system

Abstract

We provide the system. [Solution] A means of acquiring video data, An analysis method for analyzing acquired video data in real time, A detection means that performs anomaly detection based on the analysis results, A notification means that notifies when an anomaly is detected, 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 the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in the activity of watching over children in the community, with the aging of volunteers and the increase in dual-income families of guardians, the number of activity participants has been decreasing. As a result, it has become difficult to maintain a sustainable monitoring system for ensuring the safety of children. In the conventional method, due to dependence on human resources, the accuracy and scope of monitoring are limited, and there is a problem that it is difficult to quickly detect suspicious behavior.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides a system including an acquisition means for acquiring video data, an analysis means for analyzing the acquired video data in real time, a detection means for detecting anomalies based on the analysis results, and a notification means for notifying when an anomaly is detected. This system enables wide-area and highly accurate monitoring while reducing the human burden on volunteers. Furthermore, by acquiring data over a wide area using drones and fixed cameras, it enables real-time situational awareness, and provides a mechanism that allows users to remotely monitor and respond quickly in the event of an anomaly.

[0006] "Means of acquisition" refers to devices and methods for collecting video data, such as drones and cameras.

[0007] "Analysis means" refers to algorithms and systems that analyze acquired video data in real time to detect specific actions or patterns.

[0008] "Detection means" refers to a mechanism for identifying abnormal behavior or actions based on the results obtained from analysis means and notifying the system of such behavior.

[0009] "Notification means" refers to means of communicating alarms or information to the user in response to anomalies identified by detection means, and includes, for example, alerts via applications.

[0010] A "drone" refers to an aircraft that flies automatically and unmanned, used to collect video data over a wide area.

[0011] A "fixed camera" refers to a camera device that is installed at a specific location and used to continuously acquire video data.

[0012] "User" refers to an individual or organization responsible for operating the provided system, reviewing monitoring results, and taking appropriate action. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

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

[0016] In the following embodiments, a tagged 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, a tagged 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, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0034] This invention is a monitoring system for supporting child safety activities in local communities, and has an overall system structure that includes acquisition means, analysis means, detection means, and notification means. This system functions primarily based on three elements: a server, a terminal, and a user.

[0035] The server functions as a central processor, receiving video data from terminals and performing real-time analysis using AI. The analysis tools within the server execute algorithms to detect suspicious behavior from the collected video. The data analyzed by these detection tools is immediately reported to the user via notification tools when an anomaly occurs. Furthermore, it is possible to automatically generate reports on daily observations and anomaly cases for administrators in the region where the system is implemented, and provide them via email or other means.

[0036] As terminals, drones and fixed cameras are responsible for collecting data over a wide area. Drones patrol pre-set routes and collect video data via acquisition devices during their movement. Fixed cameras, often installed at specific locations in a region, also play a role in continuous monitoring and transmitting video data to a server. In this way, terminals primarily function as acquisition devices, constantly monitoring the surrounding environment.

[0037] On the other hand, users can remotely check the monitoring results using a dedicated monitoring application and take prompt action as needed. Through this application, users can visually check the video transmitted in real time and have a means to directly notify local safety response agencies if an anomaly is detected. For example, users can check the safety of their school route via camera footage from their homes and immediately contact the appropriate agency using the in-app notification function if an anomaly is detected.

[0038] In this way, the server, terminal, and user elements work together to support sustainable monitoring activities. It is expected that the organic connection of the entire system will further enhance the safety of children in local communities.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device automatically activates according to a set schedule and collects video data from the monitored area. For example, a drone begins patrolling along a programmed flight route and continuously acquires video with its onboard camera.

[0042] Step 2:

[0043] The terminal immediately transmits the collected video data to the server. This transmission is performed using low-latency communication to enable real-time data analysis. The server prepares for processing while storing the received data in temporary storage.

[0044] Step 3:

[0045] The server analyzes the received video data in real time using AI algorithms. Here, it analyzes movement and people in the video to detect suspicious behavior and unusual patterns.

[0046] Step 4:

[0047] If the server detects any suspicious behavior as a result of the analysis, it immediately performs an anomaly detection. This detection information is logged in the internal system and sent to the notification process.

[0048] Step 5:

[0049] The server activates a notification system to alert the user about any detected anomalies. This sends real-time alerts to the user's monitoring application.

[0050] Step 6:

[0051] Users can review the details of alerts received through the application. They can use the video replay function to confirm the situation when an anomaly occurred and, if necessary, report it to dispatch the appropriate agency to the site.

[0052] Step 7:

[0053] Users send feedback to the server via the app once they have completed the necessary response measures for an anomaly. This feedback is then used to improve the overall monitoring accuracy and operational efficiency of the system.

[0054] (Example 1)

[0055] 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."

[0056] Ensuring safety in local communities requires widespread and continuous monitoring, rapid detection of anomalies, and appropriate responses. However, conventional monitoring systems have faced challenges such as limitations on camera placement, inaccuracies in anomaly detection, and difficulties in rapid response. As a result, timely information provision, particularly for protecting the safety of children, is often insufficient.

[0057] 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.

[0058] In this invention, the server includes information gathering means, information processing means, state detection means, information transmission means, and report generation means. This enables the collection and analysis of a wide range of information in real time, allowing for rapid detection of anomalies and immediate notification to users. Furthermore, the automatically generated reports allow local administrators to confirm the situation in detail, contributing to improved local safety.

[0059] "Video data" refers to visual information collected by cameras and sensors, and is the subject of analysis.

[0060] "Information gathering means" refers to devices and technologies used to acquire data, such as cameras and sensors.

[0061] "Information processing means" refers to computer systems and algorithms used to analyze collected data.

[0062] "State detection means" refers to algorithms and technologies used to identify abnormal events based on processed data.

[0063] "Information transmission means" refers to communication means used to transmit detected information or anomalies to other devices or users.

[0064] "Report generation means" refers to technology for creating reports that summarize information for administrators and users based on collected and analyzed data.

[0065] "Aircraft that move through the air" refers to devices that can fly and move, such as unmanned aerial vehicles like drones.

[0066] "Information presentation function" refers to a function that displays information in a way that users can understand and provides an interface for taking necessary actions.

[0067] In this invention, the server plays a central role in realizing safety monitoring in the local community. The server receives video data transmitted from terminals and analyzes that data in real time using AI. Specifically, it uses AI frameworks such as "TENSORFLOW®" and "PyTorch" to perform object detection and abnormal behavior analysis on the received video data. This method enables highly accurate and rapid anomaly detection.

[0068] The devices used as terminals include fixed cameras and aircraft that move through the air. In particular, drones regularly orbit the area based on planned routes, collecting wide-area video data. This enables comprehensive data collection from both the ground and the air. Fixed cameras continuously collect video 24 hours a day at specific locations, providing a stable data supply.

[0069] Users can remotely view video data provided by the server using a dedicated monitoring application. The application visualizes real-time video and anomaly notifications, and has a mechanism that allows users to quickly contact local response agencies as needed. In addition, when an anomaly is detected, users can respond quickly using the integrated notification function within the app.

[0070] For example, users can view footage from cameras installed along school routes from the comfort of their homes and immediately notify patrol agencies if any anomalies are detected. This system is designed to support sustainable community watch activities and helps users and local administrators conduct safety monitoring efficiently.

[0071] An example of a prompt would be, "Please describe in detail how the AI ​​model for monitoring children in the community performs behavioral analysis, detects anomalies, and sends notifications." This prompt allows the generated AI model to provide a detailed description of the system.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The terminal collects video data using a camera and flight sensors. The input is real-time video from various locations within the region. The terminal captures this video data frame by frame and converts it into a digital format that can be sent to the server. The output is transferred to the server as compressed video data.

[0075] Step 2:

[0076] The server receives video data sent from the terminal. It prepares to run an AI algorithm to analyze the video data as input. Specifically, it uses an AI framework to start analyzing the received video. Data processing includes screen subdivision and feature extraction, and the analysis results are used in the next processing step as output.

[0077] Step 3:

[0078] The server uses an AI algorithm to analyze video data and detect suspicious behavior. The input is the video frames received in the previous step. The AI ​​model analyzes the movement and changes of objects to detect anomalies. The output generates information about the detected anomaly and its details, and the process proceeds to the next notification step.

[0079] Step 4:

[0080] The server notifies users of detected anomalies. The input for this step is the analyzed anomaly information. The server notifies the target users via monitoring applications or email. The output is an anomaly alert to the user.

[0081] Step 5:

[0082] Users check notifications through a monitoring application. Inputs include received anomaly reports and video data. Users review the video within the app and take action using the reporting function as needed. Outputs include sending notifications to local response agencies if necessary.

[0083] Step 6:

[0084] The server automatically generates daily reports based on all monitoring data and detected information. Inputs include collected video data and detection results. The server organizes the information, creates reports for administrators, and sends them via email. Outputs are detailed reports on the local safety situation.

[0085] (Application Example 1)

[0086] 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."

[0087] To ensure the safety of local communities, a system is needed that can monitor the surrounding environment in real time and respond quickly and appropriately when an anomaly is detected. However, conventional systems have difficulty monitoring, especially while on the move, and responding immediately, which can sometimes prevent early response to anomalies. In addition, there is a need for new methods to easily acquire and analyze information while monitoring a wide area.

[0088] 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.

[0089] In this invention, the server includes means for acquiring video information, means for analyzing the acquired video information in real time, means for detecting anomalies based on the analysis results, and means for providing a user interface that allows the user to check the surrounding situation in real time using a visual device and report anomalies. This enables the user to monitor the safety of their surroundings even while on the move and to take immediate action if an anomaly is detected.

[0090] "Visual information" refers to video or still image data captured by cameras or other visual devices and processed in digital format.

[0091] "Means" refer to the devices or methods used to achieve a specific purpose, and are elements within a system that perform a specific function.

[0092] "Analysis means" refers to a method or apparatus used to analyze acquired data and understand its contents.

[0093] "Visual devices" are devices used by users to visually perceive their external environment, and include smart glasses and headsets.

[0094] A "user interface" is a means of interaction used when information is exchanged between a system and a user.

[0095] An "abnormality" refers to a situation that deviates from the normal operation or expected state of a system, and is an operation or state that may threaten safety.

[0096] An "unmanned aerial vehicle" is an aircraft that operates remotely or automatically without human control and is used for data collection and surveillance tasks.

[0097] This invention is a security system that uses visual devices to monitor the surroundings and provides real-time notifications when an anomaly is detected. The server is the central processing unit that acquires video information and analyzes that data. Specifically, the server receives and processes video data transmitted from smart glasses or other visual devices in real time. Using an AI model installed on the server (e.g., TensorFlow or PyTorch), the system analyzes the video data and detects suspicious behavior or anomalies.

[0098] Smart glasses, acting as a terminal device, are designed to allow users to monitor their surroundings even while on the move. A camera equipped in the device acquires video data for a library and transmits it to a server via Wi-Fi or Bluetooth. If an anomaly is detected, a notification is displayed on the glasses' screen, allowing the user to quickly respond while checking the visual information. The user interface incorporates mechanisms for rapid anomaly reporting and additional verification operations through voice recognition and simple gestures.

[0099] A concrete example of implementing the system is a scenario where security guards on nighttime patrols wear smart glasses. The guards use the device to check for safety while walking around, and if a suspicious person or intruder is detected, a notification such as "Intruder detected, warning notification sent" is immediately displayed in a visible format. An example of a prompt message in this case would be, "Monitor your surroundings through your smart glasses and report any suspicious activity."

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The device continuously acquires surrounding video information using the camera of the visual device. It receives real-time video of the visual environment displayed on the device as input and converts it into digital data. The output is the acquired video data.

[0103] Step 2:

[0104] The terminal transmits the acquired video data to the server via wireless communication (e.g., Wi-Fi or Bluetooth). This process uses the video data acquired by the terminal as input. The output is the video data received by the server.

[0105] Step 3:

[0106] The server analyzes the received video data using an AI model (e.g., TensorFlow or PyTorch). The AI ​​model receives video data as input and analyzes movement, people, and other important features within the video. The output consists of the analyzed information and anomaly detection results.

[0107] Step 4:

[0108] The server detects the presence or absence of anomalies based on the analysis results. It uses the analysis results from an AI model as input. The output is alert information if an anomaly is detected. At this stage, the specific behavior or situation deemed anomaly is identified.

[0109] Step 5:

[0110] If an anomaly is detected, the server notifies the terminal's visual device of the detailed information. A warning message is instantly displayed on the screen for the user. The input is the anomaly detection information. The output is a visual notification that the user can see.

[0111] Step 6:

[0112] The user receives notifications and, if necessary, uses voice commands or gestures to report anomalies or take further action. Input includes user interaction based on notifications from the visual device. Output is reported information and additional instructions.

[0113] 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.

[0114] This invention is a system for supporting community child safety monitoring activities, and combines acquisition means, analysis means, detection means, notification means, and an emotion engine. This system consists of a server, a terminal, and a user.

[0115] The server receives video data transmitted from the terminal and performs real-time data analysis using AI algorithms. It also uses an emotion engine to evaluate the user's emotions based on the results of anomaly detection. The emotion engine detects the user's emotional state and adjusts the content and format of notifications accordingly. This evaluation incorporates features that sense the user's voice tone and facial expressions, enabling optimized notifications. For example, if the system evaluates the user as fatigued, it softens the notification tone and provides essential information concisely.

[0116] The drones and fixed cameras, acting as terminals, acquire data over a wide area and transmit that data to the server in real time. This allows them to serve as data acquisition tools, continuously monitoring video footage of the target area. Furthermore, the terminals autonomously detect predetermined patrol routes while efficiently collecting data.

[0117] Users can remotely view information notified by the system using a monitoring application. They can understand the video and notification content received in real time, and if an anomaly is detected, they can take appropriate action quickly. For example, when a user receives a danger notification via smartphone from work, the notification is designed with consideration for reducing stress through an emotion engine, allowing them to calmly assess the situation and take necessary safety measures.

[0118] In this way, the functions of acquisition, analysis, detection, notification, and emotion engine are integrated, reducing the burden on the user while enabling sustainable and reliable monitoring activities. This system allows communities to create a safer and more protected environment.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The terminal activates drones and fixed cameras according to a pre-set schedule to collect video data from the surveillance area. The drones patrol along designated flight routes and save the footage captured by the cameras to memory.

[0122] Step 2:

[0123] The terminal transmits the collected video data to the server in real time. The data is streamed with low latency via wireless communication technology as a video signal.

[0124] Step 3:

[0125] The server processes the received video data using an AI analysis module, recognizing people and actions while analyzing for any abnormal behavior. If the analysis detects suspicious behavior in a specific location, it triggers an alert.

[0126] Step 4:

[0127] The server activates the emotion engine based on the anomaly detection results and evaluates the user's emotional state. This evaluation includes a process of determining appropriate notification content, taking into account past user responses and historical data.

[0128] Step 5:

[0129] The server sends optimized notifications to the user based on the results of the emotion engine's evaluation. For example, if the server detects that the user is in a high-stress state, the notification will be sent in a considerate format and content.

[0130] Step 6:

[0131] The user reviews the notification received through a dedicated application and confirms the situation on site by replaying the provided video. If necessary, the user then makes the appropriate report to the local response agency.

[0132] Step 7:

[0133] After an interaction, the user provides feedback to the system, sending information to the server that the emotion engine uses to optimize future notifications. This improves the system's adaptability and user satisfaction.

[0134] (Example 2)

[0135] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0136] In community-based child safety monitoring activities, there is a need to provide a method that enables efficient situational monitoring over a wide area without placing an excessive burden on users. Conventional monitoring systems have the problem of not providing timely and appropriate notifications in the event of an emergency, which increases the psychological burden on users.

[0137] 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.

[0138] In this invention, the server includes a device for acquiring information, a processing device for analyzing the acquired information in real time, a device for detecting anomalies based on the analysis results, an emotion evaluation device for evaluating the user's emotional state, and a device with a function for adjusting notification content based on the emotion evaluation results. This enables safe and rapid response to anomalies and notification provision that reduces the psychological burden on the user.

[0139] "Information acquisition devices" refer to means of collecting data over a wide range of areas, and are composed of aircraft and stationary equipment.

[0140] A "processing device that analyzes acquired information in real time" refers to a computer system that immediately analyzes received data, and is implemented using machine learning algorithms.

[0141] An "anomaly detection device" is a mechanism that recognizes unusual situations based on the results of information analysis and identifies those events.

[0142] An "emotion evaluation device" is a system used to analyze a user's voice tone and past data to infer their emotional state.

[0143] A "device equipped with a function to adjust notification content" is a mechanism for optimizing the content and method of notifications to the user based on the results of sentiment evaluation.

[0144] In order to implement this invention, it is necessary to construct a system in which three elements—a server, a terminal, and a user—cooperate with each other.

[0145] First, the terminal will be equipped with aircraft or stationary devices to acquire information over a wide area. These devices will autonomously patrol a designated area and collect the necessary data. It is possible to acquire images and video data of the monitoring area using equipment such as drones and fixed cameras. A specific example would be cameras placed around a school to check on the safety of children as they go to school.

[0146] Next, the server receives information transmitted from these terminals in real time and performs a computer-based analysis on the received data. It is preferable to use TensorFlow or PyTorch as the machine learning platform. An AI algorithm analyzes people and movements in the video, constantly monitoring for any abnormal behavior.

[0147] When an anomaly is detected, the server activates an emotion assessment system. This system generates appropriate notification content based on the user's tone of voice and past emotion history. Through this assessment, a relaxed tone and concise information delivery are achieved to reduce the user's psychological burden.

[0148] Users can receive system notifications remotely using their smartphones or tablets. They can open a monitoring application to instantly check the situation and take swift action in case of anomalies. For example, if a child enters a dangerous area, they can receive a notification that includes appropriate evacuation instructions, enabling rapid rescue.

[0149] Examples of prompts to input into the generating AI model include, "How can we optimize the anomaly detection process in the monitoring system?" Based on this prompt, a more efficient monitoring plan will be proposed.

[0150] In this way, the server, terminal, and user components function together, enabling safe monitoring activities in the local community.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The terminal acquires video data from a designated monitoring area using aircraft or stationary equipment. In this step, the acquisition device autonomously patrols the monitoring area, continuously capturing data with cameras and sensors. The input is real-time video of the monitored area, and the output is a data stream sent to the server.

[0154] Step 2:

[0155] The server receives video data transmitted from the terminal in real time and analyzes the images using an AI algorithm. The input is a video data stream, and the AI ​​model (e.g., using TensorFlow) detects the movement of people and objects. The output is the analyzed data, which includes suspicious movements and signs of anomalies.

[0156] Step 3:

[0157] The server executes an anomaly detection module based on the analysis results. In this step, it identifies anomalies that are different from normal from the detected movements and states. The input is the analyzed data obtained in step 2, and the output is information identifying the anomaly.

[0158] Step 4:

[0159] The server activates the emotion evaluation system when an anomaly is detected, and evaluates the user's emotional state. Using the analyzed anomaly information and past emotional data as input, it estimates the user's current emotional state. The output is an emotion evaluation result corresponding to the user's emotions.

[0160] Step 5:

[0161] The server generates appropriate notification content based on the sentiment evaluation results and sends it to the user. The input consists of the sentiment evaluation results and anomaly information, which are then used to create a notification with a tone and content appropriate for the user through a specific communication channel (e.g., a notification app). The output is the notification message provided to the user.

[0162] Step 6:

[0163] The user reviews the received notification and takes appropriate action as needed. In this step, the user checks the notification on their smartphone or tablet and plans and implements appropriate countermeasures based on the situation. The input is the notification message, and the output is the specific action the user takes.

[0164] (Application Example 2)

[0165] 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".

[0166] In modern society, community safety monitoring is a crucial issue, and there is a growing need for efficient and effective surveillance systems, particularly to guarantee the safety of children and vulnerable individuals. However, existing technologies have limitations in acquiring and analyzing surveillance data, and they cannot provide flexible information based on the user's psychological state.

[0167] 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.

[0168] In this invention, the server includes an acquisition means for acquiring video data, an analysis means for analyzing the acquired video data in real time, a detection means for detecting anomalies based on the analysis results, and a notification means including an emotion engine that evaluates the user's emotional state and adjusts the notification content when an anomaly is detected. This enables the collection and analysis of a wide range of real-time monitoring data and appropriate status notifications to the user.

[0169] "Acquisition means" refers to the function of collecting video data and providing it to a server in preparation for subsequent processing.

[0170] "Analysis means" refers to a device or function that analyzes acquired video data in real time and immediately evaluates whether or not there are any abnormalities.

[0171] A "detection mechanism" is a system that identifies anomalies based on the results obtained by the analysis mechanism and enables appropriate responses such as alerts.

[0172] A "notification system including an emotion engine" is a function that analyzes the user's emotional state when an anomaly is detected, and appropriately adjusts and provides the content and format of the notification according to that state.

[0173] In a mode for carrying out the invention, this system mainly consists of a server, a terminal, and a user. The following specific hardware and software are used to realize the operation of this system.

[0174] The server utilizes machine learning libraries such as TensorFlow to execute powerful AI algorithms. Video data is transmitted to the server in real time via devices such as drones and fixed cameras. The server uses OpenCV to capture the video, and machine learning models are used as analytical tools for anomaly detection. When an anomaly is detected, an emotion engine built within the server analyzes the user's voice tone and facial expressions, and appropriately adjusts the content and format of the notification.

[0175] The terminals, such as drones and fixed cameras, cover a wide area of ​​the target region and constantly provide the latest data to the server. Therefore, these terminals are an indispensable element for data acquisition.

[0176] Users receive information from the server via smartphones or tablets. Notifications allow users to receive immediate reports of anomalies, even from remote locations, enabling safe responses. Furthermore, notifications utilizing an emotion engine reduce the user's psychological burden, allowing them to deal with the situation more calmly.

[0177] For example, if a suspicious person is detected while a child is playing in a park, a gentle warning is automatically sent to the parent's smartphone. This notification is optimized for the parent's current emotional state and is designed to avoid unnecessary anxiety.

[0178] An example of a prompt statement used as input to a generative AI model is: "Generate a notification message that will allow you to calmly respond when a suspicious person is detected while a child is playing in a park using a monitoring system."

[0179] In this way, this system technically supports safe and secure monitoring activities.

[0180] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0181] Step 1:

[0182] The terminal (drone or fixed camera) acquires video data from a wide monitoring area. This video data is transmitted to the server in real time. The input is the surrounding video, and the output is the video data transmitted to the server. This makes it possible to continuously capture the situation in the monitored area.

[0183] Step 2:

[0184] The server analyzes received video data in real time using machine learning libraries such as TensorFlow. The input is video data sent from the terminal, and the output is features for anomaly detection. The server evaluates whether there are any anomalies based on the features and prepares for alerts.

[0185] Step 3:

[0186] The server performs anomaly detection based on the analysis results. The input is the features generated in step 2, and the output is a flag indicating the presence or absence of an anomaly. If an anomaly is detected, the process proceeds to the next step.

[0187] Step 4:

[0188] If an anomaly is detected, the server uses an emotion engine to evaluate the user's emotional state. The input is data on the user's voice tone and facial expressions, and the output is the evaluation result of the user's emotional state. This generates notification content that takes the user's stress level into consideration.

[0189] Step 5:

[0190] The server adjusts the notification content based on the emotion engine's evaluation results and sends the notification to the user's terminal in an appropriate format. The input is the result of anomaly detection and the emotion engine's evaluation, and the output is an optimized notification message for the user.

[0191] Step 6:

[0192] Users view received notifications on their smartphones or tablets and take appropriate action as needed. The input is the notification message from the server, and the output is the user's response action. This allows users to calmly respond to the situation.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Second Embodiment]

[0197] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0198] 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.

[0199] 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).

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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".

[0209] This invention is a monitoring system for supporting child safety activities in local communities, and has an overall system structure that includes acquisition means, analysis means, detection means, and notification means. This system functions primarily based on three elements: a server, a terminal, and a user.

[0210] The server functions as a central processor, receiving video data from terminals and performing real-time analysis using AI. The analysis tools within the server execute algorithms to detect suspicious behavior from the collected video. The data analyzed by these detection tools is immediately reported to the user via notification tools when an anomaly occurs. Furthermore, it is possible to automatically generate reports on daily observations and anomaly cases for administrators in the region where the system is implemented, and provide them via email or other means.

[0211] As terminals, drones and fixed cameras are responsible for collecting data over a wide area. Drones patrol pre-set routes and collect video data via acquisition devices during their movement. Fixed cameras, often installed at specific locations in a region, also play a role in continuous monitoring and transmitting video data to a server. In this way, terminals primarily function as acquisition devices, constantly monitoring the surrounding environment.

[0212] On the other hand, users can remotely check the monitoring results using a dedicated monitoring application and take prompt action as needed. Through this application, users can visually check the video transmitted in real time and have a means to directly notify local safety response agencies if an anomaly is detected. For example, users can check the safety of their school route via camera footage from their homes and immediately contact the appropriate agency using the in-app notification function if an anomaly is detected.

[0213] In this way, the server, terminal, and user elements work together to support sustainable monitoring activities. It is expected that the organic connection of the entire system will further enhance the safety of children in local communities.

[0214] The following describes the processing flow.

[0215] Step 1:

[0216] The device automatically activates according to a set schedule and collects video data from the monitored area. For example, a drone begins patrolling along a programmed flight route and continuously acquires video with its onboard camera.

[0217] Step 2:

[0218] The terminal immediately transmits the collected video data to the server. This transmission is performed using low-latency communication to enable real-time data analysis. The server prepares for processing while storing the received data in temporary storage.

[0219] Step 3:

[0220] The server analyzes the received video data in real time using AI algorithms. Here, it analyzes movement and people in the video to detect suspicious behavior and unusual patterns.

[0221] Step 4:

[0222] If the server detects any suspicious behavior as a result of the analysis, it immediately performs an anomaly detection. This detection information is logged in the internal system and sent to the notification process.

[0223] Step 5:

[0224] The server activates a notification system to alert the user about any detected anomalies. This sends real-time alerts to the user's monitoring application.

[0225] Step 6:

[0226] Users can review the details of alerts received through the application. They can use the video replay function to confirm the situation when an anomaly occurred and, if necessary, report it to dispatch the appropriate agency to the site.

[0227] Step 7:

[0228] Users send feedback to the server via the app once they have completed the necessary response measures for an anomaly. This feedback is then used to improve the overall monitoring accuracy and operational efficiency of the system.

[0229] (Example 1)

[0230] 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."

[0231] Ensuring safety in local communities requires widespread and continuous monitoring, rapid detection of anomalies, and appropriate responses. However, conventional monitoring systems have faced challenges such as limitations on camera placement, inaccuracies in anomaly detection, and difficulties in rapid response. As a result, timely information provision, particularly for protecting the safety of children, is often insufficient.

[0232] 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.

[0233] In this invention, the server includes information gathering means, information processing means, state detection means, information transmission means, and report generation means. This enables the collection and analysis of a wide range of information in real time, allowing for rapid detection of anomalies and immediate notification to users. Furthermore, the automatically generated reports allow local administrators to confirm the situation in detail, contributing to improved local safety.

[0234] "Video data" refers to visual information collected by cameras and sensors, and is the subject of analysis.

[0235] "Information gathering means" refers to devices and technologies used to acquire data, such as cameras and sensors.

[0236] "Information processing means" refers to computer systems and algorithms used to analyze collected data.

[0237] "State detection means" refers to algorithms and technologies used to identify abnormal events based on processed data.

[0238] "Information transmission means" refers to communication means used to transmit detected information or anomalies to other devices or users.

[0239] "Report generation means" refers to technology for creating reports that summarize information for administrators and users based on collected and analyzed data.

[0240] "Aircraft that move through the air" refers to devices that can fly and move, such as unmanned aerial vehicles like drones.

[0241] "Information presentation function" refers to a function that displays information in a way that users can understand and provides an interface for taking necessary actions.

[0242] In this invention, the server plays a central role in realizing safety monitoring in the local community. The server receives video data transmitted from terminals and analyzes that data in real time using AI. Specifically, it uses AI frameworks such as "TensorFlow" and "PyTorch" to perform object detection and abnormal behavior analysis on the received video data. This method enables highly accurate and rapid anomaly detection.

[0243] The devices used as terminals include fixed cameras and aircraft that move through the air. In particular, drones regularly orbit the area based on planned routes, collecting wide-area video data. This enables comprehensive data collection from both the ground and the air. Fixed cameras continuously collect video 24 hours a day at specific locations, providing a stable data supply.

[0244] Users can remotely view video data provided by the server using a dedicated monitoring application. The application visualizes real-time video and anomaly notifications, and has a mechanism that allows users to quickly contact local response agencies as needed. In addition, when an anomaly is detected, users can respond quickly using the integrated notification function within the app.

[0245] For example, users can view footage from cameras installed along school routes from the comfort of their homes and immediately notify patrol agencies if any anomalies are detected. This system is designed to support sustainable community watch activities and helps users and local administrators conduct safety monitoring efficiently.

[0246] An example of a prompt would be, "Please describe in detail how the AI ​​model for monitoring children in the community performs behavioral analysis, detects anomalies, and sends notifications." This prompt allows the generated AI model to provide a detailed description of the system.

[0247] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0248] Step 1:

[0249] The terminal collects video data using a camera and flight sensors. The input is real-time video from various locations within the region. The terminal captures this video data frame by frame and converts it into a digital format that can be sent to the server. The output is transferred to the server as compressed video data.

[0250] Step 2:

[0251] The server receives video data sent from the terminal. It prepares to run an AI algorithm to analyze the video data as input. Specifically, it uses an AI framework to start analyzing the received video. Data processing includes screen subdivision and feature extraction, and the analysis results are used in the next processing step as output.

[0252] Step 3:

[0253] The server uses an AI algorithm to analyze video data and detect suspicious behavior. The input is the video frames received in the previous step. The AI ​​model analyzes the movement and changes of objects to detect anomalies. The output generates information about the detected anomaly and its details, and the process proceeds to the next notification step.

[0254] Step 4:

[0255] The server notifies users of detected anomalies. The input for this step is the analyzed anomaly information. The server notifies the target users via monitoring applications or email. The output is an anomaly alert to the user.

[0256] Step 5:

[0257] Users check notifications through a monitoring application. Inputs include received anomaly reports and video data. Users review the video within the app and take action using the reporting function as needed. Outputs include sending notifications to local response agencies if necessary.

[0258] Step 6:

[0259] The server automatically generates daily reports based on all monitoring data and detected information. Inputs include collected video data and detection results. The server organizes the information, creates reports for administrators, and sends them via email. Outputs are detailed reports on the local safety situation.

[0260] (Application Example 1)

[0261] 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."

[0262] To ensure the safety of local communities, a system is needed that can monitor the surrounding environment in real time and respond quickly and appropriately when an anomaly is detected. However, conventional systems have difficulty monitoring, especially while on the move, and responding immediately, which can sometimes prevent early response to anomalies. In addition, there is a need for new methods to easily acquire and analyze information while monitoring a wide area.

[0263] 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.

[0264] In this invention, the server includes means for acquiring video information, means for analyzing the acquired video information in real time, means for detecting anomalies based on the analysis results, and means for providing a user interface that allows the user to check the surrounding situation in real time using a visual device and report anomalies. This enables the user to monitor the safety of their surroundings even while on the move and to take immediate action if an anomaly is detected.

[0265] "Visual information" refers to video or still image data captured by cameras or other visual devices and processed in digital format.

[0266] "Means" refer to the devices or methods used to achieve a specific purpose, and are elements within a system that perform a specific function.

[0267] "Analysis means" refers to a method or apparatus used to analyze acquired data and understand its contents.

[0268] "Visual devices" are devices used by users to visually perceive their external environment, and include smart glasses and headsets.

[0269] A "user interface" is a means of interaction used when information is exchanged between a system and a user.

[0270] An "abnormality" refers to a situation that deviates from the normal operation or expected state of a system, and is an operation or state that may threaten safety.

[0271] An "unmanned aerial vehicle" is an aircraft that operates remotely or automatically without human control and is used for data collection and surveillance tasks.

[0272] This invention is a security system that uses visual devices to monitor the surroundings and provides real-time notifications when an anomaly is detected. The server is the central processing unit that acquires video information and analyzes that data. Specifically, the server receives and processes video data transmitted from smart glasses or other visual devices in real time. Using an AI model installed on the server (e.g., TensorFlow or PyTorch), the system analyzes the video data and detects suspicious behavior or anomalies.

[0273] Smart glasses, acting as a terminal device, are designed to allow users to monitor their surroundings even while on the move. A camera equipped in the device acquires video data for a library and transmits it to a server via Wi-Fi or Bluetooth. If an anomaly is detected, a notification is displayed on the glasses' screen, allowing the user to quickly respond while checking the visual information. The user interface incorporates mechanisms for rapid anomaly reporting and additional verification operations through voice recognition and simple gestures.

[0274] A concrete example of implementing the system is a scenario where security guards on nighttime patrols wear smart glasses. The guards use the device to check for safety while walking around, and if a suspicious person or intruder is detected, a notification such as "Intruder detected, warning notification sent" is immediately displayed in a visible format. An example of a prompt message in this case would be, "Monitor your surroundings through your smart glasses and report any suspicious activity."

[0275] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0276] Step 1:

[0277] The device continuously acquires surrounding video information using the camera of the visual device. It receives real-time video of the visual environment displayed on the device as input and converts it into digital data. The output is the acquired video data.

[0278] Step 2:

[0279] The terminal transmits the acquired video data to the server via wireless communication (e.g., Wi-Fi or Bluetooth). This process uses the video data acquired by the terminal as input. The output is the video data received by the server.

[0280] Step 3:

[0281] The server analyzes the received video data using an AI model (e.g., TensorFlow or PyTorch). The AI ​​model receives video data as input and analyzes movement, people, and other important features within the video. The output consists of the analyzed information and anomaly detection results.

[0282] Step 4:

[0283] The server detects the presence or absence of anomalies based on the analysis results. It uses the analysis results from the AI model as input. The output is alert information when an anomaly is detected. At this stage, specific actions or situations determined to be anomalies are identified.

[0284] Step 5:

[0285] When the server detects an anomaly, it notifies the visual device of the terminal of the detailed information. A warning message is instantly displayed on the display for the user. It uses the anomaly detection information as input. The output is a visual notification visible to the user.

[0286] Step 6:

[0287] The user receives the notification and reports the anomaly or performs further actions using voice commands or gestures as needed. The input includes the user's interaction based on the notification of the visual device. The output is the reported information and additional operation instructions.

[0288] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0289] The present invention is a system for supporting the child monitoring activities in the region, which combines an acquisition means, an analysis means, a detection means, a notification means, and an emotion engine. This system is composed of elements such as a server, a terminal, and a user.

[0290] The server receives video data transmitted from the terminal and performs real-time data analysis using AI algorithms. It also uses an emotion engine to evaluate the user's emotions based on the results of anomaly detection. The emotion engine detects the user's emotional state and adjusts the content and format of notifications accordingly. This evaluation incorporates features that sense the user's voice tone and facial expressions, enabling optimized notifications. For example, if the system evaluates the user as fatigued, it softens the notification tone and provides essential information concisely.

[0291] The drones and fixed cameras, acting as terminals, acquire data over a wide area and transmit that data to the server in real time. This allows them to serve as data acquisition tools, continuously monitoring video footage of the target area. Furthermore, the terminals autonomously detect predetermined patrol routes while efficiently collecting data.

[0292] Users can remotely view information notified by the system using a monitoring application. They can understand the video and notification content received in real time, and if an anomaly is detected, they can take appropriate action quickly. For example, when a user receives a danger notification via smartphone from work, the notification is designed with consideration for reducing stress through an emotion engine, allowing them to calmly assess the situation and take necessary safety measures.

[0293] In this way, the functions of acquisition, analysis, detection, notification, and emotion engine are integrated, reducing the burden on the user while enabling sustainable and reliable monitoring activities. This system allows communities to create a safer and more protected environment.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The terminal activates drones and fixed cameras according to a pre-set schedule to collect video data from the surveillance area. The drones patrol along designated flight routes and save the footage captured by the cameras to memory.

[0297] Step 2:

[0298] The terminal transmits the collected video data to the server in real time. The data is streamed with low latency via wireless communication technology as a video signal.

[0299] Step 3:

[0300] The server processes the received video data using an AI analysis module, recognizing people and actions while analyzing for any abnormal behavior. If the analysis detects suspicious behavior in a specific location, it triggers an alert.

[0301] Step 4:

[0302] The server activates the emotion engine based on the anomaly detection results and evaluates the user's emotional state. This evaluation includes a process of determining appropriate notification content, taking into account past user responses and historical data.

[0303] Step 5:

[0304] The server sends optimized notifications to the user based on the results of the emotion engine's evaluation. For example, if the server detects that the user is in a high-stress state, the notification will be sent in a considerate format and content.

[0305] Step 6:

[0306] The user reviews the notification received through a dedicated application and confirms the situation on site by replaying the provided video. If necessary, the user then makes the appropriate report to the local response agency.

[0307] Step 7:

[0308] After the correspondence, the user provides feedback to the system and sends information to the server for the emotion engine to utilize in subsequent notification optimization. This improves the adaptability of the system and user satisfaction.

[0309] (Example 2)

[0310] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In the activity of monitoring children in a region, there is a need to provide a method that can achieve efficient situation monitoring over a wide area without imposing an excessive burden on users. In conventional monitoring systems, timely and appropriate notifications were not made in response to abnormalities, resulting in an increase in the psychological burden on users.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0313] In this invention, the server includes a device for acquiring information, a processing device for analyzing the acquired information in real time, a device for detecting abnormalities based on the analysis results, an emotion evaluation device for evaluating the user's emotional state, and a device having a function of adjusting the notification content based on the emotion evaluation results. This enables safe and rapid response to abnormalities and provides notifications with reduced psychological burden on the user.

[0314] The "device for acquiring information" refers to means for collecting data over a wide area and is composed of aircraft and stationary devices.

[0315] The "processing device for analyzing the acquired information in real time" refers to a computer system that immediately analyzes the received data and is realized using machine learning algorithms.

[0316] An "anomaly detection device" is a mechanism that recognizes unusual situations based on the results of information analysis and identifies those events.

[0317] An "emotion evaluation device" is a system used to analyze a user's voice tone and past data to infer their emotional state.

[0318] A "device equipped with a function to adjust notification content" is a mechanism for optimizing the content and method of notifications to the user based on the results of sentiment evaluation.

[0319] In order to implement this invention, it is necessary to construct a system in which three elements—a server, a terminal, and a user—cooperate with each other.

[0320] First, the terminal will be equipped with aircraft or stationary devices to acquire information over a wide area. These devices will autonomously patrol a designated area and collect the necessary data. It is possible to acquire images and video data of the monitoring area using equipment such as drones and fixed cameras. A specific example would be cameras placed around a school to check on the safety of children as they go to school.

[0321] Next, the server receives information transmitted from these terminals in real time and performs a computer-based analysis on the received data. It is preferable to use TensorFlow or PyTorch as the machine learning platform. An AI algorithm analyzes people and movements in the video, constantly monitoring for any abnormal behavior.

[0322] When an anomaly is detected, the server activates an emotion assessment system. This system generates appropriate notification content based on the user's tone of voice and past emotion history. Through this assessment, a relaxed tone and concise information delivery are achieved to reduce the user's psychological burden.

[0323] Users can receive system notifications remotely using their smartphones or tablets. They can open a monitoring application to instantly check the situation and take swift action in case of anomalies. For example, if a child enters a dangerous area, they can receive a notification that includes appropriate evacuation instructions, enabling rapid rescue.

[0324] Examples of prompts to input into the generating AI model include, "How can we optimize the anomaly detection process in the monitoring system?" Based on this prompt, a more efficient monitoring plan will be proposed.

[0325] In this way, the server, terminal, and user components function together, enabling safe monitoring activities in the local community.

[0326] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0327] Step 1:

[0328] The terminal acquires video data from a designated monitoring area using aircraft or stationary equipment. In this step, the acquisition device autonomously patrols the monitoring area, continuously capturing data with cameras and sensors. The input is real-time video of the monitored area, and the output is a data stream sent to the server.

[0329] Step 2:

[0330] The server receives video data transmitted from the terminal in real time and analyzes the images using an AI algorithm. The input is a video data stream, and the AI ​​model (e.g., using TensorFlow) detects the movement of people and objects. The output is the analyzed data, which includes suspicious movements and signs of anomalies.

[0331] Step 3:

[0332] The server executes an anomaly detection module based on the analysis results. In this step, it identifies anomalies that are different from normal from the detected movements and states. The input is the analyzed data obtained in step 2, and the output is information identifying the anomaly.

[0333] Step 4:

[0334] The server activates the emotion evaluation system when an anomaly is detected, and evaluates the user's emotional state. Using the analyzed anomaly information and past emotional data as input, it estimates the user's current emotional state. The output is an emotion evaluation result corresponding to the user's emotions.

[0335] Step 5:

[0336] The server generates appropriate notification content based on the sentiment evaluation results and sends it to the user. The input consists of the sentiment evaluation results and anomaly information, which are then used to create a notification with a tone and content appropriate for the user through a specific communication channel (e.g., a notification app). The output is the notification message provided to the user.

[0337] Step 6:

[0338] The user reviews the received notification and takes appropriate action as needed. In this step, the user checks the notification on their smartphone or tablet and plans and implements appropriate countermeasures based on the situation. The input is the notification message, and the output is the specific action the user takes.

[0339] (Application Example 2)

[0340] 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 as the "terminal".

[0341] In modern society, community safety monitoring is a crucial issue, and there is a growing need for efficient and effective surveillance systems, particularly to guarantee the safety of children and vulnerable individuals. However, existing technologies have limitations in acquiring and analyzing surveillance data, and they cannot provide flexible information based on the user's psychological state.

[0342] 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.

[0343] In this invention, the server includes an acquisition means for acquiring video data, an analysis means for analyzing the acquired video data in real time, a detection means for detecting anomalies based on the analysis results, and a notification means including an emotion engine that evaluates the user's emotional state and adjusts the notification content when an anomaly is detected. This enables the collection and analysis of a wide range of real-time monitoring data and appropriate status notifications to the user.

[0344] "Acquisition means" refers to the function of collecting video data and providing it to a server in preparation for subsequent processing.

[0345] "Analysis means" refers to a device or function that analyzes acquired video data in real time and immediately evaluates whether or not there are any abnormalities.

[0346] A "detection mechanism" is a system that identifies anomalies based on the results obtained by the analysis mechanism and enables appropriate responses such as alerts.

[0347] A "notification system including an emotion engine" is a function that analyzes the user's emotional state when an anomaly is detected, and appropriately adjusts and provides the content and format of the notification according to that state.

[0348] In a mode for carrying out the invention, this system mainly consists of a server, a terminal, and a user. The following specific hardware and software are used to realize the operation of this system.

[0349] The server utilizes machine learning libraries such as TensorFlow to execute powerful AI algorithms. Video data is transmitted to the server in real time via devices such as drones and fixed cameras. The server uses OpenCV to capture the video, and machine learning models are used as analytical tools for anomaly detection. When an anomaly is detected, an emotion engine built within the server analyzes the user's voice tone and facial expressions, and appropriately adjusts the content and format of the notification.

[0350] The terminals, such as drones and fixed cameras, cover a wide area of ​​the target region and constantly provide the latest data to the server. Therefore, these terminals are an indispensable element for data acquisition.

[0351] Users receive information from the server via smartphones or tablets. Notifications allow users to receive immediate reports of anomalies, even from remote locations, enabling safe responses. Furthermore, notifications utilizing an emotion engine reduce the user's psychological burden, allowing them to deal with the situation more calmly.

[0352] For example, if a suspicious person is detected while a child is playing in a park, a gentle warning is automatically sent to the parent's smartphone. This notification is optimized for the parent's current emotional state and is designed to avoid unnecessary anxiety.

[0353] An example of a prompt statement used as input to a generative AI model is: "Generate a notification message that will allow you to calmly respond when a suspicious person is detected while a child is playing in a park using a monitoring system."

[0354] In this way, this system technically supports safe and secure monitoring activities.

[0355] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0356] Step 1:

[0357] The terminal (drone or fixed camera) acquires video data from a wide monitoring area. This video data is transmitted to the server in real time. The input is the surrounding video, and the output is the video data transmitted to the server. This makes it possible to continuously capture the situation in the monitored area.

[0358] Step 2:

[0359] The server analyzes received video data in real time using machine learning libraries such as TensorFlow. The input is video data sent from the terminal, and the output is features for anomaly detection. The server evaluates whether there are any anomalies based on the features and prepares for alerts.

[0360] Step 3:

[0361] The server performs anomaly detection based on the analysis results. The input is the features generated in step 2, and the output is a flag indicating the presence or absence of an anomaly. If an anomaly is detected, the process proceeds to the next step.

[0362] Step 4:

[0363] If an anomaly is detected, the server uses an emotion engine to evaluate the user's emotional state. The input is data on the user's voice tone and facial expressions, and the output is the evaluation result of the user's emotional state. This generates notification content that takes the user's stress level into consideration.

[0364] Step 5:

[0365] The server adjusts the notification content based on the emotion engine's evaluation results and sends the notification to the user's terminal in an appropriate format. The input is the result of anomaly detection and the emotion engine's evaluation, and the output is an optimized notification message for the user.

[0366] Step 6:

[0367] Users view received notifications on their smartphones or tablets and take appropriate action as needed. The input is the notification message from the server, and the output is the user's response action. This allows users to calmly respond to the situation.

[0368] 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.

[0369] 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.

[0370] 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.

[0371] [Third Embodiment]

[0372] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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).

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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".

[0384] This invention is a monitoring system for supporting child safety activities in local communities, and has an overall system structure that includes acquisition means, analysis means, detection means, and notification means. This system functions primarily based on three elements: a server, a terminal, and a user.

[0385] The server functions as a central processor, receiving video data from terminals and performing real-time analysis using AI. The analysis tools within the server execute algorithms to detect suspicious behavior from the collected video. The data analyzed by these detection tools is immediately reported to the user via notification tools when an anomaly occurs. Furthermore, it is possible to automatically generate reports on daily observations and anomaly cases for administrators in the region where the system is implemented, and provide them via email or other means.

[0386] As terminals, drones and fixed cameras are responsible for collecting data over a wide area. Drones patrol pre-set routes and collect video data via acquisition devices during their movement. Fixed cameras, often installed at specific locations in a region, also play a role in continuous monitoring and transmitting video data to a server. In this way, terminals primarily function as acquisition devices, constantly monitoring the surrounding environment.

[0387] On the other hand, users can remotely check the monitoring results using a dedicated monitoring application and take prompt action as needed. Through this application, users can visually check the video transmitted in real time and have a means to directly notify local safety response agencies if an anomaly is detected. For example, users can check the safety of their school route via camera footage from their homes and immediately contact the appropriate agency using the in-app notification function if an anomaly is detected.

[0388] In this way, the server, terminal, and user elements work together to support sustainable monitoring activities. It is expected that the organic connection of the entire system will further enhance the safety of children in local communities.

[0389] The following describes the processing flow.

[0390] Step 1:

[0391] The device automatically activates according to a set schedule and collects video data from the monitored area. For example, a drone begins patrolling along a programmed flight route and continuously acquires video with its onboard camera.

[0392] Step 2:

[0393] The terminal immediately transmits the collected video data to the server. This transmission is performed using low-latency communication to enable real-time data analysis. The server prepares for processing while storing the received data in temporary storage.

[0394] Step 3:

[0395] The server analyzes the received video data in real time using AI algorithms. Here, it analyzes movement and people in the video to detect suspicious behavior and unusual patterns.

[0396] Step 4:

[0397] If the server detects any suspicious behavior as a result of the analysis, it immediately performs an anomaly detection. This detection information is logged in the internal system and sent to the notification process.

[0398] Step 5:

[0399] The server activates a notification system to alert the user about any detected anomalies. This sends real-time alerts to the user's monitoring application.

[0400] Step 6:

[0401] Users can review the details of alerts received through the application. They can use the video replay function to confirm the situation when an anomaly occurred and, if necessary, report it to dispatch the appropriate agency to the site.

[0402] Step 7:

[0403] Users send feedback to the server via the app once they have completed the necessary response measures for an anomaly. This feedback is then used to improve the overall monitoring accuracy and operational efficiency of the system.

[0404] (Example 1)

[0405] 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."

[0406] Ensuring safety in local communities requires widespread and continuous monitoring, rapid detection of anomalies, and appropriate responses. However, conventional monitoring systems have faced challenges such as limitations on camera placement, inaccuracies in anomaly detection, and difficulties in rapid response. As a result, timely information provision, particularly for protecting the safety of children, is often insufficient.

[0407] 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.

[0408] In this invention, the server includes information gathering means, information processing means, state detection means, information transmission means, and report generation means. This enables the collection and analysis of a wide range of information in real time, allowing for rapid detection of anomalies and immediate notification to users. Furthermore, the automatically generated reports allow local administrators to confirm the situation in detail, contributing to improved local safety.

[0409] "Video data" refers to visual information collected by cameras and sensors, and is the subject of analysis.

[0410] "Information gathering means" refers to devices and technologies used to acquire data, such as cameras and sensors.

[0411] "Information processing means" refers to computer systems and algorithms used to analyze collected data.

[0412] "State detection means" refers to algorithms and technologies used to identify abnormal events based on processed data.

[0413] "Information transmission means" refers to communication means used to transmit detected information or anomalies to other devices or users.

[0414] "Report generation means" refers to technology for creating reports that summarize information for administrators and users based on collected and analyzed data.

[0415] "Aircraft that move through the air" refers to devices that can fly and move, such as unmanned aerial vehicles like drones.

[0416] "Information presentation function" refers to a function that displays information in a way that users can understand and provides an interface for taking necessary actions.

[0417] In this invention, the server plays a central role in realizing safety monitoring in the local community. The server receives video data transmitted from terminals and analyzes that data in real time using AI. Specifically, it uses AI frameworks such as "TensorFlow" and "PyTorch" to perform object detection and abnormal behavior analysis on the received video data. This method enables highly accurate and rapid anomaly detection.

[0418] The devices used as terminals include fixed cameras and aircraft that move through the air. In particular, drones regularly orbit the area based on planned routes, collecting wide-area video data. This enables comprehensive data collection from both the ground and the air. Fixed cameras continuously collect video 24 hours a day at specific locations, providing a stable data supply.

[0419] Users can remotely view video data provided by the server using a dedicated monitoring application. The application visualizes real-time video and anomaly notifications, and has a mechanism that allows users to quickly contact local response agencies as needed. In addition, when an anomaly is detected, users can respond quickly using the integrated notification function within the app.

[0420] For example, users can view footage from cameras installed along school routes from the comfort of their homes and immediately notify patrol agencies if any anomalies are detected. This system is designed to support sustainable community watch activities and helps users and local administrators conduct safety monitoring efficiently.

[0421] An example of a prompt would be, "Please describe in detail how the AI ​​model for monitoring children in the community performs behavioral analysis, detects anomalies, and sends notifications." This prompt allows the generated AI model to provide a detailed description of the system.

[0422] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0423] Step 1:

[0424] The terminal collects video data using a camera and flight sensors. The input is real-time video from various locations within the region. The terminal captures this video data frame by frame and converts it into a digital format that can be sent to the server. The output is transferred to the server as compressed video data.

[0425] Step 2:

[0426] The server receives video data sent from the terminal. It prepares to run an AI algorithm to analyze the video data as input. Specifically, it uses an AI framework to start analyzing the received video. Data processing includes screen subdivision and feature extraction, and the analysis results are used in the next processing step as output.

[0427] Step 3:

[0428] The server uses an AI algorithm to analyze video data and detect suspicious behavior. The input is the video frames received in the previous step. The AI ​​model analyzes the movement and changes of objects to detect anomalies. The output generates information about the detected anomaly and its details, and the process proceeds to the next notification step.

[0429] Step 4:

[0430] The server notifies users of detected anomalies. The input for this step is the analyzed anomaly information. The server notifies the target users via monitoring applications or email. The output is an anomaly alert to the user.

[0431] Step 5:

[0432] Users check notifications through a monitoring application. Inputs include received anomaly reports and video data. Users review the video within the app and take action using the reporting function as needed. Outputs include sending notifications to local response agencies if necessary.

[0433] Step 6:

[0434] The server automatically generates daily reports based on all monitoring data and detected information. Inputs include collected video data and detection results. The server organizes the information, creates reports for administrators, and sends them via email. Outputs are detailed reports on the local safety situation.

[0435] (Application Example 1)

[0436] 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."

[0437] To ensure the safety of local communities, a system is needed that can monitor the surrounding environment in real time and respond quickly and appropriately when an anomaly is detected. However, conventional systems have difficulty monitoring, especially while on the move, and responding immediately, which can sometimes prevent early response to anomalies. In addition, there is a need for new methods to easily acquire and analyze information while monitoring a wide area.

[0438] 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.

[0439] In this invention, the server includes means for acquiring video information, means for analyzing the acquired video information in real time, means for detecting anomalies based on the analysis results, and means for providing a user interface that allows the user to check the surrounding situation in real time using a visual device and report anomalies. This enables the user to monitor the safety of their surroundings even while on the move and to take immediate action if an anomaly is detected.

[0440] "Visual information" refers to video or still image data captured by cameras or other visual devices and processed in digital format.

[0441] "Means" refer to the devices or methods used to achieve a specific purpose, and are elements within a system that perform a specific function.

[0442] "Analysis means" refers to a method or apparatus used to analyze acquired data and understand its contents.

[0443] "Visual devices" are devices used by users to visually perceive their external environment, and include smart glasses and headsets.

[0444] A "user interface" is a means of interaction used when information is exchanged between a system and a user.

[0445] An "abnormality" refers to a situation that deviates from the normal operation or expected state of a system, and is an operation or state that may threaten safety.

[0446] An "unmanned aerial vehicle" is an aircraft that operates remotely or automatically without human control and is used for data collection and surveillance tasks.

[0447] This invention is a security system that uses visual devices to monitor the surroundings and provides real-time notifications when an anomaly is detected. The server is the central processing unit that acquires video information and analyzes that data. Specifically, the server receives and processes video data transmitted from smart glasses or other visual devices in real time. Using an AI model installed on the server (e.g., TensorFlow or PyTorch), the system analyzes the video data and detects suspicious behavior or anomalies.

[0448] Smart glasses, acting as a terminal device, are designed to allow users to monitor their surroundings even while on the move. A camera equipped in the device acquires video data for a library and transmits it to a server via Wi-Fi or Bluetooth. If an anomaly is detected, a notification is displayed on the glasses' screen, allowing the user to quickly respond while checking the visual information. The user interface incorporates mechanisms for rapid anomaly reporting and additional verification operations through voice recognition and simple gestures.

[0449] A concrete example of implementing the system is a scenario where security guards on nighttime patrols wear smart glasses. The guards use the device to check for safety while walking around, and if a suspicious person or intruder is detected, a notification such as "Intruder detected, warning notification sent" is immediately displayed in a visible format. An example of a prompt message in this case would be, "Monitor your surroundings through your smart glasses and report any suspicious activity."

[0450] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0451] Step 1:

[0452] The device continuously acquires surrounding video information using the camera of the visual device. It receives real-time video of the visual environment displayed on the device as input and converts it into digital data. The output is the acquired video data.

[0453] Step 2:

[0454] The terminal transmits the acquired video data to the server via wireless communication (e.g., Wi-Fi or Bluetooth). This process uses the video data acquired by the terminal as input. The output is the video data received by the server.

[0455] Step 3:

[0456] The server analyzes the received video data using an AI model (e.g., TensorFlow or PyTorch). The AI ​​model receives video data as input and analyzes movement, people, and other important features within the video. The output consists of the analyzed information and anomaly detection results.

[0457] Step 4:

[0458] The server detects the presence or absence of anomalies based on the analysis results. It uses the analysis results from an AI model as input. The output is alert information if an anomaly is detected. At this stage, the specific behavior or situation deemed anomaly is identified.

[0459] Step 5:

[0460] If an anomaly is detected, the server notifies the terminal's visual device of the detailed information. A warning message is instantly displayed on the screen for the user. The input is the anomaly detection information. The output is a visual notification that the user can see.

[0461] Step 6:

[0462] The user receives notifications and, if necessary, uses voice commands or gestures to report anomalies or take further action. Input includes user interaction based on notifications from the visual device. Output is reported information and additional instructions.

[0463] 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.

[0464] This invention is a system for supporting community child safety monitoring activities, and combines acquisition means, analysis means, detection means, notification means, and an emotion engine. This system consists of a server, a terminal, and a user.

[0465] The server receives video data transmitted from the terminal and performs real-time data analysis using AI algorithms. It also uses an emotion engine to evaluate the user's emotions based on the results of anomaly detection. The emotion engine detects the user's emotional state and adjusts the content and format of notifications accordingly. This evaluation incorporates features that sense the user's voice tone and facial expressions, enabling optimized notifications. For example, if the system evaluates the user as fatigued, it softens the notification tone and provides essential information concisely.

[0466] The drones and fixed cameras, acting as terminals, acquire data over a wide area and transmit that data to the server in real time. This allows them to serve as data acquisition tools, continuously monitoring video footage of the target area. Furthermore, the terminals autonomously detect predetermined patrol routes while efficiently collecting data.

[0467] Users can remotely view information notified by the system using a monitoring application. They can understand the video and notification content received in real time, and if an anomaly is detected, they can take appropriate action quickly. For example, when a user receives a danger notification via smartphone from work, the notification is designed with consideration for reducing stress through an emotion engine, allowing them to calmly assess the situation and take necessary safety measures.

[0468] In this way, the functions of acquisition, analysis, detection, notification, and emotion engine are integrated, reducing the burden on the user while enabling sustainable and reliable monitoring activities. This system allows communities to create a safer and more protected environment.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The terminal activates drones and fixed cameras according to a pre-set schedule to collect video data from the surveillance area. The drones patrol along designated flight routes and save the footage captured by the cameras to memory.

[0472] Step 2:

[0473] The terminal transmits the collected video data to the server in real time. The data is streamed with low latency via wireless communication technology as a video signal.

[0474] Step 3:

[0475] The server processes the received video data using an AI analysis module, recognizing people and actions while analyzing for any abnormal behavior. If the analysis detects suspicious behavior in a specific location, it triggers an alert.

[0476] Step 4:

[0477] The server activates the emotion engine based on the anomaly detection results and evaluates the user's emotional state. This evaluation includes a process of determining appropriate notification content, taking into account past user responses and historical data.

[0478] Step 5:

[0479] The server sends optimized notifications to the user based on the results of the emotion engine's evaluation. For example, if the server detects that the user is in a high-stress state, the notification will be sent in a considerate format and content.

[0480] Step 6:

[0481] The user reviews the notification received through a dedicated application and confirms the situation on site by replaying the provided video. If necessary, the user then makes the appropriate report to the local response agency.

[0482] Step 7:

[0483] After an interaction, the user provides feedback to the system, sending information to the server that the emotion engine uses to optimize future notifications. This improves the system's adaptability and user satisfaction.

[0484] (Example 2)

[0485] 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."

[0486] In community-based child safety monitoring activities, there is a need to provide a method that enables efficient situational monitoring over a wide area without placing an excessive burden on users. Conventional monitoring systems have the problem of not providing timely and appropriate notifications in the event of an emergency, which increases the psychological burden on users.

[0487] 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.

[0488] In this invention, the server includes a device for acquiring information, a processing device for analyzing the acquired information in real time, a device for detecting anomalies based on the analysis results, an emotion evaluation device for evaluating the user's emotional state, and a device with a function for adjusting notification content based on the emotion evaluation results. This enables safe and rapid response to anomalies and notification provision that reduces the psychological burden on the user.

[0489] "Information acquisition devices" refer to means of collecting data over a wide range of areas, and are composed of aircraft and stationary equipment.

[0490] A "processing device that analyzes acquired information in real time" refers to a computer system that immediately analyzes received data, and is implemented using machine learning algorithms.

[0491] An "anomaly detection device" is a mechanism that recognizes unusual situations based on the results of information analysis and identifies those events.

[0492] An "emotion evaluation device" is a system used to analyze a user's voice tone and past data to infer their emotional state.

[0493] A "device equipped with a function to adjust notification content" is a mechanism for optimizing the content and method of notifications to the user based on the results of sentiment evaluation.

[0494] In order to implement this invention, it is necessary to construct a system in which three elements—a server, a terminal, and a user—cooperate with each other.

[0495] First, the terminal will be equipped with aircraft or stationary devices to acquire information over a wide area. These devices will autonomously patrol a designated area and collect the necessary data. It is possible to acquire images and video data of the monitoring area using equipment such as drones and fixed cameras. A specific example would be cameras placed around a school to check on the safety of children as they go to school.

[0496] Next, the server receives information transmitted from these terminals in real time and performs a computer-based analysis on the received data. It is preferable to use TensorFlow or PyTorch as the machine learning platform. An AI algorithm analyzes people and movements in the video, constantly monitoring for any abnormal behavior.

[0497] When an anomaly is detected, the server activates an emotion assessment system. This system generates appropriate notification content based on the user's tone of voice and past emotion history. Through this assessment, a relaxed tone and concise information delivery are achieved to reduce the user's psychological burden.

[0498] Users can receive system notifications remotely using their smartphones or tablets. They can open a monitoring application to instantly check the situation and take swift action in case of anomalies. For example, if a child enters a dangerous area, they can receive a notification that includes appropriate evacuation instructions, enabling rapid rescue.

[0499] Examples of prompts to input into the generating AI model include, "How can we optimize the anomaly detection process in the monitoring system?" Based on this prompt, a more efficient monitoring plan will be proposed.

[0500] In this way, the server, terminal, and user components function together, enabling safe monitoring activities in the local community.

[0501] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0502] Step 1:

[0503] The terminal acquires video data from a designated monitoring area using aircraft or stationary equipment. In this step, the acquisition device autonomously patrols the monitoring area, continuously capturing data with cameras and sensors. The input is real-time video of the monitored area, and the output is a data stream sent to the server.

[0504] Step 2:

[0505] The server receives video data transmitted from the terminal in real time and analyzes the images using an AI algorithm. The input is a video data stream, and the AI ​​model (e.g., using TensorFlow) detects the movement of people and objects. The output is the analyzed data, which includes suspicious movements and signs of anomalies.

[0506] Step 3:

[0507] The server executes an anomaly detection module based on the analysis results. In this step, it identifies anomalies that are different from normal from the detected movements and states. The input is the analyzed data obtained in step 2, and the output is information identifying the anomaly.

[0508] Step 4:

[0509] The server activates the emotion evaluation system when an anomaly is detected, and evaluates the user's emotional state. Using the analyzed anomaly information and past emotional data as input, it estimates the user's current emotional state. The output is an emotion evaluation result corresponding to the user's emotions.

[0510] Step 5:

[0511] The server generates appropriate notification content based on the sentiment evaluation results and sends it to the user. The input consists of the sentiment evaluation results and anomaly information, which are then used to create a notification with a tone and content appropriate for the user through a specific communication channel (e.g., a notification app). The output is the notification message provided to the user.

[0512] Step 6:

[0513] The user reviews the received notification and takes appropriate action as needed. In this step, the user checks the notification on their smartphone or tablet and plans and implements appropriate countermeasures based on the situation. The input is the notification message, and the output is the specific action the user takes.

[0514] (Application Example 2)

[0515] 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."

[0516] In modern society, community safety monitoring is a crucial issue, and there is a growing need for efficient and effective surveillance systems, particularly to guarantee the safety of children and vulnerable individuals. However, existing technologies have limitations in acquiring and analyzing surveillance data, and they cannot provide flexible information based on the user's psychological state.

[0517] 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.

[0518] In this invention, the server includes an acquisition means for acquiring video data, an analysis means for analyzing the acquired video data in real time, a detection means for detecting anomalies based on the analysis results, and a notification means including an emotion engine that evaluates the user's emotional state and adjusts the notification content when an anomaly is detected. This enables the collection and analysis of a wide range of real-time monitoring data and appropriate status notifications to the user.

[0519] "Acquisition means" refers to the function of collecting video data and providing it to a server in preparation for subsequent processing.

[0520] "Analysis means" refers to a device or function that analyzes acquired video data in real time and immediately evaluates whether or not there are any abnormalities.

[0521] A "detection mechanism" is a system that identifies anomalies based on the results obtained by the analysis mechanism and enables appropriate responses such as alerts.

[0522] A "notification system including an emotion engine" is a function that analyzes the user's emotional state when an anomaly is detected, and appropriately adjusts and provides the content and format of the notification according to that state.

[0523] In a mode for carrying out the invention, this system mainly consists of a server, a terminal, and a user. The following specific hardware and software are used to realize the operation of this system.

[0524] The server utilizes machine learning libraries such as TensorFlow to execute powerful AI algorithms. Video data is transmitted to the server in real time via devices such as drones and fixed cameras. The server uses OpenCV to capture the video, and machine learning models are used as analytical tools for anomaly detection. When an anomaly is detected, an emotion engine built within the server analyzes the user's voice tone and facial expressions, and appropriately adjusts the content and format of the notification.

[0525] The terminals, such as drones and fixed cameras, cover a wide area of ​​the target region and constantly provide the latest data to the server. Therefore, these terminals are an indispensable element for data acquisition.

[0526] Users receive information from the server via smartphones or tablets. Notifications allow users to receive immediate reports of anomalies, even from remote locations, enabling safe responses. Furthermore, notifications utilizing an emotion engine reduce the user's psychological burden, allowing them to deal with the situation more calmly.

[0527] For example, if a suspicious person is detected while a child is playing in a park, a gentle warning is automatically sent to the parent's smartphone. This notification is optimized for the parent's current emotional state and is designed to avoid unnecessary anxiety.

[0528] An example of a prompt statement used as input to a generative AI model is: "Generate a notification message that will allow you to calmly respond when a suspicious person is detected while a child is playing in a park using a monitoring system."

[0529] In this way, this system technically supports safe and secure monitoring activities.

[0530] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0531] Step 1:

[0532] The terminal (drone or fixed camera) acquires video data from a wide monitoring area. This video data is transmitted to the server in real time. The input is the surrounding video, and the output is the video data transmitted to the server. This makes it possible to continuously capture the situation in the monitored area.

[0533] Step 2:

[0534] The server analyzes received video data in real time using machine learning libraries such as TensorFlow. The input is video data sent from the terminal, and the output is features for anomaly detection. The server evaluates whether there are any anomalies based on the features and prepares for alerts.

[0535] Step 3:

[0536] The server performs anomaly detection based on the analysis results. The input is the features generated in step 2, and the output is a flag indicating the presence or absence of an anomaly. If an anomaly is detected, the process proceeds to the next step.

[0537] Step 4:

[0538] If an anomaly is detected, the server uses an emotion engine to evaluate the user's emotional state. The input is data on the user's voice tone and facial expressions, and the output is the evaluation result of the user's emotional state. This generates notification content that takes the user's stress level into consideration.

[0539] Step 5:

[0540] The server adjusts the notification content based on the emotion engine's evaluation results and sends the notification to the user's terminal in an appropriate format. The input is the result of anomaly detection and the emotion engine's evaluation, and the output is an optimized notification message for the user.

[0541] Step 6:

[0542] Users view received notifications on their smartphones or tablets and take appropriate action as needed. The input is the notification message from the server, and the output is the user's response action. This allows users to calmly respond to the situation.

[0543] 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.

[0544] 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.

[0545] 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.

[0546] [Fourth Embodiment]

[0547] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0548] 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.

[0549] 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).

[0550] 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.

[0551] 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.

[0552] 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).

[0553] 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.

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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".

[0560] This invention is a monitoring system for supporting child safety activities in local communities, and has an overall system structure that includes acquisition means, analysis means, detection means, and notification means. This system functions primarily based on three elements: a server, a terminal, and a user.

[0561] The server functions as a central processor, receiving video data from terminals and performing real-time analysis using AI. The analysis tools within the server execute algorithms to detect suspicious behavior from the collected video. The data analyzed by these detection tools is immediately reported to the user via notification tools when an anomaly occurs. Furthermore, it is possible to automatically generate reports on daily observations and anomaly cases for administrators in the region where the system is implemented, and provide them via email or other means.

[0562] As terminals, drones and fixed cameras are responsible for collecting data over a wide area. Drones patrol pre-set routes and collect video data via acquisition devices during their movement. Fixed cameras, often installed at specific locations in a region, also play a role in continuous monitoring and transmitting video data to a server. In this way, terminals primarily function as acquisition devices, constantly monitoring the surrounding environment.

[0563] On the other hand, users can remotely check the monitoring results using a dedicated monitoring application and take prompt action as needed. Through this application, users can visually check the video transmitted in real time and have a means to directly notify local safety response agencies if an anomaly is detected. For example, users can check the safety of their school route via camera footage from their homes and immediately contact the appropriate agency using the in-app notification function if an anomaly is detected.

[0564] In this way, the server, terminal, and user elements work together to support sustainable monitoring activities. It is expected that the organic connection of the entire system will further enhance the safety of children in local communities.

[0565] The following describes the processing flow.

[0566] Step 1:

[0567] The device automatically activates according to a set schedule and collects video data from the monitored area. For example, a drone begins patrolling along a programmed flight route and continuously acquires video with its onboard camera.

[0568] Step 2:

[0569] The terminal immediately transmits the collected video data to the server. This transmission is performed using low-latency communication to enable real-time data analysis. The server prepares for processing while storing the received data in temporary storage.

[0570] Step 3:

[0571] The server analyzes the received video data in real time using AI algorithms. Here, it analyzes movement and people in the video to detect suspicious behavior and unusual patterns.

[0572] Step 4:

[0573] If the server detects any suspicious behavior as a result of the analysis, it immediately performs an anomaly detection. This detection information is logged in the internal system and sent to the notification process.

[0574] Step 5:

[0575] The server activates a notification system to alert the user about any detected anomalies. This sends real-time alerts to the user's monitoring application.

[0576] Step 6:

[0577] Users can review the details of alerts received through the application. They can use the video replay function to confirm the situation when an anomaly occurred and, if necessary, report it to dispatch the appropriate agency to the site.

[0578] Step 7:

[0579] Users send feedback to the server via the app once they have completed the necessary response measures for an anomaly. This feedback is then used to improve the overall monitoring accuracy and operational efficiency of the system.

[0580] (Example 1)

[0581] 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".

[0582] Ensuring safety in local communities requires widespread and continuous monitoring, rapid detection of anomalies, and appropriate responses. However, conventional monitoring systems have faced challenges such as limitations on camera placement, inaccuracies in anomaly detection, and difficulties in rapid response. As a result, timely information provision, particularly for protecting the safety of children, is often insufficient.

[0583] 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.

[0584] In this invention, the server includes information gathering means, information processing means, state detection means, information transmission means, and report generation means. This enables the collection and analysis of a wide range of information in real time, allowing for rapid detection of anomalies and immediate notification to users. Furthermore, the automatically generated reports allow local administrators to confirm the situation in detail, contributing to improved local safety.

[0585] "Video data" refers to visual information collected by cameras and sensors, and is the subject of analysis.

[0586] "Information gathering means" refers to devices and technologies used to acquire data, such as cameras and sensors.

[0587] "Information processing means" refers to computer systems and algorithms used to analyze collected data.

[0588] "State detection means" refers to algorithms and technologies used to identify abnormal events based on processed data.

[0589] "Information transmission means" refers to communication means used to transmit detected information or anomalies to other devices or users.

[0590] "Report generation means" refers to technology for creating reports that summarize information for administrators and users based on collected and analyzed data.

[0591] "Aircraft that move through the air" refers to devices that can fly and move, such as unmanned aerial vehicles like drones.

[0592] "Information presentation function" refers to a function that displays information in a way that users can understand and provides an interface for taking necessary actions.

[0593] In this invention, the server plays a central role in realizing safety monitoring in the local community. The server receives video data transmitted from terminals and analyzes that data in real time using AI. Specifically, it uses AI frameworks such as "TensorFlow" and "PyTorch" to perform object detection and abnormal behavior analysis on the received video data. This method enables highly accurate and rapid anomaly detection.

[0594] The devices used as terminals include fixed cameras and aircraft that move through the air. In particular, drones regularly orbit the area based on planned routes, collecting wide-area video data. This enables comprehensive data collection from both the ground and the air. Fixed cameras continuously collect video 24 hours a day at specific locations, providing a stable data supply.

[0595] Users can remotely view video data provided by the server using a dedicated monitoring application. The application visualizes real-time video and anomaly notifications, and has a mechanism that allows users to quickly contact local response agencies as needed. In addition, when an anomaly is detected, users can respond quickly using the integrated notification function within the app.

[0596] For example, users can view footage from cameras installed along school routes from the comfort of their homes and immediately notify patrol agencies if any anomalies are detected. This system is designed to support sustainable community watch activities and helps users and local administrators conduct safety monitoring efficiently.

[0597] An example of a prompt would be, "Please describe in detail how the AI ​​model for monitoring children in the community performs behavioral analysis, detects anomalies, and sends notifications." This prompt allows the generated AI model to provide a detailed description of the system.

[0598] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0599] Step 1:

[0600] The terminal collects video data using a camera and flight sensors. The input is real-time video from various locations within the region. The terminal captures this video data frame by frame and converts it into a digital format that can be sent to the server. The output is transferred to the server as compressed video data.

[0601] Step 2:

[0602] The server receives video data sent from the terminal. It prepares to run an AI algorithm to analyze the video data as input. Specifically, it uses an AI framework to start analyzing the received video. Data processing includes screen subdivision and feature extraction, and the analysis results are used in the next processing step as output.

[0603] Step 3:

[0604] The server uses an AI algorithm to analyze video data and detect suspicious behavior. The input is the video frames received in the previous step. The AI ​​model analyzes the movement and changes of objects to detect anomalies. The output generates information about the detected anomaly and its details, and the process proceeds to the next notification step.

[0605] Step 4:

[0606] The server notifies users of detected anomalies. The input for this step is the analyzed anomaly information. The server notifies the target users via monitoring applications or email. The output is an anomaly alert to the user.

[0607] Step 5:

[0608] Users check notifications through a monitoring application. Inputs include received anomaly reports and video data. Users review the video within the app and take action using the reporting function as needed. Outputs include sending notifications to local response agencies if necessary.

[0609] Step 6:

[0610] The server automatically generates daily reports based on all monitoring data and detected information. Inputs include collected video data and detection results. The server organizes the information, creates reports for administrators, and sends them via email. Outputs are detailed reports on the local safety situation.

[0611] (Application Example 1)

[0612] 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".

[0613] To ensure the safety of local communities, a system is needed that can monitor the surrounding environment in real time and respond quickly and appropriately when an anomaly is detected. However, conventional systems have difficulty monitoring, especially while on the move, and responding immediately, which can sometimes prevent early response to anomalies. In addition, there is a need for new methods to easily acquire and analyze information while monitoring a wide area.

[0614] 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.

[0615] In this invention, the server includes means for acquiring video information, means for analyzing the acquired video information in real time, means for detecting anomalies based on the analysis results, and means for providing a user interface that allows the user to check the surrounding situation in real time using a visual device and report anomalies. This enables the user to monitor the safety of their surroundings even while on the move and to take immediate action if an anomaly is detected.

[0616] "Visual information" refers to video or still image data captured by cameras or other visual devices and processed in digital format.

[0617] "Means" refer to the devices or methods used to achieve a specific purpose, and are elements within a system that perform a specific function.

[0618] "Analysis means" refers to a method or apparatus used to analyze acquired data and understand its contents.

[0619] "Visual devices" are devices used by users to visually perceive their external environment, and include smart glasses and headsets.

[0620] A "user interface" is a means of interaction used when information is exchanged between a system and a user.

[0621] An "abnormality" refers to a situation that deviates from the normal operation or expected state of a system, and is an operation or state that may threaten safety.

[0622] An "unmanned aerial vehicle" is an aircraft that operates remotely or automatically without human control and is used for data collection and surveillance tasks.

[0623] This invention is a security system that uses visual devices to monitor the surroundings and provides real-time notifications when an anomaly is detected. The server is the central processing unit that acquires video information and analyzes that data. Specifically, the server receives and processes video data transmitted from smart glasses or other visual devices in real time. Using an AI model installed on the server (e.g., TensorFlow or PyTorch), the system analyzes the video data and detects suspicious behavior or anomalies.

[0624] Smart glasses, acting as a terminal device, are designed to allow users to monitor their surroundings even while on the move. A camera equipped in the device acquires video data for a library and transmits it to a server via Wi-Fi or Bluetooth. If an anomaly is detected, a notification is displayed on the glasses' screen, allowing the user to quickly respond while checking the visual information. The user interface incorporates mechanisms for rapid anomaly reporting and additional verification operations through voice recognition and simple gestures.

[0625] A concrete example of implementing the system is a scenario where security guards on nighttime patrols wear smart glasses. The guards use the device to check for safety while walking around, and if a suspicious person or intruder is detected, a notification such as "Intruder detected, warning notification sent" is immediately displayed in a visible format. An example of a prompt message in this case would be, "Monitor your surroundings through your smart glasses and report any suspicious activity."

[0626] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0627] Step 1:

[0628] The device continuously acquires surrounding video information using the camera of the visual device. It receives real-time video of the visual environment displayed on the device as input and converts it into digital data. The output is the acquired video data.

[0629] Step 2:

[0630] The terminal transmits the acquired video data to the server via wireless communication (e.g., Wi-Fi or Bluetooth). This process uses the video data acquired by the terminal as input. The output is the video data received by the server.

[0631] Step 3:

[0632] The server analyzes the received video data using an AI model (e.g., TensorFlow or PyTorch). The AI ​​model receives video data as input and analyzes movement, people, and other important features within the video. The output consists of the analyzed information and anomaly detection results.

[0633] Step 4:

[0634] The server detects the presence or absence of anomalies based on the analysis results. It uses the analysis results from an AI model as input. The output is alert information if an anomaly is detected. At this stage, the specific behavior or situation deemed anomaly is identified.

[0635] Step 5:

[0636] If an anomaly is detected, the server notifies the terminal's visual device of the detailed information. A warning message is instantly displayed on the screen for the user. The input is the anomaly detection information. The output is a visual notification that the user can see.

[0637] Step 6:

[0638] The user receives notifications and, if necessary, uses voice commands or gestures to report anomalies or take further action. Input includes user interaction based on notifications from the visual device. Output is reported information and additional instructions.

[0639] 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.

[0640] This invention is a system for supporting community child safety monitoring activities, and combines acquisition means, analysis means, detection means, notification means, and an emotion engine. This system consists of a server, a terminal, and a user.

[0641] The server receives video data transmitted from the terminal and performs real-time data analysis using AI algorithms. It also uses an emotion engine to evaluate the user's emotions based on the results of anomaly detection. The emotion engine detects the user's emotional state and adjusts the content and format of notifications accordingly. This evaluation incorporates features that sense the user's voice tone and facial expressions, enabling optimized notifications. For example, if the system evaluates the user as fatigued, it softens the notification tone and provides essential information concisely.

[0642] The drones and fixed cameras, acting as terminals, acquire data over a wide area and transmit that data to the server in real time. This allows them to serve as data acquisition tools, continuously monitoring video footage of the target area. Furthermore, the terminals autonomously detect predetermined patrol routes while efficiently collecting data.

[0643] Users can remotely view information notified by the system using a monitoring application. They can understand the video and notification content received in real time, and if an anomaly is detected, they can take appropriate action quickly. For example, when a user receives a danger notification via smartphone from work, the notification is designed with consideration for reducing stress through an emotion engine, allowing them to calmly assess the situation and take necessary safety measures.

[0644] In this way, the functions of acquisition, analysis, detection, notification, and emotion engine are integrated, reducing the burden on the user while enabling sustainable and reliable monitoring activities. This system allows communities to create a safer and more protected environment.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The terminal activates drones and fixed cameras according to a pre-set schedule to collect video data from the surveillance area. The drones patrol along designated flight routes and save the footage captured by the cameras to memory.

[0648] Step 2:

[0649] The terminal transmits the collected video data to the server in real time. The data is streamed with low latency via wireless communication technology as a video signal.

[0650] Step 3:

[0651] The server processes the received video data using an AI analysis module, recognizing people and actions while analyzing for any abnormal behavior. If the analysis detects suspicious behavior in a specific location, it triggers an alert.

[0652] Step 4:

[0653] The server activates the emotion engine based on the anomaly detection results and evaluates the user's emotional state. This evaluation includes a process of determining appropriate notification content, taking into account past user responses and historical data.

[0654] Step 5:

[0655] The server sends optimized notifications to the user based on the results of the emotion engine's evaluation. For example, if the server detects that the user is in a high-stress state, the notification will be sent in a considerate format and content.

[0656] Step 6:

[0657] The user reviews the notification received through a dedicated application and confirms the situation on site by replaying the provided video. If necessary, the user then makes the appropriate report to the local response agency.

[0658] Step 7:

[0659] After an interaction, the user provides feedback to the system, sending information to the server that the emotion engine uses to optimize future notifications. This improves the system's adaptability and user satisfaction.

[0660] (Example 2)

[0661] 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".

[0662] In community-based child safety monitoring activities, there is a need to provide a method that enables efficient situational monitoring over a wide area without placing an excessive burden on users. Conventional monitoring systems have the problem of not providing timely and appropriate notifications in the event of an emergency, which increases the psychological burden on users.

[0663] 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.

[0664] In this invention, the server includes a device for acquiring information, a processing device for analyzing the acquired information in real time, a device for detecting anomalies based on the analysis results, an emotion evaluation device for evaluating the user's emotional state, and a device with a function for adjusting notification content based on the emotion evaluation results. This enables safe and rapid response to anomalies and notification provision that reduces the psychological burden on the user.

[0665] "Information acquisition devices" refer to means of collecting data over a wide range of areas, and are composed of aircraft and stationary equipment.

[0666] A "processing device that analyzes acquired information in real time" refers to a computer system that immediately analyzes received data, and is implemented using machine learning algorithms.

[0667] An "anomaly detection device" is a mechanism that recognizes unusual situations based on the results of information analysis and identifies those events.

[0668] An "emotion evaluation device" is a system used to analyze a user's voice tone and past data to infer their emotional state.

[0669] A "device equipped with a function to adjust notification content" is a mechanism for optimizing the content and method of notifications to the user based on the results of sentiment evaluation.

[0670] In order to implement this invention, it is necessary to construct a system in which three elements—a server, a terminal, and a user—cooperate with each other.

[0671] First, the terminal will be equipped with aircraft or stationary devices to acquire information over a wide area. These devices will autonomously patrol a designated area and collect the necessary data. It is possible to acquire images and video data of the monitoring area using equipment such as drones and fixed cameras. A specific example would be cameras placed around a school to check on the safety of children as they go to school.

[0672] Next, the server receives information transmitted from these terminals in real time and performs a computer-based analysis on the received data. It is preferable to use TensorFlow or PyTorch as the machine learning platform. An AI algorithm analyzes people and movements in the video, constantly monitoring for any abnormal behavior.

[0673] When an anomaly is detected, the server activates an emotion assessment system. This system generates appropriate notification content based on the user's tone of voice and past emotion history. Through this assessment, a relaxed tone and concise information delivery are achieved to reduce the user's psychological burden.

[0674] Users can receive system notifications remotely using their smartphones or tablets. They can open a monitoring application to instantly check the situation and take swift action in case of anomalies. For example, if a child enters a dangerous area, they can receive a notification that includes appropriate evacuation instructions, enabling rapid rescue.

[0675] Examples of prompts to input into the generating AI model include, "How can we optimize the anomaly detection process in the monitoring system?" Based on this prompt, a more efficient monitoring plan will be proposed.

[0676] In this way, the server, terminal, and user components function together, enabling safe monitoring activities in the local community.

[0677] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0678] Step 1:

[0679] The terminal acquires video data from a designated monitoring area using aircraft or stationary equipment. In this step, the acquisition device autonomously patrols the monitoring area, continuously capturing data with cameras and sensors. The input is real-time video of the monitored area, and the output is a data stream sent to the server.

[0680] Step 2:

[0681] The server receives video data transmitted from the terminal in real time and analyzes the images using an AI algorithm. The input is a video data stream, and the AI ​​model (e.g., using TensorFlow) detects the movement of people and objects. The output is the analyzed data, which includes suspicious movements and signs of anomalies.

[0682] Step 3:

[0683] The server executes an anomaly detection module based on the analysis results. In this step, it identifies anomalies that are different from normal from the detected movements and states. The input is the analyzed data obtained in step 2, and the output is information identifying the anomaly.

[0684] Step 4:

[0685] The server activates the emotion evaluation system when an anomaly is detected, and evaluates the user's emotional state. Using the analyzed anomaly information and past emotional data as input, it estimates the user's current emotional state. The output is an emotion evaluation result corresponding to the user's emotions.

[0686] Step 5:

[0687] The server generates appropriate notification content based on the sentiment evaluation results and sends it to the user. The input consists of the sentiment evaluation results and anomaly information, which are then used to create a notification with a tone and content appropriate for the user through a specific communication channel (e.g., a notification app). The output is the notification message provided to the user.

[0688] Step 6:

[0689] The user reviews the received notification and takes appropriate action as needed. In this step, the user checks the notification on their smartphone or tablet and plans and implements appropriate countermeasures based on the situation. The input is the notification message, and the output is the specific action the user takes.

[0690] (Application Example 2)

[0691] 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".

[0692] In modern society, community safety monitoring is a crucial issue, and there is a growing need for efficient and effective surveillance systems, particularly to guarantee the safety of children and vulnerable individuals. However, existing technologies have limitations in acquiring and analyzing surveillance data, and they cannot provide flexible information based on the user's psychological state.

[0693] 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.

[0694] In this invention, the server includes an acquisition means for acquiring video data, an analysis means for analyzing the acquired video data in real time, a detection means for detecting anomalies based on the analysis results, and a notification means including an emotion engine that evaluates the user's emotional state and adjusts the notification content when an anomaly is detected. This enables the collection and analysis of a wide range of real-time monitoring data and appropriate status notifications to the user.

[0695] "Acquisition means" refers to the function of collecting video data and providing it to a server in preparation for subsequent processing.

[0696] "Analysis means" refers to a device or function that analyzes acquired video data in real time and immediately evaluates whether or not there are any abnormalities.

[0697] A "detection mechanism" is a system that identifies anomalies based on the results obtained by the analysis mechanism and enables appropriate responses such as alerts.

[0698] A "notification system including an emotion engine" is a function that analyzes the user's emotional state when an anomaly is detected, and appropriately adjusts and provides the content and format of the notification according to that state.

[0699] In a mode for carrying out the invention, this system mainly consists of a server, a terminal, and a user. The following specific hardware and software are used to realize the operation of this system.

[0700] The server utilizes machine learning libraries such as TensorFlow to execute powerful AI algorithms. Video data is transmitted to the server in real time via devices such as drones and fixed cameras. The server uses OpenCV to capture the video, and machine learning models are used as analytical tools for anomaly detection. When an anomaly is detected, an emotion engine built within the server analyzes the user's voice tone and facial expressions, and appropriately adjusts the content and format of the notification.

[0701] The terminals, such as drones and fixed cameras, cover a wide area of ​​the target region and constantly provide the latest data to the server. Therefore, these terminals are an indispensable element for data acquisition.

[0702] Users receive information from the server via smartphones or tablets. Notifications allow users to receive immediate reports of anomalies, even from remote locations, enabling safe responses. Furthermore, notifications utilizing an emotion engine reduce the user's psychological burden, allowing them to deal with the situation more calmly.

[0703] For example, if a suspicious person is detected while a child is playing in a park, a gentle warning is automatically sent to the parent's smartphone. This notification is optimized for the parent's current emotional state and is designed to avoid unnecessary anxiety.

[0704] An example of a prompt statement used as input to a generative AI model is: "Generate a notification message that will allow you to calmly respond when a suspicious person is detected while a child is playing in a park using a monitoring system."

[0705] In this way, this system technically supports safe and secure monitoring activities.

[0706] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0707] Step 1:

[0708] The terminal (drone or fixed camera) acquires video data from a wide monitoring area. This video data is transmitted to the server in real time. The input is the surrounding video, and the output is the video data transmitted to the server. This makes it possible to continuously capture the situation in the monitored area.

[0709] Step 2:

[0710] The server analyzes received video data in real time using machine learning libraries such as TensorFlow. The input is video data sent from the terminal, and the output is features for anomaly detection. The server evaluates whether there are any anomalies based on the features and prepares for alerts.

[0711] Step 3:

[0712] The server performs anomaly detection based on the analysis results. The input is the features generated in step 2, and the output is a flag indicating the presence or absence of an anomaly. If an anomaly is detected, the process proceeds to the next step.

[0713] Step 4:

[0714] If an anomaly is detected, the server uses an emotion engine to evaluate the user's emotional state. The input is data on the user's voice tone and facial expressions, and the output is the evaluation result of the user's emotional state. This generates notification content that takes the user's stress level into consideration.

[0715] Step 5:

[0716] The server adjusts the notification content based on the emotion engine's evaluation results and sends the notification to the user's terminal in an appropriate format. The input is the result of anomaly detection and the emotion engine's evaluation, and the output is an optimized notification message for the user.

[0717] Step 6:

[0718] Users view received notifications on their smartphones or tablets and take appropriate action as needed. The input is the notification message from the server, and the output is the user's response action. This allows users to calmly respond to the situation.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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."

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] The following is further disclosed regarding the embodiments described above.

[0741] (Claim 1)

[0742] A means of acquiring video data,

[0743] An analysis method for analyzing acquired video data in real time,

[0744] A detection means that performs anomaly detection based on the analysis results,

[0745] A notification means that notifies when an anomaly is detected,

[0746] A system that includes this.

[0747] (Claim 2)

[0748] The system according to claim 1, which uses a drone to acquire data over a wide area.

[0749] (Claim 3)

[0750] The system according to claim 1, which provides an interface that allows the user to remotely monitor and respond to abnormalities.

[0751] "Example 1"

[0752] (Claim 1)

[0753] Information gathering means for acquiring video data,

[0754] An information processing means that analyzes acquired video data in real time using AI,

[0755] A state detection means for detecting anomalies based on analysis results,

[0756] A means for transmitting information to notify the user when an anomaly is detected,

[0757] A report generation means that automatically generates a record of monitoring results,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, which uses an aerial aircraft to collect information over a wide area.

[0761] (Claim 3)

[0762] The system according to claim 1, which provides an information presentation function that allows users to remotely monitor the situation and respond quickly when an anomaly is detected.

[0763] "Application Example 1"

[0764] (Claim 1)

[0765] Means for acquiring video information,

[0766] An analysis method for analyzing acquired video information in real time,

[0767] A means for detecting anomalies based on the analysis results,

[0768] A means of notifying when an anomaly is detected,

[0769] A means of providing a user interface that allows users to check the surrounding situation in real time using a visual device and report abnormalities,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, which uses an unmanned aerial vehicle to acquire information over a wide area.

[0773] (Claim 3)

[0774] The system according to claim 1, which provides an interface using a visual device that allows a user to monitor the safety of their surroundings while moving and report any abnormalities.

[0775] "Example 2 of combining an emotion engine"

[0776] (Claim 1)

[0777] A device for acquiring information,

[0778] A processing unit that analyzes the acquired information in real time,

[0779] A device that detects anomalies based on the analysis results,

[0780] A device that notifies when an abnormality is detected,

[0781] An emotion evaluation device that evaluates the user's emotional state,

[0782] A device equipped with a function to adjust notification content based on emotion evaluation results,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, which uses an aircraft or stationary equipment to acquire information over a wide area.

[0786] (Claim 3)

[0787] The system according to claim 1, which provides a screen that allows the user to remotely monitor and respond in the event of an anomaly.

[0788] "Application example 2 when combining with an emotional engine"

[0789] (Claim 1)

[0790] A means of acquiring video data,

[0791] An analysis method for analyzing acquired video data in real time,

[0792] A detection means that performs anomaly detection based on the analysis results,

[0793] A notification means including an emotion engine that evaluates the user's emotional state and adjusts the notification content when an anomaly is detected,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, which uses a drone to acquire data over a wide area.

[0797] (Claim 3)

[0798] The system according to claim 1, which provides an interface that allows the user to remotely monitor and respond to anomalies, and adjusts notifications based on the user's emotional state. [Explanation of symbols]

[0799] 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. A means of acquiring video data, An analysis method for analyzing acquired video data in real time, A detection means that performs anomaly detection based on the analysis results, A notification means that notifies when an anomaly is detected, A system that includes this.

2. The system according to claim 1, which uses a drone to acquire data over a wide area.

3. The system according to claim 1, which provides an interface that allows the user to remotely monitor and respond to abnormalities.

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