system

Abstract

The system according to this embodiment aims to automatically monitor security camera footage, detect anomalies, and promptly report them. [Solution] The system according to the embodiment comprises a monitoring unit, a detection unit, a notification unit, and a transmission unit. The monitoring unit monitors the video from security cameras. The detection unit detects abnormalities from the video monitored by the monitoring unit. The notification unit notifies the police or security company based on the abnormalities detected by the detection unit. The transmission unit analyzes the information reported by the notification unit and transmits details of the crime, characteristics of the perpetrator, escape route, etc.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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 conventional technology, the process of monitoring the video of a security camera and detecting abnormalities is often performed manually, and there is a problem that it is difficult to respond quickly.

[0005] The system according to the embodiment aims to automatically monitor the video of a security camera, detect abnormalities, and report them quickly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a detection unit, a notification unit, and a transmission unit. The monitoring unit monitors the video feed from security cameras. The detection unit detects anomalies from the video feed monitored by the monitoring unit. The notification unit notifies the police or security company based on the anomalies detected by the detection unit. The transmission unit analyzes the information reported by the notification unit and transmits details of the crime, characteristics of the perpetrator, escape route, etc. [Effects of the Invention]

[0007] The system according to this embodiment can automatically monitor security camera footage, detect anomalies, and promptly report them. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​system according to an embodiment of the present invention is a system for the early resolution of incidents and accidents in the world. This AI system consists of the following steps. First, the AI ​​monitors security camera footage. Next, the AI ​​detects accidents or crimes and immediately notifies the police or security company. When notifying, information analyzed by the AI ​​is also transmitted. This includes details of the crime, characteristics of the perpetrator, and escape route. Specifically, security camera footage is input to the AI, which analyzes the footage in real time and detects abnormal movements or behaviors. For example, if violent acts or suspicious movements are detected, the AI ​​determines that it is an accident or a crime. Next, based on the detected information, the AI ​​immediately notifies the police or security company. At this time, the report also includes detailed information analyzed by the AI. For example, details of the crime, characteristics of the perpetrator, and escape route are included. This allows the police and security company to respond quickly and accurately. Furthermore, the AI ​​continues to monitor the situation even after the notification and provides additional information as needed. For example, if a suspect is on the run, the system tracks their escape route in real time and provides this information to the police and security companies. This can lead to the early arrest of the suspect. This system enables the early resolution of crimes and accidents, improving public safety. For example, it is effective as a crime prevention measure in places where many people gather, such as commercial facilities and public transportation. It can also be used to protect the safety of local communities, such as residential areas and schools. In this way, AI-powered crime prevention systems make a significant contribution to the early resolution of crimes and accidents. By monitoring security camera footage and detecting anomalies, AI can immediately report them, enabling a swift and accurate response. This improves the safety of society as a whole, creating an environment where people can live with peace of mind. In short, AI systems can achieve the early resolution of crimes and accidents and improve public safety.

[0029] The AI ​​system according to this embodiment comprises a monitoring unit, a detection unit, a notification unit, and a transmission unit. The monitoring unit monitors the video feed from security cameras. The monitoring unit monitors the video feed from security cameras in places such as commercial facilities and public transportation in real time. The monitoring unit can also monitor the video feed from security cameras in residential areas and schools. Furthermore, the monitoring unit uses AI to analyze the video feed and detect abnormal movements and behaviors. For example, the monitoring unit can detect acts of violence and suspicious movements. The detection unit detects abnormalities from the video feed monitored by the monitoring unit. The detection unit uses AI to analyze the video feed and detect abnormal movements and behaviors. For example, the detection unit can detect acts of violence and suspicious movements. Furthermore, if the detection unit detects an abnormality, it immediately transmits the information to the notification unit. The notification unit notifies the police or security company based on the abnormality detected by the detection unit. The notification unit generates notification content using AI and immediately notifies the police or security company. For example, the reporting unit can generate a report containing detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route. The transmission unit analyzes the information reported by the reporting unit and transmits the nature of the crime, the characteristics of the perpetrator, the escape route, etc. The transmission unit can, for example, use AI to analyze the report and generate detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route. For example, the transmission unit can provide detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route to the police or security companies. As a result, the AI ​​system according to this embodiment can monitor security camera footage, detect anomalies, and immediately report them, thereby enabling the early resolution of incidents and accidents.

[0030] The monitoring unit monitors security camera footage. For example, it monitors footage from security cameras in commercial facilities and public transportation in real time. Specifically, it centrally manages footage from multiple cameras installed in various areas of a commercial facility and transmits the video data to a central monitoring system in real time. In public transportation, it similarly monitors footage from cameras installed in stations and train cars to detect abnormal behavior or suspicious movements. The monitoring unit can also monitor footage from security cameras in residential areas and schools. In residential areas, it collects footage from cameras installed at the entrances and parking lots of each house, and in schools, it monitors footage from cameras installed in corridors, classrooms, and schoolyards. Furthermore, the monitoring unit uses AI to analyze the footage and detect abnormal movements and behaviors. The AI ​​uses video analysis algorithms to detect movements that differ from normal behavior patterns in real time. For example, the monitoring unit can detect acts of violence and suspicious movements. Specifically, the AI ​​analyzes a person's movements, posture, and speed, and identifies abnormal movements by comparing them to normal behavior patterns. This allows the monitoring unit to efficiently monitor a wide area and detect anomalies early.

[0031] The detection unit detects anomalies from video footage monitored by the monitoring unit. The detection unit analyzes the video footage using AI, for example, to detect abnormal movements and behaviors. Specifically, the AI ​​analyzes the movements of people and objects in the video and identifies abnormal movements by comparing them to normal behavior patterns. For example, the detection unit can detect violent acts or suspicious movements. Using a deep learning model, the AI ​​analyzes the video in real time based on patterns of abnormal behavior learned from past data. For example, the AI ​​determines that actions such as a person suddenly running, staying in a specific area for a long time, or throwing objects are abnormal. Furthermore, if the detection unit detects an anomaly, it immediately transmits the information to the notification unit. When an anomaly is detected, the AI ​​automatically generates detailed information such as the type, location, and time of the anomaly and transmits it to the notification unit. This allows the detection unit to quickly and accurately detect anomalies and respond immediately. In addition, to improve the accuracy of anomaly detection, the detection unit periodically updates its AI model and learns from the latest data. This allows the detection unit to consistently detect anomalies with high accuracy, improving the overall reliability of the system.

[0032] The reporting unit notifies the police and security companies based on anomalies detected by the detection unit. The reporting unit generates report content using AI, for example, and immediately notifies the police and security companies. Specifically, the AI ​​automatically generates report content based on anomaly information transmitted from the detection unit. For example, the reporting unit can generate report content that includes detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route. Based on video analysis results and past data, the AI ​​automatically extracts the characteristics of the perpetrator (clothing, height, build, etc.) and details of the crime (location, time, actions, etc.) and reflects them in the report content. In addition, the reporting unit uses multiple communication methods to immediately transmit the report content to the police and security companies. For example, the reporting unit uses a digital reporting system via the internet and a voice reporting system using telephone lines in combination to reliably transmit information. Furthermore, even after transmitting the report content, the reporting unit continuously updates the report content based on information updated in real time, providing the police and security companies with the latest situation. This allows the reporting unit to report anomalies quickly and accurately, supporting the early resolution of incidents and accidents.

[0033] The transmission department analyzes information reported by the reporting department and transmits details of the crime, the perpetrator's characteristics, and escape routes. For example, the transmission department uses AI to analyze the reported information and generate detailed information such as the crime, the perpetrator's characteristics, and escape routes. Specifically, the AI ​​automatically analyzes the details of the crime and the perpetrator's characteristics based on the information transmitted from the reporting department, and provides detailed information such as the crime, the perpetrator's characteristics, and escape routes to the police and security companies. The AI ​​automatically extracts the perpetrator's characteristics (clothing, height, build, etc.) and details of the crime (location, time, actions, etc.) based on video analysis results and past data, and reflects this in the transmitted information. Furthermore, the transmission department provides the police and security companies with the latest information based on real-time updates. For example, if the perpetrator's escape route changes or new information is obtained, the transmission department immediately updates the information and provides it to the police and security companies. In addition, the transmission department uses multiple communication methods to ensure reliable information transmission. For example, it uses a combination of digital and voice reporting systems to ensure that important information is delivered reliably. This allows the communications department to provide information quickly and accurately to the police and security companies, supporting the early resolution of incidents and accidents.

[0034] The filtering unit has filtering capabilities. For example, the filtering unit uses AI to remove unnecessary information from security camera footage and extract important information. For example, the filtering unit can perform noise reduction and extract important information. For example, the filtering unit removes noise from the video and highlights abnormal movements and actions. The filtering unit can also extract important information and transmit it to the detection unit. For example, the filtering unit detects abnormal movements and actions and transmits them to the detection unit. This allows the filtering unit to remove unnecessary information and extract important information. Some or all of the above processing in the filtering unit may be performed using AI, or without AI. For example, the filtering unit can input security camera footage into a generating AI and have the generating AI perform noise reduction and extract important information.

[0035] The selection unit selects training data. For example, the selection unit uses AI to select training data from security camera footage. For example, the selection unit can select training data such as image data, text data, and audio data. For example, the selection unit selects video data that includes abnormal movements or behaviors and uses it as training data for the AI. The selection unit can also make selections considering the quality and diversity of the training data. For example, the selection unit selects high-quality video data and data that includes diverse behavior patterns. This allows the selection unit to select training data that improves the accuracy of the AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input security camera footage into a generating AI and have the generating AI perform the selection of training data.

[0036] The communications department provides means of communication. The communications department provides means of communication with the police and security companies, for example, using AI. For example, the communications department can provide means of communication such as the internet, telephone, and wireless communication. For example, the communications department can transmit information to the police and security companies via the internet. The communications department can also make emergency calls using telephone or wireless communication. For example, the communications department can use the telephone to notify the police in an emergency. The communications department can also transmit information to security companies using wireless communication. This allows the communications department to cooperate smoothly with the police and security companies. Some or all of the above-described processes in the communications department may be performed using AI, for example, or without AI. For example, the communications department can input information about a report into a generating AI and have the generating AI select the means of communication and transmit the information about the report.

[0037] The management department manages privacy protection regulations. The management department manages privacy protection regulations such as data encryption, access restrictions, and data retention periods. For example, the management department can use AI to encrypt data and prevent the leakage of personal information. The management department can also implement access restrictions, ensuring that only specific users can access the data. For example, the management department can set access permissions to restrict access to data. Furthermore, the management department can set data retention periods and delete data after a certain period. For example, the management department can set data retention periods and automatically delete data after a certain period. This allows the management department to manage privacy protection regulations and prevent the leakage of personal information. Some or all of the above processes performed by the management department may be carried out using AI, for example, or without AI. For example, the management department can have a generating AI perform data encryption and access restriction settings.

[0038] The detection unit can detect abnormal movements and behaviors. For example, the detection unit can use AI to detect abnormal movements and behaviors from security camera footage. For example, the detection unit can detect sudden movements or unnatural behavioral patterns. For example, the detection unit can detect acts of violence or suspicious movements and determine them to be abnormal. Furthermore, if the detection unit detects an abnormality, it can immediately transmit the information to the reporting unit. This allows the detection unit to detect abnormal movements and behaviors and respond quickly. 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 security camera footage into a generating AI and have the generating AI perform the detection of abnormal movements and behaviors.

[0039] The reporting unit can include detailed information in the report, such as the nature of the crime, the characteristics of the perpetrator, and the escape route. For example, the reporting unit can use AI to generate the report, which will include detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route. For example, the reporting unit can generate a report that describes in detail the modus operandi, the characteristics of the perpetrator, and the escape route. For example, the reporting unit may include specific information such as theft, assault, or property damage as the nature of the crime. The reporting unit may also include height, weight, clothing, and facial features as the characteristics of the perpetrator. Furthermore, the reporting unit may include camera locations, eyewitness accounts, and geographical information as the escape route. This allows the reporting unit to generate a report containing detailed information and provide it to the police or security companies. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the report content into a generating AI and have the generating AI perform the generation of a report containing detailed information.

[0040] The monitoring unit can improve the accuracy of anomaly detection by referring to past incident data during monitoring. For example, the monitoring unit can use AI to refer to past incident data and improve the accuracy of anomaly detection. For example, the monitoring unit can learn specific behavioral patterns based on past incident data and detect anomalies. The monitoring unit can also analyze past incident data and consider the rate of anomaly occurrence at specific time periods and locations. Furthermore, the monitoring unit can build a predictive model of abnormal behavior by referring to past incident data. This allows the monitoring unit to improve the accuracy of anomaly detection by referring to past incident data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past incident data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0041] The monitoring unit can adjust the intensity of monitoring based on specific time periods and locations during monitoring. For example, the monitoring unit can use AI to adjust the intensity of monitoring based on specific time periods and locations. For example, the monitoring unit can increase the intensity of monitoring at night or in places with little foot traffic. It can also maintain normal monitoring intensity during the day or in places with a lot of foot traffic. Furthermore, the monitoring unit can temporarily increase the intensity of monitoring in places where specific events are being held. In this way, the monitoring unit can adjust the intensity of monitoring based on specific time periods and locations, enabling more effective monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on specific time periods and locations into a generating AI and have the generating AI perform the adjustment of the monitoring intensity.

[0042] The monitoring unit can change its monitoring method when monitoring, taking into account weather and environmental conditions. For example, the monitoring unit can use AI to change its monitoring method considering weather and environmental conditions. For example, the monitoring unit can adjust its monitoring method when it rains because the camera's visibility is poor. The monitoring unit can also use an infrared camera for monitoring at night. Furthermore, the monitoring unit can increase the intensity of monitoring when there is fog. In this way, the monitoring unit can change its monitoring method considering weather and environmental conditions and perform more effective monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input weather and environmental condition data into a generating AI and have the generating AI execute the change in the monitoring method.

[0043] The monitoring unit can integrate multiple camera images to detect anomalies during monitoring. The monitoring unit can, for example, use AI to integrate multiple camera images and detect anomalies. For example, the monitoring unit can integrate multiple camera images in real time and detect anomalies. The monitoring unit can also integrate images from different angles to detect abnormal behavior. Furthermore, the monitoring unit can analyze multiple camera images to identify patterns of abnormal behavior. This allows the monitoring unit to integrate multiple camera images to detect anomalies and improve the accuracy of anomaly detection. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input multiple camera images into a generating AI and have the generating AI perform anomaly detection.

[0044] The detection unit can improve its detection accuracy by learning patterns of abnormal behavior upon detection. The detection unit can improve detection accuracy by learning patterns of abnormal behavior using, for example, AI. For example, the detection unit learns patterns of abnormal behavior based on past abnormal behavior data. The detection unit can also learn patterns of abnormal behavior and detect new abnormal behaviors. Furthermore, the detection unit can improve detection accuracy by learning patterns of abnormal behavior. In this way, the detection unit can improve detection accuracy by learning patterns of abnormal behavior. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without using AI. For example, the detection unit can input abnormal behavior data into a generating AI and have the generating AI perform learning of abnormal behavior patterns.

[0045] The detection unit can apply different detection algorithms depending on the type of abnormal behavior upon detection. For example, the detection unit can use AI to apply different detection algorithms depending on the type of abnormal behavior. For example, the detection unit can apply an algorithm to detect violent behavior. The detection unit can also apply an algorithm to detect suspicious movements. Furthermore, the detection unit can apply an algorithm to detect escape behavior. In this way, the detection unit can apply different detection algorithms depending on the type of abnormal behavior and improve detection accuracy. 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 the type of abnormal behavior into a generating AI and cause the generating AI to execute the application of an appropriate detection algorithm.

[0046] The detection unit can adjust the detection intensity based on the location where the abnormal behavior occurs. For example, the detection unit can use AI to adjust the detection intensity based on the location of the abnormal behavior. For example, the detection unit can increase the detection intensity in areas with little foot traffic. It can also maintain a normal detection intensity in areas with a lot of foot traffic. Furthermore, the detection unit can temporarily increase the detection intensity in locations where specific events are held. In this way, the detection unit can adjust the detection intensity based on the location of the abnormal behavior and perform more effective anomaly detection. 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 data on the location of the abnormal behavior into a generating AI and have the generating AI perform the adjustment of the detection intensity.

[0047] The detection unit can adjust the detection frequency based on the time of occurrence of abnormal behavior when it detects it. The detection unit can adjust the detection frequency based on the time of occurrence of abnormal behavior using, for example, AI. For example, the detection unit can increase the detection frequency at night. The detection unit can also maintain a normal detection frequency during the day. Furthermore, if abnormal behavior occurs frequently during a particular time period, the detection unit can increase the detection frequency during that time period. In this way, the detection unit can adjust the detection frequency based on the time of occurrence of abnormal behavior and perform more effective anomaly detection. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input data on the time of occurrence of abnormal behavior into a generating AI and have the generating AI perform the adjustment of the detection frequency.

[0048] The reporting unit can improve the accuracy of reports by referring to past reporting data when a report is made. For example, the reporting unit can use AI to refer to past reporting data and improve the accuracy of reports. For example, the reporting unit can scrutinize the content of a report based on past reporting data. The reporting unit can also analyze past reporting data to improve the accuracy of reports. Furthermore, the reporting unit can optimize the content of a report by referring to past reporting data. In this way, the reporting unit can improve the accuracy of reports by referring to past reporting data. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without using AI. For example, the reporting unit can input past reporting data into a generating AI and have the generating AI perform a scrutiny of the report content.

[0049] The reporting unit can apply different reporting methods depending on the type of anomaly when a report is made. For example, the reporting unit can use AI to apply different reporting methods depending on the type of anomaly. For example, in the case of violent behavior, the reporting unit can make an emergency report. In addition, in the case of suspicious activity, the reporting unit can make a report that includes tracking information. This allows the reporting unit to select the appropriate reporting method according to the type of anomaly and respond quickly and accurately. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the type of anomaly into a generating AI and have the generating AI execute the application of the appropriate reporting method.

[0050] The reporting unit can select the optimal reporting method when a report is made, taking into account the geographical location information of the recipient. For example, the reporting unit can use AI to select the optimal reporting method by considering the geographical location information of the recipient. For example, if the recipient is nearby, the reporting unit can select a rapid reporting method. If the recipient is far away, the reporting unit can select a standard reporting method. Furthermore, the reporting unit can select the optimal reporting method based on the geographical location information of the recipient. This allows the reporting unit to select the optimal reporting method by considering the geographical location information of the recipient and to make a quick and accurate report. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the geographical location information of the recipient into a generating AI and have the generating AI select the optimal reporting method.

[0051] The reporting unit can provide additional information related to the reported content at the time of reporting. For example, the reporting unit may use AI to provide additional information related to the reported content. For example, the reporting unit may include details of the crime in the reported content. The reporting unit may also include characteristics of the perpetrator in the reported content. Furthermore, the reporting unit may include the escape route in the reported content. This allows the reporting unit to provide additional information related to the reported content and convey more detailed information to the police or security company. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit may input the reported content into a generating AI and have the generating AI perform the provision of related additional information.

[0052] The transmission unit can improve the accuracy of transmission by referring to past transmission data during transmission. For example, the transmission unit can use AI to refer to past transmission data and improve the accuracy of transmission. For example, the transmission unit can scrutinize the content of transmission based on past transmission data. The transmission unit can also analyze past transmission data and improve the accuracy of transmission. Furthermore, the transmission unit can optimize the content of transmission by referring to past transmission data. In this way, the transmission unit can improve the accuracy of transmission by referring to past transmission data. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without using AI. For example, the transmission unit can input past transmission data into a generating AI and have the generating AI perform a scrutiny of the content of transmission.

[0053] The communication unit can apply different communication methods depending on the type of anomaly during communication. For example, the communication unit can use AI to apply different communication methods depending on the type of anomaly. For example, in the case of violent behavior, the communication unit can make an emergency communication. In addition, in the case of suspicious movement, the communication unit can make a normal communication. Furthermore, in the case of escape behavior, the communication unit can make a communication that includes tracking information. This allows the communication unit to select an appropriate communication method according to the type of anomaly and respond quickly and accurately. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the type of anomaly into a generating AI and have the generating AI execute the application of an appropriate communication method.

[0054] The transmission unit can select the optimal transmission method when transmitting information, taking into account the geographical location of the recipient. For example, the transmission unit can use AI to select the optimal transmission method by considering the geographical location of the recipient. For example, if the recipient is nearby, the transmission unit can select a rapid transmission method. If the recipient is far away, the transmission unit can select a normal transmission method. Furthermore, the transmission unit can select the optimal transmission method based on the geographical location of the recipient. As a result, the transmission unit can select the optimal transmission method by considering the geographical location of the recipient and perform rapid and accurate transmission. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the geographical location of the recipient into a generating AI and have the generating AI select the optimal transmission method.

[0055] The transmission unit can provide additional information related to the transmitted content at the time of transmission. The transmission unit can provide additional information related to the transmitted content using, for example, AI. For example, the transmission unit may include details of the crime in the transmitted content. The transmission unit may also include characteristics of the perpetrator in the transmitted content. Furthermore, the transmission unit may include escape routes in the transmitted content. This allows the transmission unit to provide additional information related to the transmitted content and convey more detailed information to the police or security companies. Some or all of the above processing in the transmission unit may be performed using, for example, AI, or not using AI. For example, the transmission unit can input the transmitted content into a generating AI and have the generating AI perform the provision of related additional information.

[0056] The filtering unit can improve the accuracy of filtering by referring to past filtering data during the filtering process. For example, the filtering unit can use AI to refer to past filtering data and improve the accuracy of filtering. For example, the filtering unit can scrutinize the filtering content based on past filtering data. The filtering unit can also analyze past filtering data to improve the accuracy of filtering. Furthermore, the filtering unit can optimize the filtering content by referring to past filtering data. In this way, the filtering unit can improve the accuracy of filtering by referring to past filtering data. Some or all of the above processes in the filtering unit may be performed using AI, for example, or without using AI. For example, the filtering unit can input past filtering data into a generating AI and have the generating AI perform a scrutiny of the filtering content.

[0057] The filtering unit can select the optimal filtering method by considering the attribute information of the data to be filtered during the filtering process. For example, the filtering unit may use AI to select the optimal filtering method by considering the attribute information of the data to be filtered. For example, if the data to be filtered is important information, the filtering unit may select a strict filtering method. If the data to be filtered is general information, the filtering unit may select a normal filtering method. Furthermore, the filtering unit may select the optimal filtering method based on the attribute information of the data to be filtered. In this way, the filtering unit can select the optimal filtering method by considering the attribute information of the data to be filtered and perform effective filtering. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit may input the attribute information of the data to be filtered into a generating AI and have the generating AI select the optimal filtering method.

[0058] The selection unit can improve the accuracy of its selections by referring to past selection data during the selection process. For example, the selection unit can use AI to refer to past selection data and improve the accuracy of its selections. For example, the selection unit can scrutinize the selection content based on past selection data. The selection unit can also analyze past selection data to improve the accuracy of its selections. Furthermore, the selection unit can optimize the selection content by referring to past selection data. In this way, the selection unit can improve the accuracy of its selections by referring to past selection data. Some or all of the above processes in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input past selection data into a generating AI and have the generating AI perform a scrutiny of the selection content.

[0059] The selection unit can select the optimal selection method by considering the attribute information of the items to be selected during the selection process. For example, the selection unit can use AI to select the optimal selection method by considering the attribute information of the items to be selected. For example, if the items to be selected are important information, the selection unit can select a strict selection method. Also, if the items to be selected are general information, the selection unit can select a normal selection method. Furthermore, the selection unit can select the optimal selection method based on the attribute information of the items to be selected. In this way, the selection unit can select the optimal selection method by considering the attribute information of the items to be selected and perform an effective selection. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input the attribute information of the items to be selected into a generating AI and have the generating AI perform the selection of the optimal selection method.

[0060] The communication unit can improve the accuracy of communication by referring to past communication data during communication. For example, the communication unit can use AI to refer to past communication data and improve the accuracy of communication. For example, the communication unit can scrutinize the content of communication based on past communication data. The communication unit can also analyze past communication data to improve the accuracy of communication. Furthermore, the communication unit can optimize the content of communication by referring to past communication data. In this way, the communication unit can improve the accuracy of communication by referring to past communication data. Some or all of the above processing in the communication unit may be performed using AI, for example, or without using AI. For example, the communication unit can input past communication data into a generating AI and have the generating AI perform a scrutiny of the communication content.

[0061] The communication unit can select the optimal communication method when communicating, taking into account the geographical location information of the communication destination. For example, the communication unit can use AI to select the optimal communication method by considering the geographical location information of the communication destination. For example, if the communication destination is nearby, the communication unit can select a rapid communication method. If the communication destination is far away, the communication unit can select a normal communication method. Furthermore, the communication unit can select the optimal communication method based on the geographical location information of the communication destination. As a result, the communication unit can select the optimal communication method by taking into account the geographical location information of the communication destination and perform rapid and accurate communication. Some or all of the above processing in the communication unit may be performed using AI, for example, or without using AI. For example, the communication unit can input the geographical location information of the communication destination into a generating AI and have the generating AI perform the selection of the optimal communication method.

[0062] The management department can improve the accuracy of its management by referring to past privacy protection data during the management process. For example, the management department can use AI to refer to past privacy protection data and improve the accuracy of its management. For example, the management department can scrutinize management content based on past privacy protection data. The management department can also analyze past privacy protection data to improve the accuracy of its management. Furthermore, the management department can optimize management content by referring to past privacy protection data. In this way, the management department can improve the accuracy of its management by referring to past privacy protection data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past privacy protection data into a generating AI and have the generating AI perform a scrutiny of management content.

[0063] The management department can select the optimal management method when managing data, taking into account the attribute information of the data being managed. For example, the management department can use AI to select the optimal management method by considering the attribute information of the data being managed. For example, if the data being managed is important information, the management department can select a strict management method. If the data being managed is general information, the management department can select a normal management method. Furthermore, the management department can select the optimal management method based on the attribute information of the data being managed. This allows the management department to select the optimal management method by considering the attribute information of the data being managed and to perform effective management. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the attribute information of the data being managed into a generating AI and have the generating AI select the optimal management method.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The AI ​​system may also include a prediction unit. The prediction unit, for example, uses AI to analyze past data and predict future incidents or accidents. For instance, the prediction unit can learn specific patterns based on past incident data and predict future abnormal behavior. Furthermore, the prediction unit can predict the rate of anomalies in specific time periods or locations and adjust monitoring priorities accordingly. Additionally, the prediction unit can predict anomalies at locations where specific events are held and notify police or security companies in advance. This allows the prediction unit to anticipate future incidents or accidents and take preventative measures. Some or all of the above-described processes in the prediction unit may be performed using AI, or not. For example, the prediction unit can input past data into a generating AI and have the generating AI predict future abnormal behavior.

[0066] The AI ​​system may also include a notification unit. The notification unit can, for example, use AI to notify the user of anomaly detection. For example, the notification unit can send anomaly detection notifications to the user via a smartphone app. The notification unit can also notify the user via email or SMS. Furthermore, the notification unit can customize the notification method according to the user's settings. For example, if the user requests an emergency notification, the notification unit can send a notification immediately. This allows the notification unit to provide the user with quick and accurate anomaly detection notifications. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly detection data into a generating AI and have the generating AI generate the notification content.

[0067] The AI ​​system may also include an analysis unit. The analysis unit can, for example, use AI to analyze security camera footage in detail and identify the cause of abnormal behavior. For instance, the analysis unit can analyze the time period, location, and behavioral patterns of the abnormal behavior. It can also analyze the frequency and trends of the abnormal behavior and propose preventative measures. Furthermore, the analysis unit can identify the factors behind the abnormal behavior and provide information for taking countermeasures. This allows the analysis unit to identify the cause of the abnormal behavior and propose preventative measures. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input security camera footage into a generating AI and have the generating AI identify the cause of the abnormal behavior.

[0068] The AI ​​system may also include a recording unit. The recording unit can, for example, use AI to record security camera footage and anomaly detection data. For instance, the recording unit can record the date, time, location, and behavioral patterns of abnormal behavior. Furthermore, the recording unit can store detailed information about the abnormal behavior for later analysis and verification. Additionally, the recording unit can encrypt the recorded data to enhance privacy protection. This allows the recording unit to record security camera footage and anomaly detection data for later analysis and verification. Some or all of the above-described processes in the recording unit may be performed using AI, or not. For example, the recording unit can input security camera footage into a generating AI and have the generating AI generate the recorded content.

[0069] The AI ​​system may also include a feedback unit. The feedback unit, for example, uses AI to collect user feedback and use it to improve the system. For instance, the feedback unit can provide an interface where users can input their experience with the system and areas for improvement. Furthermore, the feedback unit can analyze the collected feedback to identify areas for system improvement. Additionally, the feedback unit can automatically adjust system settings based on user feedback. This allows the feedback unit to collect user feedback and use it to improve the system. Some or all of the above-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user feedback data into a generating AI and have the generating AI identify areas for system improvement.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The monitoring unit monitors security camera footage. The monitoring unit monitors footage from security cameras in commercial facilities, public transportation, residential areas, schools, etc., in real time. The monitoring unit also uses AI to analyze the footage and detect abnormal movements and behaviors. Step 2: The detection unit detects anomalies from the video footage monitored by the monitoring unit. The detection unit uses AI to analyze the video and detect anomalies such as violent acts or suspicious movements. If an anomaly is detected, it immediately transmits the information to the reporting unit. Step 3: The reporting unit notifies the police or security company based on the anomaly detected by the detection unit. The reporting unit uses AI to generate the report content and immediately sends the report, which includes detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route, to the police or security company. Step 4: The transmission department analyzes the information reported by the reporting department and transmits details of the crime, the perpetrator's characteristics, and escape route. The transmission department uses AI to analyze the reported information, generates detailed information, and provides it to the police and security companies.

[0072] (Example of form 2) The AI ​​system according to an embodiment of the present invention is a system for the early resolution of incidents and accidents in the world. This AI system consists of the following steps. First, the AI ​​monitors security camera footage. Next, the AI ​​detects accidents or crimes and immediately notifies the police or security company. When notifying, information analyzed by the AI ​​is also transmitted. This includes details of the crime, characteristics of the perpetrator, and escape route. Specifically, security camera footage is input to the AI, which analyzes the footage in real time and detects abnormal movements or behaviors. For example, if violent acts or suspicious movements are detected, the AI ​​determines that it is an accident or a crime. Next, based on the detected information, the AI ​​immediately notifies the police or security company. At this time, the report also includes detailed information analyzed by the AI. For example, details of the crime, characteristics of the perpetrator, and escape route are included. This allows the police and security company to respond quickly and accurately. Furthermore, the AI ​​continues to monitor the situation even after the notification and provides additional information as needed. For example, if a suspect is on the run, the system tracks their escape route in real time and provides this information to the police and security companies. This can lead to the early arrest of the suspect. This system enables the early resolution of crimes and accidents, improving public safety. For example, it is effective as a crime prevention measure in places where many people gather, such as commercial facilities and public transportation. It can also be used to protect the safety of local communities, such as residential areas and schools. In this way, AI-powered crime prevention systems make a significant contribution to the early resolution of crimes and accidents. By monitoring security camera footage and detecting anomalies, AI can immediately report them, enabling a swift and accurate response. This improves the safety of society as a whole, creating an environment where people can live with peace of mind. In short, AI systems can achieve the early resolution of crimes and accidents and improve public safety.

[0073] The AI ​​system according to this embodiment comprises a monitoring unit, a detection unit, a notification unit, and a transmission unit. The monitoring unit monitors the video feed from security cameras. The monitoring unit monitors the video feed from security cameras in places such as commercial facilities and public transportation in real time. The monitoring unit can also monitor the video feed from security cameras in residential areas and schools. Furthermore, the monitoring unit uses AI to analyze the video feed and detect abnormal movements and behaviors. For example, the monitoring unit can detect acts of violence and suspicious movements. The detection unit detects abnormalities from the video feed monitored by the monitoring unit. The detection unit uses AI to analyze the video feed and detect abnormal movements and behaviors. For example, the detection unit can detect acts of violence and suspicious movements. Furthermore, if the detection unit detects an abnormality, it immediately transmits the information to the notification unit. The notification unit notifies the police or security company based on the abnormality detected by the detection unit. The notification unit generates notification content using AI and immediately notifies the police or security company. For example, the reporting unit can generate a report containing detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route. The transmission unit analyzes the information reported by the reporting unit and transmits the nature of the crime, the characteristics of the perpetrator, the escape route, etc. The transmission unit can, for example, use AI to analyze the report and generate detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route. For example, the transmission unit can provide detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route to the police or security companies. As a result, the AI ​​system according to this embodiment can monitor security camera footage, detect anomalies, and immediately report them, thereby enabling the early resolution of incidents and accidents.

[0074] The monitoring unit monitors security camera footage. For example, it monitors footage from security cameras in commercial facilities and public transportation in real time. Specifically, it centrally manages footage from multiple cameras installed in various areas of a commercial facility and transmits the video data to a central monitoring system in real time. In public transportation, it similarly monitors footage from cameras installed in stations and train cars to detect abnormal behavior or suspicious movements. The monitoring unit can also monitor footage from security cameras in residential areas and schools. In residential areas, it collects footage from cameras installed at the entrances and parking lots of each house, and in schools, it monitors footage from cameras installed in corridors, classrooms, and schoolyards. Furthermore, the monitoring unit uses AI to analyze the footage and detect abnormal movements and behaviors. The AI ​​uses video analysis algorithms to detect movements that differ from normal behavior patterns in real time. For example, the monitoring unit can detect acts of violence and suspicious movements. Specifically, the AI ​​analyzes a person's movements, posture, and speed, and identifies abnormal movements by comparing them to normal behavior patterns. This allows the monitoring unit to efficiently monitor a wide area and detect anomalies early.

[0075] The detection unit detects anomalies from video footage monitored by the monitoring unit. The detection unit analyzes the video footage using AI, for example, to detect abnormal movements and behaviors. Specifically, the AI ​​analyzes the movements of people and objects in the video and identifies abnormal movements by comparing them to normal behavior patterns. For example, the detection unit can detect violent acts or suspicious movements. Using a deep learning model, the AI ​​analyzes the video in real time based on patterns of abnormal behavior learned from past data. For example, the AI ​​determines that actions such as a person suddenly running, staying in a specific area for a long time, or throwing objects are abnormal. Furthermore, if the detection unit detects an anomaly, it immediately transmits the information to the notification unit. When an anomaly is detected, the AI ​​automatically generates detailed information such as the type, location, and time of the anomaly and transmits it to the notification unit. This allows the detection unit to quickly and accurately detect anomalies and respond immediately. In addition, to improve the accuracy of anomaly detection, the detection unit periodically updates its AI model and learns from the latest data. This allows the detection unit to consistently detect anomalies with high accuracy, improving the overall reliability of the system.

[0076] The reporting unit notifies the police and security companies based on anomalies detected by the detection unit. The reporting unit generates report content using AI, for example, and immediately notifies the police and security companies. Specifically, the AI ​​automatically generates report content based on anomaly information transmitted from the detection unit. For example, the reporting unit can generate report content that includes detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route. Based on video analysis results and past data, the AI ​​automatically extracts the characteristics of the perpetrator (clothing, height, build, etc.) and details of the crime (location, time, actions, etc.) and reflects them in the report content. In addition, the reporting unit uses multiple communication methods to immediately transmit the report content to the police and security companies. For example, the reporting unit uses a digital reporting system via the internet and a voice reporting system using telephone lines in combination to reliably transmit information. Furthermore, even after transmitting the report content, the reporting unit continuously updates the report content based on information updated in real time, providing the police and security companies with the latest situation. This allows the reporting unit to report anomalies quickly and accurately, supporting the early resolution of incidents and accidents.

[0077] The transmission department analyzes information reported by the reporting department and transmits details of the crime, the perpetrator's characteristics, and escape routes. For example, the transmission department uses AI to analyze the reported information and generate detailed information such as the crime, the perpetrator's characteristics, and escape routes. Specifically, the AI ​​automatically analyzes the details of the crime and the perpetrator's characteristics based on the information transmitted from the reporting department, and provides detailed information such as the crime, the perpetrator's characteristics, and escape routes to the police and security companies. The AI ​​automatically extracts the perpetrator's characteristics (clothing, height, build, etc.) and details of the crime (location, time, actions, etc.) based on video analysis results and past data, and reflects this in the transmitted information. Furthermore, the transmission department provides the police and security companies with the latest information based on real-time updates. For example, if the perpetrator's escape route changes or new information is obtained, the transmission department immediately updates the information and provides it to the police and security companies. In addition, the transmission department uses multiple communication methods to ensure reliable information transmission. For example, it uses a combination of digital and voice reporting systems to ensure that important information is delivered reliably. This allows the communications department to provide information quickly and accurately to the police and security companies, supporting the early resolution of incidents and accidents.

[0078] The filtering unit has filtering capabilities. For example, the filtering unit uses AI to remove unnecessary information from security camera footage and extract important information. For example, the filtering unit can perform noise reduction and extract important information. For example, the filtering unit removes noise from the video and highlights abnormal movements and actions. The filtering unit can also extract important information and transmit it to the detection unit. For example, the filtering unit detects abnormal movements and actions and transmits them to the detection unit. This allows the filtering unit to remove unnecessary information and extract important information. Some or all of the above processing in the filtering unit may be performed using AI, or without AI. For example, the filtering unit can input security camera footage into a generating AI and have the generating AI perform noise reduction and extract important information.

[0079] The selection unit selects training data. For example, the selection unit uses AI to select training data from security camera footage. For example, the selection unit can select training data such as image data, text data, and audio data. For example, the selection unit selects video data that includes abnormal movements or behaviors and uses it as training data for the AI. The selection unit can also make selections considering the quality and diversity of the training data. For example, the selection unit selects high-quality video data and data that includes diverse behavior patterns. This allows the selection unit to select training data that improves the accuracy of the AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input security camera footage into a generating AI and have the generating AI perform the selection of training data.

[0080] The communications department provides means of communication. The communications department provides means of communication with the police and security companies, for example, using AI. For example, the communications department can provide means of communication such as the internet, telephone, and wireless communication. For example, the communications department can transmit information to the police and security companies via the internet. The communications department can also make emergency calls using telephone or wireless communication. For example, the communications department can use the telephone to notify the police in an emergency. The communications department can also transmit information to security companies using wireless communication. This allows the communications department to cooperate smoothly with the police and security companies. Some or all of the above-described processes in the communications department may be performed using AI, for example, or without AI. For example, the communications department can input information about a report into a generating AI and have the generating AI select the means of communication and transmit the information about the report.

[0081] The management department manages privacy protection regulations. The management department manages privacy protection regulations such as data encryption, access restrictions, and data retention periods. For example, the management department can use AI to encrypt data and prevent the leakage of personal information. The management department can also implement access restrictions, ensuring that only specific users can access the data. For example, the management department can set access permissions to restrict access to data. Furthermore, the management department can set data retention periods and delete data after a certain period. For example, the management department can set data retention periods and automatically delete data after a certain period. This allows the management department to manage privacy protection regulations and prevent the leakage of personal information. Some or all of the above processes performed by the management department may be carried out using AI, for example, or without AI. For example, the management department can have a generating AI perform data encryption and access restriction settings.

[0082] The detection unit can detect abnormal movements and behaviors. For example, the detection unit can use AI to detect abnormal movements and behaviors from security camera footage. For example, the detection unit can detect sudden movements or unnatural behavioral patterns. For example, the detection unit can detect acts of violence or suspicious movements and determine them to be abnormal. Furthermore, if the detection unit detects an abnormality, it can immediately transmit the information to the reporting unit. This allows the detection unit to detect abnormal movements and behaviors and respond quickly. 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 security camera footage into a generating AI and have the generating AI perform the detection of abnormal movements and behaviors.

[0083] The reporting unit can include detailed information in the report, such as the nature of the crime, the characteristics of the perpetrator, and the escape route. For example, the reporting unit can use AI to generate the report, which will include detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route. For example, the reporting unit can generate a report that describes in detail the modus operandi, the characteristics of the perpetrator, and the escape route. For example, the reporting unit may include specific information such as theft, assault, or property damage as the nature of the crime. The reporting unit may also include height, weight, clothing, and facial features as the characteristics of the perpetrator. Furthermore, the reporting unit may include camera locations, eyewitness accounts, and geographical information as the escape route. This allows the reporting unit to generate a report containing detailed information and provide it to the police or security companies. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the report content into a generating AI and have the generating AI perform the generation of a report containing detailed information.

[0084] The communication unit can continue to monitor the situation after the report is made and provide additional information as needed. For example, the communication unit can use AI to monitor security camera footage after the report is made and detect any unusual movements or behaviors. For example, if the suspect is on the run, the communication unit can track the escape route in real time and provide that information to the police or security company. The communication unit can also provide additional eyewitness accounts and real-time footage. For example, the communication unit can track the suspect's escape route based on camera locations, eyewitness accounts, geographical information, etc. This allows the communication unit to continue monitoring the situation after the report is made and provide additional information as needed. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not using AI. For example, the communication unit can input security camera footage into a generative AI and have the generative AI perform the detection of unusual movements or behaviors.

[0085] The monitoring unit can estimate the user's emotions and adjust its monitoring focus based on the estimated emotions. For example, the monitoring unit can use AI to estimate the user's emotions and adjust its monitoring focus. For instance, if the user is feeling anxious, the monitoring unit will focus its monitoring on a specific area. Conversely, if the user is feeling at ease, the monitoring unit can maintain its normal monitoring range. Furthermore, if the user is stressed, the monitoring unit can increase the monitoring frequency to detect anomalies earlier. This allows the monitoring unit to adjust its monitoring focus according to the user's emotions, enabling more effective monitoring. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring focus.

[0086] The monitoring unit can improve the accuracy of anomaly detection by referring to past incident data during monitoring. For example, the monitoring unit can use AI to refer to past incident data and improve the accuracy of anomaly detection. For example, the monitoring unit can learn specific behavioral patterns based on past incident data and detect anomalies. The monitoring unit can also analyze past incident data and consider the rate of anomaly occurrence at specific time periods and locations. Furthermore, the monitoring unit can build a predictive model of abnormal behavior by referring to past incident data. This allows the monitoring unit to improve the accuracy of anomaly detection by referring to past incident data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past incident data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0087] The monitoring unit can adjust the intensity of monitoring based on specific time periods and locations during monitoring. For example, the monitoring unit can use AI to adjust the intensity of monitoring based on specific time periods and locations. For example, the monitoring unit can increase the intensity of monitoring at night or in places with little foot traffic. It can also maintain normal monitoring intensity during the day or in places with a lot of foot traffic. Furthermore, the monitoring unit can temporarily increase the intensity of monitoring in places where specific events are being held. In this way, the monitoring unit can adjust the intensity of monitoring based on specific time periods and locations, enabling more effective monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on specific time periods and locations into a generating AI and have the generating AI perform the adjustment of the monitoring intensity.

[0088] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, the monitoring unit can use AI to estimate the user's emotions and adjust the monitoring frequency. For example, if the user is feeling anxious, the monitoring unit can increase the monitoring frequency. Conversely, if the user is feeling at ease, the monitoring unit can maintain the normal monitoring frequency. Furthermore, if the user is feeling stressed, the monitoring unit can further increase the monitoring frequency to detect anomalies early. In this way, the monitoring unit can adjust the monitoring frequency according to the user's emotions and perform more effective monitoring. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI adjust the monitoring frequency.

[0089] The monitoring unit can change its monitoring method when monitoring, taking into account weather and environmental conditions. For example, the monitoring unit can use AI to change its monitoring method considering weather and environmental conditions. For example, the monitoring unit can adjust its monitoring method when it rains because the camera's visibility is poor. The monitoring unit can also use an infrared camera for monitoring at night. Furthermore, the monitoring unit can increase the intensity of monitoring when there is fog. In this way, the monitoring unit can change its monitoring method considering weather and environmental conditions and perform more effective monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input weather and environmental condition data into a generating AI and have the generating AI execute the change in the monitoring method.

[0090] The monitoring unit can integrate multiple camera images to detect anomalies during monitoring. The monitoring unit can, for example, use AI to integrate multiple camera images and detect anomalies. For example, the monitoring unit can integrate multiple camera images in real time and detect anomalies. The monitoring unit can also integrate images from different angles to detect abnormal behavior. Furthermore, the monitoring unit can analyze multiple camera images to identify patterns of abnormal behavior. This allows the monitoring unit to integrate multiple camera images to detect anomalies and improve the accuracy of anomaly detection. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input multiple camera images into a generating AI and have the generating AI perform anomaly detection.

[0091] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, the detection unit can use AI to estimate the user's emotions and adjust the anomaly detection criteria. For example, if the user is feeling anxious, the detection unit can tighten the anomaly detection criteria. If the user is feeling at ease, the detection unit can maintain the normal anomaly detection criteria. Furthermore, if the user is feeling stressed, the detection unit can tighten the anomaly detection criteria even more. In this way, the detection unit can adjust the anomaly detection criteria according to the user's emotions and perform more effective anomaly detection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the anomaly detection criteria.

[0092] The detection unit can improve its detection accuracy by learning patterns of abnormal behavior upon detection. The detection unit can improve detection accuracy by learning patterns of abnormal behavior using, for example, AI. For example, the detection unit learns patterns of abnormal behavior based on past abnormal behavior data. The detection unit can also learn patterns of abnormal behavior and detect new abnormal behaviors. Furthermore, the detection unit can improve detection accuracy by learning patterns of abnormal behavior. In this way, the detection unit can improve detection accuracy by learning patterns of abnormal behavior. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without using AI. For example, the detection unit can input abnormal behavior data into a generating AI and have the generating AI perform learning of abnormal behavior patterns.

[0093] The detection unit can apply different detection algorithms depending on the type of abnormal behavior upon detection. For example, the detection unit can use AI to apply different detection algorithms depending on the type of abnormal behavior. For example, the detection unit can apply an algorithm to detect violent behavior. The detection unit can also apply an algorithm to detect suspicious movements. Furthermore, the detection unit can apply an algorithm to detect escape behavior. In this way, the detection unit can apply different detection algorithms depending on the type of abnormal behavior and improve detection accuracy. 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 the type of abnormal behavior into a generating AI and cause the generating AI to execute the application of an appropriate detection algorithm.

[0094] The detection unit can estimate the user's emotions and determine the priority of anomaly detection based on the estimated user emotions. For example, the detection unit can use AI to estimate the user's emotions and determine the priority of anomaly detection. For example, if the user is feeling anxious, the detection unit will prioritize anomaly detection higher. If the user is feeling at ease, the detection unit can maintain the normal priority of anomaly detection. Furthermore, if the user is feeling stressed, the detection unit can further increase the priority of anomaly detection. In this way, the detection unit can determine the priority of anomaly detection according to the user's emotions and perform more effective anomaly detection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generative AI and have the generative AI perform the determination of anomaly detection priorities.

[0095] The detection unit can adjust the detection intensity based on the location where the abnormal behavior occurs. For example, the detection unit can use AI to adjust the detection intensity based on the location of the abnormal behavior. For example, the detection unit can increase the detection intensity in areas with little foot traffic. It can also maintain a normal detection intensity in areas with a lot of foot traffic. Furthermore, the detection unit can temporarily increase the detection intensity in locations where specific events are held. In this way, the detection unit can adjust the detection intensity based on the location of the abnormal behavior and perform more effective anomaly detection. 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 data on the location of the abnormal behavior into a generating AI and have the generating AI perform the adjustment of the detection intensity.

[0096] The detection unit can adjust the detection frequency based on the time of occurrence of abnormal behavior when it detects it. The detection unit can adjust the detection frequency based on the time of occurrence of abnormal behavior using, for example, AI. For example, the detection unit can increase the detection frequency at night. The detection unit can also maintain a normal detection frequency during the day. Furthermore, if abnormal behavior occurs frequently during a particular time period, the detection unit can increase the detection frequency during that time period. In this way, the detection unit can adjust the detection frequency based on the time of occurrence of abnormal behavior and perform more effective anomaly detection. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input data on the time of occurrence of abnormal behavior into a generating AI and have the generating AI perform the adjustment of the detection frequency.

[0097] The reporting unit can estimate the user's emotions and adjust the level of detail in the report based on the estimated emotions. For example, the reporting unit can use AI to estimate the user's emotions and adjust the level of detail in the report. For example, if the user is feeling anxious, the reporting unit can provide a detailed report. If the user is feeling at ease, the reporting unit can provide a normal report. Furthermore, if the user is feeling stressed, the reporting unit can provide an even more detailed report. In this way, the reporting unit can adjust the level of detail in the report according to the user's emotions and make more effective reports. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in the report.

[0098] The reporting unit can improve the accuracy of reports by referring to past reporting data when a report is made. For example, the reporting unit can use AI to refer to past reporting data and improve the accuracy of reports. For example, the reporting unit can scrutinize the content of a report based on past reporting data. The reporting unit can also analyze past reporting data to improve the accuracy of reports. Furthermore, the reporting unit can optimize the content of a report by referring to past reporting data. In this way, the reporting unit can improve the accuracy of reports by referring to past reporting data. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without using AI. For example, the reporting unit can input past reporting data into a generating AI and have the generating AI perform a scrutiny of the report content.

[0099] The reporting unit can apply different reporting methods depending on the type of anomaly when a report is made. For example, the reporting unit can use AI to apply different reporting methods depending on the type of anomaly. For example, in the case of violent behavior, the reporting unit can make an emergency report. In addition, in the case of suspicious activity, the reporting unit can make a report that includes tracking information. This allows the reporting unit to select the appropriate reporting method according to the type of anomaly and respond quickly and accurately. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the type of anomaly into a generating AI and have the generating AI execute the application of the appropriate reporting method.

[0100] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. The reporting unit can use AI, for example, to estimate the user's emotions and determine the priority of reports. For example, the reporting unit will give a higher priority to reports when the user is feeling anxious. The reporting unit can maintain the normal priority when the user is feeling at ease. Furthermore, the reporting unit can give an even higher priority to reports when the user is feeling stressed. This allows the reporting unit to determine the priority of reports according to the user's emotions and make more effective reports. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI determine the priority of reports.

[0101] The reporting unit can select the optimal reporting method when a report is made, taking into account the geographical location information of the recipient. For example, the reporting unit can use AI to select the optimal reporting method by considering the geographical location information of the recipient. For example, if the recipient is nearby, the reporting unit can select a rapid reporting method. If the recipient is far away, the reporting unit can select a standard reporting method. Furthermore, the reporting unit can select the optimal reporting method based on the geographical location information of the recipient. This allows the reporting unit to select the optimal reporting method by considering the geographical location information of the recipient and to make a quick and accurate report. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the geographical location information of the recipient into a generating AI and have the generating AI select the optimal reporting method.

[0102] The reporting unit can provide additional information related to the reported content at the time of reporting. For example, the reporting unit may use AI to provide additional information related to the reported content. For example, the reporting unit may include details of the crime in the reported content. The reporting unit may also include characteristics of the perpetrator in the reported content. Furthermore, the reporting unit may include the escape route in the reported content. This allows the reporting unit to provide additional information related to the reported content and convey more detailed information to the police or security company. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit may input the reported content into a generating AI and have the generating AI perform the provision of related additional information.

[0103] The communication unit can estimate the user's emotions and adjust the level of detail in the message based on the estimated emotions. For example, the communication unit can use AI to estimate the user's emotions and adjust the level of detail in the message. For example, if the user is feeling anxious, the communication unit can provide detailed message content. If the user is feeling at ease, the communication unit can provide normal message content. Furthermore, if the user is feeling stressed, the communication unit can provide even more detailed message content. In this way, the communication unit can adjust the level of detail in the message content according to the user's emotions and deliver more effective messages. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in the message content.

[0104] The transmission unit can improve the accuracy of transmission by referring to past transmission data during transmission. For example, the transmission unit can use AI to refer to past transmission data and improve the accuracy of transmission. For example, the transmission unit can scrutinize the content of transmission based on past transmission data. The transmission unit can also analyze past transmission data and improve the accuracy of transmission. Furthermore, the transmission unit can optimize the content of transmission by referring to past transmission data. In this way, the transmission unit can improve the accuracy of transmission by referring to past transmission data. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without using AI. For example, the transmission unit can input past transmission data into a generating AI and have the generating AI perform a scrutiny of the content of transmission.

[0105] The communication unit can apply different communication methods depending on the type of anomaly during communication. For example, the communication unit can use AI to apply different communication methods depending on the type of anomaly. For example, in the case of violent behavior, the communication unit can make an emergency communication. In addition, in the case of suspicious movement, the communication unit can make a normal communication. Furthermore, in the case of escape behavior, the communication unit can make a communication that includes tracking information. This allows the communication unit to select an appropriate communication method according to the type of anomaly and respond quickly and accurately. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the type of anomaly into a generating AI and have the generating AI execute the application of an appropriate communication method.

[0106] The communication unit can estimate the user's emotions and determine the priority of communication based on the estimated emotions. For example, the communication unit can use AI to estimate the user's emotions and determine the priority of communication. For example, if the user is feeling anxious, the communication unit will give a higher priority to communication. If the user is feeling at ease, the communication unit can maintain the normal priority of communication. Furthermore, if the user is feeling stressed, the communication unit can give an even higher priority to communication. In this way, the communication unit can determine the priority of communication according to the user's emotions and deliver more effective communication. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input user emotion data into a generative AI and have the generative AI determine the priority of communication.

[0107] The transmission unit can select the optimal transmission method when transmitting information, taking into account the geographical location of the recipient. For example, the transmission unit can use AI to select the optimal transmission method by considering the geographical location of the recipient. For example, if the recipient is nearby, the transmission unit can select a rapid transmission method. If the recipient is far away, the transmission unit can select a normal transmission method. Furthermore, the transmission unit can select the optimal transmission method based on the geographical location of the recipient. As a result, the transmission unit can select the optimal transmission method by considering the geographical location of the recipient and perform rapid and accurate transmission. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the geographical location of the recipient into a generating AI and have the generating AI select the optimal transmission method.

[0108] The transmission unit can provide additional information related to the transmitted content at the time of transmission. The transmission unit can provide additional information related to the transmitted content using, for example, AI. For example, the transmission unit may include details of the crime in the transmitted content. The transmission unit may also include characteristics of the perpetrator in the transmitted content. Furthermore, the transmission unit may include escape routes in the transmitted content. This allows the transmission unit to provide additional information related to the transmitted content and convey more detailed information to the police or security companies. Some or all of the above processing in the transmission unit may be performed using, for example, AI, or not using AI. For example, the transmission unit can input the transmitted content into a generating AI and have the generating AI perform the provision of related additional information.

[0109] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated emotions. For example, the filtering unit might use AI to estimate the user's emotions and adjust the filtering criteria. For instance, if the user is feeling anxious, the filtering unit might tighten the filtering criteria. Conversely, if the user is feeling at ease, the filtering unit can maintain the normal filtering criteria. Furthermore, if the user is feeling stressed, the filtering unit might tighten the filtering criteria even more. This allows the filtering unit to adjust the filtering criteria according to the user's emotions, enabling more effective filtering. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the filtering unit may be performed using AI or not. For example, the filtering unit can input user emotion data into the generative AI and have the generative AI adjust the filtering criteria.

[0110] The filtering unit can improve the accuracy of filtering by referring to past filtering data during the filtering process. For example, the filtering unit can use AI to refer to past filtering data and improve the accuracy of filtering. For example, the filtering unit can scrutinize the filtering content based on past filtering data. The filtering unit can also analyze past filtering data to improve the accuracy of filtering. Furthermore, the filtering unit can optimize the filtering content by referring to past filtering data. In this way, the filtering unit can improve the accuracy of filtering by referring to past filtering data. Some or all of the above processes in the filtering unit may be performed using AI, for example, or without using AI. For example, the filtering unit can input past filtering data into a generating AI and have the generating AI perform a scrutiny of the filtering content.

[0111] The filtering unit can estimate the user's emotions and determine filtering priorities based on the estimated emotions. For example, the filtering unit might use AI to estimate the user's emotions and determine filtering priorities. For instance, if the user is feeling anxious, the filtering unit might prioritize filtering more highly. If the user is feeling at ease, the filtering unit can maintain its normal filtering priority. Furthermore, if the user is feeling stressed, the filtering unit might prioritize filtering even more highly. This allows the filtering unit to determine filtering priorities according to the user's emotions, enabling more effective filtering. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 filtering unit may be performed using AI or not. For example, the filtering unit can input user emotion data into a generative AI and have the generative AI determine the filtering priorities.

[0112] The filtering unit can select the optimal filtering method by considering the attribute information of the data to be filtered during the filtering process. For example, the filtering unit may use AI to select the optimal filtering method by considering the attribute information of the data to be filtered. For example, if the data to be filtered is important information, the filtering unit may select a strict filtering method. If the data to be filtered is general information, the filtering unit may select a normal filtering method. Furthermore, the filtering unit may select the optimal filtering method based on the attribute information of the data to be filtered. In this way, the filtering unit can select the optimal filtering method by considering the attribute information of the data to be filtered and perform effective filtering. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit may input the attribute information of the data to be filtered into a generating AI and have the generating AI select the optimal filtering method.

[0113] The selection unit can estimate the user's emotions and adjust the selection criteria for training data based on the estimated user emotions. For example, the selection unit can use AI to estimate the user's emotions and adjust the selection criteria for training data. For example, if the user is feeling anxious, the selection unit can tighten the selection criteria for training data. If the user is feeling at ease, the selection unit can maintain the normal selection criteria for training data. Furthermore, if the user is feeling stressed, the selection unit can tighten the selection criteria for training data even more. In this way, the selection unit can adjust the selection criteria for training data according to the user's emotions and perform more effective selection of training data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the training data selection criteria.

[0114] The selection unit can improve the accuracy of its selections by referring to past selection data during the selection process. For example, the selection unit can use AI to refer to past selection data and improve the accuracy of its selections. For example, the selection unit can scrutinize the selection content based on past selection data. The selection unit can also analyze past selection data to improve the accuracy of its selections. Furthermore, the selection unit can optimize the selection content by referring to past selection data. In this way, the selection unit can improve the accuracy of its selections by referring to past selection data. Some or all of the above processes in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input past selection data into a generating AI and have the generating AI perform a scrutiny of the selection content.

[0115] The selection unit can estimate the user's emotions and determine the priority of training data based on the estimated user emotions. For example, the selection unit can use AI to estimate the user's emotions and determine the priority of training data. For example, if the user is feeling anxious, the selection unit will give higher priority to the training data. If the user is feeling at ease, the selection unit can maintain the normal priority of the training data. Furthermore, if the user is feeling stressed, the selection unit can give even higher priority to the training data. In this way, the selection unit can determine the priority of training data according to the user's emotions and perform more effective selection of training data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user emotion data into the generative AI and have the generative AI perform the determination of the priority of the training data.

[0116] The selection unit can select the optimal selection method by considering the attribute information of the items to be selected during the selection process. For example, the selection unit can use AI to select the optimal selection method by considering the attribute information of the items to be selected. For example, if the items to be selected are important information, the selection unit can select a strict selection method. Also, if the items to be selected are general information, the selection unit can select a normal selection method. Furthermore, the selection unit can select the optimal selection method based on the attribute information of the items to be selected. In this way, the selection unit can select the optimal selection method by considering the attribute information of the items to be selected and perform an effective selection. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input the attribute information of the items to be selected into a generating AI and have the generating AI perform the selection of the optimal selection method.

[0117] The communication unit can estimate the user's emotions and adjust the level of detail in the communication content based on the estimated emotions. For example, the communication unit can use AI to estimate the user's emotions and adjust the level of detail in the communication content. For example, if the user is feeling anxious, the communication unit can provide detailed communication content. If the user is feeling at ease, the communication unit can provide normal communication content. Furthermore, if the user is feeling stressed, the communication unit can provide even more detailed communication content. In this way, the communication unit can adjust the level of detail in the communication content according to the user's emotions and communicate more effectively. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in the communication content.

[0118] The communication unit can improve the accuracy of communication by referring to past communication data during communication. For example, the communication unit can use AI to refer to past communication data and improve the accuracy of communication. For example, the communication unit can scrutinize the content of communication based on past communication data. The communication unit can also analyze past communication data to improve the accuracy of communication. Furthermore, the communication unit can optimize the content of communication by referring to past communication data. In this way, the communication unit can improve the accuracy of communication by referring to past communication data. Some or all of the above processing in the communication unit may be performed using AI, for example, or without using AI. For example, the communication unit can input past communication data into a generating AI and have the generating AI perform a scrutiny of the communication content.

[0119] The communications unit can estimate the user's emotions and determine communication priorities based on the estimated emotions. For example, the communications unit can use AI to estimate the user's emotions and determine communication priorities. For example, if the communications unit is feeling anxious, it can prioritize the communication higher. If the user is feeling at ease, it can maintain the normal communication priority. Furthermore, if the user is feeling stressed, it can prioritize the communication even higher. This allows the communications unit to determine communication priorities according to the user's emotions and conduct more effective communication. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit can input user emotion data into a generative AI and have the generative AI determine communication priorities.

[0120] The communication unit can select the optimal communication method when communicating, taking into account the geographical location information of the communication destination. For example, the communication unit can use AI to select the optimal communication method by considering the geographical location information of the communication destination. For example, if the communication destination is nearby, the communication unit can select a rapid communication method. If the communication destination is far away, the communication unit can select a normal communication method. Furthermore, the communication unit can select the optimal communication method based on the geographical location information of the communication destination. As a result, the communication unit can select the optimal communication method by taking into account the geographical location information of the communication destination and perform rapid and accurate communication. Some or all of the above processing in the communication unit may be performed using AI, for example, or without using AI. For example, the communication unit can input the geographical location information of the communication destination into a generating AI and have the generating AI perform the selection of the optimal communication method.

[0121] The management department can estimate the user's emotions and adjust privacy protection provisions based on the estimated user emotions. For example, the management department can use AI to estimate the user's emotions and adjust privacy protection provisions. For example, if the user is feeling anxious, the management department can tighten privacy protection provisions. Conversely, if the user is feeling secure, the management department can maintain normal privacy protection provisions. Furthermore, if the user is feeling stressed, the management department can tighten privacy protection provisions even more. In this way, the management department can adjust privacy protection provisions according to the user's emotions and provide more effective privacy protection. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user emotion data into a generative AI and have the generative AI perform adjustments to privacy protection provisions.

[0122] The management department can improve the accuracy of its management by referring to past privacy protection data during the management process. For example, the management department can use AI to refer to past privacy protection data and improve the accuracy of its management. For example, the management department can scrutinize management content based on past privacy protection data. The management department can also analyze past privacy protection data to improve the accuracy of its management. Furthermore, the management department can optimize management content by referring to past privacy protection data. In this way, the management department can improve the accuracy of its management by referring to past privacy protection data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past privacy protection data into a generating AI and have the generating AI perform a scrutiny of management content.

[0123] The management department can estimate the user's emotions and determine privacy protection priorities based on those estimated emotions. For example, the management department might use AI to estimate the user's emotions and determine privacy protection priorities. For instance, if the user is feeling anxious, the management department might prioritize privacy protection higher. If the user is feeling secure, the management department might maintain the normal privacy protection priorities. Furthermore, if the user is feeling stressed, the management department might prioritize privacy protection even higher. This allows the management department to determine privacy protection priorities according to the user's emotions, enabling more effective privacy protection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 management department may be performed using AI or not. For example, the management department could input user emotion data into a generative AI and have the generative AI determine privacy protection priorities.

[0124] The management department can select the optimal management method when managing data, taking into account the attribute information of the data being managed. For example, the management department can use AI to select the optimal management method by considering the attribute information of the data being managed. For example, if the data being managed is important information, the management department can select a strict management method. If the data being managed is general information, the management department can select a normal management method. Furthermore, the management department can select the optimal management method based on the attribute information of the data being managed. This allows the management department to select the optimal management method by considering the attribute information of the data being managed and to perform effective management. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the attribute information of the data being managed into a generating AI and have the generating AI select the optimal management method.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The AI ​​system may also include a prediction unit. The prediction unit, for example, uses AI to analyze past data and predict future incidents or accidents. For instance, the prediction unit can learn specific patterns based on past incident data and predict future abnormal behavior. Furthermore, the prediction unit can predict the rate of anomalies in specific time periods or locations and adjust monitoring priorities accordingly. Additionally, the prediction unit can predict anomalies at locations where specific events are held and notify police or security companies in advance. This allows the prediction unit to anticipate future incidents or accidents and take preventative measures. Some or all of the above-described processes in the prediction unit may be performed using AI, or not. For example, the prediction unit can input past data into a generating AI and have the generating AI predict future abnormal behavior.

[0127] The AI ​​system may also include a notification unit. The notification unit can, for example, use AI to notify the user of anomaly detection. For example, the notification unit can send anomaly detection notifications to the user via a smartphone app. The notification unit can also notify the user via email or SMS. Furthermore, the notification unit can customize the notification method according to the user's settings. For example, if the user requests an emergency notification, the notification unit can send a notification immediately. This allows the notification unit to provide the user with quick and accurate anomaly detection notifications. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly detection data into a generating AI and have the generating AI generate the notification content.

[0128] The AI ​​system may also include an analysis unit. The analysis unit can, for example, use AI to analyze security camera footage in detail and identify the cause of abnormal behavior. For instance, the analysis unit can analyze the time period, location, and behavioral patterns of the abnormal behavior. It can also analyze the frequency and trends of the abnormal behavior and propose preventative measures. Furthermore, the analysis unit can identify the factors behind the abnormal behavior and provide information for taking countermeasures. This allows the analysis unit to identify the cause of the abnormal behavior and propose preventative measures. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input security camera footage into a generating AI and have the generating AI identify the cause of the abnormal behavior.

[0129] The AI ​​system may also include a recording unit. The recording unit can, for example, use AI to record security camera footage and anomaly detection data. For instance, the recording unit can record the date, time, location, and behavioral patterns of abnormal behavior. Furthermore, the recording unit can store detailed information about the abnormal behavior for later analysis and verification. Additionally, the recording unit can encrypt the recorded data to enhance privacy protection. This allows the recording unit to record security camera footage and anomaly detection data for later analysis and verification. Some or all of the above-described processes in the recording unit may be performed using AI, or not. For example, the recording unit can input security camera footage into a generating AI and have the generating AI generate the recorded content.

[0130] The AI ​​system may also include a feedback unit. The feedback unit, for example, uses AI to collect user feedback and use it to improve the system. For instance, the feedback unit can provide an interface where users can input their experience with the system and areas for improvement. Furthermore, the feedback unit can analyze the collected feedback to identify areas for system improvement. Additionally, the feedback unit can automatically adjust system settings based on user feedback. This allows the feedback unit to collect user feedback and use it to improve the system. Some or all of the above-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user feedback data into a generating AI and have the generating AI identify areas for system improvement.

[0131] The AI ​​system may further include an emotion estimation unit. The emotion estimation unit, for example, uses AI to estimate the user's emotions and adjusts the system's behavior accordingly. For instance, if the user is feeling anxious, the emotion estimation unit can increase the frequency of system notifications. Conversely, if the user is feeling at ease, the emotion estimation unit can maintain the normal notification frequency. Furthermore, if the user is feeling stressed, the emotion estimation unit can increase the system's monitoring intensity. This allows the emotion estimation unit to adjust the system's behavior according to the user's emotions, enabling more effective monitoring. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the emotion estimation unit may be performed using AI, or not. For example, the emotion estimation unit can input user emotion data into the generative AI and have the generative AI perform the system behavior adjustments.

[0132] The AI ​​system may further include a customization unit. This customization unit, for example, uses AI to customize the system's operation based on user settings. For instance, the customization unit can be configured to focus monitoring on specific areas. It can also be configured to receive notifications during specific time periods. Furthermore, it can be configured to receive notifications only when it detects specific abnormal behavior. This allows the customization unit to customize the system's operation based on user settings, enabling more effective monitoring. Some or all of the above-described processes in the customization unit may be performed using AI, or without AI. For example, the customization unit can input user setting data into a generating AI and have the generating AI perform the system operation customization.

[0133] The AI ​​system may further include an emotion analysis unit. The emotion analysis unit, for example, uses AI to analyze the user's emotions in detail and optimize the system's operation. For instance, if the user is feeling anxious, the emotion analysis unit can identify the cause and adjust system settings. If the user is feeling at ease, the emotion analysis unit can make settings to maintain that state. Furthermore, if the user is feeling stressed, the emotion analysis unit can provide notifications or alerts to alleviate that stress. This allows the emotion analysis unit to analyze the user's emotions in detail and optimize the system's operation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the emotion analysis unit may be performed using AI or not. For example, the emotion analysis unit can input user emotion data into the generative AI and have the generative AI perform system operation optimization.

[0134] The AI ​​system may further include an emotional feedback unit. The emotional feedback unit provides system feedback based on the user's emotions, for example, using AI. For instance, if the user is feeling anxious, the emotional feedback unit can provide reassuring feedback. If the user is feeling reassured, the emotional feedback unit can provide feedback to maintain that state. Furthermore, if the user is feeling tense, the emotional feedback unit can provide feedback to alleviate that tension. This allows the emotional feedback unit to provide system feedback based on the user's emotions, enabling more effective monitoring. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotional feedback unit may be performed using AI, or not. For example, the emotional feedback unit can input user emotion data into a generative AI and have the generative AI generate the feedback content.

[0135] The AI ​​system may further include an emotion monitoring unit. The emotion monitoring unit, for example, continuously monitors the user's emotions using AI and adjusts the system's operation accordingly. For instance, if the user is feeling anxious, the emotion monitoring unit can monitor whether that state persists and adjust system settings as needed. Similarly, if the user is feeling at ease, the emotion monitoring unit can configure settings to maintain that state. Furthermore, if the user is feeling tense, the emotion monitoring unit can monitor whether that tension persists and provide notifications or alerts as needed. This allows the emotion monitoring unit to continuously monitor the user's emotions and adjust the system's operation accordingly. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the emotion monitoring unit may be performed using AI or not. For example, the emotion monitoring unit can input user emotion data into a generative AI and have the generative AI perform system operation adjustments.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The monitoring unit monitors security camera footage. The monitoring unit monitors footage from security cameras in commercial facilities, public transportation, residential areas, schools, etc., in real time. The monitoring unit also uses AI to analyze the footage and detect abnormal movements and behaviors. Step 2: The detection unit detects anomalies from the video footage monitored by the monitoring unit. The detection unit uses AI to analyze the video and detect anomalies such as violent acts or suspicious movements. If an anomaly is detected, it immediately transmits the information to the reporting unit. Step 3: The reporting unit notifies the police or security company based on the anomaly detected by the detection unit. The reporting unit uses AI to generate the report content and immediately sends the report, which includes detailed information such as the nature of the crime, the characteristics of the perpetrator, and the escape route, to the police or security company. Step 4: The transmission department analyzes the information reported by the reporting department and transmits details of the crime, the perpetrator's characteristics, and escape route. The transmission department uses AI to analyze the reported information, generates detailed information, and provides it to the police and security companies.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the monitoring unit, detection unit, notification unit, transmission unit, filtering unit, selection unit, communication unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors security camera footage using the camera 42 of the smart device 14 and analyzes the footage using the control unit 46A. The detection unit detects anomalies using the specific processing unit 290 of the data processing unit 12, and the notification unit generates notification content using the specific processing unit 290 and immediately notifies the police or security company. The transmission unit analyzes the notification content using the specific processing unit 290 of the data processing unit 12 and generates detailed information. The filtering unit performs noise reduction and extracts important information using the control unit 46A of the smart device 14. The selection unit selects training data using the specific processing unit 290 of the data processing unit 12. The communication unit provides means of communication with the police or security company using the communication I / F 44 of the smart device 14. The management unit manages privacy protection regulations using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the monitoring unit, detection unit, notification unit, transmission unit, filtering unit, selection unit, communication unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors security camera footage using the camera 42 of the smart glasses 214 and analyzes the footage using the control unit 46A. The detection unit detects anomalies using the identification processing unit 290 of the data processing unit 12, and the notification unit generates notification content using the identification processing unit 290 and immediately notifies the police or security company. The transmission unit analyzes the notification content using the identification processing unit 290 of the data processing unit 12 and generates detailed information. The filtering unit performs noise reduction and extracts important information using the control unit 46A of the smart glasses 214. The selection unit selects training data using the identification processing unit 290 of the data processing unit 12. The communication unit provides means of communication with the police or security company using the communication I / F 44 of the smart glasses 214. The management unit manages the privacy protection regulations through the specific processing unit 290 of the data processing device 12. 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.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

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

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

[0168] In 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.

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

[0170] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0172] The data processing system 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.

[0173] Each of the multiple elements described above, including the monitoring unit, detection unit, notification unit, transmission unit, filtering unit, selection unit, communication unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the video from security cameras using the camera 42 of the headset terminal 314 and analyzes the video using the control unit 46A. The detection unit detects abnormalities using the specific processing unit 290 of the data processing unit 12, and the notification unit generates notification content using the specific processing unit 290 and immediately notifies the police or security company. The transmission unit analyzes the notification content using the specific processing unit 290 of the data processing unit 12 and generates detailed information. The filtering unit performs noise reduction and extracts important information using the control unit 46A of the headset terminal 314. The selection unit selects training data using the specific processing unit 290 of the data processing unit 12. The communication unit provides means of communication with the police or security company using the communication I / F 44 of the headset terminal 314. The management unit manages the privacy protection regulations through the specific processing unit 290 of the data processing device 12. 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.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the monitoring unit, detection unit, notification unit, transmission unit, filtering unit, selection unit, communication unit, and management unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors security camera footage using the camera 42 of the robot 414 and analyzes the footage using the control unit 46A. The detection unit detects anomalies using the specific processing unit 290 of the data processing unit 12, and the notification unit generates notification content using the specific processing unit 290 and immediately notifies the police or security company. The transmission unit analyzes the notification content using the specific processing unit 290 of the data processing unit 12 and generates detailed information. The filtering unit performs noise reduction and extracts important information using the control unit 46A of the robot 414. The selection unit selects training data using the specific processing unit 290 of the data processing unit 12. The communication unit provides means of communication with the police or security company using the communication I / F 44 of the robot 414. The management unit manages privacy protection regulations using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) The surveillance department monitors the footage from security cameras, A detection unit that detects anomalies from the video footage monitored by the aforementioned monitoring unit, A notification unit that notifies the police or security company based on the abnormality detected by the aforementioned detection unit, The system includes a transmission unit that analyzes the information reported by the aforementioned reporting unit and transmits details of the crime, characteristics of the perpetrator, escape route, etc. A system characterized by the following features. (Note 2) Equipped with a filtering unit that has a filtering function. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a selection unit for selecting training data. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a communication unit that provides means of communication. The system described in Appendix 1, characterized by the features described herein. (Note 5) It has a management department that oversees privacy protection regulations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The detection unit is Detects abnormal movements and behaviors. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reporting unit, The report should include detailed information such as the nature of the crime, the perpetrator's characteristics, and the escape route. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned transmission unit is We will continue to monitor the situation after the report is made and provide additional information as needed. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring focus based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, During monitoring, past incident data is referenced to improve the accuracy of anomaly detection. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, During monitoring, adjust the monitoring intensity based on specific time periods and locations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned monitoring unit, When monitoring, the monitoring method will be changed to take weather and environmental conditions into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned monitoring unit, During monitoring, multiple camera feeds are integrated to detect anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is The system estimates the user's emotions and adjusts the anomaly detection criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is When detection occurs, the system learns patterns of abnormal behavior to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is When detection occurs, different detection algorithms are applied depending on the type of abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is The system estimates the user's emotions and determines the priority of anomaly detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is When detection occurs, the detection intensity is adjusted based on the location where the abnormal behavior occurred. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is When abnormal behavior is detected, the detection frequency is adjusted based on the duration of the abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the level of detail in the report based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting unit, When reporting, past reporting data is referenced to improve the accuracy of the report. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting unit, When reporting an issue, different reporting methods will be applied depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, The system estimates the user's emotions and prioritizes reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, When making a report, the most suitable reporting method is selected considering the geographical location of the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, When making a report, provide additional information related to the content of the report. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned transmission unit is It estimates the user's emotions and adjusts the level of detail in the message based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned transmission unit is During transmission, past transmission data is referenced to improve the accuracy of the transmission. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned transmission unit is During transmission, different transmission methods are applied depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned transmission unit is It estimates the user's emotions and determines the priority of communication based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned transmission unit is When transmitting information, the most suitable transmission method is selected, taking into account the geographical location of the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned transmission unit is Provide additional information related to the content of the message during transmission. The system described in Appendix 1, characterized by the features described herein. (Note 33) The filtering unit is It estimates the user's sentiment and adjusts the filtering criteria based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The filtering unit is During filtering, past filtering data is referenced to improve the accuracy of the filtering process. The system described in Appendix 2, characterized by the features described herein. (Note 35) The filtering unit is It estimates the user's emotions and determines filtering priorities based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The filtering unit is During filtering, the optimal filtering method is selected by considering the attribute information of the target to be filtered. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned selection unit is It estimates the user's emotions and adjusts the selection criteria for training data based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned selection unit is During the selection process, we improve the accuracy of the selection by referring to past selection data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned selection unit is It estimates the user's emotions and prioritizes the training data based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned selection unit is During the selection process, the optimal selection method is chosen by considering the attribute information of the items to be selected. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned communications unit is It estimates the user's emotions and adjusts the level of detail in the communication content based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned communications unit is During communication, past communication data is referenced to improve communication accuracy. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned communications unit is It estimates the user's emotions and determines communication priorities based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned communications unit is During communication, the system selects the optimal communication method by considering the geographical location information of the communication destination. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned management department, We estimate user sentiment and adjust privacy protection provisions based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 46) The aforementioned management department, During management, we refer to past privacy protection data to improve the accuracy of management. The system described in Appendix 5, characterized by the features described herein. (Note 47) The aforementioned management department, It estimates user sentiment and determines privacy protection priorities based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 48) The aforementioned management department, During management, the optimal management method is selected by considering the attribute information of the managed items. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0210] 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 surveillance department monitors the footage from security cameras, A detection unit that detects anomalies from the video footage monitored by the aforementioned monitoring unit, A notification unit that notifies the police or security company based on the abnormality detected by the aforementioned detection unit, The system includes a transmission unit that analyzes the information reported by the aforementioned reporting unit and transmits details of the crime, characteristics of the perpetrator, escape route, etc. A system characterized by the following features.

2. Equipped with a filtering unit that has a filtering function. The system according to feature 1.

3. It includes a selection unit for selecting training data. The system according to feature 1.

4. Equipped with a communication unit that provides means of communication. The system according to feature 1.

5. It has a management department that oversees privacy protection regulations. The system according to feature 1.

6. The detection unit, Detects abnormal movements and behaviors. The system according to feature 1.

7. The aforementioned reporting unit, The report should include detailed information such as the nature of the crime, the perpetrator's characteristics, and the escape route. The system according to feature 1.

8. The aforementioned transmission unit is We will continue to monitor the situation after the report is made and provide additional information as needed. The system according to feature 1.

9. The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring focus based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned monitoring unit, During monitoring, past incident data is referenced to improve the accuracy of anomaly detection. The system according to feature 1.

11. The aforementioned monitoring unit, During monitoring, adjust the monitoring intensity based on specific time periods and locations. The system according to feature 1.

12. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

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