system

Abstract

We provide the system. [Solution] Means for collecting and integrating local information, A means of analyzing integrated information, prioritizing it, and automatically generating tasks, A means of notifying the appropriate collaborators of the generated tasks, A means of providing information in multiple languages ​​and supporting communication among users, A means of displaying local emergency information and support information in real time through visual information presentation on user terminals, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In local communities, due to the progress of disaster prevention and aging, it has become difficult to transmit information to and support isolated residents. In particular, the lack of effective information sharing and cooperation systems in emergencies is prominent, and the issue is that the connection between residents and the proper utilization of resources are not sufficient.

Means for Solving the Problems

[0005] This invention aims to solve problems by providing a means for efficiently collecting and integrating local information. By analyzing the integrated information and automatically generating prioritized tasks using machine learning algorithms, it becomes possible to respond quickly to local emergencies. Furthermore, by quickly notifying appropriate collaborators of the generated tasks and incorporating a multilingual information display function, the system supports smooth communication and promotes cooperation among residents.

[0006] "Local information" refers to all data related to a specific region, including weather, disasters, residents' health status, and infrastructure conditions.

[0007] "Integration" is the process of combining information obtained from different data sources to make it possible to understand the overall situation.

[0008] "Analysis" is the process of examining collected information in detail to derive hidden patterns and useful insights.

[0009] "Automatic task generation" refers to the process of creating specific action items that the system should autonomously execute based on the analyzed information.

[0010] A "collaborator" is a resident or volunteer within a community who can provide support or cooperation for a specific task.

[0011] "Notification" is the process of informing appropriate collaborators about the existence and content of a task.

[0012] "Multilingual support" refers to the ability to handle different languages ​​and the function of making information easier to understand for people from different cultures. [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It 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 an 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 an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of a 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 labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple 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 labeled 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 labeled 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, etc.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[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 aims to promote cooperation among residents through a system that centrally manages information gathering, analysis, and notification within local communities. This system consists of terminals placed in each home and public facility, a server with a central database, and users with smartphones and personal computers.

[0035] Users provide information to the system by accessing a smartphone app and entering their health status and the support they need. For example, if an elderly person who is unable to shop wants groceries delivered, they can enter this information through the app.

[0036] The terminal periodically acquires information from sensors and public data sources within the area, collecting weather information and disaster alerts. By immediately transmitting the collected data to a server, the terminal plays a role in monitoring the situation across the entire area in real time.

[0037] The server integrates information sent from users and devices and stores it in a database. The server then analyzes the stored data and uses machine learning algorithms to generate prioritized tasks. This allows for the rapid identification of individuals in need of specific assistance or situations requiring evacuation.

[0038] The generated tasks are notified from the server to users and volunteers who can help. This process efficiently spreads necessary support information in specific areas, enabling immediate response. Furthermore, the system supports multiple languages, allowing for accurate information transmission to residents who speak different languages.

[0039] For example, if roads in a region are closed due to heavy rain, the server quickly analyzes this information and notifies residents who urgently need help. It also informs nearby volunteers about detours and requests for evacuation assistance. This allows danger to be prevented through cooperation between residents and volunteers.

[0040] By implementing this invention, it is expected that efficient information sharing and support systems will be strengthened throughout the community, and that the bonds between residents will deepen.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users use a smartphone app to input their health status and support needs. For example, they can register detailed information such as, "I am currently healthy, but I need assistance with grocery shopping."

[0044] Step 2:

[0045] The device continuously acquires data from various sensors within the area (e.g., temperature sensors and rain gauges). It also uses public data APIs to collect the latest weather information and disaster occurrence information.

[0046] Step 3:

[0047] The server integrates all data received from users and devices. This includes creating a database in an appropriate manner while taking personal information protection into consideration.

[0048] Step 4:

[0049] The server runs machine learning algorithms to analyze the integrated data. Based on the analysis results, it identifies high-priority issues and areas requiring assistance.

[0050] Step 5:

[0051] Based on the analysis results, the server generates specific tasks. These tasks include specific instructions for action and support, such as "delivering food to the elderly" or "notifying people of evacuation advisories."

[0052] Step 6:

[0053] The server selects appropriate collaborators and notifies them of the generated tasks. Push notifications are used to quickly disseminate information and solicit their cooperation.

[0054] Step 7:

[0055] The device prepares multilingual information and provides it to users as needed. Foreign users receive automatically translated messages, ensuring accurate instructions and information that transcend language barriers.

[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] There is a need to improve the efficiency of information sharing and resident support within local communities, as well as to support communication through multilingual support. Furthermore, advanced information management and analysis methods are required to respond quickly to environmental changes and emergencies. There is a lack of methods to respond quickly and accurately to various events occurring within the community.

[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 means for collecting and integrating local information, means for analyzing the integrated information using a machine learning algorithm and automatically generating issues with priority, and means for notifying appropriate collaborators of the generated issues. This enables improved efficiency in supporting residents in local communities, multilingual information sharing, and rapid response in emergencies.

[0061] "Local information" is a general term for data collected within a specific region regarding weather, traffic, health conditions, and the need for assistance.

[0062] "Integration" is the process of combining data obtained from multiple sources into a single dataset and making it analyzable.

[0063] A "machine learning algorithm" is a computational method used to learn regularities and patterns from large amounts of data and perform analysis and prediction.

[0064] "Challenges" refer to specific problems or issues that need to be resolved or addressed within a local community, and require appropriate responses.

[0065] "Multilingualization" refers to a system that provides information in an accurate and understandable format to residents who speak different languages.

[0066] An "electronic terminal" refers to a device such as a smartphone or personal computer used for inputting or acquiring information.

[0067] A "server" is a central computing system that stores, analyzes, generates, and notifies data.

[0068] This invention is a system that effectively collects, analyzes, and notifies information within a local community, thereby promoting cooperation among residents. The system mainly consists of three elements: a server, terminals, and users.

[0069] Users provide the system with information about their health status and support needs by accessing a dedicated application using their smartphone or computer. This allows the system to understand the situation of each individual resident in the community. For example, elderly people who have difficulty shopping can request fresh food delivery through the app.

[0070] The terminals are installed in homes and public facilities and periodically collect environmental information from sensors and public data sources within the area. This includes temperature measurement using temperature sensors and acquisition of weather information from the internet. The collected data is immediately transferred to a server, enabling real-time monitoring of the entire area.

[0071] The server integrates information sent from users and devices into a central database. The server implements advanced machine learning algorithms to analyze the data and automatically generate prioritized tasks. For example, during heavy rain, it can use road closure information to instruct alternative routes and notify residents who need to evacuate.

[0072] The generated challenges are notified from the server to users and local volunteers who can help. This ensures that necessary support information is disseminated quickly and accurately, and because it is available in multiple languages, the information can be reliably delivered to residents who speak various languages.

[0073] For example, in a situation where roads in a region are closed due to heavy rain, the server analyzes this information and immediately notifies residents of the warning. Volunteers are notified of detours and requests for evacuation assistance. An example of a prompt message would be: "If heavy rain is expected in the region, what information should be collected to notify residents, and what procedures should be followed to send a request for assistance to volunteers?"

[0074] In this way, the entire system works in coordination, providing an environment where residents of the local community can live their daily lives more safely and conveniently.

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

[0076] Step 1:

[0077] Users access a dedicated app from their smartphone or computer and input their health status and necessary support information. This input information is sent to a server and stored in a regional database. The input is data related to individual health status and support requests, while the output is integrated data stored in a central database. Specifically, users select support options on the app and input specific details.

[0078] Step 2:

[0079] The terminal automatically acquires environmental information from sensors installed within the area and from external information sources, and collects this data. The collected data is sent to a server. The input is environmental data such as temperature, humidity, and weather information, and the output is real-time information transferred to the server. Specific operations include periodic data acquisition from sensors and downloading weather information via the internet.

[0080] Step 3:

[0081] The server integrates data obtained from users and terminals into a central database. Inputs include the aforementioned health information, support information, and environmental data, while output is an integrated dataset. The server uses this dataset to perform analysis using machine learning algorithms. Specifically, this involves organizing and registering the data in the database.

[0082] Step 4:

[0083] The server analyzes integrated data and automatically generates tasks according to priority. A generative AI model is used for this process. The input is the dataset to be analyzed, and the output is a list of tasks generated according to priority. Specific operations include data pattern analysis and the execution of task generation algorithms.

[0084] Step 5:

[0085] The server notifies users and local volunteers who can help based on the generated tasks. The input is a list of generated tasks, and the output is the notified assistance request information. Specifically, a process is in place to notify in the appropriate language through multilingual support. Notification recipients receive instructions tailored to the specific nature and urgency of the assistance needed and take appropriate action.

[0086] (Application Example 1)

[0087] 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."

[0088] Real-time information gathering and effective information sharing are essential for the rapid dissemination of emergency and support information within local communities and for promoting cooperation among residents. However, current systems lack sufficient multilingual support and visual information presentation capabilities, posing challenges to information transmission and understanding. Therefore, there is a need to build a system that addresses these challenges and strengthens community safety and cooperation among residents.

[0089] 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.

[0090] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information, prioritizing it, and automatically generating tasks, means for notifying appropriate collaborators of the generated tasks, and means for displaying local emergency and support information in real time through visual information presentation on user terminals. This makes it possible to effectively transmit information in multiple languages ​​and promote cooperation among residents.

[0091] "Local information" refers to all kinds of data related to a specific region, including weather, traffic, disaster information, residents' health status, and assistance requests.

[0092] An "information storage device" refers to a digital database used to record and manage collected information.

[0093] "Visual information presentation" refers to the act of visually displaying information using a user terminal through means such as text, images, and maps.

[0094] "Generated tasks" refer to work items that are automatically created to request and instruct specific actions based on collected and analyzed information.

[0095] A "suitable collaborator" refers to an individual or group that possesses the skills and resources best suited to performing a particular task.

[0096] "Multilingual support" refers to the ability to support multiple languages ​​and accurately convey information to users who speak each language.

[0097] An "information processing algorithm" refers to a mathematical or digital procedure used to analyze collected data, evaluate the current situation, and generate the necessary response.

[0098] The system that realizes this invention aims to collect local information, generate necessary tasks, and promote cooperation among residents. The system mainly consists of a server, terminals, and users, each playing a specific role.

[0099] The servers are built using cloud computing services such as AWS® EC2. The servers use database management systems such as MongoDB to aggregate and analyze regional information. Machine learning algorithms such as Tensorflow® and PyTorch are used. These algorithms analyze the collected data, automatically generate necessary tasks, and notify the most suitable collaborators.

[0100] The devices are deployed in homes and public facilities to acquire data from local sensors and external information sources. They communicate with servers via Wi-Fi or Bluetooth, transmitting data in real time to monitor weather information and emergency situations.

[0101] Users can access the system using devices such as smartphones and smart glasses. An application built with React Native is installed on the user's device, allowing them to check their own information and local conditions. The system supports multiple languages, ensuring accurate information is provided to users who speak different languages.

[0102] For example, if some roads in a region are closed due to heavy rain, the server analyzes this information and immediately notifies residents who need emergency assistance. Furthermore, it provides nearby volunteers with information on safe detours. Residents can visually access this information using a smartphone app.

[0103] An example of a prompt to input into a generating AI model would be: "Create a plan for how to notify residents of safe routes if several roads in a certain area are closed due to heavy rain."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The device periodically collects data from local sensors and external data sources. This includes weather information, traffic conditions, and disaster information. This information is transmitted to a server via Wi-Fi.

[0107] Step 2:

[0108] The server receives data sent from the terminal and stores it in a database such as MongoDB. It takes raw sensor data as input data and converts it into time-series data for storage.

[0109] Step 3:

[0110] The server analyzes the stored data and uses machine learning frameworks like TensorFlow to generate the necessary tasks. Prioritized tasks are then classified using various algorithms to determine whether immediate action is required based on specific conditions.

[0111] Step 4:

[0112] The server notifies the most suitable collaborators of the generated tasks. These notifications are sent to local volunteers and residents via smartphones and other device applications.

[0113] Step 5:

[0114] Users receive notifications from the server using a smartphone application. This process utilizes multilingual support to visually display necessary support information and evacuation routes.

[0115] Step 6:

[0116] Users make decisions based on the information they receive and send feedback to the server as needed. This facilitates continuous information exchange and optimization within the region.

[0117] 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.

[0118] This invention provides a system that combines an emotion engine to streamline support activities in local communities. This system has the function of recognizing the user's emotions, in addition to information gathering, analysis, and notification, in order to address challenges associated with local disasters and aging populations.

[0119] Users regularly use the app to input their status and the support they need. The emotion engine analyzes the user's text and voice input to recognize emotions such as stress and anxiety in real time. For example, if a user inputs "I've been feeling anxious frequently lately," that emotion data is incorporated into the system.

[0120] The terminal transmits environmental data collected through sensors within the area, as well as user emotional information, to the server. This allows for a comprehensive assessment of the local situation and the psychological state of individual users.

[0121] The server integrates information from terminals and users and performs analysis using an emotion engine. In particular, it utilizes emotion data to understand the well-being and stress levels of residents and adjust priorities accordingly. For example, if anxiety is high in a specific area, the server will prioritize emergency response in that area. Furthermore, based on emotion information, it determines whether a user needs emotional support and generates tasks to facilitate necessary assistance and communication.

[0122] For example, after a large-scale disaster, the server identifies areas in need of psychological care based on the stress levels of residents recognized by the emotion engine, and notifies volunteers and counselors of this information. This notification enables prompt psychological support.

[0123] Therefore, the present invention aims to significantly improve disaster response and daily support activities by utilizing information technology to respond quickly and accurately to the mental and physical needs of local residents.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] Users input information about their daily lives and current moods using a smartphone app. For example, they can input emotional information such as "I feel anxious living alone."

[0127] Step 2:

[0128] The terminal collects weather and disaster prevention information from local sensors and sends it to a server along with user input data. This data includes environmental changes and disaster information.

[0129] Step 3:

[0130] The server integrates environmental data sent from the terminal and emotional data from the user. Next, it uses an emotion engine to analyze the user's emotional state and quantify emotions such as stress and anxiety.

[0131] Step 4:

[0132] The server automatically generates tasks with adjusted priorities based on the analyzed emotional information. For example, it can generate psychological support tasks for users with unstable moods based on emotional data.

[0133] Step 5:

[0134] The server sends notifications to the most suitable collaborators and volunteers as the tasks are generated. Emotion-based priority tasks, which require a quick response, are immediately notified via push notifications.

[0135] Step 6:

[0136] The device provides information to users in multiple languages ​​and offers content and instructions that cater to users who require emotional support. This enables smooth communication among users.

[0137] This series of processes enables effective responses tailored to local conditions and the feelings of individual users, providing a support system that enhances the safety and security of residents.

[0138] (Example 2)

[0139] 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 will be referred to as the "terminal."

[0140] For swift and effective support activities in local communities, it is crucial to collect real-time information on the diverse challenges that arise during disasters and in daily life, and to set appropriate priorities based on that information. However, the current system does not adequately consider emotional support and the quality of communication, which makes it difficult to provide prompt psychological care to residents.

[0141] 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.

[0142] In this invention, the server includes means for collecting and integrating regional data, means for analyzing the integrated information and individual emotional information, prioritizing and automatically generating support plans, and means for notifying appropriate support providers of the generated support plans. This enables a comprehensive understanding of the situation in each region and the emotional state of its residents, allowing for the rapid and appropriate provision of support and psychological care.

[0143] "Local data" refers to environmental information and data related to the conditions of residents collected within a specific region.

[0144] "Integrated information" refers to data collected from various sources that has been centralized and compiled for analysis.

[0145] "Emotional information" refers to data about emotions analyzed from text and voice input by the user.

[0146] A "support plan" refers to specific action plans and support methods that are automatically generated based on the analyzed information.

[0147] A "supporter" is an individual or group identified by the system that provides support to a specific region or population.

[0148] An "emotion recognition engine" is software or an algorithm that analyzes a user's text or voice input to evaluate their psychological state in real time.

[0149] "Real-time" refers to the instantaneous acquisition and processing of data without delay.

[0150] "Notification" refers to the process by which generated support plans and important information are communicated to support providers.

[0151] The system according to the present invention functions as a comprehensive platform for streamlining community support activities. The embodiments for carrying out the present invention are described below in detail.

[0152] Hardware and software

[0153] This system primarily consists of three components: servers, terminals, and users. The coordinated operation of these components ensures smooth information gathering, data analysis, notification, and support activities.

[0154] The server plays a central role in integrating and analyzing information. Analysis software, including an emotion recognition engine, runs on the server, processing data sent from users and terminals. This engine utilizes natural language processing technology to convert text and voice input into emotion data. Furthermore, machine learning algorithms are used to identify support needs specific to each region.

[0155] The devices include smartphones, tablets, and personal computers, and their role is to send user input information to the server and collect data in real time from sensors within the region. This data is transmitted to the server via a secure protocol.

[0156] Users input their feelings and support requests through applications they use daily. The entered data is sent to a server via the device and analyzed by an emotion recognition engine. Based on notifications sent from the server, users also collaborate with support providers as needed.

[0157] Specific examples and prompt statements

[0158] As a concrete example, suppose a user enters "I've been feeling anxious frequently lately" into the app. This information is analyzed by the server, and if it determines that the user's psychological state in that region is unstable, an immediate support plan is generated.

[0159] Examples of prompts for a generative AI model are as follows:

[0160] "Analyze user input data to identify psychological support needs in specific regions."

[0161] This will enable the system to respond quickly and appropriately to the diverse needs of local residents, with the aim of contributing to the creation of a safe and secure community.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] Users input their state and emotions through the application via text or voice. This input provides information about the user's everyday feelings and level of support needs. This input data is received by the application and prepared for transmission to the emotion recognition engine. A specific example of this process would be the user typing "I've been feeling anxious lately," and the data being appropriately formatted.

[0165] Step 2:

[0166] The terminal receives information entered by the user and collects environmental data within the area through sensors. The input in this step includes user sentiment information and environmental data. The terminal uses a secure communication protocol to organize and prepare this data for transmission to the server. Specifically, this involves encrypting temperature and humidity measurements and voice data.

[0167] Step 3:

[0168] The server receives emotional information and environmental data sent from the terminal and stores it in a database. At this point, the input consists of organized emotional information and environmental data. The server utilizes an emotion recognition engine to analyze the input data and evaluate stress and anxiety levels. The output is the emotional evaluation result for each individual user. Specifically, emotion analysis is performed using natural language processing and emotion evaluation is performed using machine learning algorithms.

[0169] Step 4:

[0170] The server automatically generates support plans by setting support priorities for each region based on the analyzed emotion evaluation results and environmental data. The inputs at this time are the analysis results and environmental data. Using a generation AI model, the optimal support plan is created, and the support plan is obtained as output. Specifically, the output is the output of specific support content generated based on the prompt text.

[0171] Step 5:

[0172] The server notifies the relevant supporters of the generated support plan. The input is the generated support plan. As an output of the notification, tasks are assigned to the supporters. Specifically, notification emails are sent to the list of supporters and alerts are displayed in the app.

[0173] Through these steps, the system enables the efficient and rapid provision of support to local communities.

[0174] (Application Example 2)

[0175] 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".

[0176] Efficiently understanding the emotional state of residents in a community and providing prompt support has been difficult with conventional technologies. In particular, there is a need to respond immediately to the psychological and physical needs of residents during disasters or in aging communities. This invention aims to solve these problems.

[0177] 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.

[0178] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information, recognizing emotional states in real time, prioritizing tasks, and automatically generating tasks, and means for notifying appropriate collaborators of the generated tasks and providing psychological support information. This enables a comprehensive understanding of the emotions and needs of residents in the local community, and allows for swift and accurate support activities.

[0179] "Local information" refers to environmental data, residents' sentiment data, and all related data for a specific region.

[0180] "Emotional state recognition" refers to the process of analyzing the user's text or voice data to recognize emotions such as stress and anxiety.

[0181] A "method for automatically generating tasks with prioritization" is a mechanism that generates tasks based on urgency and importance, using collected information.

[0182] "Appropriate collaborators" refer to professionals, volunteers, and related organizations involved in local support activities.

[0183] "Psychological support information" refers to information that provides support and advice tailored to the emotional state of residents.

[0184] The system for implementing this invention aims to effectively carry out support activities in the local community by analyzing the emotional state of users using an emotion engine. This system consists of a terminal for collecting local information, a server for integrating and analyzing the data, and an application used by resident users.

[0185] The server integrates data acquired from sensors and users within the region and uses an emotion engine to recognize emotional states in real time. Based on this data, the server automatically generates responses for high-priority tasks and situations where residents require psychological support. In this process, machine learning algorithms are used to create action plans based on residents' emotions.

[0186] The device sends text and voice data entered by the user to the emotion engine and transmits the analysis results to the server. Furthermore, it continuously collects environmental data from sensors within the area and transmits it to the server along with the user's emotion data.

[0187] Users input their emotional state through the application. This application visualizes the emotional state of residents and provides psychological support information when needed. For example, if a user inputs "I've been feeling anxious lately," the emotion engine analyzes that information and, if necessary, provides countermeasures or notifies relevant parties.

[0188] As a concrete example, an example of a prompt message using a generative AI model is, "Based on user sentiment data and local environmental information, analyze which areas need emergency assistance." In this way, by implementing the invention, it becomes possible to understand the sentiments and needs of the entire community and to carry out efficient support activities.

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The device receives text or voice input data from the user. This data is sent to the emotion engine for analysis. The received data is classified by the emotion engine into emotions such as stress and anxiety, and the corresponding emotional state data is output.

[0192] Step 2:

[0193] The device collects environmental data in real time from sensors within the area. This data includes temperature, humidity, noise levels, and more. The collected environmental data is sent directly to a server and used as basic data to understand the local conditions.

[0194] Step 3:

[0195] The server integrates user emotional state data and environmental data received from the terminal. By integrating this input data and simultaneously analyzing the residents' emotional state and the local environmental conditions, it determines whether or not psychological support is needed for the residents. The results of this processing are stored as internal data.

[0196] Step 4:

[0197] The server automatically generates tasks and their priorities based on the emotional state of each resident, using data analyzed with machine learning algorithms. Here, the algorithm compares the current data with past data and patterns to identify areas with high urgency, and the corresponding tasks are formed as an output.

[0198] Step 5:

[0199] The server notifies appropriate collaborators of the generated tasks and provides psychological support information as needed. The notifications also include information about the residents' emotional state and the local situation. This information distribution helps collaborators take swift and appropriate action.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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".

[0216] This invention aims to promote cooperation among residents through a system that centrally manages information gathering, analysis, and notification within local communities. This system consists of terminals placed in each home and public facility, a server with a central database, and users with smartphones and personal computers.

[0217] Users provide information to the system by accessing a smartphone app and entering their health status and the support they need. For example, if an elderly person who is unable to shop wants groceries delivered, they can enter this information through the app.

[0218] The terminal periodically acquires information from sensors and public data sources within the area, collecting weather information and disaster alerts. By immediately transmitting the collected data to a server, the terminal plays a role in monitoring the situation across the entire area in real time.

[0219] The server integrates information sent from users and devices and stores it in a database. The server then analyzes the stored data and uses machine learning algorithms to generate prioritized tasks. This allows for the rapid identification of individuals in need of specific assistance or situations requiring evacuation.

[0220] The generated tasks are notified from the server to users and volunteers who can help. This process efficiently spreads necessary support information in specific areas, enabling immediate response. Furthermore, the system supports multiple languages, allowing for accurate information transmission to residents who speak different languages.

[0221] For example, if roads in a region are closed due to heavy rain, the server quickly analyzes this information and notifies residents who urgently need help. It also informs nearby volunteers about detours and requests for evacuation assistance. This allows danger to be prevented through cooperation between residents and volunteers.

[0222] By implementing this invention, it is expected that efficient information sharing and support systems will be strengthened throughout the community, and that the bonds between residents will deepen.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] Users use a smartphone app to input their health status and support needs. For example, they can register detailed information such as, "I am currently healthy, but I need assistance with grocery shopping."

[0226] Step 2:

[0227] The device continuously acquires data from various sensors within the area (e.g., temperature sensors and rain gauges). It also uses public data APIs to collect the latest weather information and disaster occurrence information.

[0228] Step 3:

[0229] The server integrates all data received from users and devices. This includes creating a database in an appropriate manner while taking personal information protection into consideration.

[0230] Step 4:

[0231] The server runs machine learning algorithms to analyze the integrated data. Based on the analysis results, it identifies high-priority issues and areas requiring assistance.

[0232] Step 5:

[0233] Based on the analysis results, the server generates specific tasks. These tasks include specific instructions for action and support, such as "delivering food to the elderly" or "notifying people of evacuation advisories."

[0234] Step 6:

[0235] The server selects appropriate collaborators and notifies them of the generated tasks. Push notifications are used to quickly disseminate information and solicit their cooperation.

[0236] Step 7:

[0237] The device prepares multilingual information and provides it to users as needed. Foreign users receive automatically translated messages, ensuring accurate instructions and information that transcend language barriers.

[0238] (Example 1)

[0239] 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."

[0240] There is a need to improve the efficiency of information sharing and resident support within local communities, as well as to support communication through multilingual support. Furthermore, advanced information management and analysis methods are required to respond quickly to environmental changes and emergencies. There is a lack of methods to respond quickly and accurately to various events occurring within the community.

[0241] 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.

[0242] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information using a machine learning algorithm and automatically generating issues with priority, and means for notifying appropriate collaborators of the generated issues. This enables improved efficiency in supporting residents in local communities, multilingual information sharing, and rapid response in emergencies.

[0243] "Local information" is a general term for data collected within a specific region regarding weather, traffic, health conditions, and the need for assistance.

[0244] "Integration" is the process of combining data obtained from multiple sources into a single dataset and making it analyzable.

[0245] A "machine learning algorithm" is a computational method used to learn regularities and patterns from large amounts of data and perform analysis and prediction.

[0246] "Challenges" refer to specific problems or issues that need to be resolved or addressed within a local community, and require appropriate responses.

[0247] "Multilingualization" refers to a system that provides information in an accurate and understandable format to residents who speak different languages.

[0248] An "electronic terminal" refers to a device such as a smartphone or personal computer used for inputting or acquiring information.

[0249] A "server" is a central computing system that stores, analyzes, generates, and notifies data.

[0250] This invention is a system that effectively collects, analyzes, and notifies information within a local community, thereby promoting cooperation among residents. The system mainly consists of three elements: a server, terminals, and users.

[0251] Users provide the system with information about their health status and support needs by accessing a dedicated application using their smartphone or computer. This allows the system to understand the situation of each individual resident in the community. For example, elderly people who have difficulty shopping can request fresh food delivery through the app.

[0252] The terminals are installed in homes and public facilities and periodically collect environmental information from sensors and public data sources within the area. This includes temperature measurement using temperature sensors and acquisition of weather information from the internet. The collected data is immediately transferred to a server, enabling real-time monitoring of the entire area.

[0253] The server integrates information sent from users and devices into a central database. The server implements advanced machine learning algorithms to analyze the data and automatically generate prioritized tasks. For example, during heavy rain, it can use road closure information to instruct alternative routes and notify residents who need to evacuate.

[0254] The generated challenges are notified from the server to users and local volunteers who can help. This ensures that necessary support information is disseminated quickly and accurately, and because it is available in multiple languages, the information can be reliably delivered to residents who speak various languages.

[0255] For example, in a situation where roads in a region are closed due to heavy rain, the server analyzes this information and immediately notifies residents of the warning. Volunteers are notified of detours and requests for evacuation assistance. An example of a prompt message would be: "If heavy rain is expected in the region, what information should be collected to notify residents, and what procedures should be followed to send a request for assistance to volunteers?"

[0256] In this way, the entire system works in coordination, providing an environment where residents of the local community can live their daily lives more safely and conveniently.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] Users access a dedicated app from their smartphone or computer and input their health status and necessary support information. This input information is sent to a server and stored in a regional database. The input is data related to individual health status and support requests, while the output is integrated data stored in a central database. Specifically, users select support options on the app and input specific details.

[0260] Step 2:

[0261] The terminal automatically acquires environmental information from sensors installed within the area and from external information sources, and collects this data. The collected data is sent to a server. The input is environmental data such as temperature, humidity, and weather information, and the output is real-time information transferred to the server. Specific operations include periodic data acquisition from sensors and downloading weather information via the internet.

[0262] Step 3:

[0263] The server integrates data obtained from users and terminals into a central database. Inputs include the aforementioned health information, support information, and environmental data, while output is an integrated dataset. The server uses this dataset to perform analysis using machine learning algorithms. Specifically, this involves organizing and registering the data in the database.

[0264] Step 4:

[0265] The server analyzes integrated data and automatically generates tasks according to priority. A generative AI model is used for this process. The input is the dataset to be analyzed, and the output is a list of tasks generated according to priority. Specific operations include data pattern analysis and the execution of task generation algorithms.

[0266] Step 5:

[0267] The server notifies users and local volunteers who can help based on the generated tasks. The input is a list of generated tasks, and the output is the notified assistance request information. Specifically, a process is in place to notify in the appropriate language through multilingual support. Notification recipients receive instructions tailored to the specific nature and urgency of the assistance needed and take appropriate action.

[0268] (Application Example 1)

[0269] 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."

[0270] Real-time information gathering and effective information sharing are essential for the rapid dissemination of emergency and support information within local communities and for promoting cooperation among residents. However, current systems lack sufficient multilingual support and visual information presentation capabilities, posing challenges to information transmission and understanding. Therefore, there is a need to build a system that addresses these challenges and strengthens community safety and cooperation among residents.

[0271] 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.

[0272] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information, prioritizing it, and automatically generating tasks, means for notifying appropriate collaborators of the generated tasks, and means for displaying local emergency and support information in real time through visual information presentation on user terminals. This makes it possible to effectively transmit information in multiple languages ​​and promote cooperation among residents.

[0273] "Local information" refers to all kinds of data related to a specific region, including weather, traffic, disaster information, residents' health status, and assistance requests.

[0274] An "information storage device" refers to a digital database used to record and manage collected information.

[0275] "Visual information presentation" refers to the act of visually displaying information using a user terminal through means such as text, images, and maps.

[0276] "Generated tasks" refer to work items that are automatically created to request and instruct specific actions based on collected and analyzed information.

[0277] A "suitable collaborator" refers to an individual or group that possesses the skills and resources best suited to performing a particular task.

[0278] "Multilingual support" refers to the ability to support multiple languages ​​and accurately convey information to users who speak each language.

[0279] An "information processing algorithm" refers to a mathematical or digital procedure used to analyze collected data, evaluate the current situation, and generate the necessary response.

[0280] The system that realizes this invention aims to collect local information, generate necessary tasks, and promote cooperation among residents. The system mainly consists of a server, terminals, and users, each playing a specific role.

[0281] The servers are built using cloud computing services such as AWS EC2. The servers use database management systems such as MongoDB to aggregate and analyze regional information. For machine learning algorithms, frameworks such as TensorFlow and PyTorch are used. These algorithms analyze the collected data, automatically generate necessary tasks, and notify the most suitable collaborators.

[0282] The devices are deployed in homes and public facilities to acquire data from local sensors and external information sources. They communicate with servers via Wi-Fi or Bluetooth, transmitting data in real time to monitor weather information and emergency situations.

[0283] Users can access the system using devices such as smartphones and smart glasses. An application built with React Native is installed on the user's device, allowing them to check their own information and local conditions. The system supports multiple languages, ensuring accurate information is provided to users who speak different languages.

[0284] As a specific example, when some roads in a region are blocked due to heavy rain, the server analyzes this information and immediately notifies the residents who need emergency support. Furthermore, it provides information on safe detours to neighboring volunteers. Residents can visually confirm this information using a smartphone app.

[0285] Examples of prompt sentences to be input into the generative AI model include "When several roads in a certain region are blocked due to heavy rain, please create a plan on how to notify residents of a safe route."

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The terminal periodically collects data from regional sensors and external data sources. This includes weather information, traffic conditions, and disaster information. This information is transmitted to the server via Wi-Fi.

[0289] Step 2:

[0290] The server receives the data transmitted from the terminal and stores it in a database such as MongoDB. As input data, raw sensor data is acquired, and as data to be stored, it is converted into time-series data.

[0291] Step 3:

[0292] The server analyzes the stored data and uses a machine learning framework such as TensorFlow to generate the necessary tasks. The prioritized tasks are classified by various algorithms to determine whether immediate response is required according to specific conditions.

[0293] Step 4:

[0294] The server notifies the most suitable collaborators of the generated tasks. These notifications are sent to local volunteers and residents via smartphones and other device applications.

[0295] Step 5:

[0296] Users receive notifications from the server using a smartphone application. This process utilizes multilingual support to visually display necessary support information and evacuation routes.

[0297] Step 6:

[0298] Users make decisions based on the information they receive and send feedback to the server as needed. This facilitates continuous information exchange and optimization within the region.

[0299] 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.

[0300] This invention provides a system that combines an emotion engine to streamline support activities in local communities. This system has the function of recognizing the user's emotions, in addition to information gathering, analysis, and notification, in order to address challenges associated with local disasters and aging populations.

[0301] Users regularly use the app to input their status and the support they need. The emotion engine analyzes the user's text and voice input to recognize emotions such as stress and anxiety in real time. For example, if a user inputs "I've been feeling anxious frequently lately," that emotion data is incorporated into the system.

[0302] The terminal has the role of transmitting the user's emotional information to the server in addition to the environmental data collected through sensors within the region. This enables a comprehensive judgment of the regional situation and the psychological state of individual users.

[0303] The server integrates information from the terminal and the user, and performs analysis using an emotion engine. In particular, it utilizes emotional data to grasp the happiness and tension levels of residents and adjust priorities. For example, when anxiety is increasing in a specific area, the priority of emergency response in that area is raised. Also, based on emotional information, it determines whether the user needs mental support and generates tasks to promote necessary assistance and communication.

[0304] For example, after a large-scale disaster occurs, the server identifies areas where psychological care is needed based on the stress levels of residents recognized by the emotion engine, and notifies volunteers and counselors of that information. This notification enables prompt provision of psychological support.

[0305] Thus, the present invention aims to rapidly and accurately respond to the mental and physical needs of regional residents by making full use of information technology, and to significantly improve disaster response and daily support activities.

[0306] The processing flow will be described below.

[0307] Step 1:

[0308] The user uses a smartphone app to input information about their daily life status and current mood. For example, emotional information such as "feeling anxious living alone" can be input.

[0309] Step 2:

[0310] The terminal collects meteorological and disaster prevention information obtained from regional sensors, and transmits it to the server together with the input data from the user. This data includes environmental changes and disaster information.

[0311] Step 3:

[0312] The server integrates environmental data sent from the terminal and emotional data from the user. Next, it uses an emotion engine to analyze the user's emotional state and quantify emotions such as stress and anxiety.

[0313] Step 4:

[0314] The server automatically generates tasks with adjusted priorities based on the analyzed emotional information. For example, it can generate psychological support tasks for users with unstable moods based on emotional data.

[0315] Step 5:

[0316] The server sends notifications to the most suitable collaborators and volunteers as the tasks are generated. Emotion-based priority tasks, which require a quick response, are immediately notified via push notifications.

[0317] Step 6:

[0318] The device provides information to users in multiple languages ​​and offers content and instructions that cater to users who require emotional support. This enables smooth communication among users.

[0319] This series of processes enables effective responses tailored to local conditions and the feelings of individual users, providing a support system that enhances the safety and security of residents.

[0320] (Example 2)

[0321] 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".

[0322] For swift and effective support activities in local communities, it is crucial to collect real-time information on the diverse challenges that arise during disasters and in daily life, and to set appropriate priorities based on that information. However, the current system does not adequately consider emotional support and the quality of communication, which makes it difficult to provide prompt psychological care to residents.

[0323] 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.

[0324] In this invention, the server includes means for collecting and integrating regional data, means for analyzing the integrated information and individual emotional information, prioritizing and automatically generating support plans, and means for notifying appropriate support providers of the generated support plans. This enables a comprehensive understanding of the situation in each region and the emotional state of its residents, allowing for the rapid and appropriate provision of support and psychological care.

[0325] "Local data" refers to environmental information and data related to the conditions of residents collected within a specific region.

[0326] "Integrated information" refers to data collected from various sources that has been centralized and compiled for analysis.

[0327] "Emotional information" refers to data about emotions analyzed from text and voice input by the user.

[0328] A "support plan" refers to specific action plans and support methods that are automatically generated based on the analyzed information.

[0329] A "supporter" is an individual or group identified by the system that provides support to a specific region or population.

[0330] An "emotion recognition engine" is software or an algorithm that analyzes a user's text or voice input to evaluate their psychological state in real time.

[0331] "Real-time" refers to the instantaneous acquisition and processing of data without delay.

[0332] "Notification" refers to the process by which generated support plans and important information are communicated to support providers.

[0333] The system according to the present invention functions as a comprehensive platform for streamlining community support activities. The embodiments for carrying out the present invention are described below in detail.

[0334] Hardware and software

[0335] This system primarily consists of three components: servers, terminals, and users. The coordinated operation of these components ensures smooth information gathering, data analysis, notification, and support activities.

[0336] The server plays a central role in integrating and analyzing information. Analysis software, including an emotion recognition engine, runs on the server, processing data sent from users and terminals. This engine utilizes natural language processing technology to convert text and voice input into emotion data. Furthermore, machine learning algorithms are used to identify support needs specific to each region.

[0337] The devices include smartphones, tablets, and personal computers, and their role is to send user input information to the server and collect data in real time from sensors within the region. This data is transmitted to the server via a secure protocol.

[0338] Users input their feelings and support requests through applications they use daily. The entered data is sent to a server via the device and analyzed by an emotion recognition engine. Based on notifications sent from the server, users also collaborate with support providers as needed.

[0339] Specific examples and prompt statements

[0340] As a concrete example, suppose a user enters "I've been feeling anxious frequently lately" into the app. This information is analyzed by the server, and if it determines that the user's psychological state in that region is unstable, an immediate support plan is generated.

[0341] Examples of prompts for a generative AI model are as follows:

[0342] "Analyze user input data to identify psychological support needs in specific regions."

[0343] This will enable the system to respond quickly and appropriately to the diverse needs of local residents, with the aim of contributing to the creation of a safe and secure community.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] Users input their state and emotions through the application via text or voice. This input provides information about the user's everyday feelings and level of support needs. This input data is received by the application and prepared for transmission to the emotion recognition engine. A specific example of this process would be the user typing "I've been feeling anxious lately," and the data being appropriately formatted.

[0347] Step 2:

[0348] The terminal receives information entered by the user and collects environmental data within the area through sensors. The input in this step includes user sentiment information and environmental data. The terminal uses a secure communication protocol to organize and prepare this data for transmission to the server. Specifically, this involves encrypting temperature and humidity measurements and voice data.

[0349] Step 3:

[0350] The server receives emotional information and environmental data sent from the terminal and stores it in a database. At this point, the input consists of organized emotional information and environmental data. The server utilizes an emotion recognition engine to analyze the input data and evaluate stress and anxiety levels. The output is the emotional evaluation result for each individual user. Specifically, emotion analysis is performed using natural language processing and emotion evaluation is performed using machine learning algorithms.

[0351] Step 4:

[0352] The server automatically generates support plans by setting support priorities for each region based on the analyzed emotion evaluation results and environmental data. The inputs at this time are the analysis results and environmental data. Using a generation AI model, the optimal support plan is created, and the support plan is obtained as output. Specifically, the output is the output of specific support content generated based on the prompt text.

[0353] Step 5:

[0354] The server notifies the relevant supporters of the generated support plan. The input is the generated support plan. As an output of the notification, tasks are assigned to the supporters. Specifically, notification emails are sent to the list of supporters and alerts are displayed in the app.

[0355] Through these steps, the system enables the efficient and rapid provision of support to local communities.

[0356] (Application Example 2)

[0357] 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."

[0358] Efficiently understanding the emotional state of residents in a community and providing prompt support has been difficult with conventional technologies. In particular, there is a need to respond immediately to the psychological and physical needs of residents during disasters or in aging communities. This invention aims to solve these problems.

[0359] 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.

[0360] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information, recognizing emotional states in real time, prioritizing tasks, and automatically generating tasks, and means for notifying appropriate collaborators of the generated tasks and providing psychological support information. This enables a comprehensive understanding of the emotions and needs of residents in the local community, and allows for swift and accurate support activities.

[0361] "Local information" refers to environmental data, residents' sentiment data, and all related data for a specific region.

[0362] "Emotional state recognition" refers to the process of analyzing the user's text or voice data to recognize emotions such as stress and anxiety.

[0363] A "method for automatically generating tasks with prioritization" is a mechanism that generates tasks based on urgency and importance, using collected information.

[0364] "Appropriate collaborators" refer to professionals, volunteers, and related organizations involved in local support activities.

[0365] "Psychological support information" refers to information that provides support and advice tailored to the emotional state of residents.

[0366] The system for implementing this invention aims to effectively carry out support activities in the local community by analyzing the emotional state of users using an emotion engine. This system consists of a terminal for collecting local information, a server for integrating and analyzing the data, and an application used by resident users.

[0367] The server integrates data acquired from sensors and users within the region and uses an emotion engine to recognize emotional states in real time. Based on this data, the server automatically generates responses for high-priority tasks and situations where residents require psychological support. In this process, machine learning algorithms are used to create action plans based on residents' emotions.

[0368] The device sends text and voice data entered by the user to the emotion engine and transmits the analysis results to the server. Furthermore, it continuously collects environmental data from sensors within the area and transmits it to the server along with the user's emotion data.

[0369] Users input their emotional state through the application. This application visualizes the emotional state of residents and provides psychological support information when needed. For example, if a user inputs "I've been feeling anxious lately," the emotion engine analyzes that information and, if necessary, provides countermeasures or notifies relevant parties.

[0370] As a concrete example, an example of a prompt message using a generative AI model is, "Based on user sentiment data and local environmental information, analyze which areas need emergency assistance." In this way, by implementing the invention, it becomes possible to understand the sentiments and needs of the entire community and to carry out efficient support activities.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The device receives text or voice input data from the user. This data is sent to the emotion engine for analysis. The received data is classified by the emotion engine into emotions such as stress and anxiety, and the corresponding emotional state data is output.

[0374] Step 2:

[0375] The device collects environmental data in real time from sensors within the area. This data includes temperature, humidity, noise levels, and more. The collected environmental data is sent directly to a server and used as basic data to understand the local conditions.

[0376] Step 3:

[0377] The server integrates user emotional state data and environmental data received from the terminal. By integrating this input data and simultaneously analyzing the residents' emotional state and the local environmental conditions, it determines whether or not psychological support is needed for the residents. The results of this processing are stored as internal data.

[0378] Step 4:

[0379] The server automatically generates tasks and their priorities based on the emotional state of each resident, using data analyzed with machine learning algorithms. Here, the algorithm compares the current data with past data and patterns to identify areas with high urgency, and the corresponding tasks are formed as an output.

[0380] Step 5:

[0381] The server notifies appropriate collaborators of the generated tasks and provides psychological support information as needed. The notifications also include information about the residents' emotional state and the local situation. This information distribution helps collaborators take swift and appropriate action.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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".

[0398] This invention aims to promote cooperation among residents through a system that centrally manages information gathering, analysis, and notification within local communities. This system consists of terminals placed in each home and public facility, a server with a central database, and users with smartphones and personal computers.

[0399] Users provide information to the system by accessing a smartphone app and entering their health status and the support they need. For example, if an elderly person who is unable to shop wants groceries delivered, they can enter this information through the app.

[0400] The terminal periodically acquires information from sensors and public data sources within the area, collecting weather information and disaster alerts. By immediately transmitting the collected data to a server, the terminal plays a role in monitoring the situation across the entire area in real time.

[0401] The server integrates information sent from users and devices and stores it in a database. The server then analyzes the stored data and uses machine learning algorithms to generate prioritized tasks. This allows for the rapid identification of individuals in need of specific assistance or situations requiring evacuation.

[0402] The generated tasks are notified from the server to users and volunteers who can help. This process efficiently spreads necessary support information in specific areas, enabling immediate response. Furthermore, the system supports multiple languages, allowing for accurate information transmission to residents who speak different languages.

[0403] For example, if roads in a region are closed due to heavy rain, the server quickly analyzes this information and notifies residents who urgently need help. It also informs nearby volunteers about detours and requests for evacuation assistance. This allows danger to be prevented through cooperation between residents and volunteers.

[0404] By implementing this invention, it is expected that efficient information sharing and support systems will be strengthened throughout the community, and that the bonds between residents will deepen.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] Users use a smartphone app to input their health status and support needs. For example, they can register detailed information such as, "I am currently healthy, but I need assistance with grocery shopping."

[0408] Step 2:

[0409] The device continuously acquires data from various sensors within the area (e.g., temperature sensors and rain gauges). It also uses public data APIs to collect the latest weather information and disaster occurrence information.

[0410] Step 3:

[0411] The server integrates all data received from users and devices. This includes creating a database in an appropriate manner while taking personal information protection into consideration.

[0412] Step 4:

[0413] The server runs machine learning algorithms to analyze the integrated data. Based on the analysis results, it identifies high-priority issues and areas requiring assistance.

[0414] Step 5:

[0415] Based on the analysis results, the server generates specific tasks. These tasks include specific instructions for action and support, such as "delivering food to the elderly" or "notifying people of evacuation advisories."

[0416] Step 6:

[0417] The server selects appropriate collaborators and notifies them of the generated tasks. Push notifications are used to quickly disseminate information and solicit their cooperation.

[0418] Step 7:

[0419] The device prepares multilingual information and provides it to users as needed. Foreign users receive automatically translated messages, ensuring accurate instructions and information that transcend language barriers.

[0420] (Example 1)

[0421] 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."

[0422] There is a need to improve the efficiency of information sharing and resident support within local communities, as well as to support communication through multilingual support. Furthermore, advanced information management and analysis methods are required to respond quickly to environmental changes and emergencies. There is a lack of methods to respond quickly and accurately to various events occurring within the community.

[0423] 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.

[0424] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information using a machine learning algorithm and automatically generating issues with priority, and means for notifying appropriate collaborators of the generated issues. This enables improved efficiency in supporting residents in local communities, multilingual information sharing, and rapid response in emergencies.

[0425] "Local information" is a general term for data collected within a specific region regarding weather, traffic, health conditions, and the need for assistance.

[0426] "Integration" is the process of combining data obtained from multiple sources into a single dataset and making it analyzable.

[0427] A "machine learning algorithm" is a computational method used to learn regularities and patterns from large amounts of data and perform analysis and prediction.

[0428] "Challenges" refer to specific problems or issues that need to be resolved or addressed within a local community, and require appropriate responses.

[0429] "Multilingualization" refers to a system that provides information in an accurate and understandable format to residents who speak different languages.

[0430] An "electronic terminal" refers to a device such as a smartphone or personal computer used for inputting or acquiring information.

[0431] A "server" is a central computing system that stores, analyzes, generates, and notifies data.

[0432] This invention is a system that effectively collects, analyzes, and notifies information within a local community, thereby promoting cooperation among residents. The system mainly consists of three elements: a server, terminals, and users.

[0433] Users provide the system with information about their health status and support needs by accessing a dedicated application using their smartphone or computer. This allows the system to understand the situation of each individual resident in the community. For example, elderly people who have difficulty shopping can request fresh food delivery through the app.

[0434] The terminals are installed in homes and public facilities and periodically collect environmental information from sensors and public data sources within the area. This includes temperature measurement using temperature sensors and acquisition of weather information from the internet. The collected data is immediately transferred to a server, enabling real-time monitoring of the entire area.

[0435] The server integrates information sent from users and devices into a central database. The server implements advanced machine learning algorithms to analyze the data and automatically generate prioritized tasks. For example, during heavy rain, it can use road closure information to instruct alternative routes and notify residents who need to evacuate.

[0436] The generated challenges are notified from the server to users and local volunteers who can help. This ensures that necessary support information is disseminated quickly and accurately, and because it is available in multiple languages, the information can be reliably delivered to residents who speak various languages.

[0437] For example, in a situation where roads in a region are closed due to heavy rain, the server analyzes this information and immediately notifies residents of the warning. Volunteers are notified of detours and requests for evacuation assistance. An example of a prompt message would be: "If heavy rain is expected in the region, what information should be collected to notify residents, and what procedures should be followed to send a request for assistance to volunteers?"

[0438] In this way, the entire system works in coordination, providing an environment where residents of the local community can live their daily lives more safely and conveniently.

[0439] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0440] Step 1:

[0441] Users access a dedicated app from their smartphone or computer and input their health status and necessary support information. This input information is sent to a server and stored in a regional database. The input is data related to individual health status and support requests, while the output is integrated data stored in a central database. Specifically, users select support options on the app and input specific details.

[0442] Step 2:

[0443] The terminal automatically acquires environmental information from sensors installed within the area and from external information sources, and collects this data. The collected data is sent to a server. The input is environmental data such as temperature, humidity, and weather information, and the output is real-time information transferred to the server. Specific operations include periodic data acquisition from sensors and downloading weather information via the internet.

[0444] Step 3:

[0445] The server integrates data obtained from users and terminals into a central database. Inputs include the aforementioned health information, support information, and environmental data, while output is an integrated dataset. The server uses this dataset to perform analysis using machine learning algorithms. Specifically, this involves organizing and registering the data in the database.

[0446] Step 4:

[0447] The server analyzes integrated data and automatically generates tasks according to priority. A generative AI model is used for this process. The input is the dataset to be analyzed, and the output is a list of tasks generated according to priority. Specific operations include data pattern analysis and the execution of task generation algorithms.

[0448] Step 5:

[0449] The server notifies users and local volunteers who can help based on the generated tasks. The input is a list of generated tasks, and the output is the notified assistance request information. Specifically, a process is in place to notify in the appropriate language through multilingual support. Notification recipients receive instructions tailored to the specific nature and urgency of the assistance needed and take appropriate action.

[0450] (Application Example 1)

[0451] 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."

[0452] Real-time information gathering and effective information sharing are essential for the rapid dissemination of emergency and support information within local communities and for promoting cooperation among residents. However, current systems lack sufficient multilingual support and visual information presentation capabilities, posing challenges to information transmission and understanding. Therefore, there is a need to build a system that addresses these challenges and strengthens community safety and cooperation among residents.

[0453] 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.

[0454] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information, prioritizing it, and automatically generating tasks, means for notifying appropriate collaborators of the generated tasks, and means for displaying local emergency and support information in real time through visual information presentation on user terminals. This makes it possible to effectively transmit information in multiple languages ​​and promote cooperation among residents.

[0455] "Local information" refers to all kinds of data related to a specific region, including weather, traffic, disaster information, residents' health status, and assistance requests.

[0456] An "information storage device" refers to a digital database used to record and manage collected information.

[0457] "Visual information presentation" refers to the act of visually displaying information using a user terminal through means such as text, images, and maps.

[0458] "Generated tasks" refer to work items that are automatically created to request and instruct specific actions based on collected and analyzed information.

[0459] A "suitable collaborator" refers to an individual or group that possesses the skills and resources best suited to performing a particular task.

[0460] "Multilingual support" refers to the ability to support multiple languages ​​and accurately convey information to users who speak each language.

[0461] An "information processing algorithm" refers to a mathematical or digital procedure used to analyze collected data, evaluate the current situation, and generate the necessary response.

[0462] The system that realizes this invention aims to collect local information, generate necessary tasks, and promote cooperation among residents. The system mainly consists of a server, terminals, and users, each playing a specific role.

[0463] The servers are built using cloud computing services such as AWS EC2. The servers use database management systems such as MongoDB to aggregate and analyze regional information. For machine learning algorithms, frameworks such as TensorFlow and PyTorch are used. These algorithms analyze the collected data, automatically generate necessary tasks, and notify the most suitable collaborators.

[0464] The devices are deployed in homes and public facilities to acquire data from local sensors and external information sources. They communicate with servers via Wi-Fi or Bluetooth, transmitting data in real time to monitor weather information and emergency situations.

[0465] Users can access the system using devices such as smartphones and smart glasses. An application built with React Native is installed on the user's device, allowing them to check their own information and local conditions. The system supports multiple languages, ensuring accurate information is provided to users who speak different languages.

[0466] For example, if some roads in a region are closed due to heavy rain, the server analyzes this information and immediately notifies residents who need emergency assistance. Furthermore, it provides nearby volunteers with information on safe detours. Residents can visually access this information using a smartphone app.

[0467] An example of a prompt to input into a generating AI model would be: "Create a plan for how to notify residents of safe routes if several roads in a certain area are closed due to heavy rain."

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The device periodically collects data from local sensors and external data sources. This includes weather information, traffic conditions, and disaster information. This information is transmitted to a server via Wi-Fi.

[0471] Step 2:

[0472] The server receives data sent from the terminal and stores it in a database such as MongoDB. It takes raw sensor data as input data and converts it into time-series data for storage.

[0473] Step 3:

[0474] The server analyzes the stored data and uses machine learning frameworks like TensorFlow to generate the necessary tasks. Prioritized tasks are then classified using various algorithms to determine whether immediate action is required based on specific conditions.

[0475] Step 4:

[0476] The server notifies the most suitable collaborators of the generated tasks. These notifications are sent to local volunteers and residents via smartphones and other device applications.

[0477] Step 5:

[0478] Users receive notifications from the server using a smartphone application. This process utilizes multilingual support to visually display necessary support information and evacuation routes.

[0479] Step 6:

[0480] Users make decisions based on the information they receive and send feedback to the server as needed. This facilitates continuous information exchange and optimization within the region.

[0481] 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.

[0482] This invention provides a system that combines an emotion engine to streamline support activities in local communities. This system has the function of recognizing the user's emotions, in addition to information gathering, analysis, and notification, in order to address challenges associated with local disasters and aging populations.

[0483] Users regularly use the app to input their status and the support they need. The emotion engine analyzes the user's text and voice input to recognize emotions such as stress and anxiety in real time. For example, if a user inputs "I've been feeling anxious frequently lately," that emotion data is incorporated into the system.

[0484] The terminal transmits environmental data collected through sensors within the area, as well as user emotional information, to the server. This allows for a comprehensive assessment of the local situation and the psychological state of individual users.

[0485] The server integrates information from terminals and users and performs analysis using an emotion engine. In particular, it utilizes emotion data to understand the well-being and stress levels of residents and adjust priorities accordingly. For example, if anxiety is high in a specific area, the server will prioritize emergency response in that area. Furthermore, based on emotion information, it determines whether a user needs emotional support and generates tasks to facilitate necessary assistance and communication.

[0486] For example, after a large-scale disaster, the server identifies areas in need of psychological care based on the stress levels of residents recognized by the emotion engine, and notifies volunteers and counselors of this information. This notification enables prompt psychological support.

[0487] Therefore, the present invention aims to significantly improve disaster response and daily support activities by utilizing information technology to respond quickly and accurately to the mental and physical needs of local residents.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] Users input information about their daily lives and current moods using a smartphone app. For example, they can input emotional information such as "I feel anxious living alone."

[0491] Step 2:

[0492] The terminal collects weather and disaster prevention information from local sensors and sends it to a server along with user input data. This data includes environmental changes and disaster information.

[0493] Step 3:

[0494] The server integrates environmental data sent from the terminal and emotional data from the user. Next, it uses an emotion engine to analyze the user's emotional state and quantify emotions such as stress and anxiety.

[0495] Step 4:

[0496] The server automatically generates tasks with adjusted priorities based on the analyzed emotional information. For example, it can generate psychological support tasks for users with unstable moods based on emotional data.

[0497] Step 5:

[0498] The server sends notifications to the most suitable collaborators and volunteers as the tasks are generated. Emotion-based priority tasks, which require a quick response, are immediately notified via push notifications.

[0499] Step 6:

[0500] The device provides information to users in multiple languages ​​and offers content and instructions that cater to users who require emotional support. This enables smooth communication among users.

[0501] This series of processes enables effective responses tailored to local conditions and the feelings of individual users, providing a support system that enhances the safety and security of residents.

[0502] (Example 2)

[0503] 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."

[0504] For swift and effective support activities in local communities, it is crucial to collect real-time information on the diverse challenges that arise during disasters and in daily life, and to set appropriate priorities based on that information. However, the current system does not adequately consider emotional support and the quality of communication, which makes it difficult to provide prompt psychological care to residents.

[0505] 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.

[0506] In this invention, the server includes means for collecting and integrating regional data, means for analyzing the integrated information and individual emotional information, prioritizing and automatically generating support plans, and means for notifying appropriate support providers of the generated support plans. This enables a comprehensive understanding of the situation in each region and the emotional state of its residents, allowing for the rapid and appropriate provision of support and psychological care.

[0507] "Local data" refers to environmental information and data related to the conditions of residents collected within a specific region.

[0508] "Integrated information" refers to data collected from various sources that has been centralized and compiled for analysis.

[0509] "Emotional information" refers to data about emotions analyzed from text and voice input by the user.

[0510] A "support plan" refers to specific action plans and support methods that are automatically generated based on the analyzed information.

[0511] A "supporter" is an individual or group identified by the system that provides support to a specific region or population.

[0512] An "emotion recognition engine" is software or an algorithm that analyzes a user's text or voice input to evaluate their psychological state in real time.

[0513] "Real-time" refers to the instantaneous acquisition and processing of data without delay.

[0514] "Notification" refers to the process by which generated support plans and important information are communicated to support providers.

[0515] The system according to the present invention functions as a comprehensive platform for streamlining community support activities. The embodiments for carrying out the present invention are described below in detail.

[0516] Hardware and software

[0517] This system primarily consists of three components: servers, terminals, and users. The coordinated operation of these components ensures smooth information gathering, data analysis, notification, and support activities.

[0518] The server plays a central role in integrating and analyzing information. Analysis software, including an emotion recognition engine, runs on the server, processing data sent from users and terminals. This engine utilizes natural language processing technology to convert text and voice input into emotion data. Furthermore, machine learning algorithms are used to identify support needs specific to each region.

[0519] The devices include smartphones, tablets, and personal computers, and their role is to send user input information to the server and collect data in real time from sensors within the region. This data is transmitted to the server via a secure protocol.

[0520] Users input their feelings and support requests through applications they use daily. The entered data is sent to a server via the device and analyzed by an emotion recognition engine. Based on notifications sent from the server, users also collaborate with support providers as needed.

[0521] Specific examples and prompt statements

[0522] As a concrete example, suppose a user enters "I've been feeling anxious frequently lately" into the app. This information is analyzed by the server, and if it determines that the user's psychological state in that region is unstable, an immediate support plan is generated.

[0523] Examples of prompts for a generative AI model are as follows:

[0524] "Analyze user input data to identify psychological support needs in specific regions."

[0525] This will enable the system to respond quickly and appropriately to the diverse needs of local residents, with the aim of contributing to the creation of a safe and secure community.

[0526] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0527] Step 1:

[0528] Users input their state and emotions through the application via text or voice. This input provides information about the user's everyday feelings and level of support needs. This input data is received by the application and prepared for transmission to the emotion recognition engine. A specific example of this process would be the user typing "I've been feeling anxious lately," and the data being appropriately formatted.

[0529] Step 2:

[0530] The terminal receives information entered by the user and collects environmental data within the area through sensors. The input in this step includes user sentiment information and environmental data. The terminal uses a secure communication protocol to organize and prepare this data for transmission to the server. Specifically, this involves encrypting temperature and humidity measurements and voice data.

[0531] Step 3:

[0532] The server receives emotional information and environmental data sent from the terminal and stores it in a database. At this point, the input consists of organized emotional information and environmental data. The server utilizes an emotion recognition engine to analyze the input data and evaluate stress and anxiety levels. The output is the emotional evaluation result for each individual user. Specifically, emotion analysis is performed using natural language processing and emotion evaluation is performed using machine learning algorithms.

[0533] Step 4:

[0534] The server automatically generates support plans by setting support priorities for each region based on the analyzed emotion evaluation results and environmental data. The inputs at this time are the analysis results and environmental data. Using a generation AI model, the optimal support plan is created, and the support plan is obtained as output. Specifically, the output is the output of specific support content generated based on the prompt text.

[0535] Step 5:

[0536] The server notifies the relevant supporters of the generated support plan. The input is the generated support plan. As an output of the notification, tasks are assigned to the supporters. Specifically, notification emails are sent to the list of supporters and alerts are displayed in the app.

[0537] Through these steps, the system enables the efficient and rapid provision of support to local communities.

[0538] (Application Example 2)

[0539] 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."

[0540] Efficiently understanding the emotional state of residents in a community and providing prompt support has been difficult with conventional technologies. In particular, there is a need to respond immediately to the psychological and physical needs of residents during disasters or in aging communities. This invention aims to solve these problems.

[0541] 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.

[0542] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information, recognizing emotional states in real time, prioritizing tasks, and automatically generating tasks, and means for notifying appropriate collaborators of the generated tasks and providing psychological support information. This enables a comprehensive understanding of the emotions and needs of residents in the local community, and allows for swift and accurate support activities.

[0543] "Local information" refers to environmental data, residents' sentiment data, and all related data for a specific region.

[0544] "Emotional state recognition" refers to the process of analyzing the user's text or voice data to recognize emotions such as stress and anxiety.

[0545] A "method for automatically generating tasks with prioritization" is a mechanism that generates tasks based on urgency and importance, using collected information.

[0546] "Appropriate collaborators" refer to professionals, volunteers, and related organizations involved in local support activities.

[0547] "Psychological support information" refers to information that provides support and advice tailored to the emotional state of residents.

[0548] The system for implementing this invention aims to effectively carry out support activities in the local community by analyzing the emotional state of users using an emotion engine. This system consists of a terminal for collecting local information, a server for integrating and analyzing the data, and an application used by resident users.

[0549] The server integrates data acquired from sensors and users within the region and uses an emotion engine to recognize emotional states in real time. Based on this data, the server automatically generates responses for high-priority tasks and situations where residents require psychological support. In this process, machine learning algorithms are used to create action plans based on residents' emotions.

[0550] The device sends text and voice data entered by the user to the emotion engine and transmits the analysis results to the server. Furthermore, it continuously collects environmental data from sensors within the area and transmits it to the server along with the user's emotion data.

[0551] Users input their emotional state through the application. This application visualizes the emotional state of residents and provides psychological support information when needed. For example, if a user inputs "I've been feeling anxious lately," the emotion engine analyzes that information and, if necessary, provides countermeasures or notifies relevant parties.

[0552] As a concrete example, an example of a prompt message using a generative AI model is, "Based on user sentiment data and local environmental information, analyze which areas need emergency assistance." In this way, by implementing the invention, it becomes possible to understand the sentiments and needs of the entire community and to carry out efficient support activities.

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0554] Step 1:

[0555] The device receives text or voice input data from the user. This data is sent to the emotion engine for analysis. The received data is classified by the emotion engine into emotions such as stress and anxiety, and the corresponding emotional state data is output.

[0556] Step 2:

[0557] The device collects environmental data in real time from sensors within the area. This data includes temperature, humidity, noise levels, and more. The collected environmental data is sent directly to a server and used as basic data to understand the local conditions.

[0558] Step 3:

[0559] The server integrates user emotional state data and environmental data received from the terminal. By integrating this input data and simultaneously analyzing the residents' emotional state and the local environmental conditions, it determines whether or not psychological support is needed for the residents. The results of this processing are stored as internal data.

[0560] Step 4:

[0561] The server automatically generates tasks and their priorities based on the emotional state of each resident, using data analyzed with machine learning algorithms. Here, the algorithm compares the current data with past data and patterns to identify areas with high urgency, and the corresponding tasks are formed as an output.

[0562] Step 5:

[0563] The server notifies appropriate collaborators of the generated tasks and provides psychological support information as needed. The notifications also include information about the residents' emotional state and the local situation. This information distribution helps collaborators take swift and appropriate action.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] [Fourth Embodiment]

[0568] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0569] 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.

[0570] 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).

[0571] 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.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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".

[0581] This invention aims to promote cooperation among residents through a system that centrally manages information gathering, analysis, and notification within local communities. This system consists of terminals placed in each home and public facility, a server with a central database, and users with smartphones and personal computers.

[0582] Users provide information to the system by accessing a smartphone app and entering their health status and the support they need. For example, if an elderly person who is unable to shop wants groceries delivered, they can enter this information through the app.

[0583] The terminal periodically acquires information from sensors and public data sources within the area, collecting weather information and disaster alerts. By immediately transmitting the collected data to a server, the terminal plays a role in monitoring the situation across the entire area in real time.

[0584] The server integrates information sent from users and devices and stores it in a database. The server then analyzes the stored data and uses machine learning algorithms to generate prioritized tasks. This allows for the rapid identification of individuals in need of specific assistance or situations requiring evacuation.

[0585] The generated tasks are notified from the server to users and volunteers who can help. This process efficiently spreads necessary support information in specific areas, enabling immediate response. Furthermore, the system supports multiple languages, allowing for accurate information transmission to residents who speak different languages.

[0586] For example, if roads in a region are closed due to heavy rain, the server quickly analyzes this information and notifies residents who urgently need help. It also informs nearby volunteers about detours and requests for evacuation assistance. This allows danger to be prevented through cooperation between residents and volunteers.

[0587] By implementing this invention, it is expected that efficient information sharing and support systems will be strengthened throughout the community, and that the bonds between residents will deepen.

[0588] The following describes the processing flow.

[0589] Step 1:

[0590] Users use a smartphone app to input their health status and support needs. For example, they can register detailed information such as, "I am currently healthy, but I need assistance with grocery shopping."

[0591] Step 2:

[0592] The device continuously acquires data from various sensors within the area (e.g., temperature sensors and rain gauges). It also uses public data APIs to collect the latest weather information and disaster occurrence information.

[0593] Step 3:

[0594] The server integrates all data received from users and devices. This includes creating a database in an appropriate manner while taking personal information protection into consideration.

[0595] Step 4:

[0596] The server runs machine learning algorithms to analyze the integrated data. Based on the analysis results, it identifies high-priority issues and areas requiring assistance.

[0597] Step 5:

[0598] Based on the analysis results, the server generates specific tasks. These tasks include specific instructions for action and support, such as "delivering food to the elderly" or "notifying people of evacuation advisories."

[0599] Step 6:

[0600] The server selects appropriate collaborators and notifies them of the generated tasks. Push notifications are used to quickly disseminate information and solicit their cooperation.

[0601] Step 7:

[0602] The device prepares multilingual information and provides it to users as needed. Foreign users receive automatically translated messages, ensuring accurate instructions and information that transcend language barriers.

[0603] (Example 1)

[0604] 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".

[0605] There is a need to improve the efficiency of information sharing and resident support within local communities, as well as to support communication through multilingual support. Furthermore, advanced information management and analysis methods are required to respond quickly to environmental changes and emergencies. There is a lack of methods to respond quickly and accurately to various events occurring within the community.

[0606] 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.

[0607] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information using a machine learning algorithm and automatically generating issues with priority, and means for notifying appropriate collaborators of the generated issues. This enables improved efficiency in supporting residents in local communities, multilingual information sharing, and rapid response in emergencies.

[0608] "Local information" is a general term for data collected within a specific region regarding weather, traffic, health conditions, and the need for assistance.

[0609] "Integration" is the process of combining data obtained from multiple sources into a single dataset and making it analyzable.

[0610] A "machine learning algorithm" is a computational method used to learn regularities and patterns from large amounts of data and perform analysis and prediction.

[0611] "Challenges" refer to specific problems or issues that need to be resolved or addressed within a local community, and require appropriate responses.

[0612] "Multilingualization" refers to a system that provides information in an accurate and understandable format to residents who speak different languages.

[0613] An "electronic terminal" refers to a device such as a smartphone or personal computer used for inputting or acquiring information.

[0614] A "server" is a central computing system that stores, analyzes, generates, and notifies data.

[0615] This invention is a system that effectively collects, analyzes, and notifies information within a local community, thereby promoting cooperation among residents. The system mainly consists of three elements: a server, terminals, and users.

[0616] Users provide the system with information about their health status and support needs by accessing a dedicated application using their smartphone or computer. This allows the system to understand the situation of each individual resident in the community. For example, elderly people who have difficulty shopping can request fresh food delivery through the app.

[0617] The terminals are installed in homes and public facilities and periodically collect environmental information from sensors and public data sources within the area. This includes temperature measurement using temperature sensors and acquisition of weather information from the internet. The collected data is immediately transferred to a server, enabling real-time monitoring of the entire area.

[0618] The server integrates information sent from users and devices into a central database. The server implements advanced machine learning algorithms to analyze the data and automatically generate prioritized tasks. For example, during heavy rain, it can use road closure information to instruct alternative routes and notify residents who need to evacuate.

[0619] The generated challenges are notified from the server to users and local volunteers who can help. This ensures that necessary support information is disseminated quickly and accurately, and because it is available in multiple languages, the information can be reliably delivered to residents who speak various languages.

[0620] For example, in a situation where roads in a region are closed due to heavy rain, the server analyzes this information and immediately notifies residents of the warning. Volunteers are notified of detours and requests for evacuation assistance. An example of a prompt message would be: "If heavy rain is expected in the region, what information should be collected to notify residents, and what procedures should be followed to send a request for assistance to volunteers?"

[0621] In this way, the entire system works in coordination, providing an environment where residents of the local community can live their daily lives more safely and conveniently.

[0622] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0623] Step 1:

[0624] Users access a dedicated app from their smartphone or computer and input their health status and necessary support information. This input information is sent to a server and stored in a regional database. The input is data related to individual health status and support requests, while the output is integrated data stored in a central database. Specifically, users select support options on the app and input specific details.

[0625] Step 2:

[0626] The terminal automatically acquires environmental information from sensors installed within the area and from external information sources, and collects this data. The collected data is sent to a server. The input is environmental data such as temperature, humidity, and weather information, and the output is real-time information transferred to the server. Specific operations include periodic data acquisition from sensors and downloading weather information via the internet.

[0627] Step 3:

[0628] The server integrates data obtained from users and terminals into a central database. Inputs include the aforementioned health information, support information, and environmental data, while output is an integrated dataset. The server uses this dataset to perform analysis using machine learning algorithms. Specifically, this involves organizing and registering the data in the database.

[0629] Step 4:

[0630] The server analyzes integrated data and automatically generates tasks according to priority. A generative AI model is used for this process. The input is the dataset to be analyzed, and the output is a list of tasks generated according to priority. Specific operations include data pattern analysis and the execution of task generation algorithms.

[0631] Step 5:

[0632] The server notifies users and local volunteers who can help based on the generated tasks. The input is a list of generated tasks, and the output is the notified assistance request information. Specifically, a process is in place to notify in the appropriate language through multilingual support. Notification recipients receive instructions tailored to the specific nature and urgency of the assistance needed and take appropriate action.

[0633] (Application Example 1)

[0634] 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".

[0635] Real-time information gathering and effective information sharing are essential for the rapid dissemination of emergency and support information within local communities and for promoting cooperation among residents. However, current systems lack sufficient multilingual support and visual information presentation capabilities, posing challenges to information transmission and understanding. Therefore, there is a need to build a system that addresses these challenges and strengthens community safety and cooperation among residents.

[0636] 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.

[0637] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information, prioritizing it, and automatically generating tasks, means for notifying appropriate collaborators of the generated tasks, and means for displaying local emergency and support information in real time through visual information presentation on user terminals. This makes it possible to effectively transmit information in multiple languages ​​and promote cooperation among residents.

[0638] "Local information" refers to all kinds of data related to a specific region, including weather, traffic, disaster information, residents' health status, and assistance requests.

[0639] An "information storage device" refers to a digital database used to record and manage collected information.

[0640] "Visual information presentation" refers to the act of visually displaying information using a user terminal through means such as text, images, and maps.

[0641] "Generated tasks" refer to work items that are automatically created to request and instruct specific actions based on collected and analyzed information.

[0642] A "suitable collaborator" refers to an individual or group that possesses the skills and resources best suited to performing a particular task.

[0643] "Multilingual support" refers to the ability to support multiple languages ​​and accurately convey information to users who speak each language.

[0644] An "information processing algorithm" refers to a mathematical or digital procedure used to analyze collected data, evaluate the current situation, and generate the necessary response.

[0645] The system that realizes this invention aims to collect local information, generate necessary tasks, and promote cooperation among residents. The system mainly consists of a server, terminals, and users, each playing a specific role.

[0646] The servers are built using cloud computing services such as AWS EC2. The servers use database management systems such as MongoDB to aggregate and analyze regional information. For machine learning algorithms, frameworks such as TensorFlow and PyTorch are used. These algorithms analyze the collected data, automatically generate necessary tasks, and notify the most suitable collaborators.

[0647] The devices are deployed in homes and public facilities to acquire data from local sensors and external information sources. They communicate with servers via Wi-Fi or Bluetooth, transmitting data in real time to monitor weather information and emergency situations.

[0648] Users can access the system using devices such as smartphones and smart glasses. An application built with React Native is installed on the user's device, allowing them to check their own information and local conditions. The system supports multiple languages, ensuring accurate information is provided to users who speak different languages.

[0649] For example, if some roads in a region are closed due to heavy rain, the server analyzes this information and immediately notifies residents who need emergency assistance. Furthermore, it provides nearby volunteers with information on safe detours. Residents can visually access this information using a smartphone app.

[0650] An example of a prompt to input into a generating AI model would be: "Create a plan for how to notify residents of safe routes if several roads in a certain area are closed due to heavy rain."

[0651] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0652] Step 1:

[0653] The device periodically collects data from local sensors and external data sources. This includes weather information, traffic conditions, and disaster information. This information is transmitted to a server via Wi-Fi.

[0654] Step 2:

[0655] The server receives data sent from the terminal and stores it in a database such as MongoDB. It takes raw sensor data as input data and converts it into time-series data for storage.

[0656] Step 3:

[0657] The server analyzes the stored data and uses machine learning frameworks like TensorFlow to generate the necessary tasks. Prioritized tasks are then classified using various algorithms to determine whether immediate action is required based on specific conditions.

[0658] Step 4:

[0659] The server notifies the most suitable collaborators of the generated tasks. These notifications are sent to local volunteers and residents via smartphones and other device applications.

[0660] Step 5:

[0661] Users receive notifications from the server using a smartphone application. This process utilizes multilingual support to visually display necessary support information and evacuation routes.

[0662] Step 6:

[0663] Users make decisions based on the information they receive and send feedback to the server as needed. This facilitates continuous information exchange and optimization within the region.

[0664] 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.

[0665] This invention provides a system that combines an emotion engine to streamline support activities in local communities. This system has the function of recognizing the user's emotions, in addition to information gathering, analysis, and notification, in order to address challenges associated with local disasters and aging populations.

[0666] Users regularly use the app to input their status and the support they need. The emotion engine analyzes the user's text and voice input to recognize emotions such as stress and anxiety in real time. For example, if a user inputs "I've been feeling anxious frequently lately," that emotion data is incorporated into the system.

[0667] The terminal transmits environmental data collected through sensors within the area, as well as user emotional information, to the server. This allows for a comprehensive assessment of the local situation and the psychological state of individual users.

[0668] The server integrates information from terminals and users and performs analysis using an emotion engine. In particular, it utilizes emotion data to understand the well-being and stress levels of residents and adjust priorities accordingly. For example, if anxiety is high in a specific area, the server will prioritize emergency response in that area. Furthermore, based on emotion information, it determines whether a user needs emotional support and generates tasks to facilitate necessary assistance and communication.

[0669] For example, after a large-scale disaster, the server identifies areas in need of psychological care based on the stress levels of residents recognized by the emotion engine, and notifies volunteers and counselors of this information. This notification enables prompt psychological support.

[0670] Therefore, the present invention aims to significantly improve disaster response and daily support activities by utilizing information technology to respond quickly and accurately to the mental and physical needs of local residents.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] Users input information about their daily lives and current moods using a smartphone app. For example, they can input emotional information such as "I feel anxious living alone."

[0674] Step 2:

[0675] The terminal collects weather and disaster prevention information from local sensors and sends it to a server along with user input data. This data includes environmental changes and disaster information.

[0676] Step 3:

[0677] The server integrates environmental data sent from the terminal and emotional data from the user. Next, it uses an emotion engine to analyze the user's emotional state and quantify emotions such as stress and anxiety.

[0678] Step 4:

[0679] The server automatically generates tasks with adjusted priorities based on the analyzed emotional information. For example, it can generate psychological support tasks for users with unstable moods based on emotional data.

[0680] Step 5:

[0681] The server sends notifications to the most suitable collaborators and volunteers as the tasks are generated. Emotion-based priority tasks, which require a quick response, are immediately notified via push notifications.

[0682] Step 6:

[0683] The device provides information to users in multiple languages ​​and offers content and instructions that cater to users who require emotional support. This enables smooth communication among users.

[0684] This series of processes enables effective responses tailored to local conditions and the feelings of individual users, providing a support system that enhances the safety and security of residents.

[0685] (Example 2)

[0686] 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".

[0687] For swift and effective support activities in local communities, it is crucial to collect real-time information on the diverse challenges that arise during disasters and in daily life, and to set appropriate priorities based on that information. However, the current system does not adequately consider emotional support and the quality of communication, which makes it difficult to provide prompt psychological care to residents.

[0688] 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.

[0689] In this invention, the server includes means for collecting and integrating regional data, means for analyzing the integrated information and individual emotional information, prioritizing and automatically generating support plans, and means for notifying appropriate support providers of the generated support plans. This enables a comprehensive understanding of the situation in each region and the emotional state of its residents, allowing for the rapid and appropriate provision of support and psychological care.

[0690] "Local data" refers to environmental information and data related to the conditions of residents collected within a specific region.

[0691] "Integrated information" refers to data collected from various sources that has been centralized and compiled for analysis.

[0692] "Emotional information" refers to data about emotions analyzed from text and voice input by the user.

[0693] A "support plan" refers to specific action plans and support methods that are automatically generated based on the analyzed information.

[0694] A "supporter" is an individual or group identified by the system that provides support to a specific region or population.

[0695] An "emotion recognition engine" is software or an algorithm that analyzes a user's text or voice input to evaluate their psychological state in real time.

[0696] "Real-time" refers to the instantaneous acquisition and processing of data without delay.

[0697] "Notification" refers to the process by which generated support plans and important information are communicated to support providers.

[0698] The system according to the present invention functions as a comprehensive platform for streamlining community support activities. The embodiments for carrying out the present invention are described below in detail.

[0699] Hardware and software

[0700] This system primarily consists of three components: servers, terminals, and users. The coordinated operation of these components ensures smooth information gathering, data analysis, notification, and support activities.

[0701] The server plays a central role in integrating and analyzing information. Analysis software, including an emotion recognition engine, runs on the server, processing data sent from users and terminals. This engine utilizes natural language processing technology to convert text and voice input into emotion data. Furthermore, machine learning algorithms are used to identify support needs specific to each region.

[0702] The devices include smartphones, tablets, and personal computers, and their role is to send user input information to the server and collect data in real time from sensors within the region. This data is transmitted to the server via a secure protocol.

[0703] Users input their feelings and support requests through applications they use daily. The entered data is sent to a server via the device and analyzed by an emotion recognition engine. Based on notifications sent from the server, users also collaborate with support providers as needed.

[0704] Specific examples and prompt statements

[0705] As a concrete example, suppose a user enters "I've been feeling anxious frequently lately" into the app. This information is analyzed by the server, and if it determines that the user's psychological state in that region is unstable, an immediate support plan is generated.

[0706] Examples of prompts for a generative AI model are as follows:

[0707] "Analyze user input data to identify psychological support needs in specific regions."

[0708] This will enable the system to respond quickly and appropriately to the diverse needs of local residents, with the aim of contributing to the creation of a safe and secure community.

[0709] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0710] Step 1:

[0711] Users input their state and emotions through the application via text or voice. This input provides information about the user's everyday feelings and level of support needs. This input data is received by the application and prepared for transmission to the emotion recognition engine. A specific example of this process would be the user typing "I've been feeling anxious lately," and the data being appropriately formatted.

[0712] Step 2:

[0713] The terminal receives information entered by the user and collects environmental data within the area through sensors. The input in this step includes user sentiment information and environmental data. The terminal uses a secure communication protocol to organize and prepare this data for transmission to the server. Specifically, this involves encrypting temperature and humidity measurements and voice data.

[0714] Step 3:

[0715] The server receives emotional information and environmental data sent from the terminal and stores it in a database. At this point, the input consists of organized emotional information and environmental data. The server utilizes an emotion recognition engine to analyze the input data and evaluate stress and anxiety levels. The output is the emotional evaluation result for each individual user. Specifically, emotion analysis is performed using natural language processing and emotion evaluation is performed using machine learning algorithms.

[0716] Step 4:

[0717] The server automatically generates support plans by setting support priorities for each region based on the analyzed emotion evaluation results and environmental data. The inputs at this time are the analysis results and environmental data. Using a generation AI model, the optimal support plan is created, and the support plan is obtained as output. Specifically, the output is the output of specific support content generated based on the prompt text.

[0718] Step 5:

[0719] The server notifies the relevant supporters of the generated support plan. The input is the generated support plan. As an output of the notification, tasks are assigned to the supporters. Specifically, notification emails are sent to the list of supporters and alerts are displayed in the app.

[0720] Through these steps, the system enables the efficient and rapid provision of support to local communities.

[0721] (Application Example 2)

[0722] 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".

[0723] Efficiently understanding the emotional state of residents in a community and providing prompt support has been difficult with conventional technologies. In particular, there is a need to respond immediately to the psychological and physical needs of residents during disasters or in aging communities. This invention aims to solve these problems.

[0724] 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.

[0725] In this invention, the server includes means for collecting and integrating local information, means for analyzing the integrated information, recognizing emotional states in real time, prioritizing tasks, and automatically generating tasks, and means for notifying appropriate collaborators of the generated tasks and providing psychological support information. This enables a comprehensive understanding of the emotions and needs of residents in the local community, and allows for swift and accurate support activities.

[0726] "Local information" refers to environmental data, residents' sentiment data, and all related data for a specific region.

[0727] "Emotional state recognition" refers to the process of analyzing the user's text or voice data to recognize emotions such as stress and anxiety.

[0728] A "method for automatically generating tasks with prioritization" is a mechanism that generates tasks based on urgency and importance, using collected information.

[0729] "Appropriate collaborators" refer to professionals, volunteers, and related organizations involved in local support activities.

[0730] "Psychological support information" refers to information that provides support and advice tailored to the emotional state of residents.

[0731] The system for implementing this invention aims to effectively carry out support activities in the local community by analyzing the emotional state of users using an emotion engine. This system consists of a terminal for collecting local information, a server for integrating and analyzing the data, and an application used by resident users.

[0732] The server integrates data acquired from sensors and users within the region and uses an emotion engine to recognize emotional states in real time. Based on this data, the server automatically generates responses for high-priority tasks and situations where residents require psychological support. In this process, machine learning algorithms are used to create action plans based on residents' emotions.

[0733] The device sends text and voice data entered by the user to the emotion engine and transmits the analysis results to the server. Furthermore, it continuously collects environmental data from sensors within the area and transmits it to the server along with the user's emotion data.

[0734] Users input their emotional state through the application. This application visualizes the emotional state of residents and provides psychological support information when needed. For example, if a user inputs "I've been feeling anxious lately," the emotion engine analyzes that information and, if necessary, provides countermeasures or notifies relevant parties.

[0735] As a concrete example, an example of a prompt message using a generative AI model is, "Based on user sentiment data and local environmental information, analyze which areas need emergency assistance." In this way, by implementing the invention, it becomes possible to understand the sentiments and needs of the entire community and to carry out efficient support activities.

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] The device receives text or voice input data from the user. This data is sent to the emotion engine for analysis. The received data is classified by the emotion engine into emotions such as stress and anxiety, and the corresponding emotional state data is output.

[0739] Step 2:

[0740] The device collects environmental data in real time from sensors within the area. This data includes temperature, humidity, noise levels, and more. The collected environmental data is sent directly to a server and used as basic data to understand the local conditions.

[0741] Step 3:

[0742] The server integrates user emotional state data and environmental data received from the terminal. By integrating this input data and simultaneously analyzing the residents' emotional state and the local environmental conditions, it determines whether or not psychological support is needed for the residents. The results of this processing are stored as internal data.

[0743] Step 4:

[0744] The server automatically generates tasks and their priorities based on the emotional state of each resident, using data analyzed with machine learning algorithms. Here, the algorithm compares the current data with past data and patterns to identify areas with high urgency, and the corresponding tasks are formed as an output.

[0745] Step 5:

[0746] The server notifies appropriate collaborators of the generated tasks and provides psychological support information as needed. The notifications also include information about the residents' emotional state and the local situation. This information distribution helps collaborators take swift and appropriate action.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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."

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] The following is further disclosed regarding the embodiments described above.

[0769] (Claim 1)

[0770] Means for collecting and integrating local information,

[0771] A means of analyzing integrated information, prioritizing it, and automatically generating tasks,

[0772] A means of notifying the appropriate collaborators of the generated tasks,

[0773] A means of providing information in multiple languages ​​and supporting communication among users,

[0774] A system that includes this.

[0775] (Claim 2)

[0776] The system according to claim 1, further comprising means for acquiring information in real time from sensors within the region and from external data sources and storing that information in a database.

[0777] (Claim 3)

[0778] The system according to claim 1, further comprising means for identifying problems using machine learning algorithms based on analyzed information and automatically generating corresponding action plans.

[0779] "Example 1"

[0780] (Claim 1)

[0781] Means for collecting and integrating local information,

[0782] A method for analyzing integrated information using machine learning algorithms, prioritizing, and automatically generating issues,

[0783] A means of notifying the appropriate collaborators of the generated tasks,

[0784] A means of providing information in multiple languages ​​and supporting communication among users,

[0785] A means of inputting individual status and support request information via an electronic terminal and storing it in a database,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, further comprising means for acquiring information in real time from sensors within the area and from external information sources and storing the information in a database.

[0789] (Claim 3)

[0790] The system according to claim 1, further comprising means for identifying problems using a generative AI model based on analyzed information and automatically generating corresponding action plans.

[0791] "Application Example 1"

[0792] (Claim 1)

[0793] Means for collecting and integrating local information,

[0794] A means of analyzing integrated information, prioritizing it, and automatically generating tasks,

[0795] A means of notifying the appropriate collaborators of the generated tasks,

[0796] A means of providing information in multiple languages ​​and supporting communication among users,

[0797] A means of displaying local emergency information and support information in real time through visual information presentation on user terminals,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, further comprising means for acquiring information in real time from sensor devices within the region and external information sources, and storing the information in an information storage device.

[0801] (Claim 3)

[0802] The system according to claim 1, further comprising means for identifying problems using an information processing algorithm based on analyzed information and automatically generating a corresponding action plan.

[0803] "Example 2 of combining an emotion engine"

[0804] (Claim 1)

[0805] Means for collecting and integrating regional data,

[0806] A means to analyze integrated information and individual emotional information, prioritize it, and automatically generate a support plan,

[0807] A means of notifying the appropriate support person of the generated support plan,

[0808] A means of providing information to local residents in multiple languages ​​and supporting interpersonal communication,

[0809] A method using an emotion recognition engine to analyze emotional data from users,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, further comprising means for acquiring data in real time from sensors within the region and from external information sources and storing the data in a database.

[0813] (Claim 3)

[0814] The system according to claim 1, further comprising means for identifying local issues using a machine learning algorithm based on analyzed data and automatically generating specific action plans accordingly.

[0815] "Application example 2 when combining with an emotional engine"

[0816] (Claim 1)

[0817] Means for collecting and integrating local information,

[0818] A means of analyzing integrated information, recognizing emotional states in real time, prioritizing them, and automatically generating tasks,

[0819] A means of notifying appropriate collaborators of the generated tasks and providing psychological support information,

[0820] A means of providing information in multiple languages ​​and supporting communication among users,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising means for acquiring information in real time from sensors within the region and from external data sources, integrating it with user sentiment data, and storing it in a database.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising means for automatically generating action plans that correspond to the emotional state of residents using a machine learning algorithm based on the analyzed information. [Explanation of symbols]

[0826] 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. Means for collecting and integrating local information, A means of analyzing integrated information, prioritizing it, and automatically generating tasks, A means of notifying the appropriate collaborators of the generated tasks, A means of providing information in multiple languages ​​and supporting communication among users, A means of displaying local emergency information and support information in real time through visual information presentation on user terminals, A system that includes this.

2. The system according to claim 1, further comprising means for acquiring information in real time from sensor devices within the region and external information sources, and storing the information in an information storage device.

3. The system according to claim 1, further comprising means for identifying problems using an information processing algorithm based on analyzed information and automatically generating a corresponding action plan.

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