system

Abstract

The system according to this embodiment aims to efficiently detect suspicious individuals from security camera footage and extract only the necessary parts. [Solution] The system according to the embodiment comprises a collection unit, a detection unit, a cropping unit, and a report creation unit. The collection unit collects images from security cameras. The detection unit analyzes the images collected by the collection unit and detects suspicious individuals. The cropping unit crops out only the necessary parts based on the suspicious individuals detected by the detection unit. The report creation unit creates a report based on the images cropped by the cropping unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 prior art, there is a problem that the video of the security camera is huge, and it is difficult to efficiently detect suspicious persons and extract only the necessary parts.

[0005] The system according to the embodiment aims to efficiently detect suspicious persons from the video of the security camera and extract only the necessary parts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a detection unit, a cropping unit, and a report generation unit. The collection unit collects video footage from security cameras. The detection unit analyzes the video footage collected by the collection unit and detects suspicious individuals. The cropping unit crops out only the necessary parts based on the suspicious individuals detected by the detection unit. The report generation unit creates a report based on the video footage cropped by the cropping unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently detect suspicious individuals from security camera footage and extract only the necessary parts. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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 program 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 shoplifting prevention AI according to an embodiment of the present invention is a security system for supermarkets and convenience stores. This security system reads video from multiple security cameras and provides the following functions. First, it detects suspicious individuals and contacts the person in charge. The AI ​​analyzes the security camera footage and identifies individuals exhibiting abnormal behavior. For example, it detects behaviors such as picking up an item and immediately putting it back, or walking around the store in an unnatural manner. This allows for the early detection of individuals who may be shoplifting and notification to the person in charge. Next, it extracts only the necessary parts from a large amount of video to generate a small-capacity video. The AI ​​analyzes the security camera footage and extracts only the parts that contain the moment of shoplifting or suspicious behavior. This allows for the efficient saving of only the parts necessary as evidence and reduces data size. Furthermore, it creates a report for identifying the perpetrator. The AI ​​records the actions of the detected suspicious person in detail and compiles them into a report. For example, it describes what actions were taken at what time, where, and when. This makes it easier to identify the perpetrator and facilitates reporting to the police and submitting evidence. In this way, the Shoplifting Prevention AI aims to eliminate shoplifting by detecting suspicious individuals, extracting necessary video clips, and creating reports to identify perpetrators. This enables the Shoplifting Prevention AI to efficiently collect, analyze, extract, and report on security camera footage.

[0029] The shoplifting prevention AI according to this embodiment comprises a collection unit, a detection unit, a cropping unit, and a report generation unit. The collection unit collects video from security cameras. The collection unit can, for example, collect video from multiple security cameras. The collection unit can, for example, use cameras that cover the entire area of ​​the store or cameras that concentrate on a specific area. The collection unit can, for example, use AI to efficiently collect video from security cameras. The detection unit analyzes the video collected by the collection unit and detects suspicious individuals. The detection unit, for example, uses AI to analyze the video from security cameras and identify individuals exhibiting abnormal behavior. The detection unit can, for example, detect behaviors such as picking up an item and immediately putting it back, or walking around the store unnaturally. The detection unit can, for example, use AI to detect individuals exhibiting abnormal behavior early and notify the person in charge. The cropping unit crops out only the necessary parts based on the suspicious individuals detected by the detection unit. The clipping unit can, for example, use AI to analyze security camera footage and extract only the parts containing the moment of shoplifting or suspicious behavior. The clipping unit can, for example, efficiently save only the parts necessary as evidence, thereby reducing data size. The report creation unit creates a report based on the footage clipped by the clipping unit. The report creation unit can, for example, use AI to record in detail the actions of the suspicious person detected and compile them into a report. The report creation unit can, for example, describe what actions were taken, where, and at what time of day. As a result, the shoplifting prevention AI according to this embodiment can efficiently collect, analyze, clip, and create reports from security camera footage.

[0030] The data collection unit collects footage from security cameras. For example, the unit can collect footage from multiple security cameras. Specifically, it can use cameras that cover the entire store or cameras concentrated in specific areas. This ensures thorough surveillance of every corner of the store, reducing the risk of shoplifting. The data collection unit can efficiently collect security camera footage using AI. The AI ​​automatically adjusts the video quality and resolution to collect data in optimal conditions. For example, it can correct for distortions caused by changes in lighting or camera position, resulting in clear images. Furthermore, the data collection unit collects footage in real time and transmits it to a central database. This ensures that the latest footage is always stored and can be used for subsequent analysis and detection. Additionally, the data collection unit can efficiently manage data volume using video data compression technology. This allows for the long-term storage of large amounts of video data, enabling retrospective review of past footage. The data collection unit can also collaborate with other systems and departments, sharing data as needed. For example, it can collaborate with police and security companies, providing collected video data to enable a rapid response. This allows the data collection unit to efficiently and effectively collect security camera footage, thereby improving the overall performance of the system.

[0031] The detection unit analyzes the video footage collected by the collection unit to detect suspicious individuals. The detection unit uses AI to analyze security camera footage and identify individuals exhibiting abnormal behavior. Specifically, it can detect actions such as picking up an item and immediately putting it back, or walking around the store unnaturally. The AI ​​tracks the movements of individuals in the video and detects abnormalities by comparing them to normal behavior patterns. For example, the AI ​​analyzes the speed, direction, and duration of a person's movement to identify unusual behavior. Furthermore, the AI ​​can learn from past data and model typical shoplifting patterns, enabling more accurate detection. The detection unit can detect individuals exhibiting abnormal behavior early and notify the responsible personnel. Notifications are made in real time, allowing personnel to respond immediately. For example, alerts can be sent to smartphones or tablets, displaying the location and behavior of the suspicious individual. The detection unit can also incorporate a feedback loop to continuously improve the accuracy of abnormal behavior detection. This allows the detection unit to retrain the AI ​​model based on collected data, further improving detection accuracy. This allows the detection unit to quickly and accurately analyze the collected video footage and identify suspicious individuals early on.

[0032] The clipping unit extracts only the necessary parts based on the suspicious person detected by the detection unit. Using AI, the clipping unit analyzes security camera footage and can extract only the moments of shoplifting or other suspicious behavior. Specifically, the AI ​​tracks the movement of people in the footage and identifies the time and location where abnormal behavior occurred. This allows for efficient extraction of only the necessary parts from long periods of footage. The clipping unit efficiently saves only the necessary parts as evidence, reducing data size. For example, it can extract and save footage from the few minutes before and after the moment of shoplifting or suspicious behavior. Furthermore, the clipping unit can use video compression technology to optimize data size while maintaining video quality. This ensures that saved footage can be played back in high quality when reviewed later. Additionally, the clipping unit can share the extracted footage with other systems and departments. For example, providing it to the police or security companies enables a quicker response. In summary, the clipping unit efficiently extracts only the necessary parts and saves them as evidence.

[0033] The reporting department creates reports based on the video clippings made by the clipping department. The reporting department can meticulously record the actions of suspicious individuals detected using AI and compile them into reports. Specifically, it can record what actions were taken, where, and at what time. The AI ​​analyzes the movements of the person in the video and creates a detailed timeline of their actions. For example, it can meticulously record the time when an item was picked up, the route the person walked around the store, and the moment of shoplifting. The AI ​​can also evaluate the abnormality and risk level of the actions and reflect this in the report. This allows the person in charge to grasp the actions of suspicious individuals at a glance. The reporting department can save the created reports in digital format and print them as needed. Furthermore, the reporting department can collaborate with other systems and departments to share reports. For example, providing them to the police or security companies enables a rapid response. This allows the reporting department to meticulously record the actions of suspicious individuals and create reports efficiently.

[0034] The detection unit can analyze security camera footage and identify individuals exhibiting abnormal behavior. For example, the detection unit can use AI to analyze security camera footage and identify individuals exhibiting abnormal behavior. The detection unit can detect behaviors such as picking up an item and immediately putting it back, or walking around the store in an unnatural manner. The detection unit can use AI to detect individuals exhibiting abnormal behavior early and notify the responsible person. This allows for the early detection of shoplifting by identifying individuals exhibiting abnormal behavior. Some or all of the above-described processes in the detection unit may be performed using AI, or without AI. For example, the detection unit can input security camera footage into AI and have the AI ​​identify individuals exhibiting abnormal behavior.

[0035] The cropping unit can crop out only the necessary parts from the video detected by the detection unit. For example, the cropping unit can use AI to analyze security camera footage and extract only the parts containing the moment of shoplifting or suspicious behavior. For example, the cropping unit can efficiently save only the parts necessary as evidence, thereby reducing data size. This reduces data size by efficiently cropping out only the necessary parts. Some or all of the above processing in the cropping unit may be performed using AI, for example, or without AI. For example, the cropping unit can input security camera footage into AI and have the AI ​​perform the extraction of the necessary parts.

[0036] The report generation unit can create a report based on the video clipping unit. The report generation unit can, for example, meticulously record the actions of a suspicious person detected using AI and compile them into a report. The report generation unit can, for example, describe what actions were taken, where, and at what time. This streamlines report creation and makes it easier to identify the perpetrator. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input the clipped video into the AI ​​and have the AI ​​create the report.

[0037] The collection unit can collect video from multiple security cameras. The collection unit can use cameras that cover the entire store area or cameras that concentrate on a specific area. The collection unit can efficiently collect video from security cameras using AI, for example. This enables wide-area surveillance by collecting video from multiple security cameras. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input video from multiple security cameras into the AI ​​and have the AI ​​perform the video collection.

[0038] The detection unit can detect behaviors such as picking up an item and immediately putting it back, or wandering around the store in an unnatural manner. For example, the detection unit can use AI to analyze security camera footage and detect behaviors such as picking up an item and immediately putting it back, or wandering around the store in an unnatural manner. For example, the detection unit can use AI to quickly identify individuals exhibiting abnormal behavior and notify the responsible person. This allows for the early identification of individuals with a high probability of shoplifting by detecting specific behavioral patterns. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input security camera footage into AI and have the AI ​​perform the detection of specific behavioral patterns.

[0039] The collection unit can dynamically adjust the installation positions of security cameras to collect optimal video footage. For example, the collection unit can use AI to dynamically adjust the installation positions of security cameras and collect optimal video footage. For example, the collection unit can automatically adjust the camera installation positions according to the level of congestion in the store and collect optimal video footage. For example, if abnormal behavior is detected in a specific area, the collection unit can concentrate cameras in that area. For example, the collection unit can dynamically adjust the camera installation positions according to changes in the store layout. This enables optimal video collection by dynamically adjusting the installation positions of security cameras. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the installation positions of security cameras into the AI ​​and have the AI ​​perform the adjustment of the installation positions.

[0040] The collection unit can change its video collection method based on specific time periods or days of the week. For example, the collection unit can use AI to change the collection method based on specific time periods or days of the week when collecting video. For example, the collection unit can use a normal collection method during weekday daytime hours and increase the collection frequency at night or on weekends. For example, the collection unit can enhance its collection method to collect more detailed video on days when specific events are held. For example, the collection unit can change its collection method based on past data to capture times when shoplifting is most likely to occur. This allows for efficient video collection by changing the collection method based on specific time periods or days of the week. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data for specific time periods or days of the week into the AI ​​and have the AI ​​execute the changes to the collection method.

[0041] The collection unit can adjust the collection method based on weather and lighting conditions when collecting video. For example, the collection unit can use AI to adjust the collection method based on weather and lighting conditions when collecting video. For example, the collection unit can prioritize indoor video collection during rainy weather. For example, the collection unit can use an infrared camera to collect video when the lighting is dim. For example, the collection unit can enhance outdoor video collection during sunny weather. This allows for optimal video collection by adjusting the collection method according to weather and lighting conditions. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input weather and lighting condition data into the AI ​​and have the AI ​​adjust the collection method.

[0042] The collection unit can analyze the store's congestion level during video collection and select the optimal collection method. For example, the collection unit can use AI to analyze the store's congestion level during video collection and select the optimal collection method. For example, the collection unit can collect wide-area video when the store is crowded. For example, the collection unit can focus on collecting video from a specific area when the store is empty. For example, the collection unit can adjust the camera's collection angle according to the congestion level. This enables efficient video collection by selecting the collection method according to the store's congestion level. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the store's congestion level into AI and have AI select the collection method.

[0043] The detection unit can optimize its detection algorithm based on past shoplifting data when detecting shoplifting. The detection unit can optimize its detection algorithm based on past shoplifting data when detecting shoplifting, for example, by using AI. The detection unit can optimize its algorithm for detecting specific behavioral patterns based on past shoplifting data. The detection unit can predict shoplifting that occurs at specific times or days of the week from past data and adjust its detection algorithm accordingly. The detection unit can analyze past data and develop algorithms to deal with new shoplifting methods. This improves detection accuracy by optimizing the detection algorithm based on past shoplifting data. Some or all of the above processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past shoplifting data into AI and have AI perform the optimization of the detection algorithm.

[0044] The detection unit can identify abnormal behavior based on the person's attribute information at the time of detection. The detection unit can, for example, use AI to identify abnormal behavior based on the person's attribute information at the time of detection. The detection unit can, for example, detect specific behavioral patterns based on age and gender. The detection unit can, for example, adjust the detection criteria for abnormal behavior based on attribute information. The detection unit can, for example, detect abnormal behavior for a specific person at an early stage by considering attribute information. This improves detection accuracy by identifying abnormal behavior based on the person's attribute information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the person's attribute information into AI and have the AI ​​perform the identification of abnormal behavior.

[0045] The detection unit can identify abnormal behavior based on the store's layout information at the time of detection. The detection unit can, for example, use AI to identify abnormal behavior based on the store's layout information at the time of detection. The detection unit can, for example, detect abnormal behavior in a specific area based on the store's layout information. The detection unit can, for example, adjust the detection criteria for abnormal behavior in response to layout changes. The detection unit can, for example, take layout information into consideration to perform detection of abnormal behavior focused on a specific area. This improves detection accuracy by identifying abnormal behavior based on the store's layout information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the store's layout information into the AI ​​and have the AI ​​perform the identification of abnormal behavior.

[0046] The detection unit can identify abnormal behavior in cooperation with other security systems upon detection. For example, the detection unit can use AI to identify abnormal behavior in cooperation with other security systems upon detection. The detection unit can, for example, cooperate with an alarm system to trigger an alarm when abnormal behavior is detected. The detection unit can improve the accuracy of abnormal behavior detection based on data from other security systems. For example, if abnormal behavior is detected, the detection unit can respond quickly in cooperation with other security systems. This improves the accuracy of abnormal behavior detection through cooperation with other security systems. Some or all of the above-described processes in the detection unit may be performed using AI, or without AI. For example, the detection unit can input data from other security systems into the AI ​​and have the AI ​​identify abnormal behavior.

[0047] The cropping function can optimize the image quality of the video during cropping and extract the necessary parts. For example, the cropping function can use AI to optimize the image quality of the video during cropping and extract the necessary parts. For example, the cropping function can adjust the resolution of the video and crop the necessary parts in high quality. For example, the cropping function can remove noise from the video and crop a clear image. For example, the cropping function can adjust the brightness and contrast of the video and crop with optimal image quality. In this way, by optimizing the image quality of the video, the necessary parts can be extracted in high quality. Some or all of the above processing in the cropping function may be performed using AI, for example, or without using AI. For example, the cropping function can input video quality data into AI and have the AI ​​perform image quality optimization.

[0048] The cropping unit can integrate multiple camera feeds to perform optimal cropping. For example, the cropping unit can use AI to integrate multiple camera feeds to perform optimal cropping. For example, the cropping unit can integrate multiple camera feeds and crop footage from different angles. For example, the cropping unit can analyze multiple camera feeds and crop the most important part. For example, the cropping unit can combine multiple camera feeds to crop the image in a way that allows for an overall view. This allows for efficient cropping of footage from different angles by integrating multiple camera feeds. Some or all of the above-described processes in the cropping unit may be performed using AI, or not. For example, the cropping unit can input multiple camera feeds into an AI and have the AI ​​perform the video integration and cropping.

[0049] The cropping function can adjust the video's time axis during cropping to extract the optimal portion. For example, the cropping function can use AI to adjust the video's time axis during cropping to extract the optimal portion. For example, the cropping function can adjust the video's time axis to extract important parts. For example, the cropping function can compress the video's time axis to display important parts in a shorter time. For example, the cropping function can enlarge the video's time axis to extract detailed parts. This allows for efficient extraction of important parts by adjusting the video's time axis. Some or all of the above processing in the cropping function may be performed using AI, or without AI. For example, the cropping function can input video time axis data into AI and have AI perform the time axis adjustment.

[0050] The clipping unit can analyze the audio data of the video during the clipping process to extract the necessary parts. For example, the clipping unit can use AI to analyze the audio data of the video during the clipping process to extract the necessary parts. For example, the clipping unit can analyze the audio data of the video and extract parts containing important conversations or sounds. For example, the clipping unit can detect abnormal sounds based on the audio data and clip those parts. For example, the clipping unit can analyze the audio data and extract parts containing specific keywords. This allows for the efficient extraction of parts containing important conversations or sounds by analyzing the audio data. Some or all of the above-described processes in the clipping unit may be performed using AI, or not. For example, the clipping unit can input audio data into AI and have the AI ​​perform the extraction of the necessary parts.

[0051] The report creation unit can select the optimal report format based on past report data when creating a report. For example, the report creation unit can use AI to select the optimal report format based on past report data when creating a report. For example, the report creation unit can select the most effective report format based on past report data. For example, the report creation unit can suggest a report format suitable for a specific situation based on past report data. For example, the report creation unit can analyze past report data and develop a new report format. This allows for the creation of effective reports by selecting the optimal report format based on past report data. Some or all of the above processes in the report creation unit may be performed using AI, or not. For example, the report creation unit can input past report data into AI and have the AI ​​perform the report format selection.

[0052] The report generation unit can automatically add video metadata when generating a report. For example, the report generation unit can use AI to automatically add video metadata when generating a report. For example, the report generation unit can automatically add the date and time the video was shot to the report. For example, the report generation unit can automatically add the location where the video was shot to the report. For example, the report generation unit can create a detailed report based on the video metadata. This allows for the efficient creation of detailed reports by automatically adding video metadata. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input video metadata into AI and have AI perform the metadata addition.

[0053] The report generation unit can create reports by integrating data from other security systems. The report generation unit can, for example, use AI to integrate data from other security systems when creating reports. The report generation unit can, for example, create detailed reports based on data from other security systems. The report generation unit can, for example, collaborate with other security systems and create reports based on integrated data. The report generation unit can, for example, analyze data from other security systems to create optimal reports. This allows for the efficient creation of detailed reports by integrating data from other security systems. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input data from other security systems into AI and have the AI ​​create the reports.

[0054] The report creation unit can select the method of sending the report when creating the report. The report creation unit can, for example, use AI to select the method of sending the report when creating the report. The report creation unit can, for example, select the method of sending the report by email. The report creation unit can, for example, select the method of sharing the report on the cloud. The report creation unit can, for example, select the method of sending the report according to the user's needs. This makes it possible to send reports according to the user's needs by selecting the method of sending the report. Some or all of the above processes in the report creation unit may be performed using AI, for example, or without using AI. For example, the report creation unit can input data on the sending method into the AI ​​and have the AI ​​perform the selection of the sending method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The shoplifting detection AI can also be equipped with an audio analysis unit. The audio analysis unit can analyze audio data contained in security camera footage and detect unusual sounds and conversations. For example, the audio analysis unit can detect the sound of items being put into bags or unnatural conversations with store employees. This improves the accuracy of shoplifting detection by utilizing not only video but also audio data. The audio analysis unit can, for example, use AI to analyze audio data and detect unusual sounds and conversations. The audio analysis unit can, for example, detect conversations containing specific keywords and notify the responsible person. The audio analysis unit can, for example, identify individuals exhibiting unusual behavior based on audio data.

[0057] The shoplifting prevention AI can also be equipped with a facial recognition unit. The facial recognition unit can recognize the faces of people captured in security camera footage and identify individuals suspected of shoplifting in the past. For example, the facial recognition unit can compare the footage with a database of past shoplifting incidents to identify the same person. This allows for the early detection of individuals with a high probability of recidivism and notification to the responsible personnel. The facial recognition unit can, for example, use AI to perform facial recognition and compare it with a database of past incidents. The facial recognition unit can, for example, detect individuals with specific characteristics and issue a warning. The facial recognition unit can, for example, integrate footage from multiple cameras to improve the accuracy of facial recognition.

[0058] The shoplifting prevention AI can also be equipped with a behavior prediction unit. The behavior prediction unit can analyze security camera footage and predict a person's actions. For example, the behavior prediction unit can predict what action will be taken next based on past behavior patterns. This allows for the detection of actions that are likely to be shoplifting and notification to the person in charge. The behavior prediction unit can, for example, use AI to analyze behavior patterns and make predictions. The behavior prediction unit can, for example, predict actions in a specific area and adjust the camera focus accordingly. The behavior prediction unit can, for example, issue a warning if abnormal behavior is predicted.

[0059] The shoplifting prevention AI can also be equipped with a temperature sensor. The temperature sensor can monitor the temperature inside the store and detect abnormal temperature changes. For example, if there is a sudden temperature change in a specific area, the temperature sensor can concentrate cameras in that area. This allows for the early detection of abnormal behavior based on temperature changes and notification to the responsible personnel. The temperature sensor can, for example, analyze temperature data using AI to detect abnormal temperature changes. The temperature sensor can, for example, issue a warning if the temperature exceeds a specific temperature range. The temperature sensor can, for example, predict the possibility of abnormal behavior based on temperature data.

[0060] The shoplifting prevention AI can also be equipped with a vibration sensor. The vibration sensor can monitor vibrations within the store and detect abnormal vibrations. For example, the vibration sensor can monitor the vibrations of product shelves, and if abnormal vibrations are detected, it can concentrate cameras in that area. This allows for the early detection of abnormal behavior based on vibrations and notification to the responsible personnel. The vibration sensor can, for example, analyze vibration data using AI to detect abnormal vibrations. The vibration sensor can, for example, detect specific vibration patterns and issue warnings. The vibration sensor can, for example, predict the possibility of abnormal behavior based on vibration data.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects footage from security cameras. The collection unit can, for example, collect footage from multiple security cameras. The collection unit can, for example, use cameras that cover the entire store or cameras that concentrate on a specific area. The collection unit can, for example, use AI to efficiently collect footage from security cameras. Step 2: The detection unit analyzes the video footage collected by the collection unit to detect suspicious individuals. The detection unit can, for example, use AI to analyze security camera footage and identify individuals exhibiting abnormal behavior. The detection unit can detect behaviors such as picking up an item and immediately putting it back, or wandering around the store in an unnatural manner. The detection unit can, for example, use AI to quickly identify individuals exhibiting abnormal behavior and notify the responsible person. Step 3: The clipping unit cuts out only the necessary parts based on the suspicious person detected by the detection unit. For example, the clipping unit can use AI to analyze security camera footage and extract only the parts containing the moment of shoplifting or suspicious behavior. For example, the clipping unit can efficiently save only the parts necessary as evidence, reducing data size. Step 4: The report creation unit creates a report based on the footage extracted by the clipping unit. The report creation unit can, for example, meticulously record the actions of a suspicious person detected using AI and compile them into a report. The report creation unit can, for example, describe what actions were taken at what time, where, and when.

[0063] (Example of form 2) The shoplifting prevention AI according to an embodiment of the present invention is a security system for supermarkets and convenience stores. This security system reads video from multiple security cameras and provides the following functions. First, it detects suspicious individuals and contacts the person in charge. The AI ​​analyzes the security camera footage and identifies individuals exhibiting abnormal behavior. For example, it detects behaviors such as picking up an item and immediately putting it back, or walking around the store in an unnatural manner. This allows for the early detection of individuals who may be shoplifting and notification to the person in charge. Next, it extracts only the necessary parts from a large amount of video to generate a small-capacity video. The AI ​​analyzes the security camera footage and extracts only the parts that contain the moment of shoplifting or suspicious behavior. This allows for the efficient saving of only the parts necessary as evidence and reduces data size. Furthermore, it creates a report for identifying the perpetrator. The AI ​​records the actions of the detected suspicious person in detail and compiles them into a report. For example, it describes what actions were taken at what time, where, and when. This makes it easier to identify the perpetrator and facilitates reporting to the police and submitting evidence. In this way, the Shoplifting Prevention AI aims to eliminate shoplifting by detecting suspicious individuals, extracting necessary video clips, and creating reports to identify perpetrators. This enables the Shoplifting Prevention AI to efficiently collect, analyze, extract, and report on security camera footage.

[0064] The shoplifting prevention AI according to this embodiment comprises a collection unit, a detection unit, a cropping unit, and a report generation unit. The collection unit collects video from security cameras. The collection unit can, for example, collect video from multiple security cameras. The collection unit can, for example, use cameras that cover the entire area of ​​the store or cameras that concentrate on a specific area. The collection unit can, for example, use AI to efficiently collect video from security cameras. The detection unit analyzes the video collected by the collection unit and detects suspicious individuals. The detection unit, for example, uses AI to analyze the video from security cameras and identify individuals exhibiting abnormal behavior. The detection unit can, for example, detect behaviors such as picking up an item and immediately putting it back, or walking around the store unnaturally. The detection unit can, for example, use AI to detect individuals exhibiting abnormal behavior early and notify the person in charge. The cropping unit crops out only the necessary parts based on the suspicious individuals detected by the detection unit. The clipping unit can, for example, use AI to analyze security camera footage and extract only the parts containing the moment of shoplifting or suspicious behavior. The clipping unit can, for example, efficiently save only the parts necessary as evidence, thereby reducing data size. The report creation unit creates a report based on the footage clipped by the clipping unit. The report creation unit can, for example, use AI to record in detail the actions of the suspicious person detected and compile them into a report. The report creation unit can, for example, describe what actions were taken, where, and at what time of day. As a result, the shoplifting prevention AI according to this embodiment can efficiently collect, analyze, clip, and create reports from security camera footage.

[0065] The data collection unit collects footage from security cameras. For example, the unit can collect footage from multiple security cameras. Specifically, it can use cameras that cover the entire store or cameras concentrated in specific areas. This ensures thorough surveillance of every corner of the store, reducing the risk of shoplifting. The data collection unit can efficiently collect security camera footage using AI. The AI ​​automatically adjusts the video quality and resolution to collect data in optimal conditions. For example, it can correct for distortions caused by changes in lighting or camera position, resulting in clear images. Furthermore, the data collection unit collects footage in real time and transmits it to a central database. This ensures that the latest footage is always stored and can be used for subsequent analysis and detection. Additionally, the data collection unit can efficiently manage data volume using video data compression technology. This allows for the long-term storage of large amounts of video data, enabling retrospective review of past footage. The data collection unit can also collaborate with other systems and departments, sharing data as needed. For example, it can collaborate with police and security companies, providing collected video data to enable a rapid response. This allows the data collection unit to efficiently and effectively collect security camera footage, thereby improving the overall performance of the system.

[0066] The detection unit analyzes the video footage collected by the collection unit to detect suspicious individuals. The detection unit uses AI to analyze security camera footage and identify individuals exhibiting abnormal behavior. Specifically, it can detect actions such as picking up an item and immediately putting it back, or walking around the store unnaturally. The AI ​​tracks the movements of individuals in the video and detects abnormalities by comparing them to normal behavior patterns. For example, the AI ​​analyzes the speed, direction, and duration of a person's movement to identify unusual behavior. Furthermore, the AI ​​can learn from past data and model typical shoplifting patterns, enabling more accurate detection. The detection unit can detect individuals exhibiting abnormal behavior early and notify the responsible personnel. Notifications are made in real time, allowing personnel to respond immediately. For example, alerts can be sent to smartphones or tablets, displaying the location and behavior of the suspicious individual. The detection unit can also incorporate a feedback loop to continuously improve the accuracy of abnormal behavior detection. This allows the detection unit to retrain the AI ​​model based on collected data, further improving detection accuracy. This allows the detection unit to quickly and accurately analyze the collected video footage and identify suspicious individuals early on.

[0067] The clipping unit extracts only the necessary parts based on the suspicious person detected by the detection unit. Using AI, the clipping unit analyzes security camera footage and can extract only the moments of shoplifting or other suspicious behavior. Specifically, the AI ​​tracks the movement of people in the footage and identifies the time and location where abnormal behavior occurred. This allows for efficient extraction of only the necessary parts from long periods of footage. The clipping unit efficiently saves only the necessary parts as evidence, reducing data size. For example, it can extract and save footage from the few minutes before and after the moment of shoplifting or suspicious behavior. Furthermore, the clipping unit can use video compression technology to optimize data size while maintaining video quality. This ensures that saved footage can be played back in high quality when reviewed later. Additionally, the clipping unit can share the extracted footage with other systems and departments. For example, providing it to the police or security companies enables a quicker response. In summary, the clipping unit efficiently extracts only the necessary parts and saves them as evidence.

[0068] The reporting department creates reports based on the video clippings made by the clipping department. The reporting department can meticulously record the actions of suspicious individuals detected using AI and compile them into reports. Specifically, it can record what actions were taken, where, and at what time. The AI ​​analyzes the movements of the person in the video and creates a detailed timeline of their actions. For example, it can meticulously record the time when an item was picked up, the route the person walked around the store, and the moment of shoplifting. The AI ​​can also evaluate the abnormality and risk level of the actions and reflect this in the report. This allows the person in charge to grasp the actions of suspicious individuals at a glance. The reporting department can save the created reports in digital format and print them as needed. Furthermore, the reporting department can collaborate with other systems and departments to share reports. For example, providing them to the police or security companies enables a rapid response. This allows the reporting department to meticulously record the actions of suspicious individuals and create reports efficiently.

[0069] The detection unit can analyze security camera footage and identify individuals exhibiting abnormal behavior. For example, the detection unit can use AI to analyze security camera footage and identify individuals exhibiting abnormal behavior. The detection unit can detect behaviors such as picking up an item and immediately putting it back, or walking around the store in an unnatural manner. The detection unit can use AI to detect individuals exhibiting abnormal behavior early and notify the responsible person. This allows for the early detection of shoplifting by identifying individuals exhibiting abnormal behavior. Some or all of the above-described processes in the detection unit may be performed using AI, or without AI. For example, the detection unit can input security camera footage into AI and have the AI ​​identify individuals exhibiting abnormal behavior.

[0070] The cropping unit can crop out only the necessary parts from the video detected by the detection unit. For example, the cropping unit can use AI to analyze security camera footage and extract only the parts containing the moment of shoplifting or suspicious behavior. For example, the cropping unit can efficiently save only the parts necessary as evidence, thereby reducing data size. This reduces data size by efficiently cropping out only the necessary parts. Some or all of the above processing in the cropping unit may be performed using AI, for example, or without AI. For example, the cropping unit can input security camera footage into AI and have the AI ​​perform the extraction of the necessary parts.

[0071] The report generation unit can create a report based on the video clipping unit. The report generation unit can, for example, meticulously record the actions of a suspicious person detected using AI and compile them into a report. The report generation unit can, for example, describe what actions were taken, where, and at what time. This streamlines report creation and makes it easier to identify the perpetrator. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input the clipped video into the AI ​​and have the AI ​​create the report.

[0072] The collection unit can collect video from multiple security cameras. The collection unit can use cameras that cover the entire store area or cameras that concentrate on a specific area. The collection unit can efficiently collect video from security cameras using AI, for example. This enables wide-area surveillance by collecting video from multiple security cameras. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input video from multiple security cameras into the AI ​​and have the AI ​​perform the video collection.

[0073] The detection unit can detect behaviors such as picking up an item and immediately putting it back, or wandering around the store in an unnatural manner. For example, the detection unit can use AI to analyze security camera footage and detect behaviors such as picking up an item and immediately putting it back, or wandering around the store in an unnatural manner. For example, the detection unit can use AI to quickly identify individuals exhibiting abnormal behavior and notify the responsible person. This allows for the early identification of individuals with a high probability of shoplifting by detecting specific behavioral patterns. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input security camera footage into AI and have the AI ​​perform the detection of specific behavioral patterns.

[0074] The collection unit can estimate the user's emotions and adjust the timing of security camera video collection based on the estimated user emotions. For example, the collection unit uses AI to estimate the user's emotions and adjusts the timing of security camera video collection based on the estimated user emotions. For example, if the user is tense, the collection unit can collect video more frequently to capture detailed footage. For example, if the user is relaxed, the collection unit can collect video at longer intervals to capture only the necessary footage. For example, if the user is in a hurry, the collection unit can shorten the collection intervals to quickly capture footage. By adjusting the video collection timing according to the user's emotions, more effective surveillance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of video collection.

[0075] The collection unit can dynamically adjust the installation positions of security cameras to collect optimal video footage. For example, the collection unit can use AI to dynamically adjust the installation positions of security cameras and collect optimal video footage. For example, the collection unit can automatically adjust the camera installation positions according to the level of congestion in the store and collect optimal video footage. For example, if abnormal behavior is detected in a specific area, the collection unit can concentrate cameras in that area. For example, the collection unit can dynamically adjust the camera installation positions according to changes in the store layout. This enables optimal video collection by dynamically adjusting the installation positions of security cameras. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the installation positions of security cameras into the AI ​​and have the AI ​​perform the adjustment of the installation positions.

[0076] The collection unit can change its video collection method based on specific time periods or days of the week. For example, the collection unit can use AI to change the collection method based on specific time periods or days of the week when collecting video. For example, the collection unit can use a normal collection method during weekday daytime hours and increase the collection frequency at night or on weekends. For example, the collection unit can enhance its collection method to collect more detailed video on days when specific events are held. For example, the collection unit can change its collection method based on past data to capture times when shoplifting is most likely to occur. This allows for efficient video collection by changing the collection method based on specific time periods or days of the week. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data for specific time periods or days of the week into the AI ​​and have the AI ​​execute the changes to the collection method.

[0077] The collection unit can estimate the user's emotions and determine the priority of the videos to collect based on the estimated user emotions. For example, the collection unit can use AI to estimate the user's emotions and determine the priority of the videos to collect based on the estimated user emotions. For example, if the user is tense, the collection unit can prioritize collecting videos of important areas. For example, if the user is relaxed, the collection unit can collect videos with normal priority. For example, if the user is in a hurry, the collection unit can quickly collect important videos. In this way, important videos can be collected preferentially by determining the priority of the videos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input user emotion data into AI and have the AI ​​perform the determination of video priority.

[0078] The collection unit can adjust the collection method based on weather and lighting conditions when collecting video. For example, the collection unit can use AI to adjust the collection method based on weather and lighting conditions when collecting video. For example, the collection unit can prioritize indoor video collection during rainy weather. For example, the collection unit can use an infrared camera to collect video when the lighting is dim. For example, the collection unit can enhance outdoor video collection during sunny weather. This allows for optimal video collection by adjusting the collection method according to weather and lighting conditions. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input weather and lighting condition data into the AI ​​and have the AI ​​adjust the collection method.

[0079] The collection unit can analyze the store's congestion level during video collection and select the optimal collection method. For example, the collection unit can use AI to analyze the store's congestion level during video collection and select the optimal collection method. For example, the collection unit can collect wide-area video when the store is crowded. For example, the collection unit can focus on collecting video from a specific area when the store is empty. For example, the collection unit can adjust the camera's collection angle according to the congestion level. This enables efficient video collection by selecting the collection method according to the store's congestion level. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the store's congestion level into AI and have AI select the collection method.

[0080] The detection unit can estimate the user's emotions and adjust the detection criteria for abnormal behavior based on the estimated user emotions. For example, the detection unit can use AI to estimate the user's emotions and adjust the detection criteria for abnormal behavior based on the estimated user emotions. For example, if the user is tense, the detection unit can tighten the detection criteria to detect abnormal behavior earlier. For example, if the user is relaxed, the detection unit can use the normal detection criteria. For example, if the user is in a hurry, the detection unit can adjust the criteria to quickly detect abnormal behavior. This improves the accuracy of abnormal behavior detection by adjusting the detection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 user emotion data into the AI ​​and have the AI ​​perform the adjustment of the detection criteria.

[0081] The detection unit can optimize its detection algorithm based on past shoplifting data when detecting shoplifting. The detection unit can optimize its detection algorithm based on past shoplifting data when detecting shoplifting, for example, by using AI. The detection unit can optimize its algorithm for detecting specific behavioral patterns based on past shoplifting data. The detection unit can predict shoplifting that occurs at specific times or days of the week from past data and adjust its detection algorithm accordingly. The detection unit can analyze past data and develop algorithms to deal with new shoplifting methods. This improves detection accuracy by optimizing the detection algorithm based on past shoplifting data. Some or all of the above processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past shoplifting data into AI and have AI perform the optimization of the detection algorithm.

[0082] The detection unit can identify abnormal behavior based on the person's attribute information at the time of detection. The detection unit can, for example, use AI to identify abnormal behavior based on the person's attribute information at the time of detection. The detection unit can, for example, detect specific behavioral patterns based on age and gender. The detection unit can, for example, adjust the detection criteria for abnormal behavior based on attribute information. The detection unit can, for example, detect abnormal behavior for a specific person at an early stage by considering attribute information. This improves detection accuracy by identifying abnormal behavior based on the person's attribute information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the person's attribute information into AI and have the AI ​​perform the identification of abnormal behavior.

[0083] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. For example, the detection unit can use AI to estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. For example, if the user is nervous, the detection unit can provide a simple and highly visible display method. For example, if the user is relaxed, the detection unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the detection unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input user emotion data into the AI ​​and have the AI ​​perform the adjustment of the display method.

[0084] The detection unit can identify abnormal behavior based on the store's layout information at the time of detection. The detection unit can, for example, use AI to identify abnormal behavior based on the store's layout information at the time of detection. The detection unit can, for example, detect abnormal behavior in a specific area based on the store's layout information. The detection unit can, for example, adjust the detection criteria for abnormal behavior in response to layout changes. The detection unit can, for example, take layout information into consideration to perform detection of abnormal behavior focused on a specific area. This improves detection accuracy by identifying abnormal behavior based on the store's layout information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the store's layout information into the AI ​​and have the AI ​​perform the identification of abnormal behavior.

[0085] The detection unit can identify abnormal behavior in cooperation with other security systems upon detection. For example, the detection unit can use AI to identify abnormal behavior in cooperation with other security systems upon detection. The detection unit can, for example, cooperate with an alarm system to trigger an alarm when abnormal behavior is detected. The detection unit can improve the accuracy of abnormal behavior detection based on data from other security systems. For example, if abnormal behavior is detected, the detection unit can respond quickly in cooperation with other security systems. This improves the accuracy of abnormal behavior detection through cooperation with other security systems. Some or all of the above-described processes in the detection unit may be performed using AI, or without AI. For example, the detection unit can input data from other security systems into the AI ​​and have the AI ​​identify abnormal behavior.

[0086] The cropping function can estimate the user's emotions and adjust the cropping range based on the estimated emotions. For example, the cropping function can use AI to estimate the user's emotions and adjust the cropping range based on the estimated emotions. For example, if the user is nervous, the cropping function can crop out detailed parts. For example, if the user is relaxed, the cropping function can crop out within a normal range. For example, if the user is in a hurry, the cropping function can quickly crop out the necessary parts. This allows for efficient extraction of necessary parts by adjusting the cropping range according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the cropping function may be performed using AI, for example, or without AI. For example, the cropping function can input user emotion data into AI and have the AI ​​adjust the cropping range.

[0087] The cropping function can optimize the image quality of the video during cropping and extract the necessary parts. For example, the cropping function can use AI to optimize the image quality of the video during cropping and extract the necessary parts. For example, the cropping function can adjust the resolution of the video and crop the necessary parts in high quality. For example, the cropping function can remove noise from the video and crop a clear image. For example, the cropping function can adjust the brightness and contrast of the video and crop with optimal image quality. In this way, by optimizing the image quality of the video, the necessary parts can be extracted in high quality. Some or all of the above processing in the cropping function may be performed using AI, for example, or without using AI. For example, the cropping function can input video quality data into AI and have the AI ​​perform image quality optimization.

[0088] The cropping unit can integrate multiple camera feeds to perform optimal cropping. For example, the cropping unit can use AI to integrate multiple camera feeds to perform optimal cropping. For example, the cropping unit can integrate multiple camera feeds and crop footage from different angles. For example, the cropping unit can analyze multiple camera feeds and crop the most important part. For example, the cropping unit can combine multiple camera feeds to crop the image in a way that allows for an overall view. This allows for efficient cropping of footage from different angles by integrating multiple camera feeds. Some or all of the above-described processes in the cropping unit may be performed using AI, or not. For example, the cropping unit can input multiple camera feeds into an AI and have the AI ​​perform the video integration and cropping.

[0089] The clipping unit can estimate the user's emotions and adjust the display method of the clipped video based on the estimated user emotions. For example, the clipping unit can use AI to estimate the user's emotions and adjust the display method of the clipped video based on the estimated user emotions. For example, if the user is nervous, the clipping unit can provide a simple and highly visible display method. For example, if the user is relaxed, the clipping unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the clipping unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the clipping unit may be performed using AI, for example, or without AI. For example, the clipping unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0090] The cropping function can adjust the video's time axis during cropping to extract the optimal portion. For example, the cropping function can use AI to adjust the video's time axis during cropping to extract the optimal portion. For example, the cropping function can adjust the video's time axis to extract important parts. For example, the cropping function can compress the video's time axis to display important parts in a shorter time. For example, the cropping function can enlarge the video's time axis to extract detailed parts. This allows for efficient extraction of important parts by adjusting the video's time axis. Some or all of the above processing in the cropping function may be performed using AI, or without AI. For example, the cropping function can input video time axis data into AI and have AI perform the time axis adjustment.

[0091] The clipping unit can analyze the audio data of the video during the clipping process to extract the necessary parts. For example, the clipping unit can use AI to analyze the audio data of the video during the clipping process to extract the necessary parts. For example, the clipping unit can analyze the audio data of the video and extract parts containing important conversations or sounds. For example, the clipping unit can detect abnormal sounds based on the audio data and clip those parts. For example, the clipping unit can analyze the audio data and extract parts containing specific keywords. This allows for the efficient extraction of parts containing important conversations or sounds by analyzing the audio data. Some or all of the above-described processes in the clipping unit may be performed using AI, or not. For example, the clipping unit can input audio data into AI and have the AI ​​perform the extraction of the necessary parts.

[0092] The report generation unit can estimate the user's emotions and adjust the report content based on the estimated emotions. For example, the report generation unit can use AI to estimate the user's emotions and adjust the report content based on the estimated emotions. For example, if the user is nervous, the report generation unit can create a concise and to-the-point report. For example, if the user is relaxed, the report generation unit can create a report that includes detailed information. For example, if the user is in a hurry, the report generation unit can provide a report that can be generated quickly. This allows for the creation of more appropriate reports by adjusting the report content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input user emotion data into AI and have AI perform the adjustment of the report content.

[0093] The report creation unit can select the optimal report format based on past report data when creating a report. For example, the report creation unit can use AI to select the optimal report format based on past report data when creating a report. For example, the report creation unit can select the most effective report format based on past report data. For example, the report creation unit can suggest a report format suitable for a specific situation based on past report data. For example, the report creation unit can analyze past report data and develop a new report format. This allows for the creation of effective reports by selecting the optimal report format based on past report data. Some or all of the above processes in the report creation unit may be performed using AI, or not. For example, the report creation unit can input past report data into AI and have the AI ​​perform the report format selection.

[0094] The report generation unit can automatically add video metadata when generating a report. For example, the report generation unit can use AI to automatically add video metadata when generating a report. For example, the report generation unit can automatically add the date and time the video was shot to the report. For example, the report generation unit can automatically add the location where the video was shot to the report. For example, the report generation unit can create a detailed report based on the video metadata. This allows for the efficient creation of detailed reports by automatically adding video metadata. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input video metadata into AI and have AI perform the metadata addition.

[0095] The report generation unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, the report generation unit can use AI to estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, if the user is stressed, the report generation unit can prioritize important reports. For example, if the user is relaxed, the report generation unit can create reports with normal priority. For example, if the user is in a hurry, the report generation unit can quickly create important reports. This allows for the prioritization of important reports by determining the priority of reports according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the report generation unit may be performed using AI or not. For example, the report generation unit can input user emotion data into AI and have the AI ​​determine the priority of reports.

[0096] The report generation unit can create reports by integrating data from other security systems. The report generation unit can, for example, use AI to integrate data from other security systems when creating reports. The report generation unit can, for example, create detailed reports based on data from other security systems. The report generation unit can, for example, collaborate with other security systems and create reports based on integrated data. The report generation unit can, for example, analyze data from other security systems to create optimal reports. This allows for the efficient creation of detailed reports by integrating data from other security systems. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input data from other security systems into AI and have the AI ​​create the reports.

[0097] The report creation unit can select the method of sending the report when creating the report. The report creation unit can, for example, use AI to select the method of sending the report when creating the report. The report creation unit can, for example, select the method of sending the report by email. The report creation unit can, for example, select the method of sharing the report on the cloud. The report creation unit can, for example, select the method of sending the report according to the user's needs. This makes it possible to send reports according to the user's needs by selecting the method of sending the report. Some or all of the above processes in the report creation unit may be performed using AI, for example, or without using AI. For example, the report creation unit can input data on the sending method into the AI ​​and have the AI ​​perform the selection of the sending method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The shoplifting detection AI can also be equipped with an audio analysis unit. The audio analysis unit can analyze audio data contained in security camera footage and detect unusual sounds and conversations. For example, the audio analysis unit can detect the sound of items being put into bags or unnatural conversations with store employees. This improves the accuracy of shoplifting detection by utilizing not only video but also audio data. The audio analysis unit can, for example, use AI to analyze audio data and detect unusual sounds and conversations. The audio analysis unit can, for example, detect conversations containing specific keywords and notify the responsible person. The audio analysis unit can, for example, identify individuals exhibiting unusual behavior based on audio data.

[0100] The shoplifting prevention AI can also be equipped with a facial recognition unit. The facial recognition unit can recognize the faces of people captured in security camera footage and identify individuals suspected of shoplifting in the past. For example, the facial recognition unit can compare the footage with a database of past shoplifting incidents to identify the same person. This allows for the early detection of individuals with a high probability of recidivism and notification to the responsible personnel. The facial recognition unit can, for example, use AI to perform facial recognition and compare it with a database of past incidents. The facial recognition unit can, for example, detect individuals with specific characteristics and issue a warning. The facial recognition unit can, for example, integrate footage from multiple cameras to improve the accuracy of facial recognition.

[0101] The shoplifting prevention AI can also be equipped with a behavior prediction unit. The behavior prediction unit can analyze security camera footage and predict a person's actions. For example, the behavior prediction unit can predict what action will be taken next based on past behavior patterns. This allows for the detection of actions that are likely to be shoplifting and notification to the person in charge. The behavior prediction unit can, for example, use AI to analyze behavior patterns and make predictions. The behavior prediction unit can, for example, predict actions in a specific area and adjust the camera focus accordingly. The behavior prediction unit can, for example, issue a warning if abnormal behavior is predicted.

[0102] The shoplifting prevention AI can also be equipped with a temperature sensor. The temperature sensor can monitor the temperature inside the store and detect abnormal temperature changes. For example, if there is a sudden temperature change in a specific area, the temperature sensor can concentrate cameras in that area. This allows for the early detection of abnormal behavior based on temperature changes and notification to the responsible personnel. The temperature sensor can, for example, analyze temperature data using AI to detect abnormal temperature changes. The temperature sensor can, for example, issue a warning if the temperature exceeds a specific temperature range. The temperature sensor can, for example, predict the possibility of abnormal behavior based on temperature data.

[0103] The shoplifting prevention AI can also be equipped with a vibration sensor. The vibration sensor can monitor vibrations within the store and detect abnormal vibrations. For example, the vibration sensor can monitor the vibrations of product shelves, and if abnormal vibrations are detected, it can concentrate cameras in that area. This allows for the early detection of abnormal behavior based on vibrations and notification to the responsible personnel. The vibration sensor can, for example, analyze vibration data using AI to detect abnormal vibrations. The vibration sensor can, for example, detect specific vibration patterns and issue warnings. The vibration sensor can, for example, predict the possibility of abnormal behavior based on vibration data.

[0104] The shoplifting prevention AI can further estimate the user's emotions and adjust the security camera video analysis algorithm based on the estimated emotions. For example, if the user is tense, the analysis algorithm can be made stricter to detect abnormal behavior early. If the user is relaxed, the normal analysis algorithm can be used. In this way, the accuracy of detecting abnormal behavior is improved by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The adjustment of the analysis algorithm may be performed using AI, for example, or without using AI. For example, the adjustment of the analysis algorithm can be made to be performed by AI.

[0105] The shoplifting prevention AI can further estimate the user's emotions and adjust how security camera footage is saved based on those emotions. For example, if the user is nervous, important footage can be prioritized for saving. If the user is relaxed, the normal saving method can be used. This allows for efficient saving of important footage by adjusting the saving method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The adjustment of the saving method may be performed using AI or not using AI. For example, the adjustment of the saving method can be performed by AI.

[0106] The shoplifting prevention AI can further estimate the user's emotions and adjust the analysis speed of security camera footage based on the estimated emotions. For example, if the user is nervous, the analysis speed can be increased to quickly detect abnormal behavior. If the user is relaxed, the normal analysis speed can be used. This improves the accuracy of abnormal behavior detection by adjusting the analysis speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The adjustment of the analysis speed may be performed using AI or not using AI. For example, the adjustment of the analysis speed can be made to be performed by AI.

[0107] The shoplifting prevention AI can further estimate the user's emotions and adjust the display method of security camera footage based on the estimated emotions. For example, if the user is tense, a simple and highly visible display method can be provided. If the user is relaxed, a display method containing detailed information can be provided. This improves visibility by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The adjustment of the display method may be performed using AI, for example, or without using AI. For example, the AI ​​can be made to perform the adjustment of the display method.

[0108] The shoplifting prevention AI can further estimate the user's emotions and adjust the retention period of security camera footage based on the estimated emotions. For example, if the user is nervous, the retention period of important footage can be extended. If the user is relaxed, the normal retention period can be used. This allows for efficient management of important footage by adjusting the retention period according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The adjustment of the retention period may be performed using AI or not using AI. For example, the adjustment of the retention period can be performed by AI.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects footage from security cameras. The collection unit can, for example, collect footage from multiple security cameras. The collection unit can, for example, use cameras that cover the entire store or cameras that concentrate on a specific area. The collection unit can, for example, use AI to efficiently collect footage from security cameras. Step 2: The detection unit analyzes the video footage collected by the collection unit to detect suspicious individuals. The detection unit can, for example, use AI to analyze security camera footage and identify individuals exhibiting abnormal behavior. The detection unit can detect behaviors such as picking up an item and immediately putting it back, or wandering around the store in an unnatural manner. The detection unit can, for example, use AI to quickly identify individuals exhibiting abnormal behavior and notify the responsible person. Step 3: The clipping unit cuts out only the necessary parts based on the suspicious person detected by the detection unit. For example, the clipping unit can use AI to analyze security camera footage and extract only the parts containing the moment of shoplifting or suspicious behavior. For example, the clipping unit can efficiently save only the parts necessary as evidence, reducing data size. Step 4: The report creation unit creates a report based on the footage extracted by the clipping unit. The report creation unit can, for example, meticulously record the actions of a suspicious person detected using AI and compile them into a report. The report creation unit can, for example, describe what actions were taken at what time, where, and when.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, detection unit, clipping unit, and report generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects security camera footage using the camera 42 of the smart device 14 or the communication I / F 26 of the data processing unit 12. The detection unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the security camera footage to identify individuals exhibiting abnormal behavior. The clipping unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and extracts only the parts containing the moment of shoplifting or other suspicious behavior. The report generation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and records the actions of the detected suspicious person in detail and compiles them into a report. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, detection unit, clipping unit, and report generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects images from a security camera using the camera 42 of the smart glasses 214 or the communication I / F 26 of the data processing unit 12. The detection unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the security camera images to identify a person exhibiting abnormal behavior. The clipping unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and extracts only the parts containing the moment of shoplifting or suspicious behavior. The report generation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and records the actions of the detected suspicious person in detail and compiles them into a report. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, detection unit, clipping unit, and report generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects security camera footage using the camera 42 of the headset terminal 314 or the communication I / F 26 of the data processing unit 12. The detection unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the security camera footage to identify individuals exhibiting abnormal behavior. The clipping unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and extracts only the parts containing the moment of shoplifting or other suspicious behavior. The report generation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and records the actions of the detected suspicious person in detail and compiles them into a report. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, detection unit, clipping unit, and report generation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects security camera footage using the camera 42 of the robot 414 or the communication I / F 26 of the data processing unit 12. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the security camera footage and identifies individuals exhibiting abnormal behavior. The clipping unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which extracts only the parts containing the moment of shoplifting or other suspicious behavior. The report generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which records the actions of the detected suspicious person in detail and compiles them into a report. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The collection department collects security camera footage, A detection unit analyzes the video collected by the aforementioned collection unit to detect suspicious individuals, Based on the suspicious person detected by the aforementioned detection unit, a cropping unit cuts out only the necessary parts, The system includes a report creation unit that creates a report based on the image cropped by the cropping unit. A system characterized by the following features. (Note 2) The detection unit is Analyzing security camera footage to identify individuals exhibiting unusual behavior. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned cut-out portion is The detection unit extracts only the necessary parts from the detected video. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned report creation unit, Create a report based on the video footage extracted by the clipping section. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect footage from multiple security cameras. The system described in Appendix 1, characterized by the features described herein. (Note 6) The detection unit is The system detects behaviors such as picking up an item and immediately putting it back, or walking around the store in an unnatural manner. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of security camera footage collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Dynamically adjust the placement of security cameras to capture optimal video footage. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting video footage, the collection method is changed based on specific time periods or days of the week. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the videos to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During video collection, the collection method is adjusted based on weather and lighting conditions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting video footage, the store's congestion level is analyzed to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit is The system estimates the user's emotions and adjusts the criteria for detecting abnormal behavior based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is When detection occurs, the detection algorithm is optimized based on past shoplifting data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is Upon detection, abnormal behavior is identified based on the person's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is It estimates the user's emotions and adjusts how the detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is Upon detection, abnormal behavior is identified based on the store's layout information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is Upon detection, the system works in conjunction with other security systems to identify abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned cut-out portion is It estimates the user's emotions and adjusts the cropping range based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned cut-out portion is During the cropping process, the video quality is optimized to extract the necessary parts. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned cut-out portion is During the cropping process, multiple camera feeds are combined to create the optimal crop. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned cut-out portion is The system estimates the user's emotions and adjusts how the clipped video is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned cut-out portion is During the cropping process, the video's timeline is adjusted to extract the optimal portion. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned cut-out portion is During the trimming process, the audio data of the video is analyzed to extract the necessary parts. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned report creation unit, It estimates the user's emotions and adjusts the report content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned report creation unit, When creating a report, the optimal report format is selected based on past report data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned report creation unit, Automatically add video metadata when creating reports. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned report creation unit, It estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned report creation unit, When creating a report, data from other security systems is integrated to generate the report. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned report creation unit, When creating a report, select the method for sending the report. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects security camera footage, A detection unit analyzes the video collected by the aforementioned collection unit to detect suspicious individuals, Based on the suspicious person detected by the aforementioned detection unit, a cropping unit cuts out only the necessary parts, The system includes a report creation unit that creates a report based on the image cropped by the cropping unit. A system characterized by the following features.

2. The detection unit is Analyzing security camera footage to identify individuals exhibiting unusual behavior. The system according to feature 1.

3. The aforementioned cut-out portion is The detection unit extracts only the necessary portion from the image detected. The system according to feature 1.

4. The aforementioned report creation unit, Create a report based on the video footage extracted by the clipping section. The system according to feature 1.

5. The aforementioned collection unit is Collect footage from multiple security cameras. The system according to feature 1.

6. The detection unit is The system detects behaviors such as picking up an item and immediately putting it back, or walking around the store in an unnatural manner. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of security camera footage collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Dynamically adjust the placement of security cameras to capture optimal video footage. The system according to feature 1.

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