering the interrelationships of disaster relief supplies. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the selection unit can input data on the interrelationships of disaster relief supplies into a generation AI and have the generation AI perform the task of improving the accuracy of the selection.

[0100] The selection unit can make selections by considering the attribute information of local governments. For example, the selection unit can select appropriate candidate local governments by considering the population size of the local governments. The selection unit can select local governments that can be provided to by considering the economic situation of the local governments. The selection unit can select local governments that can be provided to quickly by considering the geographical conditions of the local governments. In this way, appropriate candidate local governments can be selected by considering the attribute information of the local governments. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the selection unit can input local government attribute information data into a generation AI and have the generation AI perform the selection.

[0101] The selection unit can estimate the user's emotions and adjust the display order of the selection results based on the estimated emotions. For example, if the user is nervous, the selection unit can provide a simple and highly visible display order. If the user is relaxed, the selection unit can provide a display order that includes detailed information. If the user is in a hurry, the selection unit can provide a display order that gets straight to the point. By adjusting the display order based on the user's emotions, it becomes possible to provide a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The selection unit can perform selections while considering the geographical distribution of municipalities. For example, the selection unit can prioritize selecting neighboring municipalities based on their geographical distribution. The selection unit can prioritize selecting areas with high disaster risk based on their geographical distribution. The selection unit can prioritize selecting areas with good transportation access based on their geographical distribution. This allows for rapid and efficient candidate selection by considering the geographical distribution of municipalities. Some or all of the above-described processes in the selection unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the selection unit can input geographical distribution data of municipalities into a generation AI and have the generation AI perform the selection.

[0103] The selection unit can improve the accuracy of its selection by referring to relevant literature on disaster relief supplies. For example, the selection unit can refer to relevant literature on disaster relief supplies and select the most suitable candidate municipalities for provision. Based on the relevant literature on disaster relief supplies, the selection unit can select municipalities that can provide the supplies efficiently. The selection unit can analyze relevant literature on disaster relief supplies and make highly accurate selections. As a result, by referring to relevant literature on disaster relief supplies, it becomes possible to select candidates with high accuracy. Some or all of the above processing in the selection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the selection unit can input data on relevant literature on disaster relief supplies into a generation AI and have the generation AI perform the task of improving the accuracy of the selection.

[0104] The service provider can estimate the user's emotions and adjust the way the information is displayed based on those emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can provide a display that includes detailed information. If the user is in a hurry, the service provider can provide a display that gets straight to the point. By adjusting the display method based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The provisioning unit can adjust the level of detail provided based on the importance of the disaster relief supplies. For example, the provisioning unit can provide detailed information for disaster relief supplies of high importance. For disaster relief supplies of low importance, the provisioning unit can provide simplified information. The provisioning unit can determine the priority of provision according to importance. This allows for efficient information provision by adjusting the level of detail provided based on the importance of the disaster relief supplies. Some or all of the above processing in the provisioning unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the provisioning unit can input the importance data of the disaster relief supplies into a generation AI and have the generation AI determine the level of detail provided.

[0106] The supply unit can apply different supply algorithms depending on the category of disaster relief supplies. For example, for food-related disaster relief supplies, the supply unit can apply a supply algorithm that emphasizes the expiration date. For pharmaceutical-related disaster relief supplies, the supply unit can apply a supply algorithm that emphasizes the expiration date and inventory status. For clothing and bedding-related disaster relief supplies, the supply unit can apply a supply algorithm that emphasizes the quantity and condition. By applying different supply algorithms depending on the category of disaster relief supplies, it becomes possible to provide highly accurate information. Some or all of the above processing in the supply unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the supply unit can input disaster relief supply category data into a generation AI and have the generation AI execute the application of the supply algorithm.

[0107] The information provider can estimate the user's emotions and prioritize the information provided based on those emotions. For example, if the user is feeling anxious, the provider will prioritize providing important information. If the user is feeling at ease, the provider can provide detailed information. If the user is facing an emergency, the provider can immediately provide the most necessary information. This allows for the priority provision of important information by prioritizing it based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The distribution department can determine the priority of provision based on the submission date of disaster relief supplies. For example, the distribution department can prioritize the provision of disaster relief supplies that have been submitted recently. The distribution department can postpone the provision of disaster relief supplies that have been submitted older. The distribution department can adjust the provision schedule based on the submission date. This allows for efficient provision of information by determining the priority of provision based on the submission date of disaster relief supplies. Some or all of the above processing in the distribution department may be performed using a generation AI, or it may be performed without a generation AI. For example, the distribution department can input data on the submission date of disaster relief supplies into a generation AI and have the generation AI determine the priority of provision.

[0109] The distribution unit can adjust the order of distribution based on the relevance of the disaster relief supplies. For example, the distribution unit can prioritize the distribution of highly relevant disaster relief supplies. The distribution unit can postpone the distribution of less relevant disaster relief supplies. The distribution unit can adjust the distribution schedule based on relevance. This allows for efficient information provision by adjusting the order of distribution based on the relevance of the disaster relief supplies. Some or all of the above processing in the distribution unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the distribution unit can input relevance data of disaster relief supplies into a generation AI and have the generation AI execute the distribution order.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The collection department can adjust its collection methods when gathering information on disaster relief supplies, taking into account the budget situation of local governments. For example, for local governments with limited budgets, the collection department will adopt cost-effective collection methods. For local governments with abundant budgets, the collection department can use advanced technologies to collect detailed information. The collection department can adjust the collection frequency and scope according to the budget situation. This allows for efficient information collection by adjusting the collection method based on the local government's budget.

[0112] The analysis unit can adjust the level of detail in its analysis of disaster preparedness information, taking into account the disaster response capabilities of local governments. For example, for local governments with high disaster response capabilities, the analysis unit can perform detailed analysis and provide highly accurate data. For local governments with low disaster response capabilities, the analysis unit can perform simplified analysis and provide results quickly. The analysis unit can determine the priority of analysis according to the disaster response capabilities. This allows for efficient analysis by adjusting the level of detail based on the disaster response capabilities of local governments.

[0113] The registration unit can adjust the registration method when registering disaster preparedness information, taking into account the population density of the local government. For example, for local governments with high population density, the registration unit can register detailed information and provide highly accurate data. For local governments with low population density, the registration unit can register simplified information and provide data quickly. The registration unit can determine the priority of registration according to population density. This allows for efficient data registration by adjusting the registration method based on the population density of the local government.

[0114] The selection department can adjust its selection criteria when selecting municipalities to receive disaster relief supplies, taking into account the municipalities' disaster history. For example, for municipalities that have experienced large-scale disasters in the past, the selection department will select candidates that can provide supplies quickly. For municipalities with little disaster history, the selection department can adopt detailed selection criteria to select highly accurate candidates. The selection department can determine the priority of selection based on the disaster history. This allows for the rapid and accurate selection of candidates by adjusting the selection criteria based on the disaster history of each municipality.

[0115] The provision department can adjust its provision method when providing information on disaster relief supplies, taking into account the infrastructure status of local governments. For example, it can provide detailed information and highly accurate data to local governments with well-developed infrastructure. For local governments with underdeveloped infrastructure, it can provide simplified information and data quickly. The provision department can determine the priority of provision according to the infrastructure status. This allows for efficient information provision by adjusting the provision method based on the infrastructure status of local governments.

[0116] The data collection unit can estimate the user's emotions and determine the priority of disaster preparedness information to collect based on those emotions. For example, if the user is feeling anxious, the unit will prioritize collecting important preparedness information. If the user is feeling at ease, the unit can collect detailed preparedness information. If the user is facing an emergency, the unit can quickly gather the most necessary preparedness information. This allows for the priority collection of critical information by prioritizing information based on the user's emotions.

[0117] The analysis unit can estimate the user's emotions and adjust the image information analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can employ a simplified analysis method and provide results quickly. If the user is relaxed, the analysis unit can employ a detailed analysis method and provide highly accurate results. If the user is facing an emergency, the analysis unit can perform an immediate analysis and provide results quickly. In this way, by adjusting the analysis method based on the user's emotions, the burden on the user is reduced and analysis can be performed efficiently.

[0118] The registration unit can estimate the user's emotions and adjust the data registration method based on those emotions. For example, if the user is stressed, the registration unit can provide a simple registration method and register data quickly. If the user is relaxed, the registration unit can provide a detailed registration method and register highly accurate data. If the user is facing an emergency, the registration unit can register data immediately. By adjusting the registration method based on the user's emotions, the burden on the user is reduced and data can be registered efficiently.

[0119] The selection unit can estimate the user's emotions and adjust the selection criteria for candidate municipalities based on those estimated emotions. For example, if the user is stressed, the selection unit can use simpler selection criteria to quickly select candidates. If the user is relaxed, the selection unit can use more detailed selection criteria to select more accurate candidates. If the user is facing an emergency, the selection unit can immediately select candidates. This allows for rapid and accurate candidate selection by adjusting the selection criteria based on the user's emotions.

[0120] The information provider can estimate the user's emotions and adjust the way the information is displayed based on those emotions. For example, if the user is stressed, the provider can provide a simple and highly visible display. If the user is relaxed, the provider can provide a display that includes detailed information. If the user is in a hurry, the provider can provide a display that gets straight to the point. By adjusting the display method based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects information on disaster relief supplies. For example, it can collect information on disaster relief supplies from the national and local governments and collect images of specified items using social media applications. Specifically, it collects information such as the inventory status and expiration dates of the supplies as images. Step 2: The analysis unit analyzes the image information collected by the collection unit and extracts data such as the inventory status and expiration dates of stockpiled items. For example, it uses a generation AI to extract text information from images and registers it in a database. Step 3: The registration unit registers the information analyzed by the analysis unit into the database. For example, it registers data extracted using the generation AI into the database. Step 4: The selection department selects candidate municipalities to provide services based on the information registered by the registration department. For example, it uses a generation AI to analyze the database information and select the most suitable candidate municipalities. Step 5: The provision department provides the information selected by the selection department. For example, it will contact the affected municipalities with contact information for potential supply municipalities and the number of days until provision. This information can also be communicated using AI generation.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, registration unit, selection unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information on disaster relief supplies using the camera 42 and communication interface 44 of the smart device 14. The analysis unit analyzes the image information using the identification processing unit 290 of the data processing unit 12 and extracts data such as the inventory status and expiration date of the supplies. The registration unit registers the extracted data in the database 24 of the data processing unit 12. The selection unit analyzes the information in the database 24 using the identification processing unit 290 of the data processing unit 12 and selects the most suitable candidate municipality for provision. The provision unit uses the communication interface 44 of the smart device 14 to contact the affected municipality with contact information of the candidate municipality for supply and the number of days until provision. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, registration unit, selection unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information on disaster relief supplies using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit analyzes the image information using the identification processing unit 290 of the data processing unit 12 and extracts data such as the inventory status and expiration date of the supplies. The registration unit registers the extracted data in the database 24 of the data processing unit 12. The selection unit analyzes the information in the database 24 using the identification processing unit 290 of the data processing unit 12 and selects the most suitable candidate municipality for provision. The provision unit uses the communication I / F 44 of the smart glasses 214 to contact the affected municipality with contact information of the candidate municipality for supply and the number of days until provision. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, registration unit, selection unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information on disaster relief supplies using the camera 42 and communication interface 44 of the headset terminal 314. The analysis unit analyzes the image information using the identification processing unit 290 of the data processing unit 12 and extracts data such as the inventory status and expiration date of the supplies. The registration unit registers the extracted data in the database 24 of the data processing unit 12. The selection unit analyzes the information in the database 24 using the identification processing unit 290 of the data processing unit 12 and selects the most suitable candidate municipality for provision. The provision unit uses the communication interface 44 of the headset terminal 314 to contact the affected municipality with contact information of the candidate municipality for supply and the number of days until provision. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, registration unit, selection unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information on disaster relief supplies using the camera 42 and communication I / F 44 of the robot 414. The analysis unit analyzes the image information using the identification processing unit 290 of the data processing unit 12 and extracts data such as the inventory status and expiration date of the supplies. The registration unit registers the extracted data in the database 24 of the data processing unit 12. The selection unit analyzes the information in the database 24 using the identification processing unit 290 of the data processing unit 12 and selects the most suitable candidate municipality for provision. The provision unit uses the communication I / F 44 of the robot 414 to contact the affected municipality with contact information of the candidate municipality for supply and the number of days until provision. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The collection department collects information on disaster relief supplies, An analysis unit analyzes the information collected by the aforementioned collection unit, A registration unit that registers the information analyzed by the aforementioned analysis unit into a database, A selection department selects candidate municipalities based on the information registered by the aforementioned registration department, The system comprises a providing unit that provides information selected by the selection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect images of specified items using a social networking application. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes image information to extract data such as the inventory status and expiration dates of stockpiled goods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned registration unit is The extracted data is registered in the database. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned selection unit is Analyze the database information and select the most suitable candidate municipalities to provide the service. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We will contact the affected municipalities with information on the contact points of potential supplying municipalities and the estimated time until provision. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting disaster preparedness information based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We will analyze the past disaster response history of each local government and select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information on disaster preparedness supplies, filtering is performed based on the local government's current disaster risk assessment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates user sentiment and prioritizes the collection of disaster preparedness information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information on disaster preparedness supplies, prioritize the collection of highly relevant information, taking into account the geographical location of local governments. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information on disaster preparedness supplies, we analyze the social media activities of local governments and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the image information analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Adjust the level of detail of the analysis based on the importance of disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Apply different analysis algorithms depending on the category of disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Prioritizing analysis based on the submission timing of disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned registration unit is The system estimates the user's emotions and adjusts the data registration method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned registration unit is Adjust the level of detail in registration based on the importance of disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned registration unit is Apply different registration algorithms depending on the category of disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned registration unit is The system estimates the user's emotions and adjusts how registered data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned registration unit is Prioritizing registration based on the timing of submission of disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned registration unit is Adjust the registration order based on the relevance of disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned selection unit is We estimate user sentiment and adjust the selection criteria for potential municipalities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned selection unit is Improve the accuracy of selection by considering the interrelationships of disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned selection unit is Selection will be made considering the attribute information of the local government. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned selection unit is It estimates the user's emotions and adjusts the display order of the selection results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned selection unit is Selection will be made considering the geographical distribution of local governments. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned selection unit is We will improve the accuracy of our selection process by referring to relevant literature on disaster preparedness supplies. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, The level of detail provided will be adjusted based on the importance of the disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, Apply different distribution algorithms depending on the category of disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, Prioritizing the provision of disaster relief supplies based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, The order of distribution will be adjusted based on the relevance of the disaster relief supplies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects information on disaster relief supplies, An analysis unit analyzes the information collected by the aforementioned collection unit, A registration unit that registers the information analyzed by the aforementioned analysis unit into a database, A selection department selects candidate municipalities based on the information registered by the aforementioned registration department, The system comprises a providing unit that provides information selected by the selection unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect images of specified items using a social networking application. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes image information to extract data such as the inventory status and expiration dates of stockpiled goods. The system according to feature 1.

4. The aforementioned registration unit is The extracted data is registered in the database. The system according to feature 1.

5. The aforementioned selection unit is Analyze the database information and select the most suitable candidate municipalities to provide the service. The system according to feature 1.

6. The aforementioned supply unit is, We will contact the affected municipalities with information on the contact points of potential supplying municipalities and the estimated time until provision. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting disaster preparedness information based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is We will analyze the past disaster response history of each local government and select the most suitable collection method. The system according to feature 1.

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