system

Abstract

The system according to this embodiment aims to effectively monitor no-parking zones and other locations where illegal activities occur, and to deter such violations. [Solution] The system according to the embodiment comprises a monitoring unit, a detection unit, a capture unit, and a display unit. The monitoring unit monitors no-parking zones and other locations where illegal activities may occur. The detection unit analyzes the video monitored by the monitoring unit and detects violations. The capture unit captures the scene of the violation detected by the detection unit as an image. The display unit displays the image captured by the capture unit on a monitor.

Description

Technical Field

[0006] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the monitoring of prohibited parking areas and other places where illegal acts are committed is not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment is intended to effectively monitor places where prohibited parking areas and other illegal acts are committed and deter violations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a detection unit, a capture unit, and a display unit. The monitoring unit monitors no-parking zones and other locations where illegal activities may occur. The detection unit analyzes the video monitored by the monitoring unit and detects violations. The capture unit captures the scene of the violation detected by the detection unit as an image. The display unit displays the image captured by the capture unit on a monitor. [Effects of the Invention]

[0007] The system according to this embodiment can effectively monitor no-parking zones and other locations where illegal activities occur, thereby deterring violations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The tort deterrence system according to an embodiment of the present invention is a system that provides a function to deter torts such as illegal parking by displaying an image of the act being committed on a monitor to the person who committed the offense. This system uses an AI camera and a monitor in combination. Specifically, it consists of the following steps. First, the AI ​​camera monitors no-parking zones and other places where torts may occur. The AI ​​camera analyzes the video in real time and detects the offense. For example, it detects a person attempting to park a bicycle in a no-parking zone. Next, the AI ​​camera captures the scene of the detected offense as an image. This image is taken clearly so that the person committing the offense can be identified. After that, the captured image is displayed on the monitor. The monitor is installed in a position where the person committing the offense can see it, and the scene of the offense is displayed in real time. As a result, the person committing the offense becomes aware that they are being monitored, and a psychological deterrent effect is exerted. For example, if a person attempting to park a bicycle in a no-parking zone is detected by the AI ​​camera and the scene is displayed on the monitor, the person will become aware that they are being monitored and will be more likely to refrain from parking. This system can be applied not only to illegal parking but also to other illegal activities such as littering, soliciting, and disposing of garbage in violation of rules. By having AI cameras detect these activities and displaying them on a monitor, a similar deterrent effect can be expected. In this way, by combining AI cameras and monitors, a low-cost and effective system for deterring illegal activities can be provided. This is expected to reduce illegal activities in municipalities and commercial facilities. The illegal activity deterrent system can deter those who commit violations by displaying images of the act on a monitor, thereby discouraging illegal activities such as illegal parking.

[0029] The illegal activity deterrence system according to this embodiment comprises a monitoring unit, a detection unit, a capture unit, and a display unit. The monitoring unit monitors no-parking zones and other locations where illegal activities may occur. The monitoring unit performs monitoring using, for example, an AI camera. The AI ​​camera analyzes video in real time and detects violations. For example, it can detect a person attempting to park a bicycle in a no-parking zone. The detection unit analyzes the video monitored by the monitoring unit and detects violations. The detection unit analyzes the video using, for example, an AI algorithm and detects violations. The AI ​​algorithm can utilize technologies such as deep learning or support vector machines. The capture unit captures the scene of the violation detected by the detection unit as an image. The capture unit captures the image using, for example, a high-resolution camera. The captured image is clearly photographed so that the person committing the violation can be identified. The display unit displays the image captured by the capture unit on a monitor. The display unit displays the image using, for example, a monitor. The monitor is positioned so that the person committing the violation can see it, and the scene of the violation is displayed in real time. As a result, the person committing the violation becomes aware that they are being monitored, and a psychological deterrent effect is created. Thus, the illegal act deterrence system according to this embodiment can detect the violation and display the scene in real time, thereby deterring the person from committing the violation.

[0030] The monitoring unit monitors no-parking zones and other areas where illegal activities may occur. The monitoring unit uses, for example, AI cameras for surveillance. These AI cameras analyze video in real time to detect violations. Specifically, AI cameras acquire high-resolution video and have built-in processors for real-time video analysis. Using deep learning models, AI cameras recognize objects and people in the video and detect specific behavioral patterns. For example, they can detect a person attempting to park a bicycle in a no-parking zone. AI cameras have algorithms that track movement in the video and detect suspicious activity within specific areas. This allows the monitoring unit to efficiently monitor a wide area and detect violations early. Furthermore, by linking multiple AI cameras via a network, the monitoring unit can expand its monitoring range and conduct more detailed surveillance. For example, AI cameras can be installed at multiple entrances and exits of a no-parking zone to understand the overall situation. Additionally, the monitoring unit can use infrared cameras and waterproof cameras to maintain high monitoring capabilities even at night or in bad weather. This allows the monitoring unit to effectively conduct surveillance 24 hours a day, 365 days a year, under all circumstances, contributing to the deterrence of illegal activities.

[0031] The detection unit analyzes video footage monitored by the monitoring unit to detect violations. For example, the detection unit uses AI algorithms to analyze the video and detect violations. AI algorithms can utilize technologies such as deep learning and support vector machines. Specifically, deep learning models have the ability to learn from large amounts of video data and recognize patterns of violations. For example, they can learn the movements of a person attempting to park a bicycle in a no-parking zone and detect those movements in real time. Support vector machines analyze features in the video and function as classifiers to determine the presence or absence of violations. This allows the detection unit to detect violations with high accuracy. Furthermore, the detection unit can use anomaly detection algorithms to detect unusual behavioral patterns or suspicious movements early on. For example, it can detect a person moving in a way that differs from normal traffic patterns and issue a warning. The detection unit can also use predictive models based on past data to assess the risk of future violations. This allows the detection unit to not only detect violations in real time but also to handle long-term risk assessment and preventative measures, improving the overall reliability and safety of the system.

[0032] The capture unit captures images of the violation detected by the detection unit. The capture unit uses, for example, a high-resolution camera to capture images. Specifically, the high-resolution camera is equipped with a high-resolution sensor to clearly capture the moment the violation occurs. This ensures that the captured images are clear enough to identify the person committing the violation. Upon receiving a signal from the detection unit, the capture unit immediately activates the high-resolution camera to capture the scene of the violation. Furthermore, the capture unit has a continuous shooting function, allowing it to record a series of actions of the violation as multiple images. This provides detailed evidence of the violation. The capture unit can also use infrared cameras and high-sensitivity sensors to capture high-quality images even at night or in low-light environments. This allows the capture unit to effectively record the scene of the violation in any environment and provide it as evidence. Additionally, the capture unit immediately saves the captured images to a database for later analysis and use as evidence. This ensures that the capture unit reliably records evidence of the violation, improving the overall reliability and effectiveness of the system.

[0033] The display unit displays images captured by the capture unit on a monitor. For example, the display unit uses a monitor to display images. The monitor is positioned so that it can see the person committing the violation, and the violation is displayed in real time. Specifically, the monitor uses a high-resolution display, allowing for a clear display of the captured images. The display unit immediately displays the images transmitted from the capture unit on the monitor, issuing a visual warning to the person committing the violation. This makes the person committing the violation aware that they are being monitored, creating a psychological deterrent effect. Furthermore, by linking multiple monitors, the display unit can cover a wide area and enhance the deterrent effect against violations. For example, monitors can be installed at multiple entrances and exits of a no-parking zone to display the overall situation in real time. The display unit can also issue stronger warnings to the person committing the violation by using audio alerts and warning messages in combination. Thus, the display unit can maximize the deterrent effect against violations by combining visual and auditory warnings. Additionally, the display unit allows administrators to remotely check the monitor's display content and take appropriate countermeasures as needed. This allows the display unit to contribute to deterring violations through real-time monitoring and warnings.

[0034] The detection unit uses an AI-based algorithm to detect violations. For example, the detection unit might use an algorithm employing deep learning. Deep learning can learn from large amounts of data and detect violations with high accuracy. The detection unit can also use an algorithm employing support vector machines. Support vector machines classify data and can detect violations with high accuracy. Furthermore, the detection unit can combine different algorithms. For example, combining deep learning and support vector machines enables more accurate detection of violations. Thus, using AI algorithms improves the accuracy of violation detection.

[0035] The display unit ensures that the image displayed on the monitor is recognizable to the person committing the violation. The display unit can make the image easier to recognize by, for example, adjusting the brightness of the display. It can also make the image easier to recognize by adjusting the position of the display. Furthermore, it can make the image easier to recognize by adjusting the size of the display. For example, the display unit automatically adjusts the brightness of the monitor so that the image is displayed clearly. It also adjusts the position of the monitor so that the person committing the violation can easily see the image. Furthermore, it adjusts the size of the monitor so that the image is displayed larger. This has the effect of making the person committing the violation aware of their actions and reconsidering them.

[0036] The monitoring unit includes a placement unit that specifically explains the installation locations and arrangement methods for AI cameras and monitors. For example, the placement unit selects the camera installation locations. Camera installation locations are important for maximizing the effectiveness of monitoring. For example, installing cameras at the entrances and exits of no-parking zones allows for early detection of violations. The placement unit also selects the monitor installation locations. It is important to install monitors in positions where individuals committing violations can easily recognize the image. For example, installing monitors at the entrances and exits of no-parking zones makes it easier for individuals committing violations to recognize the image. Furthermore, the placement unit specifically explains the arrangement methods for cameras and monitors. For example, it adjusts the height and angle of the cameras to optimize the monitoring range. It also adjusts the height and angle of the monitors to make the images easy to view. This improves the effectiveness of monitoring through optimal placement.

[0037] The detection unit includes an application unit that emphasizes its applicability to other illegal activities such as littering, soliciting, and improper garbage disposal. For example, the application unit uses an algorithm to detect littering. Littering refers to the dumping of waste in public places, causing environmental pollution. The application unit can detect littering using an AI algorithm. The application unit also uses an algorithm to detect soliciting. Soliciting refers to inappropriate contact or conversation in public places, causing discomfort to others. The application unit can detect soliciting using an AI algorithm. Furthermore, the application unit uses an algorithm to detect improper garbage disposal. Improper garbage disposal refers to dumping waste outside of designated locations and times, damaging the aesthetics of the area. The application unit can detect improper garbage disposal using an AI algorithm. This allows for broad deterrence by addressing a variety of illegal activities.

[0038] The monitoring unit can optimize its monitoring areas by referring to past violation data. For example, it can prioritize monitoring areas where violations have frequently occurred in the past. Furthermore, the monitoring unit can intensify monitoring during specific time periods based on past data. In addition, it can analyze patterns of past violations and focus monitoring on areas where violations are predicted to occur. For example, it can intensify monitoring in specific areas or time periods based on past violation data. This improves monitoring efficiency by utilizing past data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past violation data into a generating AI and have the generating AI optimize the monitoring areas.

[0039] The monitoring unit can change its monitoring method based on the time of day and weather conditions. For example, the monitoring unit can use infrared cameras for monitoring at night. It can also use waterproof cameras for monitoring during rainy weather. Furthermore, the monitoring unit can increase the number of cameras to enhance monitoring during holidays and events. For example, the monitoring unit can use infrared cameras to monitor even in darkness at night. It can also use waterproof cameras to monitor even in the rain. Furthermore, the monitoring unit can increase the number of cameras to enhance monitoring during holidays and events. This improves the accuracy of monitoring by changing the monitoring method according to environmental conditions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on time of day and weather conditions into a generating AI and have the generating AI execute the change in monitoring method.

[0040] The monitoring unit can optimize the placement of surveillance cameras by considering the geographical characteristics of the monitoring area. For example, in areas with elevation differences, the monitoring unit can adjust the camera placement to reduce blind spots. In areas with many buildings, the monitoring unit can also increase the number of cameras to expand the monitoring range. Furthermore, in large areas such as parks and plazas, the monitoring unit can widen the field of view of the cameras for better monitoring. For example, in areas with elevation differences, the monitoring unit can adjust the camera placement to reduce blind spots. In areas with many buildings, the monitoring unit can also increase the number of cameras to expand the monitoring range. Furthermore, in large areas such as parks and plazas, the monitoring unit can widen the field of view of the cameras for better monitoring. This improves the effectiveness of monitoring by considering geographical characteristics in the placement. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical characteristics data into a generating AI and have the generating AI optimize the placement of surveillance cameras.

[0041] The monitoring unit can analyze ambient audio data to detect signs of violations. For example, if the monitoring unit detects loud noises or shouting, it can enhance the zoom function of the surveillance camera to acquire detailed images. Furthermore, if the monitoring unit detects noises or impact sounds, it can increase the frame rate of the surveillance camera to acquire fast-moving images. In addition, the monitoring unit can analyze ambient audio data and issue an alert if it detects an abnormal sound. This enables early detection of violations through the analysis of audio data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input ambient audio data into a generating AI and have the generating AI detect signs of violations.

[0042] The detection unit can improve its detection accuracy by learning past violation patterns. For example, the detection unit can improve its detection accuracy by learning specific patterns based on past violation data. The detection unit can also improve its detection accuracy by predicting violations at specific times or locations based on past data. Furthermore, the detection unit can improve its detection accuracy by analyzing past violation patterns and predicting actions. For example, the detection unit can improve its detection accuracy by learning specific patterns based on past violation data. Furthermore, the detection unit can improve its detection accuracy by predicting violations at specific times or locations based on past data. Furthermore, the detection unit can improve its detection accuracy by analyzing past violation patterns and predicting actions. In this way, detection accuracy is improved by learning past patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past violation data into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0043] The detection unit can improve detection accuracy by integrating video from multiple cameras. For example, the detection unit can integrate video from multiple cameras in real time and acquire detailed data. The detection unit can also analyze video from multiple cameras and integrate data from different angles to improve detection accuracy. Furthermore, the detection unit can improve detection accuracy by generating a 3D model based on video from multiple cameras. For example, the detection unit can integrate video from multiple cameras in real time and acquire detailed data. The detection unit can also analyze video from multiple cameras and integrate data from different angles to improve detection accuracy. Furthermore, the detection unit can improve detection accuracy by generating a 3D model based on video from multiple cameras. In this way, detection accuracy is improved by integrating video from multiple cameras. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input video data from multiple cameras into a generating AI and have the generating AI perform the video integration.

[0044] The detection unit can improve detection accuracy by using audio data and vibration data in combination. For example, the detection unit can analyze audio data and issue an alert if it detects an abnormal sound. The detection unit can also analyze vibration data and issue an alert if it detects an abnormal vibration. Furthermore, the detection unit can integrate audio data and vibration data and issue an alert if it detects an abnormal situation. For example, the detection unit can analyze audio data and issue an alert if it detects an abnormal sound. The detection unit can also analyze vibration data and issue an alert if it detects an abnormal vibration. Furthermore, the detection unit can integrate audio data and vibration data and issue an alert if it detects an abnormal situation. This improves detection accuracy by using audio data and vibration data in combination. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input audio data and vibration data into a generating AI and have the generating AI perform anomaly detection.

[0045] The detection unit can analyze ambient environmental data to detect signs of violations at an early stage. For example, the detection unit can analyze ambient temperature data and issue an alert if it detects an abnormal temperature change. It can also analyze ambient light data and issue an alert if it detects an abnormal light change. Furthermore, the detection unit can analyze ambient humidity data and issue an alert if it detects an abnormal humidity change. This enables early detection of violations through the analysis of environmental data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input ambient environmental data into a generating AI and have the generating AI perform the detection of signs of violations.

[0046] The capture unit can automatically adjust the image resolution to acquire the optimal image. For example, if the person committing the violation is far away, the capture unit can increase the resolution to acquire a detailed image. Also, if the person committing the violation is close by, the capture unit can decrease the resolution to acquire a wider area image. Furthermore, if the person committing the violation is moving, the capture unit can adjust the resolution to acquire a fast-moving image. For example, if the person committing the violation is far away, the capture unit can increase the resolution to acquire a detailed image. Also, if the person committing the violation is close by, the capture unit can decrease the resolution to acquire a wider area image. Furthermore, if the person committing the violation is moving, the capture unit can adjust the resolution to acquire a fast-moving image. This allows for the acquisition of the optimal image through automatic resolution adjustment. Some or all of the above processing in the capture unit may be performed using AI, for example, or without AI. For example, the capture unit can input video data acquired by the camera into a generating AI and have the generating AI perform the resolution adjustment.

[0047] The capture unit can integrate video from multiple cameras to generate high-precision images. For example, the capture unit can integrate video from multiple cameras in real time to generate detailed images. The capture unit can also analyze video from multiple cameras and integrate data from different angles to generate high-precision images. Furthermore, the capture unit can generate 3D models based on video from multiple cameras to generate high-precision images. For example, the capture unit can integrate video from multiple cameras in real time to generate detailed images. Furthermore, the capture unit can analyze video from multiple cameras and integrate data from different angles to generate high-precision images. Furthermore, the capture unit can generate 3D models based on video from multiple cameras to generate high-precision images. In this way, high-precision images are generated by integrating video from multiple cameras. Some or all of the above processing in the capture unit may be performed using AI, for example, or without AI. For example, the capture unit can input video data from multiple cameras into a generating AI and have the generating AI perform the video integration.

[0048] The capture unit can automatically adjust the color tone and brightness of the image to improve visibility. For example, the capture unit can increase brightness at night to improve visibility. It can also adjust color tone to improve visibility during the day. Furthermore, the capture unit can increase contrast to improve visibility in rainy weather. For example, the capture unit can increase brightness at night to improve visibility. It can also adjust color tone to improve visibility during the day. Furthermore, the capture unit can increase contrast to improve visibility in rainy weather. As a result, visibility is improved through automatic adjustment of color tone and brightness. Some or all of the above processing in the capture unit may be performed using AI, for example, or without AI. For example, the capture unit can input video data acquired by the camera into a generating AI and have the generating AI perform color tone and brightness adjustments.

[0049] The capture unit can analyze ambient environmental data to determine the optimal capture timing. For example, the capture unit can analyze ambient temperature data and capture if it detects an abnormal temperature change. It can also analyze ambient light data and capture if it detects an abnormal light change. Furthermore, it can analyze ambient humidity data and capture if it detects an abnormal humidity change. In this way, the optimal capture timing is determined by analyzing the environmental data. Some or all of the above processing in the capture unit may be performed using AI, for example, or without AI. For example, the capture unit can input ambient environmental data into a generating AI and have the generating AI determine the capture timing.

[0050] The display unit can adjust the level of detail displayed based on the importance of the image. For example, in the case of a serious violation, the display unit can display a detailed image to enhance the warning. In the case of a minor violation, the display unit can also display a wide-angle image to provide a warning. Furthermore, the display unit can adjust the resolution of the displayed image according to its importance. For example, in the case of a serious violation, the display unit can display a detailed image to enhance the warning. In the case of a minor violation, the display unit can display a wide-angle image to provide a warning. Furthermore, the display unit can adjust the resolution of the displayed image according to its importance. This improves the warning effect by adjusting the level of detail of the display according to its importance. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0051] The display unit can apply different display algorithms depending on the category of the image. For example, in the case of a violation of a no-parking zone, the display unit can apply a specific display algorithm to issue a warning. The display unit can also apply a different display algorithm to issue a warning in the case of a littering violation. Furthermore, the display unit can apply yet another display algorithm to issue a warning in the case of a soliciting violation. For example, the display unit can apply a specific display algorithm to issue a warning in the case of a no-parking zone. The display unit can also apply a different display algorithm to issue a warning in the case of a littering violation. Furthermore, the display unit can apply yet another display algorithm to issue a warning in the case of a soliciting violation. This improves the warning effect by applying a display algorithm according to the category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generating AI and have the generating AI execute the application of the display algorithm.

[0052] The display unit can determine the display priority based on when the images were taken. For example, the display unit may prioritize displaying images of the most recent violation. The display unit may also display images of past violations for reference. Furthermore, the display unit may adjust the order in which the images are displayed according to when they were taken. For example, the display unit may prioritize displaying images of the most recent violation. The display unit may also display images of past violations for reference. Furthermore, the display unit may adjust the order in which the images are displayed according to when they were taken. As a result, the latest information is displayed preferentially by determining the priority based on the time of capture. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input video data acquired by the camera into a generating AI and have the generating AI perform the determination of the display priority.

[0053] The display unit can adjust the display order based on the relevance of the images. For example, the display unit may prioritize the display of images of highly relevant violations. It can also display images of less relevant violations later. Furthermore, the display unit can adjust the order in which images are displayed according to their relevance. For example, the display unit may prioritize the display of images of highly relevant violations. It can also display images of less relevant violations later. Furthermore, the display unit can adjust the order in which images are displayed according to their relevance. As a result, important information is displayed preferentially by adjusting the display order based on relevance. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generating AI and have the generating AI perform the adjustment of the display order.

[0054] The deployment unit can select the optimal deployment method by referring to past violation data. For example, the deployment unit can deploy cameras and monitors to prioritize monitoring areas where violations have frequently occurred in the past. Furthermore, based on past data, the deployment unit can deploy cameras and monitors to strengthen monitoring during specific time periods. Additionally, the deployment unit can analyze patterns of past violations and deploy cameras and monitors to focus monitoring on predicted areas. This enables optimal deployment by utilizing past data. Some or all of the above processing in the deployment unit may be performed using AI, for example, or without AI. For example, the deployment unit can input past violation data into a generating AI and have the generating AI select the deployment method.

[0055] The deployment unit can select the optimal deployment method considering geographical characteristics. For example, in areas with elevation differences, the deployment unit can adjust the camera placement to reduce blind spots. In areas with many buildings, the deployment unit can also increase the number of cameras to expand the surveillance range. Furthermore, in large areas such as parks and plazas, the deployment unit can widen the camera's field of view for surveillance. For example, in areas with elevation differences, the deployment unit can adjust the camera placement to reduce blind spots. In areas with many buildings, the deployment unit can increase the number of cameras to expand the surveillance range. Furthermore, in large areas such as parks and plazas, the deployment unit can widen the camera's field of view for surveillance. This improves the surveillance effect through deployment that takes geographical characteristics into account. Some or all of the above processing in the deployment unit may be performed using AI, for example, or without AI. For example, the deployment unit can input geographical characteristics data into a generating AI and have the generating AI select the deployment method.

[0056] The application unit can optimize the scope of coverage by referring to past violation data. For example, the application unit can set the scope of coverage to prioritize monitoring areas where violations have frequently occurred in the past. The application unit can also set the scope of coverage to strengthen monitoring during specific time periods based on past data. Furthermore, the application unit can analyze patterns of past violations and set the scope of coverage to focus monitoring on predicted areas. For example, the application unit can set the scope of coverage to prioritize monitoring areas where violations have frequently occurred in the past. The application unit can also set the scope of coverage to strengthen monitoring during specific time periods based on past data. Furthermore, the application unit can analyze patterns of past violations and set the scope of coverage to focus monitoring on predicted areas. This makes it possible to optimize the scope of coverage by utilizing past data. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input past violation data into a generating AI and have the generating AI perform the optimization of the scope of coverage.

[0057] The application unit can optimize its coverage area by considering geographical characteristics. For example, in areas with elevation differences, the application unit adjusts its coverage area to reduce blind spots. Furthermore, in areas with many buildings, the application unit can expand its coverage area to broaden the monitoring range. Additionally, in large areas such as parks and plazas, the application unit can expand its coverage area to perform monitoring. This improves monitoring effectiveness by optimizing the coverage area in consideration of geographical characteristics. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input geographical characteristics data into a generating AI and have the generating AI perform the optimization of the coverage area.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The monitoring unit can learn and predict the behavioral patterns of individuals committing violations. For example, the monitoring unit can learn specific behavioral patterns based on past violation data and predict future violations. The monitoring unit can also analyze behavioral patterns in real time and detect abnormal behavior. Furthermore, the monitoring unit can detect changes in behavioral patterns and enhance monitoring based on predicted behavior. This enables early detection of violations through the learning and prediction of behavioral patterns. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past violation data into a generating AI and have the generating AI perform behavioral pattern learning and prediction.

[0060] The display unit can analyze the actions of a person committing a violation in real time and display warnings corresponding to those actions. For example, if a person committing a violation is attempting to park a bicycle in a no-parking zone, the display unit will display a warning against parking. The display unit can also display a warning against littering if a person committing a violation is attempting to throw away litter. Furthermore, the display unit can display a warning against flirting if a person committing a violation is attempting to flirt with someone. This improves the deterrent effect against violations through behavior-based warning displays. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generating AI and have the generating AI perform behavior analysis and display warnings.

[0061] The detection unit can predict the actions of a person committing a violation and issue a warning based on the predicted actions. For example, the detection unit can learn specific behavioral patterns based on past violation data and predict future violations. The detection unit can also analyze behavioral patterns in real time and detect abnormal behavior. Furthermore, the detection unit can detect changes in behavioral patterns and issue a warning based on the predicted actions. This enables early deterrence of violations through behavioral prediction and warnings. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past violation data into a generating AI and have the generating AI perform behavioral pattern learning and prediction.

[0062] The monitoring unit can optimize the monitoring area by analyzing ambient environmental data. For example, the monitoring unit can analyze ambient temperature data and adjust the monitoring area if it detects an abnormal temperature change. It can also analyze ambient light data and adjust the monitoring area if it detects an abnormal light change. Furthermore, it can analyze ambient humidity data and adjust the monitoring area if it detects an abnormal humidity change. This makes it possible to optimize the monitoring area through the analysis of environmental data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input ambient environmental data into a generating AI and have the generating AI perform the optimization of the monitoring area.

[0063] The display unit can adjust the level of detail displayed based on the importance of the image. For example, in the case of a serious violation, the display unit can display a detailed image to enhance the warning. In the case of a minor violation, the display unit can also display a wide-angle image to provide a warning. Furthermore, the display unit can adjust the resolution of the displayed image according to its importance. This improves the warning effect by adjusting the level of detail according to the importance of the image. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The monitoring unit monitors no-parking zones and other areas where illegal activities may occur. The monitoring unit uses, for example, AI cameras for monitoring. The AI ​​cameras analyze video in real time and detect violations. Step 2: The detection unit analyzes the video monitored by the monitoring unit and detects any violations. The detection unit analyzes the video using, for example, an AI algorithm to detect violations. The AI ​​algorithm can utilize technologies such as deep learning or support vector machines. Step 3: The capture unit captures the scene of the violation detected by the detection unit as an image. The capture unit captures the image using, for example, a high-resolution camera. The captured image is clearly photographed so that the person committing the violation can be identified. Step 4: The display unit displays the images captured by the capture unit on a monitor. The display unit displays the images using a monitor, for example. The monitor is positioned so that the person committing the violation can see it, and the scene of the violation is displayed in real time. As a result, the person committing the violation becomes aware that they are being monitored, and a psychological deterrent effect is created.

[0066] (Example of form 2) The tort deterrence system according to an embodiment of the present invention is a system that provides a function to deter torts such as illegal parking by displaying an image of the act being committed on a monitor to the person who committed the offense. This system uses an AI camera and a monitor in combination. Specifically, it consists of the following steps. First, the AI ​​camera monitors no-parking zones and other places where torts may occur. The AI ​​camera analyzes the video in real time and detects the offense. For example, it detects a person attempting to park a bicycle in a no-parking zone. Next, the AI ​​camera captures the scene of the detected offense as an image. This image is taken clearly so that the person committing the offense can be identified. After that, the captured image is displayed on the monitor. The monitor is installed in a position where the person committing the offense can see it, and the scene of the offense is displayed in real time. As a result, the person committing the offense becomes aware that they are being monitored, and a psychological deterrent effect is exerted. For example, if a person attempting to park a bicycle in a no-parking zone is detected by the AI ​​camera and the scene is displayed on the monitor, the person will become aware that they are being monitored and will be more likely to refrain from parking. This system can be applied not only to illegal parking but also to other illegal activities such as littering, soliciting, and disposing of garbage in violation of rules. By having AI cameras detect these activities and displaying them on a monitor, a similar deterrent effect can be expected. In this way, by combining AI cameras and monitors, a low-cost and effective system for deterring illegal activities can be provided. This is expected to reduce illegal activities in municipalities and commercial facilities. The illegal activity deterrent system can deter those who commit violations by displaying images of the act on a monitor, thereby discouraging illegal activities such as illegal parking.

[0067] The illegal activity deterrence system according to this embodiment comprises a monitoring unit, a detection unit, a capture unit, and a display unit. The monitoring unit monitors no-parking zones and other locations where illegal activities may occur. The monitoring unit performs monitoring using, for example, an AI camera. The AI ​​camera analyzes video in real time and detects violations. For example, it can detect a person attempting to park a bicycle in a no-parking zone. The detection unit analyzes the video monitored by the monitoring unit and detects violations. The detection unit analyzes the video using, for example, an AI algorithm and detects violations. The AI ​​algorithm can utilize technologies such as deep learning or support vector machines. The capture unit captures the scene of the violation detected by the detection unit as an image. The capture unit captures the image using, for example, a high-resolution camera. The captured image is clearly photographed so that the person committing the violation can be identified. The display unit displays the image captured by the capture unit on a monitor. The display unit displays the image using, for example, a monitor. The monitor is positioned so that the person committing the violation can see it, and the scene of the violation is displayed in real time. As a result, the person committing the violation becomes aware that they are being monitored, and a psychological deterrent effect is created. Thus, the illegal act deterrence system according to this embodiment can detect the violation and display the scene in real time, thereby deterring the person from committing the violation.

[0068] The monitoring unit monitors no-parking zones and other areas where illegal activities may occur. The monitoring unit uses, for example, AI cameras for surveillance. These AI cameras analyze video in real time to detect violations. Specifically, AI cameras acquire high-resolution video and have built-in processors for real-time video analysis. Using deep learning models, AI cameras recognize objects and people in the video and detect specific behavioral patterns. For example, they can detect a person attempting to park a bicycle in a no-parking zone. AI cameras have algorithms that track movement in the video and detect suspicious activity within specific areas. This allows the monitoring unit to efficiently monitor a wide area and detect violations early. Furthermore, by linking multiple AI cameras via a network, the monitoring unit can expand its monitoring range and conduct more detailed surveillance. For example, AI cameras can be installed at multiple entrances and exits of a no-parking zone to understand the overall situation. Additionally, the monitoring unit can use infrared cameras and waterproof cameras to maintain high monitoring capabilities even at night or in bad weather. This allows the monitoring unit to effectively conduct surveillance 24 hours a day, 365 days a year, under all circumstances, contributing to the deterrence of illegal activities.

[0069] The detection unit analyzes video footage monitored by the monitoring unit to detect violations. For example, the detection unit uses AI algorithms to analyze the video and detect violations. AI algorithms can utilize technologies such as deep learning and support vector machines. Specifically, deep learning models have the ability to learn from large amounts of video data and recognize patterns of violations. For example, they can learn the movements of a person attempting to park a bicycle in a no-parking zone and detect those movements in real time. Support vector machines analyze features in the video and function as classifiers to determine the presence or absence of violations. This allows the detection unit to detect violations with high accuracy. Furthermore, the detection unit can use anomaly detection algorithms to detect unusual behavioral patterns or suspicious movements early on. For example, it can detect a person moving in a way that differs from normal traffic patterns and issue a warning. The detection unit can also use predictive models based on past data to assess the risk of future violations. This allows the detection unit to not only detect violations in real time but also to handle long-term risk assessment and preventative measures, improving the overall reliability and safety of the system.

[0070] The capture unit captures images of the violation detected by the detection unit. The capture unit uses, for example, a high-resolution camera to capture images. Specifically, the high-resolution camera is equipped with a high-resolution sensor to clearly capture the moment the violation occurs. This ensures that the captured images are clear enough to identify the person committing the violation. Upon receiving a signal from the detection unit, the capture unit immediately activates the high-resolution camera to capture the scene of the violation. Furthermore, the capture unit has a continuous shooting function, allowing it to record a series of actions of the violation as multiple images. This provides detailed evidence of the violation. The capture unit can also use infrared cameras and high-sensitivity sensors to capture high-quality images even at night or in low-light environments. This allows the capture unit to effectively record the scene of the violation in any environment and provide it as evidence. Additionally, the capture unit immediately saves the captured images to a database for later analysis and use as evidence. This ensures that the capture unit reliably records evidence of the violation, improving the overall reliability and effectiveness of the system.

[0071] The display unit displays images captured by the capture unit on a monitor. For example, the display unit uses a monitor to display images. The monitor is positioned so that it can see the person committing the violation, and the violation is displayed in real time. Specifically, the monitor uses a high-resolution display, allowing for a clear display of the captured images. The display unit immediately displays the images transmitted from the capture unit on the monitor, issuing a visual warning to the person committing the violation. This makes the person committing the violation aware that they are being monitored, creating a psychological deterrent effect. Furthermore, by linking multiple monitors, the display unit can cover a wide area and enhance the deterrent effect against violations. For example, monitors can be installed at multiple entrances and exits of a no-parking zone to display the overall situation in real time. The display unit can also issue stronger warnings to the person committing the violation by using audio alerts and warning messages in combination. Thus, the display unit can maximize the deterrent effect against violations by combining visual and auditory warnings. Additionally, the display unit allows administrators to remotely check the monitor's display content and take appropriate countermeasures as needed. This allows the display unit to contribute to deterring violations through real-time monitoring and warnings.

[0072] The detection unit uses an AI-based algorithm to detect violations. For example, the detection unit might use an algorithm employing deep learning. Deep learning can learn from large amounts of data and detect violations with high accuracy. The detection unit can also use an algorithm employing support vector machines. Support vector machines classify data and can detect violations with high accuracy. Furthermore, the detection unit can combine different algorithms. For example, combining deep learning and support vector machines enables more accurate detection of violations. Thus, using AI algorithms improves the accuracy of violation detection.

[0073] The display unit ensures that the image displayed on the monitor is recognizable to the person committing the violation. The display unit can make the image easier to recognize by, for example, adjusting the brightness of the display. It can also make the image easier to recognize by adjusting the position of the display. Furthermore, it can make the image easier to recognize by adjusting the size of the display. For example, the display unit automatically adjusts the brightness of the monitor so that the image is displayed clearly. It also adjusts the position of the monitor so that the person committing the violation can easily see the image. Furthermore, it adjusts the size of the monitor so that the image is displayed larger. This has the effect of making the person committing the violation aware of their actions and reconsidering them.

[0074] The monitoring unit includes a placement unit that specifically explains the installation locations and arrangement methods for AI cameras and monitors. For example, the placement unit selects the camera installation locations. Camera installation locations are important for maximizing the effectiveness of monitoring. For example, installing cameras at the entrances and exits of no-parking zones allows for early detection of violations. The placement unit also selects the monitor installation locations. It is important to install monitors in positions where individuals committing violations can easily recognize the image. For example, installing monitors at the entrances and exits of no-parking zones makes it easier for individuals committing violations to recognize the image. Furthermore, the placement unit specifically explains the arrangement methods for cameras and monitors. For example, it adjusts the height and angle of the cameras to optimize the monitoring range. It also adjusts the height and angle of the monitors to make the images easy to view. This improves the effectiveness of monitoring through optimal placement.

[0075] The detection unit includes an application unit that emphasizes its applicability to other illegal activities such as littering, soliciting, and improper garbage disposal. For example, the application unit uses an algorithm to detect littering. Littering refers to the dumping of waste in public places, causing environmental pollution. The application unit can detect littering using an AI algorithm. The application unit also uses an algorithm to detect soliciting. Soliciting refers to inappropriate contact or conversation in public places, causing discomfort to others. The application unit can detect soliciting using an AI algorithm. Furthermore, the application unit uses an algorithm to detect improper garbage disposal. Improper garbage disposal refers to dumping waste outside of designated locations and times, damaging the aesthetics of the area. The application unit can detect improper garbage disposal using an AI algorithm. This allows for broad deterrence by addressing a variety of illegal activities.

[0076] The monitoring unit can estimate the emotions of a person committing a violation and adjust the intensity of surveillance based on the estimated emotions. For example, if the person committing the violation is tense, the monitoring unit can enhance the zoom function of the surveillance camera to acquire detailed footage. If the person committing the violation is relaxed, the monitoring unit can also widen the camera's field of view to monitor a wider area. Furthermore, if the person committing the violation is anxious, the monitoring unit can increase the frame rate of the surveillance camera to acquire fast-moving footage. This allows for more effective surveillance by adjusting the monitoring intensity according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input video data acquired by the camera into a generative AI and have the generative AI perform emotion estimation.

[0077] The monitoring unit can optimize its monitoring areas by referring to past violation data. For example, it can prioritize monitoring areas where violations have frequently occurred in the past. Furthermore, the monitoring unit can intensify monitoring during specific time periods based on past data. In addition, it can analyze patterns of past violations and focus monitoring on areas where violations are predicted to occur. For example, it can intensify monitoring in specific areas or time periods based on past violation data. This improves monitoring efficiency by utilizing past data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past violation data into a generating AI and have the generating AI optimize the monitoring areas.

[0078] The monitoring unit can change its monitoring method based on the time of day and weather conditions. For example, the monitoring unit can use infrared cameras for monitoring at night. It can also use waterproof cameras for monitoring during rainy weather. Furthermore, the monitoring unit can increase the number of cameras to enhance monitoring during holidays and events. For example, the monitoring unit can use infrared cameras to monitor even in darkness at night. It can also use waterproof cameras to monitor even in the rain. Furthermore, the monitoring unit can increase the number of cameras to enhance monitoring during holidays and events. This improves the accuracy of monitoring by changing the monitoring method according to environmental conditions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on time of day and weather conditions into a generating AI and have the generating AI execute the change in monitoring method.

[0079] The monitoring unit can estimate the emotions of a person committing a violation and adjust the angle of the surveillance camera based on the estimated emotions. For example, if the person committing the violation is tense, the monitoring unit can lower the camera angle to acquire detailed footage. Conversely, if the person committing the violation is relaxed, the monitoring unit can widen the camera angle to monitor a wider area. Furthermore, if the person committing the violation is anxious, the monitoring unit can frequently change the camera angle to track their movements. This allows for more effective monitoring by adjusting the camera angle according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data acquired by the camera into the generative AI and have the generative AI perform emotion estimation.

[0080] The monitoring unit can optimize the placement of surveillance cameras by considering the geographical characteristics of the monitoring area. For example, in areas with elevation differences, the monitoring unit can adjust the camera placement to reduce blind spots. In areas with many buildings, the monitoring unit can also increase the number of cameras to expand the monitoring range. Furthermore, in large areas such as parks and plazas, the monitoring unit can widen the field of view of the cameras for better monitoring. For example, in areas with elevation differences, the monitoring unit can adjust the camera placement to reduce blind spots. In areas with many buildings, the monitoring unit can also increase the number of cameras to expand the monitoring range. Furthermore, in large areas such as parks and plazas, the monitoring unit can widen the field of view of the cameras for better monitoring. This improves the effectiveness of monitoring by considering geographical characteristics in the placement. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical characteristics data into a generating AI and have the generating AI optimize the placement of surveillance cameras.

[0081] The monitoring unit can analyze ambient audio data to detect signs of violations. For example, if the monitoring unit detects loud noises or shouting, it can enhance the zoom function of the surveillance camera to acquire detailed images. Furthermore, if the monitoring unit detects noises or impact sounds, it can increase the frame rate of the surveillance camera to acquire fast-moving images. In addition, the monitoring unit can analyze ambient audio data and issue an alert if it detects an abnormal sound. This enables early detection of violations through the analysis of audio data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input ambient audio data into a generating AI and have the generating AI detect signs of violations.

[0082] The detection unit can estimate the emotions of a person committing a violation and adjust the detection algorithm based on the estimated emotions. For example, if the person committing the violation is tense, the detection unit can increase the sensitivity of the detection algorithm to acquire more detailed data. Conversely, if the person committing the violation is relaxed, the detection unit can also decrease the sensitivity of the detection algorithm to monitor a wider area. Furthermore, if the person committing the violation is anxious, the detection unit can increase the frame rate of the detection algorithm to acquire fast-moving data. This improves detection accuracy by adjusting the algorithm according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input video data acquired by a camera into a generative AI and have the generative AI perform emotion estimation.

[0083] The detection unit can improve its detection accuracy by learning past violation patterns. For example, the detection unit can improve its detection accuracy by learning specific patterns based on past violation data. The detection unit can also improve its detection accuracy by predicting violations at specific times or locations based on past data. Furthermore, the detection unit can improve its detection accuracy by analyzing past violation patterns and predicting actions. For example, the detection unit can improve its detection accuracy by learning specific patterns based on past violation data. Furthermore, the detection unit can improve its detection accuracy by predicting violations at specific times or locations based on past data. Furthermore, the detection unit can improve its detection accuracy by analyzing past violation patterns and predicting actions. In this way, detection accuracy is improved by learning past patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past violation data into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0084] The detection unit can improve detection accuracy by integrating video from multiple cameras. For example, the detection unit can integrate video from multiple cameras in real time and acquire detailed data. The detection unit can also analyze video from multiple cameras and integrate data from different angles to improve detection accuracy. Furthermore, the detection unit can improve detection accuracy by generating a 3D model based on video from multiple cameras. For example, the detection unit can integrate video from multiple cameras in real time and acquire detailed data. The detection unit can also analyze video from multiple cameras and integrate data from different angles to improve detection accuracy. Furthermore, the detection unit can improve detection accuracy by generating a 3D model based on video from multiple cameras. In this way, detection accuracy is improved by integrating video from multiple cameras. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input video data from multiple cameras into a generating AI and have the generating AI perform the video integration.

[0085] The detection unit can estimate the emotions of a person committing a violation and adjust the display method of the detection results based on the estimated emotions. For example, if the person committing the violation is tense, the detection unit can display detailed data to strengthen the warning. The detection unit can also display broad data to warn if the person committing the violation is relaxed. Furthermore, if the person committing the violation is anxious, the detection unit can display fast-moving data to warn. This improves the warning effect by adjusting the display method according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input video data acquired by a camera into a generative AI and have the generative AI perform emotion estimation.

[0086] The detection unit can improve detection accuracy by using audio data and vibration data in combination. For example, the detection unit can analyze audio data and issue an alert if it detects an abnormal sound. The detection unit can also analyze vibration data and issue an alert if it detects an abnormal vibration. Furthermore, the detection unit can integrate audio data and vibration data and issue an alert if it detects an abnormal situation. For example, the detection unit can analyze audio data and issue an alert if it detects an abnormal sound. The detection unit can also analyze vibration data and issue an alert if it detects an abnormal vibration. Furthermore, the detection unit can integrate audio data and vibration data and issue an alert if it detects an abnormal situation. This improves detection accuracy by using audio data and vibration data in combination. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input audio data and vibration data into a generating AI and have the generating AI perform anomaly detection.

[0087] The detection unit can analyze ambient environmental data to detect signs of violations at an early stage. For example, the detection unit can analyze ambient temperature data and issue an alert if it detects an abnormal temperature change. It can also analyze ambient light data and issue an alert if it detects an abnormal light change. Furthermore, the detection unit can analyze ambient humidity data and issue an alert if it detects an abnormal humidity change. This enables early detection of violations through the analysis of environmental data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input ambient environmental data into a generating AI and have the generating AI perform the detection of signs of violations.

[0088] The capture unit can estimate the emotions of a person committing a violation and adjust the timing of the capture based on the estimated emotions. For example, if the person committing the violation is tense, the capture unit can advance the capture timing to acquire detailed footage. Conversely, if the person committing the violation is relaxed, the capture unit can delay the capture timing to acquire a wider range of footage. Furthermore, if the person committing the violation is anxious, the capture unit can frequently change the capture timing to track their movements. This allows for more effective image acquisition by adjusting the capture timing according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the capture unit may be performed using AI, or not using AI. For example, the capture unit can input video data acquired by the camera into a generative AI and have the generative AI perform emotion estimation.

[0089] The capture unit can automatically adjust the image resolution to acquire the optimal image. For example, if the person committing the violation is far away, the capture unit can increase the resolution to acquire a detailed image. Also, if the person committing the violation is close by, the capture unit can decrease the resolution to acquire a wider area image. Furthermore, if the person committing the violation is moving, the capture unit can adjust the resolution to acquire a fast-moving image. For example, if the person committing the violation is far away, the capture unit can increase the resolution to acquire a detailed image. Also, if the person committing the violation is close by, the capture unit can decrease the resolution to acquire a wider area image. Furthermore, if the person committing the violation is moving, the capture unit can adjust the resolution to acquire a fast-moving image. This allows for the acquisition of the optimal image through automatic resolution adjustment. Some or all of the above processing in the capture unit may be performed using AI, for example, or without AI. For example, the capture unit can input video data acquired by the camera into a generating AI and have the generating AI perform the resolution adjustment.

[0090] The capture unit can integrate video from multiple cameras to generate high-precision images. For example, the capture unit can integrate video from multiple cameras in real time to generate detailed images. The capture unit can also analyze video from multiple cameras and integrate data from different angles to generate high-precision images. Furthermore, the capture unit can generate 3D models based on video from multiple cameras to generate high-precision images. For example, the capture unit can integrate video from multiple cameras in real time to generate detailed images. Furthermore, the capture unit can analyze video from multiple cameras and integrate data from different angles to generate high-precision images. Furthermore, the capture unit can generate 3D models based on video from multiple cameras to generate high-precision images. In this way, high-precision images are generated by integrating video from multiple cameras. Some or all of the above processing in the capture unit may be performed using AI, for example, or without AI. For example, the capture unit can input video data from multiple cameras into a generating AI and have the generating AI perform the video integration.

[0091] The capture unit can estimate the emotions of a person committing a violation and adjust the display method of the captured image based on the estimated emotions. For example, if the person committing the violation is tense, the capture unit can display a detailed image to strengthen the warning. The capture unit can also display a wide-angle image to warn if the person committing the violation is relaxed. Furthermore, if the person committing the violation is anxious, the capture unit can display a fast-moving image to warn. This improves the warning effect by adjusting the display method according to the emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the capture unit may be performed using AI, or not using AI. For example, the capture unit can input video data acquired by a camera into a generative AI and have the generative AI perform emotion estimation.

[0092] The capture unit can automatically adjust the color tone and brightness of the image to improve visibility. For example, the capture unit can increase brightness at night to improve visibility. It can also adjust color tone to improve visibility during the day. Furthermore, the capture unit can increase contrast to improve visibility in rainy weather. For example, the capture unit can increase brightness at night to improve visibility. It can also adjust color tone to improve visibility during the day. Furthermore, the capture unit can increase contrast to improve visibility in rainy weather. As a result, visibility is improved through automatic adjustment of color tone and brightness. Some or all of the above processing in the capture unit may be performed using AI, for example, or without AI. For example, the capture unit can input video data acquired by the camera into a generating AI and have the generating AI perform color tone and brightness adjustments.

[0093] The capture unit can analyze ambient environmental data to determine the optimal capture timing. For example, the capture unit can analyze ambient temperature data and capture if it detects an abnormal temperature change. It can also analyze ambient light data and capture if it detects an abnormal light change. Furthermore, it can analyze ambient humidity data and capture if it detects an abnormal humidity change. In this way, the optimal capture timing is determined by analyzing the environmental data. Some or all of the above processing in the capture unit may be performed using AI, for example, or without AI. For example, the capture unit can input ambient environmental data into a generating AI and have the generating AI determine the capture timing.

[0094] The display unit can estimate the emotions of a person committing a violation and adjust the displayed content based on the estimated emotions. For example, if the person committing the violation is tense, the display unit can display a detailed image to strengthen the warning. The display unit can also display a wide-angle image to warn if the person committing the violation is relaxed. Furthermore, if the person committing the violation is anxious, the display unit can display a fast-moving image to warn. This improves the warning effect by adjusting the displayed content according to the emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into the generative AI and have the generative AI perform emotion estimation.

[0095] The display unit can adjust the level of detail displayed based on the importance of the image. For example, in the case of a serious violation, the display unit can display a detailed image to enhance the warning. In the case of a minor violation, the display unit can also display a wide-angle image to provide a warning. Furthermore, the display unit can adjust the resolution of the displayed image according to its importance. For example, in the case of a serious violation, the display unit can display a detailed image to enhance the warning. In the case of a minor violation, the display unit can display a wide-angle image to provide a warning. Furthermore, the display unit can adjust the resolution of the displayed image according to its importance. This improves the warning effect by adjusting the level of detail of the display according to its importance. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0096] The display unit can apply different display algorithms depending on the category of the image. For example, in the case of a violation of a no-parking zone, the display unit can apply a specific display algorithm to issue a warning. The display unit can also apply a different display algorithm to issue a warning in the case of a littering violation. Furthermore, the display unit can apply yet another display algorithm to issue a warning in the case of a soliciting violation. For example, the display unit can apply a specific display algorithm to issue a warning in the case of a no-parking zone. The display unit can also apply a different display algorithm to issue a warning in the case of a littering violation. Furthermore, the display unit can apply yet another display algorithm to issue a warning in the case of a soliciting violation. This improves the warning effect by applying a display algorithm according to the category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generating AI and have the generating AI execute the application of the display algorithm.

[0097] The display unit can estimate the emotions of a person committing a violation and adjust the length of the display based on the estimated emotions. For example, if the person committing the violation is tense, the display unit can extend the length of the display to strengthen the warning. Conversely, if the person committing the violation is relaxed, the display unit can shorten the length of the display to issue a warning. Furthermore, if the person committing the violation is anxious, the display unit can frequently change the length of the display to issue a warning. This improves the warning effect by adjusting the length of the display according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generative AI and have the generative AI perform emotion estimation.

[0098] The display unit can determine the display priority based on when the images were taken. For example, the display unit may prioritize displaying images of the most recent violation. The display unit may also display images of past violations for reference. Furthermore, the display unit may adjust the order in which the images are displayed according to when they were taken. For example, the display unit may prioritize displaying images of the most recent violation. The display unit may also display images of past violations for reference. Furthermore, the display unit may adjust the order in which the images are displayed according to when they were taken. As a result, the latest information is displayed preferentially by determining the priority based on the time of capture. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input video data acquired by the camera into a generating AI and have the generating AI perform the determination of the display priority.

[0099] The display unit can adjust the display order based on the relevance of the images. For example, the display unit may prioritize the display of images of highly relevant violations. It can also display images of less relevant violations later. Furthermore, the display unit can adjust the order in which images are displayed according to their relevance. For example, the display unit may prioritize the display of images of highly relevant violations. It can also display images of less relevant violations later. Furthermore, the display unit can adjust the order in which images are displayed according to their relevance. As a result, important information is displayed preferentially by adjusting the display order based on relevance. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generating AI and have the generating AI perform the adjustment of the display order.

[0100] The placement unit can estimate the emotions of a person committing a violation and adjust the placement of cameras and monitors based on the estimated emotions. For example, if the person committing the violation is tense, the placement unit can change the placement of cameras and monitors to acquire detailed footage. Also, if the person committing the violation is relaxed, the placement unit can widen the placement of cameras and monitors to monitor a wider area. Furthermore, if the person committing the violation is anxious, the placement unit can frequently change the placement of cameras and monitors to track their movements. This improves the surveillance effect by adjusting the placement according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the placement unit may be performed using AI, for example, or not using AI. For example, the placement unit can input video data acquired by cameras into a generative AI and have the generative AI perform emotion estimation.

[0101] The deployment unit can select the optimal deployment method by referring to past violation data. For example, the deployment unit can deploy cameras and monitors to prioritize monitoring areas where violations have frequently occurred in the past. Furthermore, based on past data, the deployment unit can deploy cameras and monitors to strengthen monitoring during specific time periods. Additionally, the deployment unit can analyze patterns of past violations and deploy cameras and monitors to focus monitoring on predicted areas. This enables optimal deployment by utilizing past data. Some or all of the above processing in the deployment unit may be performed using AI, for example, or without AI. For example, the deployment unit can input past violation data into a generating AI and have the generating AI select the deployment method.

[0102] The deployment unit can estimate the emotions of a person committing a violation and determine deployment priorities based on the estimated emotions. For example, if the person committing the violation is tense, the deployment unit will deploy cameras and monitors to prioritize monitoring that area. If the person committing the violation is relaxed, the deployment unit can also deploy cameras and monitors to monitor a wider area. Furthermore, if the person committing the violation is anxious, the deployment unit can deploy cameras and monitors to track their movements. This enables effective monitoring through prioritization based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the deployment unit may be performed using AI, or not. For example, the deployment unit can input video data acquired by cameras into a generative AI and have the generative AI perform emotion estimation.

[0103] The deployment unit can select the optimal deployment method considering geographical characteristics. For example, in areas with elevation differences, the deployment unit can adjust the camera placement to reduce blind spots. In areas with many buildings, the deployment unit can also increase the number of cameras to expand the surveillance range. Furthermore, in large areas such as parks and plazas, the deployment unit can widen the camera's field of view for surveillance. For example, in areas with elevation differences, the deployment unit can adjust the camera placement to reduce blind spots. In areas with many buildings, the deployment unit can increase the number of cameras to expand the surveillance range. Furthermore, in large areas such as parks and plazas, the deployment unit can widen the camera's field of view for surveillance. This improves the surveillance effect through deployment that takes geographical characteristics into account. Some or all of the above processing in the deployment unit may be performed using AI, for example, or without AI. For example, the deployment unit can input geographical characteristics data into a generating AI and have the generating AI select the deployment method.

[0104] The application unit can estimate the emotions of a person committing a violation and adjust its scope of application based on the estimated emotions. For example, if the person committing the violation is tense, the application unit can widen its scope of application for more detailed monitoring. Conversely, if the person committing the violation is relaxed, the application unit can narrow its scope of application for wider-area monitoring. Furthermore, if the person committing the violation is anxious, the application unit can frequently change its scope of application to track their movements. This allows for effective monitoring by adjusting the scope of application according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input video data acquired by a camera into a generative AI and have the generative AI perform emotion estimation.

[0105] The application unit can optimize the scope of coverage by referring to past violation data. For example, the application unit can set the scope of coverage to prioritize monitoring areas where violations have frequently occurred in the past. The application unit can also set the scope of coverage to strengthen monitoring during specific time periods based on past data. Furthermore, the application unit can analyze patterns of past violations and set the scope of coverage to focus monitoring on predicted areas. For example, the application unit can set the scope of coverage to prioritize monitoring areas where violations have frequently occurred in the past. The application unit can also set the scope of coverage to strengthen monitoring during specific time periods based on past data. Furthermore, the application unit can analyze patterns of past violations and set the scope of coverage to focus monitoring on predicted areas. This makes it possible to optimize the scope of coverage by utilizing past data. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input past violation data into a generating AI and have the generating AI perform the optimization of the scope of coverage.

[0106] The application unit can estimate the emotions of a person committing a violation and determine the priority of the scope of application based on the estimated emotions. For example, if the person committing the violation is tense, the application unit can set the scope of application to prioritize monitoring that person. The application unit can also set the scope of application to monitor a wider area if the person committing the violation is relaxed. Furthermore, if the person committing the violation is anxious, the application unit can set the scope of application to track their movements. This enables effective monitoring by determining priorities according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI, for example, or not using AI. For example, the application unit can input video data acquired by a camera into a generative AI and have the generative AI perform emotion estimation.

[0107] The application unit can optimize its coverage area by considering geographical characteristics. For example, in areas with elevation differences, the application unit adjusts its coverage area to reduce blind spots. Furthermore, in areas with many buildings, the application unit can expand its coverage area to broaden the monitoring range. Additionally, in large areas such as parks and plazas, the application unit can expand its coverage area to perform monitoring. This improves monitoring effectiveness by optimizing the coverage area in consideration of geographical characteristics. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input geographical characteristics data into a generating AI and have the generating AI perform the optimization of the coverage area.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The monitoring unit can learn and predict the behavioral patterns of individuals committing violations. For example, the monitoring unit can learn specific behavioral patterns based on past violation data and predict future violations. The monitoring unit can also analyze behavioral patterns in real time and detect abnormal behavior. Furthermore, the monitoring unit can detect changes in behavioral patterns and enhance monitoring based on predicted behavior. This enables early detection of violations through the learning and prediction of behavioral patterns. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past violation data into a generating AI and have the generating AI perform behavioral pattern learning and prediction.

[0110] The detection unit can estimate the emotions of a person committing a violation and generate a warning message based on the estimated emotions. For example, if the detection unit is tense, it can generate a warning message in a gentle tone. It can also generate a warning message in a stern tone if the person committing the violation is relaxed. Furthermore, if the person committing the violation is anxious, it can generate a warning message urging a quick response. This improves the effectiveness of warnings by generating warning messages that are appropriate to the person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, or not using AI. For example, the detection unit can input video data acquired by a camera into a generative AI and have the generative AI perform emotion estimation.

[0111] The display unit can analyze the actions of a person committing a violation in real time and display warnings corresponding to those actions. For example, if a person committing a violation is attempting to park a bicycle in a no-parking zone, the display unit will display a warning against parking. The display unit can also display a warning against littering if a person committing a violation is attempting to throw away litter. Furthermore, the display unit can display a warning against flirting if a person committing a violation is attempting to flirt with someone. This improves the deterrent effect against violations through behavior-based warning displays. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generating AI and have the generating AI perform behavior analysis and display warnings.

[0112] The monitoring unit can estimate the emotions of a person committing a violation and adjust the zoom function of the surveillance camera based on the estimated emotions. For example, if the person committing the violation is tense, the monitoring unit can enhance the zoom function to acquire detailed footage. Conversely, if the person committing the violation is relaxed, the monitoring unit can also reduce the zoom function to monitor a wider area. Furthermore, if the person committing the violation is anxious, the monitoring unit can frequently change the zoom function to track their movements. This allows for more effective monitoring by adjusting the zoom function according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data acquired by the camera into the generative AI and have the generative AI perform emotion estimation.

[0113] The detection unit can predict the actions of a person committing a violation and issue a warning based on the predicted actions. For example, the detection unit can learn specific behavioral patterns based on past violation data and predict future violations. The detection unit can also analyze behavioral patterns in real time and detect abnormal behavior. Furthermore, the detection unit can detect changes in behavioral patterns and issue a warning based on the predicted actions. This enables early deterrence of violations through behavioral prediction and warnings. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past violation data into a generating AI and have the generating AI perform behavioral pattern learning and prediction.

[0114] The display unit can estimate the emotions of a person committing a violation and adjust the displayed content based on the estimated emotions. For example, if the person committing the violation is tense, the display unit can display a detailed image to strengthen the warning. The display unit can also display a wide-angle image to warn if the person committing the violation is relaxed. Furthermore, if the person committing the violation is anxious, the display unit can display a fast-moving image to warn. This improves the warning effect by adjusting the displayed content according to the emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generative AI and have the generative AI perform emotion estimation.

[0115] The monitoring unit can optimize the monitoring area by analyzing ambient environmental data. For example, the monitoring unit can analyze ambient temperature data and adjust the monitoring area if it detects an abnormal temperature change. It can also analyze ambient light data and adjust the monitoring area if it detects an abnormal light change. Furthermore, it can analyze ambient humidity data and adjust the monitoring area if it detects an abnormal humidity change. This makes it possible to optimize the monitoring area through the analysis of environmental data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input ambient environmental data into a generating AI and have the generating AI perform the optimization of the monitoring area.

[0116] The detection unit can estimate the emotions of a person committing a violation and adjust the detection algorithm based on the estimated emotions. For example, if the person committing the violation is tense, the detection unit can increase the sensitivity of the detection algorithm to acquire more detailed data. Conversely, if the person committing the violation is relaxed, the detection unit can also decrease the sensitivity of the detection algorithm to monitor a wider area. Furthermore, if the person committing the violation is anxious, the detection unit can increase the frame rate of the detection algorithm to acquire fast-moving data. This improves detection accuracy by adjusting the algorithm according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input video data acquired by a camera into a generative AI and have the generative AI perform emotion estimation.

[0117] The display unit can adjust the level of detail displayed based on the importance of the image. For example, in the case of a serious violation, the display unit can display a detailed image to enhance the warning. In the case of a minor violation, the display unit can also display a wide-angle image to provide a warning. Furthermore, the display unit can adjust the resolution of the displayed image according to its importance. This improves the warning effect by adjusting the level of detail according to the importance of the image. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input video data acquired by a camera into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0118] The monitoring unit can estimate the emotions of a person committing a violation and adjust the angle of the surveillance camera based on the estimated emotions. For example, if the person committing the violation is tense, the monitoring unit can lower the camera angle to obtain detailed footage. Conversely, if the person committing the violation is relaxed, the monitoring unit can widen the camera angle to monitor a wider area. Furthermore, if the person committing the violation is anxious, the monitoring unit can frequently change the camera angle to track their movements. This allows for more effective monitoring by adjusting the camera angle according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input video data acquired by the camera into the generative AI and have the generative AI perform emotion estimation.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The monitoring unit monitors no-parking zones and other areas where illegal activities may occur. The monitoring unit uses, for example, AI cameras for monitoring. The AI ​​cameras analyze video in real time and detect violations. Step 2: The detection unit analyzes the video monitored by the monitoring unit and detects any violations. The detection unit analyzes the video using, for example, an AI algorithm to detect violations. The AI ​​algorithm can utilize technologies such as deep learning or support vector machines. Step 3: The capture unit captures the scene of the violation detected by the detection unit as an image. The capture unit captures the image using, for example, a high-resolution camera. The captured image is clearly photographed so that the person committing the violation can be identified. Step 4: The display unit displays the images captured by the capture unit on a monitor. The display unit displays the images using a monitor, for example. The monitor is positioned so that the person committing the violation can see it, and the scene of the violation is displayed in real time. As a result, the person committing the violation becomes aware that they are being monitored, and a psychological deterrent effect is created.

[0121] 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the monitoring unit, detection unit, capture unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the smart device 14 to monitor no-parking zones and other locations where illegal activities may occur. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses an AI algorithm to analyze video and detect violations. The capture unit captures the scene of the violation detected by the camera 42 of the smart device 14 as an image. The display unit uses the display 40A of the smart device 14 to display the captured image on a monitor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] 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.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] 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.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the monitoring unit, detection unit, capture unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the smart glasses 214 to monitor no-parking zones and other locations where illegal activities may occur. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses an AI algorithm to analyze the video and detect violations. The capture unit captures the scene of the violation detected by the camera 42 of the smart glasses 214 as an image. The display unit uses the display of the smart glasses 214 to display the captured image on a monitor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] 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.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] 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.

[0148] 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.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the monitoring unit, detection unit, capture unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the headset terminal 314 to monitor no-parking zones and other locations where illegal activities may occur. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the video using an AI algorithm to detect violations. The capture unit captures the scene of the violation detected by the camera 42 of the headset terminal 314 as an image. The display unit uses the display 343 of the headset terminal 314 to display the captured image on a monitor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] 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.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] 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.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] 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.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the monitoring unit, detection unit, capture unit, and display unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the robot 414 to monitor no-parking zones and other locations where illegal activities may occur. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses an AI algorithm to analyze the video and detect violations. The capture unit captures the scene of the violation detected by the camera 42 of the robot 414 as an image. The display unit uses the display of the robot 414 to display the captured image on a monitor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] 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.

[0177] 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.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] 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."

[0180] 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.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] 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.

[0192] (Note 1) A monitoring unit that monitors no-parking zones and other areas where illegal activities may occur, A detection unit analyzes the video footage monitored by the aforementioned monitoring unit and detects any violations. A capture unit captures the scene of the violation detected by the aforementioned detection unit as an image, The system includes a display unit that displays the image captured by the capture unit on a monitor. A system characterized by the following features. (Note 2) The detection unit is Use an AI-powered algorithm to detect violations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is The image displayed on the monitor should be recognized as that of a person committing a violation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, The system includes a section that provides specific instructions on the placement and arrangement of AI cameras and monitors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit is It includes an application section that emphasizes that it can also be applied to other illegal acts such as littering, soliciting, and not following the rules for garbage disposal. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, The system estimates the emotions of the person committing the violation and adjusts the intensity of surveillance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, Optimize the monitoring area by referring to past violation data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, Monitoring methods are changed based on time of day and weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, The system estimates the emotions of the person committing the violation and adjusts the angle of the surveillance camera based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, Optimize the placement of surveillance cameras considering the geographical characteristics of the surveillance area. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, The system analyzes surrounding audio data to detect signs of violations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit is The system estimates the emotions of individuals committing violations and adjusts the detection algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit is Learn past violation patterns to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is Integrating video from multiple cameras improves detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is The system estimates the emotions of the person committing the violation and adjusts the display method of the detection results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is Improve detection accuracy by using audio data and vibration data in combination. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is By analyzing surrounding environmental data, signs of violations can be detected early. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned capture unit is The system estimates the emotions of the person committing the violation and adjusts the timing of the capture based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned capture unit is Automatically adjusts image resolution to obtain the optimal image. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned capture unit is High-precision images are generated by integrating video from multiple cameras. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned capture unit is The system estimates the emotions of the person committing the violation and adjusts how the captured images are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned capture unit is Automatically adjusts image color tone and brightness to improve visibility. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned capture unit is Analyze surrounding environmental data to determine the optimal capture timing. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is The system estimates the emotions of the person committing the violation and adjusts the displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is Adjust the level of detail displayed based on the importance of the image. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is Apply different display algorithms depending on the image category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is The system estimates the emotions of the person committing the violation and adjusts the length of the display based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is The display priority is determined based on when the images were taken. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is Adjust the display order based on the relevance of the images. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned arrangement section is, The system estimates the emotions of the person committing the violation and adjusts the placement of cameras and monitors based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned arrangement section is, Select the optimal placement method by referring to past violation data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned arrangement section is, The system estimates the emotions of individuals committing violations and determines placement priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned arrangement section is, Select the optimal placement method considering geographical characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned application unit is Estimate the emotions of the person committing the violation and adjust the scope of application based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned application unit is Optimize the scope of application by referring to past violation data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned application unit is The system estimates the emotions of the person committing the violation and determines the priority of the scope of application based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned application unit is Optimize the scope of application by considering geographical characteristics. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors no-parking zones and other areas where illegal activities may occur, A detection unit analyzes the video footage monitored by the aforementioned monitoring unit and detects any violations. A capture unit captures the scene of the violation detected by the aforementioned detection unit as an image, The system includes a display unit that displays the image captured by the capture unit on a monitor. A system characterized by the following features.

2. The detection unit is Use an AI-powered algorithm to detect violations. The system according to feature 1.

3. The aforementioned display unit is The image displayed on the monitor should be recognized as that of a person committing a violation. The system according to feature 1.

4. The aforementioned monitoring unit, It includes a section that specifically explains the installation location and placement method of the AI ​​camera and monitor. The system according to feature 1.

5. The detection unit is It includes an application section that emphasizes that it can also be applied to other illegal acts such as littering, soliciting, and not following the rules for garbage disposal. The system according to feature 1.

6. The aforementioned monitoring unit, The system estimates the emotions of the person committing the violation and adjusts the intensity of surveillance based on those estimated emotions. The system according to feature 1.

7. The aforementioned monitoring unit, Optimize the monitoring area by referring to past violation data. The system according to feature 1.

8. The aforementioned monitoring unit, Monitoring methods are changed based on time of day and weather conditions. The system according to feature 1.

9. The aforementioned monitoring unit, The system estimates the emotions of the person committing the violation and adjusts the angle of the surveillance camera based on those estimated emotions. The system according to feature 1.

10. The aforementioned monitoring unit, Optimize the placement of surveillance cameras considering the geographical characteristics of the surveillance area. The system according to feature 1.

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