system

Abstract

Provide a system. 【Solution means】 An information collection means for collecting the needs of users in the disaster area, Data analysis means for analyzing the aggregated needs and setting priorities, Resource information collation means for collating information on available materials and human resources provided from outside the disaster area, Matching means using a generative AI for matching the aggregated needs with externally provided materials and human resources, Transport route optimization means for formulating an optimal transport route, Notification means for notifying and presenting the generated support plan to users, Real-time information providing means for sharing regional information during normal times, A system including

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the event of a disaster, it is required to quickly and appropriately connect the needs of the disaster-stricken area with relief supplies and personnel from outside. However, in the conventional method, information aggregation and analysis are generally performed manually, which takes time and effort, and there are problems such as low priority setting and matching accuracy. In addition, since feedback is not appropriately carried out, it has been difficult to effectively implement and continuously improve the support. To solve these problems, a more efficient and accurate disaster support system is needed.

Means for Solving the Problems

[0005] This invention comprises an information gathering means for collecting user needs in disaster-stricken areas and a data analysis means for analyzing the aggregated needs and setting priorities. Furthermore, it possesses a resource information organization means for organizing information on supplies and personnel that can be provided from outside the disaster area, and formulates an optimal support plan by using a matching means that uses generation AI to match aggregated needs with externally provided supplies and personnel. In addition, by including a transportation route optimization means for optimizing transportation routes and a presentation means for notifying and presenting the generated support plan to the user, the invention can improve the speed and accuracy of support during disasters. Moreover, by including a feedback transmission means for collecting feedback information from users and sending it back to the server, the invention aims to continuously improve the support process.

[0006] "Information gathering means" refers to a system for effectively collecting user needs in disaster-stricken areas, and a means of obtaining necessary support information through an interface with users.

[0007] "Data analysis tools" are functions that analyze aggregated needs information and determine the priority of support, thereby supporting rapid decision-making.

[0008] A "resource information organization method" is a means of systematically organizing information on supplies and personnel that can be supplied from outside the disaster area, and clearly identifying available support resources.

[0009] "Generative AI-based matching methods" refer to the process of utilizing generative AI technology to optimally match the needs of disaster-stricken areas with external resources, thereby enhancing the efficiency and effectiveness of support.

[0010] "Transportation route optimization means" refers to technologies for optimizing the transportation routes of goods and personnel, and is a means aimed at achieving efficient and rapid support.

[0011] "Presentation means" refers to interfaces and functions that present the generated support plan to the user in an easy-to-understand manner and provide guidance for its implementation.

[0012] "Feedback transmission means" refers to the process of collecting feedback information obtained from users and sending it back to the server for the purpose of improving the entire system. [Brief explanation of the drawing]

[0013] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. **Mode for Carrying Out the Invention**

[0014] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The 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.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system for streamlining support activities during disasters, and is centered around terminals installed in each disaster-stricken area and a server. The terminals function as an interface for collecting needs information from disaster victims, allowing users to input the support supplies and services they need. For example, if a user needs a blanket, they can easily input that information into the terminal.

[0035] The server aggregates information transmitted from each terminal and analyzes the overall needs situation as needed. It pays particular attention to items deemed high-priority from the aggregated data and performs matching to ensure efficient support. The server utilizes generative AI to instantly process the information and generate an optimized support plan. This support plan includes a list of necessary supplies and personnel, as well as delivery routes.

[0036] For example, if a company provides blankets, that information is registered on the server and matched with various shelters that need these supplies. As a result, delivery arrangements can be made immediately.

[0037] The formed support plan is notified to the user via the terminal. Based on the received information, the user can quickly begin support activities. Furthermore, after the support is completed, the user sends feedback information back to the server via the terminal. This allows for the reflection of successive changes in needs and requests, improving the accuracy of support activities.

[0038] In this way, servers, terminals, and users can effectively fulfill their respective roles and accurately respond to the needs of the disaster-stricken areas. The overall support activities can be further improved through continuous feedback. This system is truly intended to function as a new standard for disaster relief activities.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The terminal provides an interface to users in evacuation centers in disaster-stricken areas, allowing them to input the necessary supplies and support. Users use this interface to input their specific needs.

[0042] Step 2:

[0043] The terminal transmits user needs information entered by the user to the server in real time. This allows information from each evacuation center to be aggregated in one place.

[0044] Step 3:

[0045] The server analyzes the aggregated needs information and prioritizes them based on their urgency and importance. The analysis results serve as foundational data for developing support plans.

[0046] Step 4:

[0047] The server organizes information on available supplies and personnel provided from external sources. This includes resource information registered in advance by donors.

[0048] Step 5:

[0049] The server uses AI generation to match aggregated needs with external resources. This generates an optimal support plan, enabling rapid implementation of assistance.

[0050] Step 6:

[0051] The server notifies the terminal of an optimized support plan. The terminal then presents this information to the user and support staff, providing specific action guidelines.

[0052] Step 7:

[0053] After assistance is provided, users input feedback on the results and any new needs into their device. This feedback is then incorporated into future plans, contributing to improvements in assistance activities.

[0054] Step 8:

[0055] The device sends user feedback to the server. The server then re-analyzes this information and prepares to update the support plan in response to changes in needs.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In the event of a disaster, it is crucial to allocate limited resources optimally in order to respond quickly and efficiently to the diverse needs of affected areas. However, conventional systems have problems with the fact that information on needs and resources is not managed uniformly, making it difficult to achieve appropriate matching. Furthermore, the formulation and implementation of support plans are not carried out quickly, hindering effective support for disaster victims. It is important to solve these problems and carry out effective support activities.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes information gathering means equipped with a terminal device for collecting user request information, data analysis means for integrating the collected request information and setting priorities using generated artificial intelligence, and resource management means for managing information on supplies and personnel supplied from remote locations. This enables a rapid and efficient response to the diverse needs of disaster-stricken areas, facilitating appropriate matching and the formulation of efficient support plans.

[0061] "Information gathering means" refers to a device or system used to collect requests from disaster victims during a disaster and organize them as necessary data.

[0062] A "terminal device" is a device or equipment installed in a disaster-stricken area that provides a user interface for users to input the support information they need.

[0063] A "data analysis tool" is a system that analyzes collected request information and uses artificial intelligence to optimally set its priorities.

[0064] A "resource management system" is a process or device that manages information on supplies and personnel supplied from outside the disaster area and optimizes their use for efficient support.

[0065] "Generative artificial intelligence" refers to a machine learning model that includes algorithms and technologies for analyzing various types of information and generating optimal support plans.

[0066] A "matching method" is a process or device for appropriately matching the needs of disaster-stricken areas with resources that can be provided from outside.

[0067] A "route planning optimization means" is a method or apparatus for efficiently planning the transportation routes of goods and personnel in a logistics process, thereby achieving the shortest and most optimal delivery.

[0068] "Information presentation means" refers to a method or device for immediately communicating the generated support plan to the user and encouraging the commencement of support activities.

[0069] "Evaluation information sharing means" refers to a process or device for collecting user feedback information after support activities have been completed and transmitting it to a processing device.

[0070] This system is designed to efficiently collect the needs of disaster victims during a disaster and to implement the most appropriate support plan. The terminals within the system are technical devices with interfaces for disaster victims to input the supplies and services they need. Touchscreen devices are used to enable intuitive operation. These terminals are installed in disaster-stricken areas, allowing users to directly operate the devices and input their needs.

[0071] Once information is entered, the terminal sends it to the server. The server aggregates the data in real time using a messaging system such as Apache® Kafka, and uses a generative AI model to prioritize the needs. Python and Tensorflow® are used to build the generative AI model, and Google® Cloud's AI platform is used as needed. Based on the results of this analysis, an efficient support plan is formulated.

[0072] Furthermore, the server is equipped with a system for collecting and managing information on available supplies and personnel from remote locations. Database technology and APIs are used for this resource management, and in particular, the Google Maps API can be used to formulate optimal transportation routes. Based on these analysis results, a support plan is generated, and the available supplies are quickly and accurately matched with the needs of the affected areas. The generated support plan is organized using the Google Sheets API, and the next support strategy is determined.

[0073] For example, if a company provides blankets, that information is registered on the server and matched with the necessary supplies at various evacuation centers. As a result, delivery arrangements can be made immediately. The generating AI model can be prompted with messages such as, "Based on the needs information from disaster area A, please propose a high-priority support plan."

[0074] The formed support plan is notified to the user via a terminal. Based on the received information, the user can quickly begin support activities. After the support activities are completed, the user returns evaluation information to the server via the terminal, which is then used to inform the next support plan. This improves the accuracy of support and allows for a more precise response to the needs of the disaster-stricken area. The purpose of this invention is to optimize support activities during disasters and enable a rapid and effective response.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] Users input information about the supplies and services they need through terminals installed in the disaster area. Specifically, they input their requests using intuitive touch controls and press the send button. The information entered includes the type of supplies, the required quantity, and the urgency of the request. The terminal temporarily stores this data and performs consistency checks.

[0078] Step 2:

[0079] Data whose integrity has been verified is sent from the terminal to the server. At this time, the information is securely transferred using an SSL / TLS encrypted protocol. The data that reaches the server is aggregated using streaming processing such as Apache Kafka. The input data is separated by disaster area and stored in real time.

[0080] Step 3:

[0081] The server feeds the aggregated data into a generating AI model. Here, Python and TensorFlow are used to perform data prioritization analysis. The input for this analysis is information on the needs of the disaster-stricken areas, and the output is prioritized requests for assistance. The AI ​​model then processes prompts to identify the most urgent requests based on the situation in the disaster-stricken areas.

[0082] Step 4:

[0083] Based on the priority information analyzed, the server performs resource matching to meet needs. It references the resource management database to obtain information on available supplies and personnel. The input is resource information and priority information, and the output is a specific support plan. The optimal delivery route is also calculated using the Google Maps API.

[0084] Step 5:

[0085] The server sends the formed support plan to the terminal and notifies the user. The support plan includes necessary supplies, delivery personnel, and estimated arrival times. The terminal uses push notifications to display a notification on the screen. After the user confirms this, they can quickly begin providing support.

[0086] Step 6:

[0087] After an assistance activity is underway or completed, the user enters feedback using a terminal and sends it to the server. This feedback includes the success rate of the assistance and any additional requests. The feedback information is stored in a database and used to inform future assistance plans. The server then analyzes this information to continuously improve the system.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In disaster relief efforts, it is crucial that the diverse needs of victims are quickly and efficiently understood, and that appropriate relief supplies and personnel are reliably delivered. However, conventional systems have suffered from delays in relief due to the time required for information gathering, matching supplies, optimizing transportation, and developing situation-specific support plans. Furthermore, there has been an insufficient system for sharing local information with residents and supporting their daily lives, not only during disasters but also during peacetime.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes an information gathering means for collecting user needs in disaster-stricken areas, a matching means using generative AI for matching aggregated needs with externally provided supplies and personnel, and a real-time information provision means for sharing local information during normal times. This enables not only rapid and efficient support activities during disasters, but also information sharing and life support in local communities during normal times.

[0093] "Information gathering means" refers to a mechanism for collecting needs information from users through terminals installed in disaster-stricken areas.

[0094] "Data analysis methods" refer to the process of analyzing aggregated needs information and determining its priorities.

[0095] A "resource information organization method" is a method for efficiently organizing information on available supplies and personnel that can be provided from outside the disaster-stricken area.

[0096] "Generative AI-based matching methods" refer to a process that utilizes generative AI technology to optimally connect users' needs with available resources.

[0097] A "transportation route optimization method" is a system for formulating routes that allow for the most efficient transportation of relief supplies and personnel.

[0098] "Notification means" refers to technology for quickly communicating the generated support plan to the user.

[0099] A "real-time information provision system" is a system that provides residents with real-time information on local traffic and events, even during normal times.

[0100] A "feedback transmission method" is a means of collecting opinions and results information from users and sending it back to the server.

[0101] A "user interface" is a display method that appears on a terminal, allowing users to easily input their needs and receive information.

[0102] This system consists primarily of terminals installed in disaster-stricken areas, a cloud server, and a smartphone application. The terminals collect needs information from disaster victims and users, and data can be easily entered through the user interface. The collected information is immediately transmitted to the cloud server.

[0103] The servers operate on large-scale cloud computing infrastructure such as Google Cloud Platform (GCP) and perform data analysis and organize resource information. Generative AI is used to analyze aggregated needs and available resources and personnel information, enabling optimized matching. This generative AI utilizes language models such as OpenAI®, enabling rapid and highly accurate analysis.

[0104] Furthermore, during normal times, the system provides users with real-time information on traffic conditions and local events via a smartphone app from the server. This allows residents to always access the latest information, which can be useful in their daily lives and for quick responses during disasters.

[0105] As a concrete example, consider the following scenario: When a typhoon is predicted to approach, if a user inputs information via their device indicating that they "need food and blankets," the server will collect this information and perform analysis using a generation AI. A support plan will then be quickly formulated, necessary supplies will be arranged via the optimal transportation route, and residents will be notified. Furthermore, residents will be provided with real-time information on alternative transportation routes and nearby evacuation shelters using a smartphone app.

[0106] Examples of prompt statements for a generative AI model are as follows:

[0107] "Based on the latest information from local governments, please generate a list of relief supplies needed for the next typhoon. Please include requests for evacuation centers as specific needs information."

[0108] Thus, this invention aims to provide a useful information infrastructure not only during disasters but also during normal times, thereby improving the quality of life in local communities.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The terminal collects needs information from disaster victims. Users input specific requests, such as "food" or "blankets needed," through the terminal's user interface, and send this information to the server. The input data is formatted and stored on the cloud server in a unified format.

[0112] Step 2:

[0113] The server performs data analysis based on the received needs information. Using data analysis tools, it analyzes which needs should be prioritized and, if necessary, clusters the needs using a generative AI model. The input is user needs data, and the output is a prioritized list of needs.

[0114] Step 3:

[0115] The server uses resource information organization tools to organize information on available supplies and personnel. It takes in information from regional collaborations and support providers as input and generates a usable support list as output. This support list includes information such as the available quantity and delivery schedule.

[0116] Step 4:

[0117] The server uses a matching mechanism based on generated AI to match prioritized needs with organized resource information. This matching process ensures the optimal allocation of resources according to the needs. Using a prioritized needs list and a support list as input data, the server provides an optimal support plan as output.

[0118] Step 5:

[0119] The server formulates transportation routes based on an optimized support plan. Using transportation route optimization tools, it plans how to efficiently deliver support supplies and personnel. It considers the optimal support route as input and creates a specific transportation route map as output.

[0120] Step 6:

[0121] The server notifies the user of the final support plan. Through notification methods, it sends the generated support plan, specific work instructions, and transportation route to the terminal or smartphone app. The input is the formulated support plan, and the output is the information notified to the user.

[0122] Step 7:

[0123] Users send feedback about the support provided to the server using the feedback submission method. The server then collects data for future system improvements, receiving feedback data as input and analysis results for system improvement as output.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention aims to build a support system that takes user emotions into consideration in order to streamline support activities during disasters. This system functions through the coordinated operation of various elements, including terminals placed in evacuation centers, a central control server, and individual users.

[0126] The terminal is a device actually used in evacuation shelters and provides an interface for users to input the necessary relief supplies and services. This interface also includes an emotion detection function, allowing the terminal to recognize emotions from the user's voice and facial expressions while they are inputting. When users input their needs, their emotions are recognized through the emotion engine, which then transmits deeper needs to the server.

[0127] The server aggregates and analyzes information obtained from the terminals. In particular, by integrating user emotional information, it adds an emotional aspect to conventional needs information, allowing for a more accurate determination of the urgency and importance of those needs. For example, if a user expresses anxiety or urgency in a clear tone, the server recognizes that emotional information as a high-priority item.

[0128] Furthermore, the server compiles information on externally provided resources and personnel, and executes a matching process using generated AI. By taking emotional information into consideration during this process, the accuracy of support matching is improved, and priority support can be given to users who require specific psychological care. For example, a user experiencing extreme anxiety will be identified as needing mental support, and personnel will be matched accordingly.

[0129] The final support plan is communicated to the user via the terminal and implemented by the support staff. After the support is completed, the user provides feedback through the terminal. This feedback includes emotional responses, and the server incorporates this information into future support plans, enabling the continuous provision of more personalized and effective support.

[0130] In this way, support activities in disaster-stricken areas are effectively deployed with the server, terminals, and users working together, based on detailed needs information, including users' emotions. This system aims to enable faster and more accurate responses than conventional support methods, and to significantly strengthen overall support capabilities during disasters.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The terminal provides an input interface to users in evacuation shelters in disaster-stricken areas. Users use this interface to input necessary supplies and support services, while the emotion engine built into the terminal recognizes the user's emotions through their voice and facial expressions.

[0134] Step 2:

[0135] The terminal transmits needs and emotional information collected from users to the server. This transmission is done in real time, enabling timely data aggregation for each evacuation center.

[0136] Step 3:

[0137] The server analyzes the received information and reflects emotional information in the urgency of the needs. For example, if strong emotions such as fear or sadness are detected, the server prioritizes that need information.

[0138] Step 4:

[0139] The server organizes information on resources and personnel provided from external sources and uses a generation AI to perform optimal matching based on user needs and emotions. Particular attention is paid to allocating appropriate resources to users who require psychological care.

[0140] Step 5:

[0141] The server notifies the terminal of the generated support plan. The terminal then presents this plan to the user and support staff, enabling the rapid implementation of specific support activities.

[0142] Step 6:

[0143] Users input feedback into their device after the assistance is provided. At this time, emotional information is collected again and used for subsequent analysis.

[0144] Step 7:

[0145] The terminal sends user feedback to the server, which uses this information to develop the next support plan. This continuously improves the accuracy and effectiveness of the entire support process.

[0146] (Example 2)

[0147] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0148] During disasters, it is crucial to provide prompt and appropriate support to victims. However, conventional systems have struggled to adequately consider the emotional state and psychological needs of victims. In particular, addressing cases where psychological care is needed in addition to physical needs remains a challenge.

[0149] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0150] In this invention, the server includes: information gathering means for collecting user requests in the disaster area and recognizing emotions through audio and video; data analysis means for analyzing the collected requests and emotion data and setting priorities; resource information organization means for integrating information on available resources and experts provided from outside the disaster area; means for matching requests with externally provided resources and experts using a generated AI model; and feedback integration means for obtaining feedback, including emotional responses from users after the completion of support and reflecting it in the next support plan. This enables accurate capture of the physical and psychological needs of disaster victims and appropriate support based on priorities.

[0151] "Information gathering means" refers to functions that collect user requests in the disaster-stricken area and recognize users' emotional states through audio and video.

[0152] "Data analysis means" refers to a function that processes collected user requests and sentiment data and sets support priorities based on that data.

[0153] A "resource information organization tool" is a function for integrating and organizing information on available resources and experts provided from outside the disaster-stricken area.

[0154] "Matching methods using generative AI models" refer to a function that uses generative AI to match users with external resources and experts based on analyzed user requests and sentiment data.

[0155] A "presentation means" is an interface function that directly notifies the user of the generated support plan and presents the details of the support.

[0156] A "feedback integration mechanism" is a function that collects feedback, including emotional responses, provided by users after the completion of support, and incorporates it into the next support plan.

[0157] This invention is a technology for effectively carrying out support activities during disasters, and it constructs a support system that takes into account the emotional state of disaster victims. This system functions through the coordinated operation of various elements, including terminals placed in evacuation centers, a central control server, and individual users.

[0158] About the device:

[0159] The terminal is a device actually used in evacuation shelters, providing an interface for users to input the necessary relief supplies and services. The terminal has a touchscreen, allowing users to easily input their physical needs through the user interface. It also has a voice recognition microphone and camera function, which are used to detect the user's emotions. For example, if a user says "I'm very anxious" while inputting food assistance, the terminal can recognize the anxiety from the voice and facial expression and send it to the server as emotion data.

[0160] About the server:

[0161] The server aggregates request data and emotional data sent from terminals and analyzes them using a generative AI model. Based on the analysis results, support needs are prioritized, with particular consideration given to emotional aspects. It also integrates resources and expert information provided from external sources to match the most suitable support staff and supplies. This process is triggered by prompt statements, which in turn trigger the generative AI model to perform optimal matching. An example of a prompt statement is, "Based on data obtained from terminals in evacuation centers, please generate a support plan that includes appropriate psychological care, taking into account the anxiety levels of users who require food assistance."

[0162] About the user:

[0163] Users input their needs by operating a terminal and receive a support plan from the server via the terminal. After the support is completed, they send feedback from the terminal, which includes their satisfaction level and emotional response to the support. This feedback data is used in the next support plan, enabling the continuous provision of more individualized and effective support.

[0164] This system aims to ensure that relief efforts in disaster-stricken areas are deployed quickly and accurately, taking into account the detailed needs and emotions of users.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The terminal provides an interface for users in evacuation shelters to input the necessary supplies and services. The user's requests (e.g., food and clothing needs) are entered via the terminal's touchscreen. Simultaneously, the terminal detects the user's voice and facial expressions, collecting data necessary to analyze their emotional state. This input data includes the user's requests and emotional information.

[0168] Step 2:

[0169] The terminal sends the collected user request data and sentiment data to the server. The server stores the received data in a database for analysis. Specifically, the server uses natural language processing technology to analyze the request content, quantifies the sentiment information, and scores it based on evaluation criteria. This process generates output indicating the urgency and importance of the request.

[0170] Step 3:

[0171] The server uses the generated evaluation score to refer to external support resources and personnel databases, and uses a generative AI model to perform optimal support matching. The generative AI model determines the best support that matches the user's needs based on specific prompt statements. Specifically, for a user who needs food assistance, it considers their anxieties and assigns staff who can provide emotional support. This process outputs a specific support plan.

[0172] Step 4:

[0173] The server generates a final support plan and notifies the user via the terminal. The user can then view details such as the type of support provided, the assigned counselor, and the scheduled time on the terminal screen. For example, the user might receive a message such as, "A psychological counselor is scheduled to visit you tomorrow at 10 AM."

[0174] Step 5:

[0175] After receiving assistance, users provide feedback via their device. This feedback includes their satisfaction rating and emotional state regarding the assistance. The server analyzes the feedback data and reflects it in a database for use in future assistance sessions. This process clarifies areas for improvement to enhance the quality of future assistance activities.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In modern urban environments, it is a challenging task to appropriately collect user sentiment and needs regarding public services, analyze them in real time, and use that information to improve services. Particularly during disasters or when citizens are facing difficulties, there is a need to take swift and appropriate measures on an individual basis. However, current methods fail to accurately grasp the deep-seated needs of citizens, resulting in problems such as insufficient service satisfaction and inadequate response capabilities.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes information gathering means, emotion recognition means, and feedback processing means. This makes it possible to collect and analyze the emotions of citizens within a city in real time. As a result, aggregated requests can be matched with externally provided resources and personnel with high accuracy, generating efficient improvement proposals for public services and enabling a quick and accurate response to citizens.

[0181] "Information gathering means" refers to devices that have the function of collecting user requests and feedback.

[0182] "Data analysis means" refers to a process for analyzing collected request information and setting priorities.

[0183] A "resource information organization method" is a system for organizing information on resources and personnel provided from external sources and applying it to support plans.

[0184] "Generative AI-based matching method" refers to a process that uses generative AI technology to match aggregated requests with externally provided resources and personnel.

[0185] A "transportation route optimization means" is a system that has the function of formulating the optimal transportation route based on the created support plan.

[0186] "Presentation method" refers to a method for notifying the user of the generated support plan and providing them with information.

[0187] A "feedback processing mechanism" is a function that collects and analyzes feedback from citizens and reflects it in the system in real time.

[0188] "Emotion recognition means" refers to technologies used to detect and analyze the emotions of citizens.

[0189] This invention realizes a system that efficiently collects and analyzes user requests and emotions in urban and disaster-stricken areas, and supports the provision and improvement of appropriate public services based on that information. As an information collection means, devices such as terminals and smartphones are used to collect user requests and feedback. The terminal accepts voice and text input through a user interface and detects emotions from the user's voice and facial expressions using emotion recognition means. The detected data is transmitted to a server equipped with data analysis means and analyzed.

[0190] The server analyzes the aggregated requests and sets priorities using data analysis tools. Furthermore, resource information organization tools organize information on resources and personnel provided from external sources, and matching tools using generation AI efficiently match the aggregated requests with externally provided resources and personnel. Based on the matching results, a transportation route optimization tool formulates the optimal transportation route, and the generated support plan is notified to the user through a presentation tool.

[0191] As a concrete example, if citizens use their smartphones to input emotions such as stress or requests for improvement regarding public transportation within the city, this data will be collected in real time and analyzed by a server to understand their underlying needs, including their emotions. Based on the analysis results, suggestions for improving the operating schedule and facilities will be made, enabling the city to respond quickly and accurately.

[0192] An example of a prompt using a generative AI model is, "Analyze the emotions citizens feel about the stress of crowded trains, and based on that feedback, what improvement suggestions can be made?" Software such as emotion recognition engines and data analysis algorithms play a crucial role throughout this entire process.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The user provides feedback via voice or text through their device. Since this feedback may contain the user's emotions, it is sent to an emotion recognition engine. The input here is data about the user's emotions and requests, while the output is data ready for analysis of the potential emotions contained within it.

[0196] Step 2:

[0197] The device inputs the received feedback data into an emotion recognition engine, which extracts emotions from the user's voice and facial expressions. This process identifies emotions based on voice tone and text context, and outputs the user's emotional state as an analysis result. Specifically, emotion labels such as "anxiety," "satisfaction," and "frustration" are assigned.

[0198] Step 3:

[0199] The server receives sentiment data sent from the terminal and applies a data analysis algorithm to evaluate the urgency and applicability of the request. This analysis determines the priority level of the user's request. The analysis results are output as a prioritized list for each request.

[0200] Step 4:

[0201] The server uses resource information organization tools to acquire and organize information on resources and personnel that can be provided from external sources. In this process, the types of resources available, the number of people who can use them, and their location information are organized and output as a list of available resources.

[0202] Step 5:

[0203] Using a generative AI model, the server matches aggregated requests with available resources. This process considers request priorities and sentiment information to select the most appropriate resources. The final output is a matching list, which forms the basis for a specific support plan.

[0204] Step 6:

[0205] The server uses a transportation route optimization system to plan the transportation routes for relief supplies and staff. Geographic data and traffic information are used as input to calculate the optimal transportation route. The output is a highly efficient transportation schedule.

[0206] Step 7:

[0207] The server notifies the user of the generated support plan via the terminal, making it clear to the user. Information provided at this stage includes specific details of the support provided and the estimated arrival time. The user receives this information and can provide further feedback if needed.

[0208] Step 8:

[0209] If a user provides new feedback during the process, this information is sent back to the server through the feedback processing mechanism, and the initial request processing is updated as needed. This allows the system to be continuously optimized.

[0210] 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.

[0211] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0217] 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.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0219] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] 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.

[0221] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The 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.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] This invention is a system for streamlining support activities during disasters, and is centered around terminals installed in each disaster-stricken area and a server. The terminals function as an interface for collecting needs information from disaster victims, allowing users to input the support supplies and services they need. For example, if a user needs a blanket, they can easily input that information into the terminal.

[0227] The server aggregates information transmitted from each terminal and analyzes the overall needs situation as needed. It pays particular attention to items deemed high-priority from the aggregated data and performs matching to ensure efficient support. The server utilizes generative AI to instantly process the information and generate an optimized support plan. This support plan includes a list of necessary supplies and personnel, as well as delivery routes.

[0228] For example, if a company provides blankets, that information is registered on the server and matched with various shelters that need these supplies. As a result, delivery arrangements can be made immediately.

[0229] The formed support plan is notified to the user via the terminal. Based on the received information, the user can quickly begin support activities. Furthermore, after the support is completed, the user sends feedback information back to the server via the terminal. This allows for the reflection of successive changes in needs and requests, improving the accuracy of support activities.

[0230] In this way, servers, terminals, and users can effectively fulfill their respective roles and accurately respond to the needs of the disaster-stricken areas. The overall support activities can be further improved through continuous feedback. This system is truly intended to function as a new standard for disaster relief activities.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The terminal provides an interface to users in evacuation centers in disaster-stricken areas, allowing them to input the necessary supplies and support. Users use this interface to input their specific needs.

[0234] Step 2:

[0235] The terminal transmits user needs information entered by the user to the server in real time. This allows information from each evacuation center to be aggregated in one place.

[0236] Step 3:

[0237] The server analyzes the aggregated needs information and prioritizes them based on their urgency and importance. The analysis results serve as foundational data for developing support plans.

[0238] Step 4:

[0239] The server organizes information on available supplies and personnel provided from external sources. This includes resource information registered in advance by donors.

[0240] Step 5:

[0241] The server uses AI generation to match aggregated needs with external resources. This generates an optimal support plan, enabling rapid implementation of assistance.

[0242] Step 6:

[0243] The server notifies the terminal of an optimized support plan. The terminal then presents this information to the user and support staff, providing specific action guidelines.

[0244] Step 7:

[0245] After assistance is provided, users input feedback on the results and any new needs into their device. This feedback is then incorporated into future plans, contributing to improvements in assistance activities.

[0246] Step 8:

[0247] The device sends user feedback to the server. The server then re-analyzes this information and prepares to update the support plan in response to changes in needs.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0250] In the event of a disaster, it is crucial to allocate limited resources optimally in order to respond quickly and efficiently to the diverse needs of affected areas. However, conventional systems have problems with the fact that information on needs and resources is not managed uniformly, making it difficult to achieve appropriate matching. Furthermore, the formulation and implementation of support plans are not carried out quickly, hindering effective support for disaster victims. It is important to solve these problems and carry out effective support activities.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes information gathering means equipped with a terminal device for collecting user request information, data analysis means for integrating the collected request information and setting priorities using generated artificial intelligence, and resource management means for managing information on supplies and personnel supplied from remote locations. This enables a rapid and efficient response to the diverse needs of disaster-stricken areas, facilitating appropriate matching and the formulation of efficient support plans.

[0253] "Information gathering means" refers to a device or system used to collect requests from disaster victims during a disaster and organize them as necessary data.

[0254] A "terminal device" is a device or equipment installed in a disaster-stricken area that provides a user interface for users to input the support information they need.

[0255] A "data analysis tool" is a system that analyzes collected request information and uses artificial intelligence to optimally set its priorities.

[0256] A "resource management system" is a process or device that manages information on supplies and personnel supplied from outside the disaster area and optimizes their use for efficient support.

[0257] "Generative artificial intelligence" refers to a machine learning model that includes algorithms and technologies for analyzing various types of information and generating optimal support plans.

[0258] A "matching method" is a process or device for appropriately matching the needs of disaster-stricken areas with resources that can be provided from outside.

[0259] A "route planning optimization means" is a method or apparatus for efficiently planning the transportation routes of goods and personnel in a logistics process, thereby achieving the shortest and most optimal delivery.

[0260] "Information presentation means" refers to a method or device for immediately communicating the generated support plan to the user and encouraging the commencement of support activities.

[0261] "Evaluation information sharing means" refers to a process or device for collecting user feedback information after support activities have been completed and transmitting it to a processing device.

[0262] This system is designed to efficiently collect the needs of disaster victims during a disaster and to implement the most appropriate support plan. The terminals within the system are technical devices with interfaces for disaster victims to input the supplies and services they need. Touchscreen devices are used to enable intuitive operation. These terminals are installed in disaster-stricken areas, allowing users to directly operate the devices and input their needs.

[0263] Once information is entered, the terminal sends it to the server. The server aggregates the data in real time using a messaging system such as Apache Kafka, and uses a generative AI model to prioritize the needs. Python and TensorFlow are used to build the generative AI model, and Google Cloud's AI platform is used as needed. Based on the results of this analysis, an efficient support plan is formulated.

[0264] Furthermore, the server is equipped with a system for collecting and managing information on available supplies and personnel from remote locations. Database technology and APIs are used for this resource management, and in particular, the Google Maps API can be used to formulate optimal transportation routes. Based on these analysis results, a support plan is generated, and the available supplies are quickly and accurately matched with the needs of the affected areas. The generated support plan is organized using the Google Sheets API, and the next support strategy is determined.

[0265] For example, if a company provides blankets, that information is registered on the server and matched with the necessary supplies at various evacuation centers. As a result, delivery arrangements can be made immediately. The generating AI model can be prompted with messages such as, "Based on the needs information from disaster area A, please propose a high-priority support plan."

[0266] The formed support plan is notified to the user via a terminal. Based on the received information, the user can quickly begin support activities. After the support activities are completed, the user returns evaluation information to the server via the terminal, which is then used to inform the next support plan. This improves the accuracy of support and allows for a more precise response to the needs of the disaster-stricken area. The purpose of this invention is to optimize support activities during disasters and enable a rapid and effective response.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] Users input information about the supplies and services they need through terminals installed in the disaster area. Specifically, they input their requests using intuitive touch controls and press the send button. The information entered includes the type of supplies, the required quantity, and the urgency of the request. The terminal temporarily stores this data and performs consistency checks.

[0270] Step 2:

[0271] Data whose integrity has been verified is sent from the terminal to the server. At this time, the information is securely transferred using an SSL / TLS encrypted protocol. The data that reaches the server is aggregated using streaming processing such as Apache Kafka. The input data is separated by disaster area and stored in real time.

[0272] Step 3:

[0273] The server feeds the aggregated data into a generating AI model. Here, Python and TensorFlow are used to perform data prioritization analysis. The input for this analysis is information on the needs of the disaster-stricken areas, and the output is prioritized requests for assistance. The AI ​​model then processes prompts to identify the most urgent requests based on the situation in the disaster-stricken areas.

[0274] Step 4:

[0275] Based on the priority information analyzed, the server performs resource matching to meet needs. It references the resource management database to obtain information on available supplies and personnel. The input is resource information and priority information, and the output is a specific support plan. The optimal delivery route is also calculated using the Google Maps API.

[0276] Step 5:

[0277] The server sends the formed support plan to the terminal and notifies the user. The support plan includes necessary supplies, delivery personnel, and estimated arrival times. The terminal uses push notifications to display a notification on the screen. After the user confirms this, they can quickly begin providing support.

[0278] Step 6:

[0279] After an assistance activity is underway or completed, the user enters feedback using a terminal and sends it to the server. This feedback includes the success rate of the assistance and any additional requests. The feedback information is stored in a database and used to inform future assistance plans. The server then analyzes this information to continuously improve the system.

[0280] (Application Example 1)

[0281] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0282] In disaster relief efforts, it is crucial that the diverse needs of victims are quickly and efficiently understood, and that appropriate relief supplies and personnel are reliably delivered. However, conventional systems have suffered from delays in relief due to the time required for information gathering, matching supplies, optimizing transportation, and developing situation-specific support plans. Furthermore, there has been an insufficient system for sharing local information with residents and supporting their daily lives, not only during disasters but also during peacetime.

[0283] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes an information collection means for collecting the needs of users in the disaster area, a matching means using generative AI for matching the aggregated needs with externally provided materials and human resources, and a real-time information providing means for sharing regional information during normal times. Thereby, in addition to rapid and efficient support activities during disasters, information sharing and life support for local communities during peacetime become possible.

[0285] The "information collection means" is a mechanism for collecting need information from users through terminals installed in the disaster area.

[0286] The "data analysis means" is a process for analyzing the aggregated need information and determining its priorities.

[0287] The "resource information arrangement means" is a method for efficiently arranging information on supportable materials and human resources provided from outside the disaster area.

[0288] The "matching means using generative AI" is a process for optimally connecting users' needs and supportable resources by utilizing generative AI technology.

[0289] The "transport route optimization means" is a mechanism for formulating the most efficient route for transporting support materials and human resources.

[0290] The "notification means" is a technology for quickly transmitting the generated support plan to users.

[0291] The "real-time information providing means" is a system for providing residents with regional traffic and event information in real time even during peacetime.

[0292] The "feedback transmission means" is a means for collecting opinions and result information from users and transmitting them back to the server again.

[0293] A "user interface" is a display method that appears on a terminal, allowing users to easily input their needs and receive information.

[0294] This system consists primarily of terminals installed in disaster-stricken areas, a cloud server, and a smartphone application. The terminals collect needs information from disaster victims and users, and data can be easily entered through the user interface. The collected information is immediately transmitted to the cloud server.

[0295] The servers operate on large-scale cloud computing infrastructure such as Google Cloud Platform (GCP) and perform data analysis and organize resource information. Generative AI is used to analyze aggregated needs and available resources and personnel information, enabling optimized matching. This generative AI utilizes, for example, OpenAI's language models to enable rapid and highly accurate analysis.

[0296] Furthermore, during normal times, the system provides users with real-time information on traffic conditions and local events via a smartphone app from the server. This allows residents to always access the latest information, which can be useful in their daily lives and for quick responses during disasters.

[0297] As a concrete example, consider the following scenario: When a typhoon is predicted to approach, if a user inputs information via their device indicating that they "need food and blankets," the server will collect this information and perform analysis using a generation AI. A support plan will then be quickly formulated, necessary supplies will be arranged via the optimal transportation route, and residents will be notified. Furthermore, residents will be provided with real-time information on alternative transportation routes and nearby evacuation shelters using a smartphone app.

[0298] Examples of prompt statements for a generative AI model are as follows:

[0299] "Please output a list of relief supplies required during the next typhoon approach based on the latest information from local governments. Consider the requests from evacuation shelters as specific need information."

[0300] Thus, this invention aims to provide an information infrastructure useful not only during disasters but also in normal times, and to improve the quality of life in local communities.

[0301] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0302] Step 1:

[0303] The terminal collects need information from disaster victims. The user inputs specific requests such as "food" or "need a blanket" through the user interface of the terminal and transmits the information to the server. The input data is formatted and stored in the cloud server in a unified format.

[0304] Step 2:

[0305] The server performs data analysis based on the received need information. Using data analysis means, it analyzes which needs should be prioritized and, if necessary, clusters the needs using a generated AI model. The input is the need data from the user, and the output is a prioritized list of needs.

[0306] Step 3:

[0307] The server uses resource information sorting means to sort information on available supplies and personnel. Taking in information on regional cooperation and offerings from support providers as input, it generates an available support list as output. This support list includes the available quantity, offering schedule, etc.

[0308] Step 4:

[0309] The server uses a matching mechanism based on generated AI to match prioritized needs with organized resource information. This matching process ensures the optimal allocation of resources according to the needs. Using a prioritized needs list and a support list as input data, the server provides an optimal support plan as output.

[0310] Step 5:

[0311] The server formulates transportation routes based on an optimized support plan. Using transportation route optimization tools, it plans how to efficiently deliver support supplies and personnel. It considers the optimal support route as input and creates a specific transportation route map as output.

[0312] Step 6:

[0313] The server notifies the user of the final support plan. Through notification methods, it sends the generated support plan, specific work instructions, and transportation route to the terminal or smartphone app. The input is the formulated support plan, and the output is the information notified to the user.

[0314] Step 7:

[0315] Users send feedback about the support provided to the server using the feedback submission method. The server then collects data for future system improvements, receiving feedback data as input and analysis results for system improvement as output.

[0316] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0317] This invention aims to build a support system that takes user emotions into consideration in order to streamline support activities during disasters. This system functions through the coordinated operation of various elements, including terminals placed in evacuation centers, a central control server, and individual users.

[0318] The terminal is a device actually used in evacuation shelters and provides an interface for users to input the necessary relief supplies and services. This interface also includes an emotion detection function, allowing the terminal to recognize emotions from the user's voice and facial expressions while they are inputting. When users input their needs, their emotions are recognized through the emotion engine, which then transmits deeper needs to the server.

[0319] The server aggregates and analyzes information obtained from the terminals. In particular, by integrating user emotional information, it adds an emotional aspect to conventional needs information, allowing for a more accurate determination of the urgency and importance of those needs. For example, if a user expresses anxiety or urgency in a clear tone, the server recognizes that emotional information as a high-priority item.

[0320] Furthermore, the server compiles information on externally provided resources and personnel, and executes a matching process using generated AI. By taking emotional information into consideration during this process, the accuracy of support matching is improved, and priority support can be given to users who require specific psychological care. For example, a user experiencing extreme anxiety will be identified as needing mental support, and personnel will be matched accordingly.

[0321] The final support plan is communicated to the user via the terminal and implemented by the support staff. After the support is completed, the user provides feedback through the terminal. This feedback includes emotional responses, and the server incorporates this information into future support plans, enabling the continuous provision of more personalized and effective support.

[0322] In this way, support activities in disaster-stricken areas are effectively deployed with the server, terminals, and users working together, based on detailed needs information, including users' emotions. This system aims to enable faster and more accurate responses than conventional support methods, and to significantly strengthen overall support capabilities during disasters.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] The terminal provides an input interface to users in evacuation shelters in disaster-stricken areas. Users use this interface to input necessary supplies and support services, while the emotion engine built into the terminal recognizes the user's emotions through their voice and facial expressions.

[0326] Step 2:

[0327] The terminal transmits needs and emotional information collected from users to the server. This transmission is done in real time, enabling timely data aggregation for each evacuation center.

[0328] Step 3:

[0329] The server analyzes the received information and reflects emotional information in the urgency of the needs. For example, if strong emotions such as fear or sadness are detected, the server prioritizes that need information.

[0330] Step 4:

[0331] The server organizes information on resources and personnel provided from external sources and uses a generation AI to perform optimal matching based on user needs and emotions. Particular attention is paid to allocating appropriate resources to users who require psychological care.

[0332] Step 5:

[0333] The server notifies the terminal of the generated support plan. The terminal then presents this plan to the user and support staff, enabling the rapid implementation of specific support activities.

[0334] Step 6:

[0335] Users input feedback into their device after the assistance is provided. At this time, emotional information is collected again and used for subsequent analysis.

[0336] Step 7:

[0337] The terminal sends user feedback to the server, which uses this information to develop the next support plan. This continuously improves the accuracy and effectiveness of the entire support process.

[0338] (Example 2)

[0339] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0340] During disasters, it is crucial to provide prompt and appropriate support to victims. However, conventional systems have struggled to adequately consider the emotional state and psychological needs of victims. In particular, addressing cases where psychological care is needed in addition to physical needs remains a challenge.

[0341] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0342] In this invention, the server includes: information gathering means for collecting user requests in the disaster area and recognizing emotions through audio and video; data analysis means for analyzing the collected requests and emotion data and setting priorities; resource information organization means for integrating information on available resources and experts provided from outside the disaster area; means for matching requests with externally provided resources and experts using a generated AI model; and feedback integration means for obtaining feedback, including emotional responses from users after the completion of support and reflecting it in the next support plan. This enables accurate capture of the physical and psychological needs of disaster victims and appropriate support based on priorities.

[0343] "Information gathering means" refers to functions that collect user requests in the disaster-stricken area and recognize users' emotional states through audio and video.

[0344] "Data analysis means" refers to a function that processes collected user requests and sentiment data and sets support priorities based on that data.

[0345] A "resource information organization tool" is a function for integrating and organizing information on available resources and experts provided from outside the disaster-stricken area.

[0346] "Matching methods using generative AI models" refer to a function that uses generative AI to match users with external resources and experts based on analyzed user requests and sentiment data.

[0347] A "presentation means" is an interface function that directly notifies the user of the generated support plan and presents the details of the support.

[0348] A "feedback integration mechanism" is a function that collects feedback, including emotional responses, provided by users after the completion of support, and incorporates it into the next support plan.

[0349] This invention is a technology for effectively carrying out support activities during disasters, and it constructs a support system that takes into account the emotional state of disaster victims. This system functions through the coordinated operation of various elements, including terminals placed in evacuation centers, a central control server, and individual users.

[0350] About the device:

[0351] The terminal is a device actually used in evacuation shelters, providing an interface for users to input the necessary relief supplies and services. The terminal has a touchscreen, allowing users to easily input their physical needs through the user interface. It also has a voice recognition microphone and camera function, which are used to detect the user's emotions. For example, if a user says "I'm very anxious" while inputting food assistance, the terminal can recognize the anxiety from the voice and facial expression and send it to the server as emotion data.

[0352] About the server:

[0353] The server aggregates request data and emotional data sent from terminals and analyzes them using a generative AI model. Based on the analysis results, support needs are prioritized, with particular consideration given to emotional aspects. It also integrates resources and expert information provided from external sources to match the most suitable support staff and supplies. This process is triggered by prompt statements, which in turn trigger the generative AI model to perform optimal matching. An example of a prompt statement is, "Based on data obtained from terminals in evacuation centers, please generate a support plan that includes appropriate psychological care, taking into account the anxiety levels of users who require food assistance."

[0354] About the user:

[0355] Users input their needs by operating a terminal and receive a support plan from the server via the terminal. After the support is completed, they send feedback from the terminal, which includes their satisfaction level and emotional response to the support. This feedback data is used in the next support plan, enabling the continuous provision of more individualized and effective support.

[0356] This system aims to ensure that relief efforts in disaster-stricken areas are deployed quickly and accurately, taking into account the detailed needs and emotions of users.

[0357] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0358] Step 1:

[0359] The terminal provides an interface for users in evacuation shelters to input the necessary supplies and services. The user's requests (e.g., food and clothing needs) are entered via the terminal's touchscreen. Simultaneously, the terminal detects the user's voice and facial expressions, collecting data necessary to analyze their emotional state. This input data includes the user's requests and emotional information.

[0360] Step 2:

[0361] The terminal sends the collected user request data and sentiment data to the server. The server stores the received data in a database for analysis. Specifically, the server uses natural language processing technology to analyze the request content, quantifies the sentiment information, and scores it based on evaluation criteria. This process generates output indicating the urgency and importance of the request.

[0362] Step 3:

[0363] The server uses the generated evaluation score to refer to external support resources and personnel databases, and uses a generative AI model to perform optimal support matching. The generative AI model determines the best support that matches the user's needs based on specific prompt statements. Specifically, for a user who needs food assistance, it considers their anxieties and assigns staff who can provide emotional support. This process outputs a specific support plan.

[0364] Step 4:

[0365] The server generates a final support plan and notifies the user via the terminal. The user can then view details such as the type of support provided, the assigned counselor, and the scheduled time on the terminal screen. For example, the user might receive a message such as, "A psychological counselor is scheduled to visit you tomorrow at 10 AM."

[0366] Step 5:

[0367] After receiving assistance, users provide feedback via their device. This feedback includes their satisfaction rating and emotional state regarding the assistance. The server analyzes the feedback data and reflects it in a database for use in future assistance sessions. This process clarifies areas for improvement to enhance the quality of future assistance activities.

[0368] (Application Example 2)

[0369] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0370] In modern urban environments, it is a challenging task to appropriately collect user sentiment and needs regarding public services, analyze them in real time, and use that information to improve services. Particularly during disasters or when citizens are facing difficulties, there is a need to take swift and appropriate measures on an individual basis. However, current methods fail to accurately grasp the deep-seated needs of citizens, resulting in problems such as insufficient service satisfaction and inadequate response capabilities.

[0371] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0372] In this invention, the server includes information gathering means, emotion recognition means, and feedback processing means. This makes it possible to collect and analyze the emotions of citizens within a city in real time. As a result, aggregated requests can be matched with externally provided resources and personnel with high accuracy, generating efficient improvement proposals for public services and enabling a quick and accurate response to citizens.

[0373] "Information gathering means" refers to devices that have the function of collecting user requests and feedback.

[0374] "Data analysis means" refers to a process for analyzing collected request information and setting priorities.

[0375] A "resource information organization method" is a system for organizing information on resources and personnel provided from external sources and applying it to support plans.

[0376] "Generative AI-based matching method" refers to a process that uses generative AI technology to match aggregated requests with externally provided resources and personnel.

[0377] A "transportation route optimization means" is a system that has the function of formulating the optimal transportation route based on the created support plan.

[0378] "Presentation method" refers to a method for notifying the user of the generated support plan and providing them with information.

[0379] A "feedback processing mechanism" is a function that collects and analyzes feedback from citizens and reflects it in the system in real time.

[0380] "Emotion recognition means" refers to technologies used to detect and analyze the emotions of citizens.

[0381] This invention realizes a system that efficiently collects and analyzes user requests and emotions in urban and disaster-stricken areas, and supports the provision and improvement of appropriate public services based on that information. As an information collection means, devices such as terminals and smartphones are used to collect user requests and feedback. The terminal accepts voice and text input through a user interface and detects emotions from the user's voice and facial expressions using emotion recognition means. The detected data is transmitted to a server equipped with data analysis means and analyzed.

[0382] The server analyzes the aggregated requests and sets priorities using data analysis tools. Furthermore, resource information organization tools organize information on resources and personnel provided from external sources, and matching tools using generation AI efficiently match the aggregated requests with externally provided resources and personnel. Based on the matching results, a transportation route optimization tool formulates the optimal transportation route, and the generated support plan is notified to the user through a presentation tool.

[0383] As a concrete example, if citizens use their smartphones to input emotions such as stress or requests for improvement regarding public transportation within the city, this data will be collected in real time and analyzed by a server to understand their underlying needs, including their emotions. Based on the analysis results, suggestions for improving the operating schedule and facilities will be made, enabling the city to respond quickly and accurately.

[0384] An example of a prompt using a generative AI model is, "Analyze the emotions citizens feel about the stress of crowded trains, and based on that feedback, what improvement suggestions can be made?" Software such as emotion recognition engines and data analysis algorithms play a crucial role throughout this entire process.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The user provides feedback via voice or text through their device. Since this feedback may contain the user's emotions, it is sent to an emotion recognition engine. The input here is data about the user's emotions and requests, while the output is data ready for analysis of the potential emotions contained within it.

[0388] Step 2:

[0389] The device inputs the received feedback data into an emotion recognition engine, which extracts emotions from the user's voice and facial expressions. This process identifies emotions based on voice tone and text context, and outputs the user's emotional state as an analysis result. Specifically, emotion labels such as "anxiety," "satisfaction," and "frustration" are assigned.

[0390] Step 3:

[0391] The server receives sentiment data sent from the terminal and applies a data analysis algorithm to evaluate the urgency and applicability of the request. This analysis determines the priority level of the user's request. The analysis results are output as a prioritized list for each request.

[0392] Step 4:

[0393] The server uses resource information organization tools to acquire and organize information on resources and personnel that can be provided from external sources. In this process, the types of resources available, the number of people who can use them, and their location information are organized and output as a list of available resources.

[0394] Step 5:

[0395] Using a generative AI model, the server matches aggregated requests with available resources. This process considers request priorities and sentiment information to select the most appropriate resources. The final output is a matching list, which forms the basis for a specific support plan.

[0396] Step 6:

[0397] The server uses a transportation route optimization system to plan the transportation routes for relief supplies and staff. Geographic data and traffic information are used as input to calculate the optimal transportation route. The output is a highly efficient transportation schedule.

[0398] Step 7:

[0399] The server notifies the user of the generated support plan via the terminal, making it clear to the user. Information provided at this stage includes specific details of the support provided and the estimated arrival time. The user receives this information and can provide further feedback if needed.

[0400] Step 8:

[0401] If a user provides new feedback during the process, this information is sent back to the server through the feedback processing mechanism, and the initial request processing is updated as needed. This allows the system to be continuously optimized.

[0402] 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.

[0403] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0409] 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.

[0410] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0411] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0412] 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.

[0413] 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.

[0414] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0415] The 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.

[0416] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0417] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0418] This invention is a system for streamlining support activities during disasters, and is centered around terminals installed in each disaster-stricken area and a server. The terminals function as an interface for collecting needs information from disaster victims, allowing users to input the support supplies and services they need. For example, if a user needs a blanket, they can easily input that information into the terminal.

[0419] The server aggregates information transmitted from each terminal and analyzes the overall needs situation as needed. It pays particular attention to items deemed high-priority from the aggregated data and performs matching to ensure efficient support. The server utilizes generative AI to instantly process the information and generate an optimized support plan. This support plan includes a list of necessary supplies and personnel, as well as delivery routes.

[0420] For example, if a company provides blankets, that information is registered on the server and matched with various shelters that need these supplies. As a result, delivery arrangements can be made immediately.

[0421] The formed support plan is notified to the user via the terminal. Based on the received information, the user can quickly begin support activities. Furthermore, after the support is completed, the user sends feedback information back to the server via the terminal. This allows for the reflection of successive changes in needs and requests, improving the accuracy of support activities.

[0422] In this way, servers, terminals, and users can effectively fulfill their respective roles and accurately respond to the needs of the disaster-stricken areas. The overall support activities can be further improved through continuous feedback. This system is truly intended to function as a new standard for disaster relief activities.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The terminal provides an interface to users in evacuation centers in disaster-stricken areas, allowing them to input the necessary supplies and support. Users use this interface to input their specific needs.

[0426] Step 2:

[0427] The terminal transmits user needs information entered by the user to the server in real time. This allows information from each evacuation center to be aggregated in one place.

[0428] Step 3:

[0429] The server analyzes the aggregated needs information and prioritizes them based on their urgency and importance. The analysis results serve as foundational data for developing support plans.

[0430] Step 4:

[0431] The server organizes information on available supplies and personnel provided from external sources. This includes resource information registered in advance by donors.

[0432] Step 5:

[0433] The server uses AI generation to match aggregated needs with external resources. This generates an optimal support plan, enabling rapid implementation of assistance.

[0434] Step 6:

[0435] The server notifies the terminal of an optimized support plan. The terminal then presents this information to the user and support staff, providing specific action guidelines.

[0436] Step 7:

[0437] After assistance is provided, users input feedback on the results and any new needs into their device. This feedback is then incorporated into future plans, contributing to improvements in assistance activities.

[0438] Step 8:

[0439] The device sends user feedback to the server. The server then re-analyzes this information and prepares to update the support plan in response to changes in needs.

[0440] (Example 1)

[0441] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0442] In the event of a disaster, it is crucial to allocate limited resources optimally in order to respond quickly and efficiently to the diverse needs of affected areas. However, conventional systems have problems with the fact that information on needs and resources is not managed uniformly, making it difficult to achieve appropriate matching. Furthermore, the formulation and implementation of support plans are not carried out quickly, hindering effective support for disaster victims. It is important to solve these problems and carry out effective support activities.

[0443] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0444] In this invention, the server includes information gathering means equipped with a terminal device for collecting user request information, data analysis means for integrating the collected request information and setting priorities using generated artificial intelligence, and resource management means for managing information on supplies and personnel supplied from remote locations. This enables a rapid and efficient response to the diverse needs of disaster-stricken areas, facilitating appropriate matching and the formulation of efficient support plans.

[0445] "Information gathering means" refers to a device or system used to collect requests from disaster victims during a disaster and organize them as necessary data.

[0446] A "terminal device" is a device or equipment installed in a disaster-stricken area that provides a user interface for users to input the support information they need.

[0447] A "data analysis tool" is a system that analyzes collected request information and uses artificial intelligence to optimally set its priorities.

[0448] A "resource management system" is a process or device that manages information on supplies and personnel supplied from outside the disaster area and optimizes their use for efficient support.

[0449] "Generative artificial intelligence" refers to a machine learning model that includes algorithms and technologies for analyzing various types of information and generating optimal support plans.

[0450] A "matching method" is a process or device for appropriately matching the needs of disaster-stricken areas with resources that can be provided from outside.

[0451] A "route planning optimization means" is a method or apparatus for efficiently planning the transportation routes of goods and personnel in a logistics process, thereby achieving the shortest and most optimal delivery.

[0452] "Information presentation means" refers to a method or device for immediately communicating the generated support plan to the user and encouraging the commencement of support activities.

[0453] "Evaluation information sharing means" refers to a process or device for collecting user feedback information after support activities have been completed and transmitting it to a processing device.

[0454] This system is designed to efficiently collect the needs of disaster victims during a disaster and to implement the most appropriate support plan. The terminals within the system are technical devices with interfaces for disaster victims to input the supplies and services they need. Touchscreen devices are used to enable intuitive operation. These terminals are installed in disaster-stricken areas, allowing users to directly operate the devices and input their needs.

[0455] Once information is entered, the terminal sends it to the server. The server aggregates the data in real time using a messaging system such as Apache Kafka, and uses a generative AI model to prioritize the needs. Python and TensorFlow are used to build the generative AI model, and Google Cloud's AI platform is used as needed. Based on the results of this analysis, an efficient support plan is formulated.

[0456] Furthermore, the server is equipped with a system for collecting and managing information on available supplies and personnel from remote locations. Database technology and APIs are used for this resource management, and in particular, the Google Maps API can be used to formulate optimal transportation routes. Based on these analysis results, a support plan is generated, and the available supplies are quickly and accurately matched with the needs of the affected areas. The generated support plan is organized using the Google Sheets API, and the next support strategy is determined.

[0457] For example, if a company provides blankets, that information is registered on the server and matched with the necessary supplies at various evacuation centers. As a result, delivery arrangements can be made immediately. The generating AI model can be prompted with messages such as, "Based on the needs information from disaster area A, please propose a high-priority support plan."

[0458] The formed support plan is notified to the user via a terminal. Based on the received information, the user can quickly begin support activities. After the support activities are completed, the user returns evaluation information to the server via the terminal, which is then used to inform the next support plan. This improves the accuracy of support and allows for a more precise response to the needs of the disaster-stricken area. The purpose of this invention is to optimize support activities during disasters and enable a rapid and effective response.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] Users input information about the supplies and services they need through terminals installed in the disaster area. Specifically, they input their requests using intuitive touch controls and press the send button. The information entered includes the type of supplies, the required quantity, and the urgency of the request. The terminal temporarily stores this data and performs consistency checks.

[0462] Step 2:

[0463] Data whose integrity has been verified is sent from the terminal to the server. At this time, the information is securely transferred using an SSL / TLS encrypted protocol. The data that reaches the server is aggregated using streaming processing such as Apache Kafka. The input data is separated by disaster area and stored in real time.

[0464] Step 3:

[0465] The server feeds the aggregated data into a generating AI model. Here, Python and TensorFlow are used to perform data prioritization analysis. The input for this analysis is information on the needs of the disaster-stricken areas, and the output is prioritized requests for assistance. The AI ​​model then processes prompts to identify the most urgent requests based on the situation in the disaster-stricken areas.

[0466] Step 4:

[0467] Based on the priority information analyzed, the server performs resource matching to meet needs. It references the resource management database to obtain information on available supplies and personnel. The input is resource information and priority information, and the output is a specific support plan. The optimal delivery route is also calculated using the Google Maps API.

[0468] Step 5:

[0469] The server sends the formed support plan to the terminal and notifies the user. The support plan includes necessary supplies, delivery personnel, and estimated arrival times. The terminal uses push notifications to display a notification on the screen. After the user confirms this, they can quickly begin providing support.

[0470] Step 6:

[0471] After an assistance activity is underway or completed, the user enters feedback using a terminal and sends it to the server. This feedback includes the success rate of the assistance and any additional requests. The feedback information is stored in a database and used to inform future assistance plans. The server then analyzes this information to continuously improve the system.

[0472] (Application Example 1)

[0473] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] In disaster relief efforts, it is crucial that the diverse needs of victims are quickly and efficiently understood, and that appropriate relief supplies and personnel are reliably delivered. However, conventional systems have suffered from delays in relief due to the time required for information gathering, matching supplies, optimizing transportation, and developing situation-specific support plans. Furthermore, there has been an insufficient system for sharing local information with residents and supporting their daily lives, not only during disasters but also during peacetime.

[0475] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0476] In this invention, the server includes an information gathering means for collecting user needs in disaster-stricken areas, a matching means using generative AI for matching aggregated needs with externally provided supplies and personnel, and a real-time information provision means for sharing local information during normal times. This enables not only rapid and efficient support activities during disasters, but also information sharing and life support in local communities during normal times.

[0477] "Information gathering means" refers to a mechanism for collecting needs information from users through terminals installed in disaster-stricken areas.

[0478] "Data analysis methods" refer to the process of analyzing aggregated needs information and determining its priorities.

[0479] A "resource information organization method" is a method for efficiently organizing information on available supplies and personnel that can be provided from outside the disaster-stricken area.

[0480] "Generative AI-based matching methods" refer to a process that utilizes generative AI technology to optimally connect users' needs with available resources.

[0481] A "transportation route optimization method" is a system for formulating routes that allow for the most efficient transportation of relief supplies and personnel.

[0482] "Notification means" refers to technology for quickly communicating the generated support plan to the user.

[0483] A "real-time information provision system" is a system that provides residents with real-time information on local traffic and events, even during normal times.

[0484] A "feedback transmission method" is a means of collecting opinions and results information from users and sending it back to the server.

[0485] A "user interface" is a display method that appears on a terminal, allowing users to easily input their needs and receive information.

[0486] This system consists primarily of terminals installed in disaster-stricken areas, a cloud server, and a smartphone application. The terminals collect needs information from disaster victims and users, and data can be easily entered through the user interface. The collected information is immediately transmitted to the cloud server.

[0487] The servers operate on large-scale cloud computing infrastructure such as Google Cloud Platform (GCP) and perform data analysis and organize resource information. Generative AI is used to analyze aggregated needs and available resources and personnel information, enabling optimized matching. This generative AI utilizes, for example, OpenAI's language models to enable rapid and highly accurate analysis.

[0488] Furthermore, during normal times, the system provides users with real-time information on traffic conditions and local events via a smartphone app from the server. This allows residents to always access the latest information, which can be useful in their daily lives and for quick responses during disasters.

[0489] As a concrete example, consider the following scenario: When a typhoon is predicted to approach, if a user inputs information via their device indicating that they "need food and blankets," the server will collect this information and perform analysis using a generation AI. A support plan will then be quickly formulated, necessary supplies will be arranged via the optimal transportation route, and residents will be notified. Furthermore, residents will be provided with real-time information on alternative transportation routes and nearby evacuation shelters using a smartphone app.

[0490] Examples of prompt statements for a generative AI model are as follows:

[0491] "Based on the latest information from local governments, please generate a list of relief supplies needed for the next typhoon. Please include requests for evacuation centers as specific needs information."

[0492] Thus, this invention aims to provide a useful information infrastructure not only during disasters but also during normal times, thereby improving the quality of life in local communities.

[0493] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0494] Step 1:

[0495] The terminal collects needs information from disaster victims. Users input specific requests, such as "food" or "blankets needed," through the terminal's user interface, and send this information to the server. The input data is formatted and stored on the cloud server in a unified format.

[0496] Step 2:

[0497] The server performs data analysis based on the received needs information. Using data analysis tools, it analyzes which needs should be prioritized and, if necessary, clusters the needs using a generative AI model. The input is user needs data, and the output is a prioritized list of needs.

[0498] Step 3:

[0499] The server uses resource information organization tools to organize information on available supplies and personnel. It takes in information from regional collaborations and support providers as input and generates a usable support list as output. This support list includes information such as the available quantity and delivery schedule.

[0500] Step 4:

[0501] The server uses a matching mechanism based on generated AI to match prioritized needs with organized resource information. This matching process ensures the optimal allocation of resources according to the needs. Using a prioritized needs list and a support list as input data, the server provides an optimal support plan as output.

[0502] Step 5:

[0503] The server formulates transportation routes based on an optimized support plan. Using transportation route optimization tools, it plans how to efficiently deliver support supplies and personnel. It considers the optimal support route as input and creates a specific transportation route map as output.

[0504] Step 6:

[0505] The server notifies the user of the final support plan. Through notification methods, it sends the generated support plan, specific work instructions, and transportation route to the terminal or smartphone app. The input is the formulated support plan, and the output is the information notified to the user.

[0506] Step 7:

[0507] Users send feedback about the support provided to the server using the feedback submission method. The server then collects data for future system improvements, receiving feedback data as input and analysis results for system improvement as output.

[0508] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0509] This invention aims to build a support system that takes user emotions into consideration in order to streamline support activities during disasters. This system functions through the coordinated operation of various elements, including terminals placed in evacuation centers, a central control server, and individual users.

[0510] The terminal is a device actually used in evacuation shelters and provides an interface for users to input the necessary relief supplies and services. This interface also includes an emotion detection function, allowing the terminal to recognize emotions from the user's voice and facial expressions while they are inputting. When users input their needs, their emotions are recognized through the emotion engine, which then transmits deeper needs to the server.

[0511] The server aggregates and analyzes information obtained from the terminals. In particular, by integrating user emotional information, it adds an emotional aspect to conventional needs information, allowing for a more accurate determination of the urgency and importance of those needs. For example, if a user expresses anxiety or urgency in a clear tone, the server recognizes that emotional information as a high-priority item.

[0512] Furthermore, the server compiles information on externally provided resources and personnel, and executes a matching process using generated AI. By taking emotional information into consideration during this process, the accuracy of support matching is improved, and priority support can be given to users who require specific psychological care. For example, a user experiencing extreme anxiety will be identified as needing mental support, and personnel will be matched accordingly.

[0513] The final support plan is communicated to the user via the terminal and implemented by the support staff. After the support is completed, the user provides feedback through the terminal. This feedback includes emotional responses, and the server incorporates this information into future support plans, enabling the continuous provision of more personalized and effective support.

[0514] In this way, support activities in disaster-stricken areas are effectively deployed with the server, terminals, and users working together, based on detailed needs information, including users' emotions. This system aims to enable faster and more accurate responses than conventional support methods, and to significantly strengthen overall support capabilities during disasters.

[0515] The following describes the processing flow.

[0516] Step 1:

[0517] The terminal provides an input interface to users in evacuation shelters in disaster-stricken areas. Users use this interface to input necessary supplies and support services, while the emotion engine built into the terminal recognizes the user's emotions through their voice and facial expressions.

[0518] Step 2:

[0519] The terminal transmits needs and emotional information collected from users to the server. This transmission is done in real time, enabling timely data aggregation for each evacuation center.

[0520] Step 3:

[0521] The server analyzes the received information and reflects emotional information in the urgency of the needs. For example, if strong emotions such as fear or sadness are detected, the server prioritizes that need information.

[0522] Step 4:

[0523] The server organizes information on resources and personnel provided from external sources and uses a generation AI to perform optimal matching based on user needs and emotions. Particular attention is paid to allocating appropriate resources to users who require psychological care.

[0524] Step 5:

[0525] The server notifies the terminal of the generated support plan. The terminal then presents this plan to the user and support staff, enabling the rapid implementation of specific support activities.

[0526] Step 6:

[0527] Users input feedback into their device after the assistance is provided. At this time, emotional information is collected again and used for subsequent analysis.

[0528] Step 7:

[0529] The terminal sends user feedback to the server, which uses this information to develop the next support plan. This continuously improves the accuracy and effectiveness of the entire support process.

[0530] (Example 2)

[0531] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0532] During disasters, it is crucial to provide prompt and appropriate support to victims. However, conventional systems have struggled to adequately consider the emotional state and psychological needs of victims. In particular, addressing cases where psychological care is needed in addition to physical needs remains a challenge.

[0533] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0534] In this invention, the server includes: information gathering means for collecting user requests in the disaster area and recognizing emotions through audio and video; data analysis means for analyzing the collected requests and emotion data and setting priorities; resource information organization means for integrating information on available resources and experts provided from outside the disaster area; means for matching requests with externally provided resources and experts using a generated AI model; and feedback integration means for obtaining feedback, including emotional responses from users after the completion of support and reflecting it in the next support plan. This enables accurate capture of the physical and psychological needs of disaster victims and appropriate support based on priorities.

[0535] "Information gathering means" refers to functions that collect user requests in the disaster-stricken area and recognize users' emotional states through audio and video.

[0536] "Data analysis means" refers to a function that processes collected user requests and sentiment data and sets support priorities based on that data.

[0537] A "resource information organization tool" is a function for integrating and organizing information on available resources and experts provided from outside the disaster-stricken area.

[0538] "Matching methods using generative AI models" refer to a function that uses generative AI to match users with external resources and experts based on analyzed user requests and sentiment data.

[0539] A "presentation means" is an interface function that directly notifies the user of the generated support plan and presents the details of the support.

[0540] A "feedback integration mechanism" is a function that collects feedback, including emotional responses, provided by users after the completion of support, and incorporates it into the next support plan.

[0541] This invention is a technology for effectively carrying out support activities during disasters, and it constructs a support system that takes into account the emotional state of disaster victims. This system functions through the coordinated operation of various elements, including terminals placed in evacuation centers, a central control server, and individual users.

[0542] About the device:

[0543] The terminal is a device actually used in evacuation shelters, providing an interface for users to input the necessary relief supplies and services. The terminal has a touchscreen, allowing users to easily input their physical needs through the user interface. It also has a voice recognition microphone and camera function, which are used to detect the user's emotions. For example, if a user says "I'm very anxious" while inputting food assistance, the terminal can recognize the anxiety from the voice and facial expression and send it to the server as emotion data.

[0544] About the server:

[0545] The server aggregates request data and emotional data sent from terminals and analyzes them using a generative AI model. Based on the analysis results, support needs are prioritized, with particular consideration given to emotional aspects. It also integrates resources and expert information provided from external sources to match the most suitable support staff and supplies. This process is triggered by prompt statements, which in turn trigger the generative AI model to perform optimal matching. An example of a prompt statement is, "Based on data obtained from terminals in evacuation centers, please generate a support plan that includes appropriate psychological care, taking into account the anxiety levels of users who require food assistance."

[0546] About the user:

[0547] Users input their needs by operating a terminal and receive a support plan from the server via the terminal. After the support is completed, they send feedback from the terminal, which includes their satisfaction level and emotional response to the support. This feedback data is used in the next support plan, enabling the continuous provision of more individualized and effective support.

[0548] This system aims to ensure that relief efforts in disaster-stricken areas are deployed quickly and accurately, taking into account the detailed needs and emotions of users.

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] The terminal provides an interface for users in evacuation shelters to input the necessary supplies and services. The user's requests (e.g., food and clothing needs) are entered via the terminal's touchscreen. Simultaneously, the terminal detects the user's voice and facial expressions, collecting data necessary to analyze their emotional state. This input data includes the user's requests and emotional information.

[0552] Step 2:

[0553] The terminal sends the collected user request data and sentiment data to the server. The server stores the received data in a database for analysis. Specifically, the server uses natural language processing technology to analyze the request content, quantifies the sentiment information, and scores it based on evaluation criteria. This process generates output indicating the urgency and importance of the request.

[0554] Step 3:

[0555] The server uses the generated evaluation score to refer to external support resources and personnel databases, and uses a generative AI model to perform optimal support matching. The generative AI model determines the best support that matches the user's needs based on specific prompt statements. Specifically, for a user who needs food assistance, it considers their anxieties and assigns staff who can provide emotional support. This process outputs a specific support plan.

[0556] Step 4:

[0557] The server generates a final support plan and notifies the user via the terminal. The user can then view details such as the type of support provided, the assigned counselor, and the scheduled time on the terminal screen. For example, the user might receive a message such as, "A psychological counselor is scheduled to visit you tomorrow at 10 AM."

[0558] Step 5:

[0559] After receiving assistance, users provide feedback via their device. This feedback includes their satisfaction rating and emotional state regarding the assistance. The server analyzes the feedback data and reflects it in a database for use in future assistance sessions. This process clarifies areas for improvement to enhance the quality of future assistance activities.

[0560] (Application Example 2)

[0561] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0562] In modern urban environments, it is a challenging task to appropriately collect user sentiment and needs regarding public services, analyze them in real time, and use that information to improve services. Particularly during disasters or when citizens are facing difficulties, there is a need to take swift and appropriate measures on an individual basis. However, current methods fail to accurately grasp the deep-seated needs of citizens, resulting in problems such as insufficient service satisfaction and inadequate response capabilities.

[0563] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0564] In this invention, the server includes information gathering means, emotion recognition means, and feedback processing means. This makes it possible to collect and analyze the emotions of citizens within a city in real time. As a result, aggregated requests can be matched with externally provided resources and personnel with high accuracy, generating efficient improvement proposals for public services and enabling a quick and accurate response to citizens.

[0565] "Information gathering means" refers to devices that have the function of collecting user requests and feedback.

[0566] "Data analysis means" refers to a process for analyzing collected request information and setting priorities.

[0567] A "resource information organization method" is a system for organizing information on resources and personnel provided from external sources and applying it to support plans.

[0568] "Generative AI-based matching method" refers to a process that uses generative AI technology to match aggregated requests with externally provided resources and personnel.

[0569] A "transportation route optimization means" is a system that has the function of formulating the optimal transportation route based on the created support plan.

[0570] "Presentation method" refers to a method for notifying the user of the generated support plan and providing them with information.

[0571] A "feedback processing mechanism" is a function that collects and analyzes feedback from citizens and reflects it in the system in real time.

[0572] "Emotion recognition means" refers to technologies used to detect and analyze the emotions of citizens.

[0573] This invention realizes a system that efficiently collects and analyzes user requests and emotions in urban and disaster-stricken areas, and supports the provision and improvement of appropriate public services based on that information. As an information collection means, devices such as terminals and smartphones are used to collect user requests and feedback. The terminal accepts voice and text input through a user interface and detects emotions from the user's voice and facial expressions using emotion recognition means. The detected data is transmitted to a server equipped with data analysis means and analyzed.

[0574] The server analyzes the aggregated requests and sets priorities using data analysis tools. Furthermore, resource information organization tools organize information on resources and personnel provided from external sources, and matching tools using generation AI efficiently match the aggregated requests with externally provided resources and personnel. Based on the matching results, a transportation route optimization tool formulates the optimal transportation route, and the generated support plan is notified to the user through a presentation tool.

[0575] As a concrete example, if citizens use their smartphones to input emotions such as stress or requests for improvement regarding public transportation within the city, this data will be collected in real time and analyzed by a server to understand their underlying needs, including their emotions. Based on the analysis results, suggestions for improving the operating schedule and facilities will be made, enabling the city to respond quickly and accurately.

[0576] An example of a prompt using a generative AI model is, "Analyze the emotions citizens feel about the stress of crowded trains, and based on that feedback, what improvement suggestions can be made?" Software such as emotion recognition engines and data analysis algorithms play a crucial role throughout this entire process.

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] The user provides feedback via voice or text through their device. Since this feedback may contain the user's emotions, it is sent to an emotion recognition engine. The input here is data about the user's emotions and requests, while the output is data ready for analysis of the potential emotions contained within it.

[0580] Step 2:

[0581] The device inputs the received feedback data into an emotion recognition engine, which extracts emotions from the user's voice and facial expressions. This process identifies emotions based on voice tone and text context, and outputs the user's emotional state as an analysis result. Specifically, emotion labels such as "anxiety," "satisfaction," and "frustration" are assigned.

[0582] Step 3:

[0583] The server receives sentiment data sent from the terminal and applies a data analysis algorithm to evaluate the urgency and applicability of the request. This analysis determines the priority level of the user's request. The analysis results are output as a prioritized list for each request.

[0584] Step 4:

[0585] The server uses resource information organization tools to acquire and organize information on resources and personnel that can be provided from external sources. In this process, the types of resources available, the number of people who can use them, and their location information are organized and output as a list of available resources.

[0586] Step 5:

[0587] Using a generative AI model, the server matches aggregated requests with available resources. This process considers request priorities and sentiment information to select the most appropriate resources. The final output is a matching list, which forms the basis for a specific support plan.

[0588] Step 6:

[0589] The server uses a transportation route optimization system to plan the transportation routes for relief supplies and staff. Geographic data and traffic information are used as input to calculate the optimal transportation route. The output is a highly efficient transportation schedule.

[0590] Step 7:

[0591] The server notifies the user of the generated support plan via the terminal, making it clear to the user. Information provided at this stage includes specific details of the support provided and the estimated arrival time. The user receives this information and can provide further feedback if needed.

[0592] Step 8:

[0593] If a user provides new feedback during the process, this information is sent back to the server through the feedback processing mechanism, and the initial request processing is updated as needed. This allows the system to be continuously optimized.

[0594] 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.

[0595] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0596] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] 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.

[0600] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0601] 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.

[0602] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0603] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0604] 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.

[0605] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0606] 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.

[0607] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0608] The 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.

[0609] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0610] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] This invention is a system for streamlining support activities during disasters, and is centered around terminals installed in each disaster-stricken area and a server. The terminals function as an interface for collecting needs information from disaster victims, allowing users to input the support supplies and services they need. For example, if a user needs a blanket, they can easily input that information into the terminal.

[0612] The server aggregates information transmitted from each terminal and analyzes the overall needs situation as needed. It pays particular attention to items deemed high-priority from the aggregated data and performs matching to ensure efficient support. The server utilizes generative AI to instantly process the information and generate an optimized support plan. This support plan includes a list of necessary supplies and personnel, as well as delivery routes.

[0613] For example, if a company provides blankets, that information is registered on the server and matched with various shelters that need these supplies. As a result, delivery arrangements can be made immediately.

[0614] The formed support plan is notified to the user via the terminal. Based on the received information, the user can quickly begin support activities. Furthermore, after the support is completed, the user sends feedback information back to the server via the terminal. This allows for the reflection of successive changes in needs and requests, improving the accuracy of support activities.

[0615] In this way, servers, terminals, and users can effectively fulfill their respective roles and accurately respond to the needs of the disaster-stricken areas. The overall support activities can be further improved through continuous feedback. This system is truly intended to function as a new standard for disaster relief activities.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The terminal provides an interface to users in evacuation centers in disaster-stricken areas, allowing them to input the necessary supplies and support. Users use this interface to input their specific needs.

[0619] Step 2:

[0620] The terminal transmits user needs information entered by the user to the server in real time. This allows information from each evacuation center to be aggregated in one place.

[0621] Step 3:

[0622] The server analyzes the aggregated needs information and prioritizes them based on their urgency and importance. The analysis results serve as foundational data for developing support plans.

[0623] Step 4:

[0624] The server organizes information on available supplies and personnel provided from external sources. This includes resource information registered in advance by donors.

[0625] Step 5:

[0626] The server uses AI generation to match aggregated needs with external resources. This generates an optimal support plan, enabling rapid implementation of assistance.

[0627] Step 6:

[0628] The server notifies the terminal of an optimized support plan. The terminal then presents this information to the user and support staff, providing specific action guidelines.

[0629] Step 7:

[0630] After assistance is provided, users input feedback on the results and any new needs into their device. This feedback is then incorporated into future plans, contributing to improvements in assistance activities.

[0631] Step 8:

[0632] The device sends user feedback to the server. The server then re-analyzes this information and prepares to update the support plan in response to changes in needs.

[0633] (Example 1)

[0634] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] In the event of a disaster, it is crucial to allocate limited resources optimally in order to respond quickly and efficiently to the diverse needs of affected areas. However, conventional systems have problems with the fact that information on needs and resources is not managed uniformly, making it difficult to achieve appropriate matching. Furthermore, the formulation and implementation of support plans are not carried out quickly, hindering effective support for disaster victims. It is important to solve these problems and carry out effective support activities.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0637] In this invention, the server includes information gathering means equipped with a terminal device for collecting user request information, data analysis means for integrating the collected request information and setting priorities using generated artificial intelligence, and resource management means for managing information on supplies and personnel supplied from remote locations. This enables a rapid and efficient response to the diverse needs of disaster-stricken areas, facilitating appropriate matching and the formulation of efficient support plans.

[0638] "Information gathering means" refers to a device or system used to collect requests from disaster victims during a disaster and organize them as necessary data.

[0639] A "terminal device" is a device or equipment installed in a disaster-stricken area that provides a user interface for users to input the support information they need.

[0640] A "data analysis tool" is a system that analyzes collected request information and uses artificial intelligence to optimally set its priorities.

[0641] A "resource management system" is a process or device that manages information on supplies and personnel supplied from outside the disaster area and optimizes their use for efficient support.

[0642] "Generative artificial intelligence" refers to a machine learning model that includes algorithms and technologies for analyzing various types of information and generating optimal support plans.

[0643] A "matching method" is a process or device for appropriately matching the needs of disaster-stricken areas with resources that can be provided from outside.

[0644] A "route planning optimization means" is a method or apparatus for efficiently planning the transportation routes of goods and personnel in a logistics process, thereby achieving the shortest and most optimal delivery.

[0645] "Information presentation means" refers to a method or device for immediately communicating the generated support plan to the user and encouraging the commencement of support activities.

[0646] "Evaluation information sharing means" refers to a process or device for collecting user feedback information after support activities have been completed and transmitting it to a processing device.

[0647] This system is designed to efficiently collect the needs of disaster victims during a disaster and to implement the most appropriate support plan. The terminals within the system are technical devices with interfaces for disaster victims to input the supplies and services they need. Touchscreen devices are used to enable intuitive operation. These terminals are installed in disaster-stricken areas, allowing users to directly operate the devices and input their needs.

[0648] Once information is entered, the terminal sends it to the server. The server aggregates the data in real time using a messaging system such as Apache Kafka, and uses a generative AI model to prioritize the needs. Python and TensorFlow are used to build the generative AI model, and Google Cloud's AI platform is used as needed. Based on the results of this analysis, an efficient support plan is formulated.

[0649] Furthermore, the server is equipped with a system for collecting and managing information on available supplies and personnel from remote locations. Database technology and APIs are used for this resource management, and in particular, the Google Maps API can be used to formulate optimal transportation routes. Based on these analysis results, a support plan is generated, and the available supplies are quickly and accurately matched with the needs of the affected areas. The generated support plan is organized using the Google Sheets API, and the next support strategy is determined.

[0650] For example, if a company provides blankets, that information is registered on the server and matched with the necessary supplies at various evacuation centers. As a result, delivery arrangements can be made immediately. The generating AI model can be prompted with messages such as, "Based on the needs information from disaster area A, please propose a high-priority support plan."

[0651] The formed support plan is notified to the user via a terminal. Based on the received information, the user can quickly begin support activities. After the support activities are completed, the user returns evaluation information to the server via the terminal, which is then used to inform the next support plan. This improves the accuracy of support and allows for a more precise response to the needs of the disaster-stricken area. The purpose of this invention is to optimize support activities during disasters and enable a rapid and effective response.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] Users input information about the supplies and services they need through terminals installed in the disaster area. Specifically, they input their requests using intuitive touch controls and press the send button. The information entered includes the type of supplies, the required quantity, and the urgency of the request. The terminal temporarily stores this data and performs consistency checks.

[0655] Step 2:

[0656] Data whose integrity has been verified is sent from the terminal to the server. At this time, the information is securely transferred using an SSL / TLS encrypted protocol. The data that reaches the server is aggregated using streaming processing such as Apache Kafka. The input data is separated by disaster area and stored in real time.

[0657] Step 3:

[0658] The server feeds the aggregated data into a generating AI model. Here, Python and TensorFlow are used to perform data prioritization analysis. The input for this analysis is information on the needs of the disaster-stricken areas, and the output is prioritized requests for assistance. The AI ​​model then processes prompts to identify the most urgent requests based on the situation in the disaster-stricken areas.

[0659] Step 4:

[0660] Based on the priority information analyzed, the server performs resource matching to meet needs. It references the resource management database to obtain information on available supplies and personnel. The input is resource information and priority information, and the output is a specific support plan. The optimal delivery route is also calculated using the Google Maps API.

[0661] Step 5:

[0662] The server sends the formed support plan to the terminal and notifies the user. The support plan includes necessary supplies, delivery personnel, and estimated arrival times. The terminal uses push notifications to display a notification on the screen. After the user confirms this, they can quickly begin providing support.

[0663] Step 6:

[0664] After an assistance activity is underway or completed, the user enters feedback using a terminal and sends it to the server. This feedback includes the success rate of the assistance and any additional requests. The feedback information is stored in a database and used to inform future assistance plans. The server then analyzes this information to continuously improve the system.

[0665] (Application Example 1)

[0666] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0667] In disaster relief efforts, it is crucial that the diverse needs of victims are quickly and efficiently understood, and that appropriate relief supplies and personnel are reliably delivered. However, conventional systems have suffered from delays in relief due to the time required for information gathering, matching supplies, optimizing transportation, and developing situation-specific support plans. Furthermore, there has been an insufficient system for sharing local information with residents and supporting their daily lives, not only during disasters but also during peacetime.

[0668] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0669] In this invention, the server includes an information gathering means for collecting user needs in disaster-stricken areas, a matching means using generative AI for matching aggregated needs with externally provided supplies and personnel, and a real-time information provision means for sharing local information during normal times. This enables not only rapid and efficient support activities during disasters, but also information sharing and life support in local communities during normal times.

[0670] "Information gathering means" refers to a mechanism for collecting needs information from users through terminals installed in disaster-stricken areas.

[0671] "Data analysis methods" refer to the process of analyzing aggregated needs information and determining its priorities.

[0672] A "resource information organization method" is a method for efficiently organizing information on available supplies and personnel that can be provided from outside the disaster-stricken area.

[0673] "Generative AI-based matching methods" refer to a process that utilizes generative AI technology to optimally connect users' needs with available resources.

[0674] A "transportation route optimization method" is a system for formulating routes that allow for the most efficient transportation of relief supplies and personnel.

[0675] "Notification means" refers to technology for quickly communicating the generated support plan to the user.

[0676] A "real-time information provision system" is a system that provides residents with real-time information on local traffic and events, even during normal times.

[0677] A "feedback transmission method" is a means of collecting opinions and results information from users and sending it back to the server.

[0678] A "user interface" is a display method that appears on a terminal, allowing users to easily input their needs and receive information.

[0679] This system consists primarily of terminals installed in disaster-stricken areas, a cloud server, and a smartphone application. The terminals collect needs information from disaster victims and users, and data can be easily entered through the user interface. The collected information is immediately transmitted to the cloud server.

[0680] The servers operate on large-scale cloud computing infrastructure such as Google Cloud Platform (GCP) and perform data analysis and organize resource information. Generative AI is used to analyze aggregated needs and available resources and personnel information, enabling optimized matching. This generative AI utilizes, for example, OpenAI's language models to enable rapid and highly accurate analysis.

[0681] Furthermore, during normal times, the system provides users with real-time information on traffic conditions and local events via a smartphone app from the server. This allows residents to always access the latest information, which can be useful in their daily lives and for quick responses during disasters.

[0682] As a concrete example, consider the following scenario: When a typhoon is predicted to approach, if a user inputs information via their device indicating that they "need food and blankets," the server will collect this information and perform analysis using a generation AI. A support plan will then be quickly formulated, necessary supplies will be arranged via the optimal transportation route, and residents will be notified. Furthermore, residents will be provided with real-time information on alternative transportation routes and nearby evacuation shelters using a smartphone app.

[0683] Examples of prompt statements for a generative AI model are as follows:

[0684] "Based on the latest information from local governments, please generate a list of relief supplies needed for the next typhoon. Please include requests for evacuation centers as specific needs information."

[0685] Thus, this invention aims to provide a useful information infrastructure not only during disasters but also during normal times, thereby improving the quality of life in local communities.

[0686] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0687] Step 1:

[0688] The terminal collects needs information from disaster victims. Users input specific requests, such as "food" or "blankets needed," through the terminal's user interface, and send this information to the server. The input data is formatted and stored on the cloud server in a unified format.

[0689] Step 2:

[0690] The server performs data analysis based on the received needs information. Using data analysis tools, it analyzes which needs should be prioritized and, if necessary, clusters the needs using a generative AI model. The input is user needs data, and the output is a prioritized list of needs.

[0691] Step 3:

[0692] The server uses resource information organization tools to organize information on available supplies and personnel. It takes in information from regional collaborations and support providers as input and generates a usable support list as output. This support list includes information such as the available quantity and delivery schedule.

[0693] Step 4:

[0694] The server uses a matching mechanism based on generated AI to match prioritized needs with organized resource information. This matching process ensures the optimal allocation of resources according to the needs. Using a prioritized needs list and a support list as input data, the server provides an optimal support plan as output.

[0695] Step 5:

[0696] The server formulates transportation routes based on an optimized support plan. Using transportation route optimization tools, it plans how to efficiently deliver support supplies and personnel. It considers the optimal support route as input and creates a specific transportation route map as output.

[0697] Step 6:

[0698] The server notifies the user of the final support plan. Through notification methods, it sends the generated support plan, specific work instructions, and transportation route to the terminal or smartphone app. The input is the formulated support plan, and the output is the information notified to the user.

[0699] Step 7:

[0700] Users send feedback about the support provided to the server using the feedback submission method. The server then collects data for future system improvements, receiving feedback data as input and analysis results for system improvement as output.

[0701] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0702] This invention aims to build a support system that takes user emotions into consideration in order to streamline support activities during disasters. This system functions through the coordinated operation of various elements, including terminals placed in evacuation centers, a central control server, and individual users.

[0703] The terminal is a device actually used in evacuation shelters and provides an interface for users to input the necessary relief supplies and services. This interface also includes an emotion detection function, allowing the terminal to recognize emotions from the user's voice and facial expressions while they are inputting. When users input their needs, their emotions are recognized through the emotion engine, which then transmits deeper needs to the server.

[0704] The server aggregates and analyzes information obtained from the terminals. In particular, by integrating user emotional information, it adds an emotional aspect to conventional needs information, allowing for a more accurate determination of the urgency and importance of those needs. For example, if a user expresses anxiety or urgency in a clear tone, the server recognizes that emotional information as a high-priority item.

[0705] Furthermore, the server compiles information on externally provided resources and personnel, and executes a matching process using generated AI. By taking emotional information into consideration during this process, the accuracy of support matching is improved, and priority support can be given to users who require specific psychological care. For example, a user experiencing extreme anxiety will be identified as needing mental support, and personnel will be matched accordingly.

[0706] The final support plan is communicated to the user via the terminal and implemented by the support staff. After the support is completed, the user provides feedback through the terminal. This feedback includes emotional responses, and the server incorporates this information into future support plans, enabling the continuous provision of more personalized and effective support.

[0707] In this way, support activities in disaster-stricken areas are effectively deployed with the server, terminals, and users working together, based on detailed needs information, including users' emotions. This system aims to enable faster and more accurate responses than conventional support methods, and to significantly strengthen overall support capabilities during disasters.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The terminal provides an input interface to users in evacuation shelters in disaster-stricken areas. Users use this interface to input necessary supplies and support services, while the emotion engine built into the terminal recognizes the user's emotions through their voice and facial expressions.

[0711] Step 2:

[0712] The terminal transmits needs and emotional information collected from users to the server. This transmission is done in real time, enabling timely data aggregation for each evacuation center.

[0713] Step 3:

[0714] The server analyzes the received information and reflects emotional information in the urgency of the needs. For example, if strong emotions such as fear or sadness are detected, the server prioritizes that need information.

[0715] Step 4:

[0716] The server organizes information on resources and personnel provided from external sources and uses a generation AI to perform optimal matching based on user needs and emotions. Particular attention is paid to allocating appropriate resources to users who require psychological care.

[0717] Step 5:

[0718] The server notifies the terminal of the generated support plan. The terminal then presents this plan to the user and support staff, enabling the rapid implementation of specific support activities.

[0719] Step 6:

[0720] Users input feedback into their device after the assistance is provided. At this time, emotional information is collected again and used for subsequent analysis.

[0721] Step 7:

[0722] The terminal sends user feedback to the server, which uses this information to develop the next support plan. This continuously improves the accuracy and effectiveness of the entire support process.

[0723] (Example 2)

[0724] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] During disasters, it is crucial to provide prompt and appropriate support to victims. However, conventional systems have struggled to adequately consider the emotional state and psychological needs of victims. In particular, addressing cases where psychological care is needed in addition to physical needs remains a challenge.

[0726] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0727] In this invention, the server includes: information gathering means for collecting user requests in the disaster area and recognizing emotions through audio and video; data analysis means for analyzing the collected requests and emotion data and setting priorities; resource information organization means for integrating information on available resources and experts provided from outside the disaster area; means for matching requests with externally provided resources and experts using a generated AI model; and feedback integration means for obtaining feedback, including emotional responses from users after the completion of support and reflecting it in the next support plan. This enables accurate capture of the physical and psychological needs of disaster victims and appropriate support based on priorities.

[0728] "Information gathering means" refers to functions that collect user requests in the disaster-stricken area and recognize users' emotional states through audio and video.

[0729] "Data analysis means" refers to a function that processes collected user requests and sentiment data and sets support priorities based on that data.

[0730] A "resource information organization tool" is a function for integrating and organizing information on available resources and experts provided from outside the disaster-stricken area.

[0731] "Matching methods using generative AI models" refer to a function that uses generative AI to match users with external resources and experts based on analyzed user requests and sentiment data.

[0732] A "presentation means" is an interface function that directly notifies the user of the generated support plan and presents the details of the support.

[0733] A "feedback integration mechanism" is a function that collects feedback, including emotional responses, provided by users after the completion of support, and incorporates it into the next support plan.

[0734] This invention is a technology for effectively carrying out support activities during disasters, and it constructs a support system that takes into account the emotional state of disaster victims. This system functions through the coordinated operation of various elements, including terminals placed in evacuation centers, a central control server, and individual users.

[0735] About the device:

[0736] The terminal is a device actually used in evacuation shelters, providing an interface for users to input the necessary relief supplies and services. The terminal has a touchscreen, allowing users to easily input their physical needs through the user interface. It also has a voice recognition microphone and camera function, which are used to detect the user's emotions. For example, if a user says "I'm very anxious" while inputting food assistance, the terminal can recognize the anxiety from the voice and facial expression and send it to the server as emotion data.

[0737] About the server:

[0738] The server aggregates request data and emotional data sent from terminals and analyzes them using a generative AI model. Based on the analysis results, support needs are prioritized, with particular consideration given to emotional aspects. It also integrates resources and expert information provided from external sources to match the most suitable support staff and supplies. This process is triggered by prompt statements, which in turn trigger the generative AI model to perform optimal matching. An example of a prompt statement is, "Based on data obtained from terminals in evacuation centers, please generate a support plan that includes appropriate psychological care, taking into account the anxiety levels of users who require food assistance."

[0739] About the user:

[0740] Users input their needs by operating a terminal and receive a support plan from the server via the terminal. After the support is completed, they send feedback from the terminal, which includes their satisfaction level and emotional response to the support. This feedback data is used in the next support plan, enabling the continuous provision of more individualized and effective support.

[0741] This system aims to ensure that relief efforts in disaster-stricken areas are deployed quickly and accurately, taking into account the detailed needs and emotions of users.

[0742] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0743] Step 1:

[0744] The terminal provides an interface for users in evacuation shelters to input the necessary supplies and services. The user's requests (e.g., food and clothing needs) are entered via the terminal's touchscreen. Simultaneously, the terminal detects the user's voice and facial expressions, collecting data necessary to analyze their emotional state. This input data includes the user's requests and emotional information.

[0745] Step 2:

[0746] The terminal sends the collected user request data and sentiment data to the server. The server stores the received data in a database for analysis. Specifically, the server uses natural language processing technology to analyze the request content, quantifies the sentiment information, and scores it based on evaluation criteria. This process generates output indicating the urgency and importance of the request.

[0747] Step 3:

[0748] The server uses the generated evaluation score to refer to external support resources and personnel databases, and uses a generative AI model to perform optimal support matching. The generative AI model determines the best support that matches the user's needs based on specific prompt statements. Specifically, for a user who needs food assistance, it considers their anxieties and assigns staff who can provide emotional support. This process outputs a specific support plan.

[0749] Step 4:

[0750] The server generates a final support plan and notifies the user via the terminal. The user can then view details such as the type of support provided, the assigned counselor, and the scheduled time on the terminal screen. For example, the user might receive a message such as, "A psychological counselor is scheduled to visit you tomorrow at 10 AM."

[0751] Step 5:

[0752] After receiving assistance, users provide feedback via their device. This feedback includes their satisfaction rating and emotional state regarding the assistance. The server analyzes the feedback data and reflects it in a database for use in future assistance sessions. This process clarifies areas for improvement to enhance the quality of future assistance activities.

[0753] (Application Example 2)

[0754] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0755] In modern urban environments, it is a challenging task to appropriately collect user sentiment and needs regarding public services, analyze them in real time, and use that information to improve services. Particularly during disasters or when citizens are facing difficulties, there is a need to take swift and appropriate measures on an individual basis. However, current methods fail to accurately grasp the deep-seated needs of citizens, resulting in problems such as insufficient service satisfaction and inadequate response capabilities.

[0756] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0757] In this invention, the server includes information gathering means, emotion recognition means, and feedback processing means. This makes it possible to collect and analyze the emotions of citizens within a city in real time. As a result, aggregated requests can be matched with externally provided resources and personnel with high accuracy, generating efficient improvement proposals for public services and enabling a quick and accurate response to citizens.

[0758] "Information gathering means" refers to devices that have the function of collecting user requests and feedback.

[0759] "Data analysis means" refers to a process for analyzing collected request information and setting priorities.

[0760] A "resource information organization method" is a system for organizing information on resources and personnel provided from external sources and applying it to support plans.

[0761] "Generative AI-based matching method" refers to a process that uses generative AI technology to match aggregated requests with externally provided resources and personnel.

[0762] A "transportation route optimization means" is a system that has the function of formulating the optimal transportation route based on the created support plan.

[0763] "Presentation method" refers to a method for notifying the user of the generated support plan and providing them with information.

[0764] A "feedback processing mechanism" is a function that collects and analyzes feedback from citizens and reflects it in the system in real time.

[0765] "Emotion recognition means" refers to technologies used to detect and analyze the emotions of citizens.

[0766] This invention realizes a system that efficiently collects and analyzes user requests and emotions in urban and disaster-stricken areas, and supports the provision and improvement of appropriate public services based on that information. As an information collection means, devices such as terminals and smartphones are used to collect user requests and feedback. The terminal accepts voice and text input through a user interface and detects emotions from the user's voice and facial expressions using emotion recognition means. The detected data is transmitted to a server equipped with data analysis means and analyzed.

[0767] The server analyzes the aggregated requests and sets priorities using data analysis tools. Furthermore, resource information organization tools organize information on resources and personnel provided from external sources, and matching tools using generation AI efficiently match the aggregated requests with externally provided resources and personnel. Based on the matching results, a transportation route optimization tool formulates the optimal transportation route, and the generated support plan is notified to the user through a presentation tool.

[0768] As a concrete example, if citizens use their smartphones to input emotions such as stress or requests for improvement regarding public transportation within the city, this data will be collected in real time and analyzed by a server to understand their underlying needs, including their emotions. Based on the analysis results, suggestions for improving the operating schedule and facilities will be made, enabling the city to respond quickly and accurately.

[0769] An example of a prompt using a generative AI model is, "Analyze the emotions citizens feel about the stress of crowded trains, and based on that feedback, what improvement suggestions can be made?" Software such as emotion recognition engines and data analysis algorithms play a crucial role throughout this entire process.

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The user provides feedback via voice or text through their device. Since this feedback may contain the user's emotions, it is sent to an emotion recognition engine. The input here is data about the user's emotions and requests, while the output is data ready for analysis of the potential emotions contained within it.

[0773] Step 2:

[0774] The device inputs the received feedback data into an emotion recognition engine, which extracts emotions from the user's voice and facial expressions. This process identifies emotions based on voice tone and text context, and outputs the user's emotional state as an analysis result. Specifically, emotion labels such as "anxiety," "satisfaction," and "frustration" are assigned.

[0775] Step 3:

[0776] The server receives sentiment data sent from the terminal and applies a data analysis algorithm to evaluate the urgency and applicability of the request. This analysis determines the priority level of the user's request. The analysis results are output as a prioritized list for each request.

[0777] Step 4:

[0778] The server uses resource information organization tools to acquire and organize information on resources and personnel that can be provided from external sources. In this process, the types of resources available, the number of people who can use them, and their location information are organized and output as a list of available resources.

[0779] Step 5:

[0780] Using a generative AI model, the server matches aggregated requests with available resources. This process considers request priorities and sentiment information to select the most appropriate resources. The final output is a matching list, which forms the basis for a specific support plan.

[0781] Step 6:

[0782] The server uses a transportation route optimization system to plan the transportation routes for relief supplies and staff. Geographic data and traffic information are used as input to calculate the optimal transportation route. The output is a highly efficient transportation schedule.

[0783] Step 7:

[0784] The server notifies the user of the generated support plan via the terminal, making it clear to the user. Information provided at this stage includes specific details of the support provided and the estimated arrival time. The user receives this information and can provide further feedback if needed.

[0785] Step 8:

[0786] If a user provides new feedback during the process, this information is sent back to the server through the feedback processing mechanism, and the initial request processing is updated as needed. This allows the system to be continuously optimized.

[0787] 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.

[0788] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0789] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0790] 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.

[0791] Figure 9 shows an 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.

[0792] 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.

[0793] 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.

[0794] 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, motorcycles, etc., 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, for example, based 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.

[0795] 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."

[0796] 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.

[0797] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0798] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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 the like 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.

[0807] 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.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] Information gathering methods for collecting user needs in disaster-stricken areas,

[0811] A data analysis tool for analyzing aggregated needs and setting priorities,

[0812] A resource information organization method for organizing information on supplies and personnel that can be provided from outside the disaster area,

[0813] A matching method using generative AI to match aggregated needs with externally provided goods and personnel,

[0814] A means for optimizing transportation routes to formulate the optimal transportation route,

[0815] A means of notifying and presenting the generated support plan to the user,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising a feedback transmission means for collecting user feedback information and sending it back to the server.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the means for collecting information is realized by terminals placed in evacuation shelters, and a user interface is displayed through the terminals.

[0821] "Example 1"

[0822] (Claim 1)

[0823] Information collection means equipped with a terminal device for collecting user request information,

[0824] A data analysis method for integrating collected request information and setting priorities using generated artificial intelligence,

[0825] Resource management means for managing goods and personnel information supplied from remote locations,

[0826] An artificial intelligence-based matching system for appropriately matching integrated request information with available goods and personnel,

[0827] A means for optimizing route planning to achieve efficient transportation of goods,

[0828] Information presentation means for communicating the planned support strategy to the user,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising evaluation information sharing means for re-sharing post-use evaluation information collected from users with a processing device.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the means for collecting information is realized by a terminal device installed in an emergency evacuation facility, and a user interface is displayed through this terminal device.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] Information gathering methods for collecting user needs in disaster-stricken areas,

[0837] A data analysis tool for analyzing aggregated demand and setting priorities,

[0838] A resource information organization method for organizing information on supplies and personnel that can be provided from outside the disaster area,

[0839] A matching method using generative AI to match aggregated demand with externally provided goods and personnel,

[0840] A means for optimizing transportation routes to formulate the optimal transportation route,

[0841] A notification method for notifying and presenting the generated support plan to the user,

[0842] A real-time information provision method for sharing local information during normal times,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising a feedback transmission means for collecting feedback information from users and transmitting it back to the device.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the means of information gathering is realized by equipment placed in the evacuation center, and a user interface is displayed through the equipment.

[0848] "Example 2 of combining an emotion engine"

[0849] (Claim 1)

[0850] Information gathering means for collecting user requests in disaster-stricken areas and recognizing emotions through audio and video,

[0851] A data analysis method for analyzing collected request and sentiment data and setting priorities,

[0852] A resource information organization method that integrates information on available resources and experts provided from outside the disaster area,

[0853] A matching method using a generative AI model to match collected requests with externally provided resources and experts,

[0854] A means of notifying and presenting the generated support plan to the user,

[0855] A feedback integration mechanism that obtains feedback from users, including emotional reactions, after the completion of support, and incorporates it into the next support plan,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, wherein a user's emotions are recognized through a user interface and transmitted to a server via an emotion engine.

[0859] (Claim 3)

[0860] The system according to claim 1, wherein the means for collecting information is realized by a device placed in the evacuation center, and a user interface is displayed through the device.

[0861] "Application example 2 when combining with an emotional engine"

[0862] (Claim 1)

[0863] Information gathering methods for collecting user requests in disaster-stricken areas,

[0864] A data analysis tool for analyzing aggregated requests and setting priorities,

[0865] A resource information organization method for organizing information on resources and personnel that can be provided from outside the disaster area,

[0866] A matching method using generative AI to match aggregated requests with externally provided resources and personnel,

[0867] A means for optimizing transportation routes to formulate the optimal transportation route,

[0868] A means of notifying and presenting the generated support plan to the user,

[0869] A feedback processing means that collects citizen feedback using devices installed in the city and analyzes it in real time,

[0870] A means for recognizing emotions that detects and analyzes citizens' feelings and generates proposals for improving public services,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, further comprising a feedback transmission means for collecting feedback information from users and sending it back to the server.

[0874] (Claim 3)

[0875] The system according to claim 1, wherein the means for collecting information is realized by a device placed in the evacuation facility, and a user interface is displayed through the device. [Explanation of symbols]

[0876] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Information gathering methods for collecting user needs in disaster-stricken areas, A data analysis tool for analyzing aggregated demand and setting priorities, A resource information organization method for organizing information on supplies and personnel that can be provided from outside the disaster area, A matching method using generative AI to match aggregated demand with externally provided goods and personnel, A means for optimizing transportation routes to formulate the optimal transportation route, A notification method for notifying and presenting the generated support plan to the user, A real-time information provision method for sharing local information during normal times, A system that includes this.

2. The system according to claim 1, further comprising a feedback transmission means for collecting feedback information from users and transmitting it back to the device.

3. The system according to claim 1, wherein the means for gathering information is realized by equipment placed in the evacuation center, and a user interface is displayed through the equipment.

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