system

Abstract

The system according to this embodiment aims to immediately detect anomalies and take appropriate action. [Solution] The system according to the embodiment comprises a monitoring unit, a dialogue unit, a deployment instruction unit, and a notification unit. The monitoring unit integrates and manages surveillance cameras and various sensors and immediately detects abnormalities. The dialogue unit analyzes the situation through voice dialogue with security guards based on the abnormalities detected by the monitoring unit and provides optimal instructions. The deployment instruction unit, based on the instructions provided by the dialogue unit, instructs agents to autonomously extract suspicious behavior and deploy staff so that they can provide focused care. The notification unit automatically records images and notifies the police when a problem occurs.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, integrated management for immediately detecting an abnormality and taking appropriate measures has not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to immediately detect an abnormality and take appropriate measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a dialogue unit, a deployment instruction unit, and a notification unit. The monitoring unit integrates and manages surveillance cameras and various sensors and immediately detects abnormalities. Based on the abnormalities detected by the monitoring unit, the dialogue unit analyzes the situation through voice dialogue with security guards and provides optimal instructions. Based on the instructions provided by the dialogue unit, the deployment instruction unit instructs agents to autonomously extract suspicious behavior and deploy staff so that they can provide focused care. The notification unit automatically records images and notifies the police when a problem occurs. [Effects of the Invention]

[0007] The system according to this embodiment can immediately detect anomalies and take appropriate action. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The next-generation security system according to an embodiment of the present invention is an AI-powered "Guardian Agent." This Guardian Agent is a system that integrates and manages surveillance cameras and various sensors to instantly detect anomalies. The Guardian Agent analyzes the situation through voice interaction with security guards and provides optimal instructions. This integrates with human judgment to achieve a swift and accurate response. Furthermore, the agent autonomously extracts suspicious behavior and instructs staff deployment to provide focused care. In the event of trouble, it automatically records images and notifies the police. This system dramatically improves security efficiency and safety. For example, the Guardian Agent integrates and manages surveillance cameras and various sensors. This enables wide-area monitoring, preventing suspicious activity from being overlooked. For example, it can monitor a wide area within a store while instantly detecting anomalies in a specific area. Next, the Guardian Agent analyzes the situation through voice interaction with security guards. The AI ​​analyzes reports from security guards and on-site audio to provide optimal instructions. For example, in the event of a sudden incident, it may be difficult for on-site staff to respond to the report, but the AI ​​can grasp the situation and provide appropriate instructions, enabling a swift response. Furthermore, the Guardian Agent autonomously identifies suspicious activity and directs staff deployment to provide focused care. This helps prevent crimes from occurring. For example, if suspicious activity is detected in a specific area, staff can be deployed to that area to enhance security. Finally, in the event of trouble, the Guardian Agent automatically records images and notifies the police. This secures evidence and enables a swift response. For example, if a theft occurs in a store, the AI ​​automatically records images and notifies the police, enabling a rapid response. In this way, the Guardian Agent, a next-generation security system utilizing AI, dramatically improves security efficiency and safety.

[0029] The guardian agent according to this embodiment comprises a monitoring unit, a dialogue unit, a deployment instruction unit, and a notification unit. The monitoring unit integrates and manages surveillance cameras and various sensors to immediately detect abnormalities. For example, the monitoring unit coordinates surveillance cameras and various sensors to perform wide-area monitoring. The monitoring unit can use AI to perform image analysis and voice analysis to detect abnormalities. For example, the monitoring unit uses AI to analyze surveillance camera footage in real time and detect suspicious movements. The monitoring unit can also use AI to analyze sensor data and detect abnormal temperature changes or abnormal sounds. Based on the abnormalities detected by the monitoring unit, the dialogue unit analyzes the situation through voice dialogue with security guards and provides optimal instructions. For example, the dialogue unit analyzes reports from security guards and on-site audio and generates optimal instructions using AI. The dialogue unit can use AI and speech recognition technology to convert the content of security guard reports into text data. For example, the dialogue unit analyzes the content reported by security guards in real time and generates appropriate instructions. Furthermore, the dialogue unit can use AI-powered speech synthesis technology to provide voice instructions to security guards. For example, the dialogue unit can use AI-generated instructions to communicate them to security guards using speech synthesis technology. Based on the instructions provided by the dialogue unit, the deployment instruction unit has agents autonomously extract suspicious behavior and instruct staff deployment to provide focused care. For example, if suspicious activity is detected in a specific area, the deployment instruction unit will instruct staff to be deployed in that area. The deployment instruction unit can use AI to extract suspicious behavior and instruct appropriate staff deployment. For example, the deployment instruction unit can use AI to analyze patterns of suspicious behavior and identify areas that require focused care. The deployment instruction unit can also use AI to monitor staff deployment status in real time and provide optimal deployment instructions. The reporting unit automatically records images and reports to the police when a problem occurs. For example, the reporting unit automatically records surveillance camera footage when a problem occurs and reports to the police. The reporting unit can use AI to determine the type of problem and make appropriate reports. For example, the reporting department uses AI to analyze video footage of incidents and determine the type of incident, such as theft or fire.Furthermore, the reporting unit can use AI to automatically generate report content and quickly report it to the police. As a result, the Guardian Agent according to this embodiment can dramatically improve security efficiency and safety.

[0030] The monitoring unit integrates and manages surveillance cameras and various sensors to immediately detect anomalies. For example, the monitoring unit coordinates surveillance cameras and various sensors to perform wide-area monitoring. Specifically, surveillance cameras capture high-resolution video in real time, and various sensors collect environmental data such as temperature, humidity, vibration, and sound. This data is transmitted to a central database and analyzed by AI. The monitoring unit can use AI to perform image analysis and sound analysis to detect anomalies. For example, the monitoring unit uses AI to analyze surveillance camera footage in real time and detect suspicious movements. The AI ​​uses image recognition technology to analyze people's movements and behavior patterns and identify abnormal behavior. The monitoring unit can also use AI to analyze sensor data and detect abnormal temperature changes or unusual sounds. For example, if a temperature sensor detects a rapid rise in temperature, the AI ​​analyzes the data and determines the possibility of a fire. If a sound sensor detects an unusual sound, the AI ​​analyzes the sound and identifies abnormal sounds such as sounds of destruction or screams. This allows the monitoring unit to perform wide-area monitoring and detect anomalies immediately. Furthermore, the monitoring unit can centrally manage this data and collaborate with other systems and departments as needed. For example, the monitoring unit can store collected data on a cloud server, making it accessible to the dialogue unit and deployment instruction unit. In addition, the monitoring unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. As a result, the monitoring unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The dialogue unit analyzes the situation through voice dialogue with security guards based on anomalies detected by the monitoring unit and provides optimal instructions. For example, the dialogue unit analyzes reports from security guards and on-site audio, and generates optimal instructions using AI. Specifically, the dialogue unit utilizes speech recognition technology to analyze the content reported by security guards in real time and generate appropriate instructions. The AI ​​converts the security guard's voice into text data and analyzes its content. For example, if a security guard reports that they have spotted a suspicious person, the AI ​​analyzes the content and generates instructions to identify the suspicious person's location and actions. The dialogue unit can also use AI and speech synthesis technology to provide instructions to security guards via voice. For example, the dialogue unit can transmit instructions generated using AI to security guards using speech synthesis technology. This allows security guards to receive instructions quickly and take appropriate action. Furthermore, the dialogue unit can record the content of conversations with security guards and analyze them later. This allows the dialogue unit to understand the security guard's response and improve the instructions as needed. For example, by analyzing past conversations, it's possible to identify problems with security guards' responses and revise instructions. This allows the dialogue unit to achieve more effective communication with security guards and improve the overall reliability and security of the system.

[0032] The deployment command unit, based on instructions provided by the dialogue unit, autonomously extracts suspicious behavior from agents and instructs staff deployment to provide focused care. For example, if suspicious activity is detected in a specific area, the deployment command unit will instruct staff to be deployed to that area. Specifically, the deployment command unit uses AI to analyze patterns of suspicious behavior and identify areas that require focused care. Based on past data and statistical information, the AI ​​evaluates the frequency and risk level of suspicious behavior and calculates the optimal staff deployment. For example, if suspicious behavior is frequent in a specific area, the deployment command unit will instruct additional staff to be deployed to that area. The deployment command unit can also use AI to monitor staff deployment status in real time and provide optimal deployment instructions. For example, it can calculate the most efficient deployment based on the current staff deployment status and instruct staff to move as needed. This allows the deployment command unit to deploy staff efficiently and effectively, improving the overall system performance. Furthermore, the deployment command unit can collect staff feedback and continuously improve the accuracy and effectiveness of deployment instructions. For example, it can review and improve deployment instructions based on feedback from staff. This allows the staffing control unit to consistently achieve optimal staffing, improving the overall reliability and safety of the system.

[0033] The reporting unit automatically records images and notifies the police when a problem occurs. For example, when a problem occurs, the reporting unit automatically records surveillance camera footage and notifies the police. Specifically, the reporting unit can use AI to identify the type of problem and make an appropriate report. The AI ​​analyzes surveillance camera footage to identify the type of problem. For example, the AI ​​analyzes people's movements and actions from the footage to identify problems such as theft or fire. The reporting unit can also use AI to automatically generate report content and quickly notify the police. For example, the AI ​​automatically generates report content based on information such as the type of problem, location, and time of occurrence, and sends it to the police. This allows the reporting unit to report problems quickly and accurately, supporting the police response. Furthermore, the reporting unit can record the content of reports and analyze them later. This allows the reporting unit to understand the circumstances of the problem and improve the content of reports as needed. For example, it can analyze past reports to evaluate the accuracy and effectiveness of reports and identify areas for improvement. This allows the reporting unit to always provide optimal reports and improve the reliability and security of the entire system.

[0034] The monitoring unit can perform wide-area monitoring and immediately detect suspicious activity. For example, to perform wide-area monitoring, the monitoring unit installs multiple surveillance cameras and manages them in an integrated manner. The monitoring unit can use AI to analyze the surveillance camera footage in real time and detect suspicious activity. For example, the monitoring unit can use AI to analyze the surveillance camera footage and immediately detect abnormal activity in a specific area. The monitoring unit can also place multiple sensors and manage them in an integrated manner to perform wide-area monitoring. For example, the monitoring unit can use AI to analyze sensor data and immediately detect abnormal temperature changes or unusual sounds. This ensures that suspicious activity is not overlooked by performing wide-area monitoring. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input surveillance camera footage into a generating AI, which can analyze the footage and detect suspicious activity.

[0035] The dialogue unit can analyze reports from security guards and on-site audio to provide optimal instructions. For example, the dialogue unit can convert reports from security guards into text data using speech recognition technology and analyze it using AI. The dialogue unit can analyze the content of security guards' reports using AI and generate optimal instructions. For example, the dialogue unit can analyze the content of reports from security guards in real time and generate appropriate instructions. The dialogue unit can also convert audio data into text data using speech recognition technology in order to analyze on-site audio. For example, the dialogue unit can analyze on-site audio in real time and detect abnormal sounds. This allows the dialogue unit to provide optimal instructions by analyzing reports from security guards and on-site audio. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input the content of reports from security guards into a generating AI, which can analyze the content of the reports and generate optimal instructions.

[0036] The deployment command unit can strengthen security by deploying staff to a specific area if suspicious activity is detected in that area. For example, if suspicious activity is detected in a specific area, the deployment command unit will issue an instruction to deploy staff to that area. The deployment command unit can use AI to extract suspicious behavior and issue instructions for appropriate staff deployment. For example, the deployment command unit can use AI to analyze patterns of suspicious behavior and identify areas that require special attention. The deployment command unit can also use AI to grasp the staff deployment status in real time and issue optimal deployment instructions. This allows for strengthened security by deploying staff to an area if suspicious activity is detected in that area. Some or all of the above processing in the deployment command unit may be performed using AI, or not using AI. For example, the deployment command unit can input patterns of suspicious behavior into a generating AI, which can then identify areas that require special attention.

[0037] The reporting unit can automatically record images and report to the police when a problem occurs. For example, the reporting unit can automatically record surveillance camera footage when a problem occurs and report it to the police. The reporting unit can use AI to determine the type of problem and make an appropriate report. For example, the reporting unit can use AI to analyze the video of the problem and determine the type, such as theft or fire. The reporting unit can also use AI to automatically generate the content of the report and quickly report it to the police. This enables a quick response by automatically recording images and reporting to the police when a problem occurs. Some or all of the above processes in the reporting unit may be performed using AI, or they may not. For example, the reporting unit can input the video of the problem into a generating AI, which can determine the type of problem and generate the content of the report.

[0038] The monitoring unit can improve detection accuracy by referring to past anomaly detection data when detecting anomalies. For example, the monitoring unit can analyze past anomaly detection data, learn similar patterns, and improve detection accuracy. The monitoring unit can also improve detection accuracy by saving anomaly detection data to the cloud and sharing it with other monitoring systems. The monitoring unit can also update anomaly detection data in real time and improve detection accuracy based on the latest information. This improves detection accuracy by referring to past anomaly detection data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past anomaly detection data into a generating AI, which can then analyze the data to improve detection accuracy.

[0039] The monitoring unit can apply different detection algorithms depending on the type of anomaly when it detects one. For example, the monitoring unit can use different algorithms for detecting suspicious activity and fire. It can also use different algorithms for detecting abnormal sounds and abnormal temperature changes. It can also use different algorithms for detecting abnormal vibrations and abnormal light changes. By applying different detection algorithms depending on the type of anomaly, detection accuracy is improved. 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 corresponding to the type of anomaly into a generating AI, and the generating AI can detect the anomaly by applying an appropriate detection algorithm.

[0040] The monitoring unit can improve detection accuracy by considering the geographical location information of the monitored object when detecting an anomaly. For example, the monitoring unit can improve detection accuracy by identifying areas where anomalies are likely to occur based on the geographical location information of the monitored object. The monitoring unit can also improve detection accuracy by identifying areas where anomalies are less likely to occur based on the geographical location information of the monitored object. The monitoring unit can also improve detection accuracy by identifying time periods when anomalies are likely to occur based on the geographical location information of the monitored object. In this way, detection accuracy is improved by considering the geographical location information of the monitored object. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the geographical location information of the monitored object into a generating AI, and the generating AI can analyze the data to improve detection accuracy.

[0041] The monitoring unit can detect related anomalies by analyzing the social media activity of the monitored system when detecting an anomaly. For example, the monitoring unit can analyze the social media activity of the monitored system, detect abnormal posts, and detect related anomalies. The monitoring unit can also analyze the social media activity of the monitored system, detect abnormal comments, and detect related anomalies. The monitoring unit can also analyze the social media activity of the monitored system, detect abnormal images, and detect related anomalies. In this way, related anomalies can be detected by analyzing the social media activity of the monitored system. 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 social media activity data of the monitored system into a generating AI, and the generating AI can analyze the data and detect related anomalies.

[0042] The dialogue unit can improve the accuracy of its analysis by referring to past dialogue data when analyzing a situation. For example, the dialogue unit can improve its analysis accuracy by analyzing past dialogue data and learning similar situations. The dialogue unit can also improve its analysis accuracy by saving dialogue data to the cloud and sharing it with other dialogue systems. The dialogue unit can also improve its analysis accuracy by updating dialogue data in real time and based on the latest information. This improves the accuracy of the analysis by referring to past dialogue data. Some or all of the above processes in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input past dialogue data into a generating AI, and the generating AI can analyze the data to improve the analysis accuracy.

[0043] The dialogue unit can apply different analysis algorithms depending on the type of anomaly when analyzing a situation. For example, the dialogue unit can use different algorithms to analyze the movements of a suspicious person and different algorithms to analyze a fire. The dialogue unit can also use different algorithms to analyze abnormal sounds and abnormal temperature changes. The dialogue unit can also use different algorithms to analyze abnormal vibrations and abnormal light changes. By applying different analysis algorithms depending on the type of anomaly, the accuracy of the analysis is improved. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data corresponding to the type of anomaly into a generating AI, and the generating AI can apply an appropriate analysis algorithm to analyze the situation.

[0044] The dialogue unit can determine the priority of analysis based on when the anomaly occurred when analyzing a situation. For example, the dialogue unit may prioritize analyzing the situation immediately after the anomaly occurred. It may also prioritize analyzing the situation after a certain period of time has elapsed since the anomaly occurred. It may also prioritize analyzing the situation during time periods when the anomaly is likely to occur. By determining the priority of analysis based on when the anomaly occurred, a faster response becomes possible. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data regarding the timing of the anomaly into a generating AI, which can then analyze the data and determine the priority of analysis.

[0045] The dialogue unit can adjust the order of analysis based on the correlation of anomalies when analyzing a situation. For example, the dialogue unit can adjust the order of analysis based on the correlation of the area where the anomaly occurred. The dialogue unit can also adjust the order of analysis based on the correlation of the time period in which the anomaly occurred. The dialogue unit can also adjust the order of analysis based on the correlation of the circumstances in which the anomaly occurred. This allows for more efficient analysis by adjusting the order of analysis based on the correlation of anomalies. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data on the correlation of anomalies into a generating AI, and the generating AI can analyze the data and adjust the order of analysis.

[0046] The deployment instruction unit can improve extraction accuracy by referring to past suspicious behavior data when extracting suspicious behavior. For example, the deployment instruction unit can analyze past suspicious behavior data, learn similar patterns, and improve extraction accuracy. The deployment instruction unit can also save suspicious behavior data to the cloud and share it with other deployment instruction systems to improve extraction accuracy. The deployment instruction unit can also update suspicious behavior data in real time and improve extraction accuracy based on the latest information. This improves extraction accuracy by referring to past suspicious behavior data. Some or all of the above processes in the deployment instruction unit may be performed using AI, for example, or without AI. For example, the deployment instruction unit can input past suspicious behavior data into a generating AI, which can then analyze the data to improve extraction accuracy.

[0047] The placement instruction unit can apply different extraction algorithms depending on the type of suspicious behavior when extracting suspicious behavior. For example, the placement instruction unit can use different algorithms for extracting suspicious person movements and for extracting fires. It can also use different algorithms for extracting abnormal sounds and for extracting abnormal temperature changes. It can also use different algorithms for extracting abnormal vibrations and for extracting abnormal light changes. By applying different extraction algorithms depending on the type of suspicious behavior, the extraction accuracy is improved. Some or all of the above processing in the placement instruction unit may be performed using AI, for example, or without AI. For example, the placement instruction unit can input data corresponding to the type of suspicious behavior into a generating AI, and the generating AI can apply an appropriate extraction algorithm to extract the suspicious behavior.

[0048] The deployment instruction unit can improve the accuracy of extracting suspicious behavior by considering the geographical location information of the suspicious behavior. For example, the deployment instruction unit can improve the accuracy of extraction by identifying areas where anomalies are likely to occur based on the geographical location information of the suspicious behavior. The deployment instruction unit can also improve the accuracy of extraction by identifying areas where anomalies are less likely to occur based on the geographical location information of the suspicious behavior. The deployment instruction unit can also improve the accuracy of extraction by identifying time periods when anomalies are likely to occur based on the geographical location information of the suspicious behavior. In this way, the accuracy of extraction is improved by considering the geographical location information of the suspicious behavior. Some or all of the above processing in the deployment instruction unit may be performed using AI, for example, or without using AI. For example, the deployment instruction unit can input the geographical location information of the suspicious behavior into a generating AI, and the generating AI can analyze the data to improve the accuracy of extraction.

[0049] The deployment instruction unit can improve the accuracy of extracting suspicious behavior by referring to relevant literature on suspicious behavior. For example, the deployment instruction unit can improve the accuracy of extraction based on the latest research findings by referring to relevant literature on suspicious behavior. The deployment instruction unit can also improve the accuracy of extraction based on past cases by referring to relevant literature on suspicious behavior. The deployment instruction unit can also improve the accuracy of extraction based on analysis from different perspectives by referring to relevant literature on suspicious behavior. In this way, the accuracy of extraction is improved by referring to relevant literature on suspicious behavior. Some or all of the above processing in the deployment instruction unit may be performed using AI, for example, or without using AI. For example, the deployment instruction unit can input data on relevant literature on suspicious behavior into a generating AI, and the generating AI can analyze the data to improve the accuracy of extraction.

[0050] The reporting unit can improve recording accuracy by referring to past trouble data when recording troubles. For example, the reporting unit can analyze past trouble data, learn similar patterns, and improve recording accuracy. The reporting unit can also improve recording accuracy by saving trouble data to the cloud and sharing it with other reporting systems. The reporting unit can also update trouble data in real time and improve recording accuracy based on the latest information. This improves recording accuracy by referring to past trouble data. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past trouble data into a generating AI, which can then analyze the data to improve recording accuracy.

[0051] The reporting unit can apply different recording algorithms depending on the type of trouble when recording it. For example, the reporting unit can use different algorithms for recording theft and fire. It can also use different algorithms for recording abnormal noise and abnormal temperature changes. It can also use different algorithms for recording abnormal vibration and abnormal light changes. By applying different recording algorithms depending on the type of trouble, the recording accuracy is improved. 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 data corresponding to the type of trouble into a generating AI, and the generating AI can apply an appropriate recording algorithm to record the trouble.

[0052] The reporting unit can improve recording accuracy by considering the geographical location of the trouble when recording it. For example, the reporting unit can improve recording accuracy by identifying areas where anomalies are likely to occur based on the geographical location of the trouble. The reporting unit can also improve recording accuracy by identifying areas where anomalies are less likely to occur based on the geographical location of the trouble. The reporting unit can also improve recording accuracy by identifying time periods when anomalies are likely to occur based on the geographical location of the trouble. In this way, recording accuracy is improved by considering the geographical location of the trouble. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without using AI. For example, the reporting unit can input the geographical location of the trouble into a generating AI, and the generating AI can analyze the data to improve recording accuracy.

[0053] The reporting department can improve the accuracy of its recordings by referring to relevant literature on the trouble when recording it. For example, the reporting department can improve the accuracy of its recordings by referring to relevant literature on the trouble and based on the latest research findings. The reporting department can also improve the accuracy of its recordings by referring to relevant literature on the trouble and based on past cases. The reporting department can also improve the accuracy of its recordings by referring to relevant literature on the trouble and based on analysis from different perspectives. In this way, the accuracy of recordings is improved by referring to relevant literature on the trouble. Some or all of the above processing in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input data on relevant literature on the trouble into a generating AI, and the generating AI can analyze the data to improve the accuracy of the recordings.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The monitoring unit can determine detection priorities based on the frequency of anomalies. For example, by prioritizing monitoring areas where anomalies have frequently occurred in the past, recurrence can be prevented. Furthermore, resources can be used more efficiently by reducing the monitoring frequency in areas with a low frequency of anomalies. It is also possible to optimize the placement of surveillance cameras based on the frequency of anomalies. This allows for more efficient monitoring by prioritizing monitoring based on the frequency of anomalies.

[0056] The deployment control unit can optimize staff assignments based on their skill sets. For example, it can prioritize the deployment of staff with the relevant skills to address suspicious behavior in a specific area. Furthermore, deploying staff with multiple skill sets allows for responses to different types of suspicious behavior. The unit can also determine the need for training based on staff skill sets and provide appropriate training. This optimization of staff assignments based on skill sets enables more effective responses.

[0057] The monitoring unit can utilize additional sensors to identify the cause of an anomaly when it detects one. For example, if an abnormal temperature change is detected, not only a temperature sensor but also humidity and gas sensors can be added to identify the cause. Similarly, if an abnormal sound is detected, not only a sound sensor but also vibration and light sensors can be added to identify the cause. Furthermore, if abnormal movement is detected, not only a motion detection sensor but also infrared and pressure sensors can be added to identify the cause. By utilizing additional sensors to identify the cause of an anomaly, more accurate anomaly detection becomes possible.

[0058] The staffing system can optimize staff assignments based on their health status. For example, it can assign other staff to allow a fatigued staff member to rest. Prioritizing the assignment of healthy staff members enables more efficient response. Furthermore, assigning staff with specific health risks to specific areas minimizes those risks. This allows for more effective responses by optimizing staff assignments based on their health status.

[0059] The monitoring unit can determine detection priorities based on the time of day the anomaly occurs. For example, by prioritizing monitoring of areas where anomalies are likely to occur at night, safety during nighttime hours can be ensured. Similarly, for areas where anomalies are likely to occur during the day, daytime monitoring can be strengthened for more efficient monitoring. Furthermore, areas with a high probability of anomalies occurring during specific time periods can be monitored intensively during those times. This allows for more efficient monitoring by determining detection priorities based on the time of day the anomaly occurs.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The monitoring unit integrates and manages surveillance cameras and various sensors to immediately detect anomalies. The monitoring unit coordinates surveillance cameras and various sensors to perform wide-area monitoring. Using AI, it performs image and sound analysis to analyze surveillance camera footage in real time and detect suspicious activity. It can also analyze sensor data to detect abnormal temperature changes and unusual sounds. Step 2: Based on the anomalies detected by the monitoring unit, the dialogue unit analyzes the situation through voice interaction with the security guard and provides optimal instructions. The dialogue unit analyzes reports from the security guard and on-site audio, and generates optimal instructions using AI. It utilizes speech recognition technology to convert the security guard's reports into text data, analyzes it in real time, and generates appropriate instructions. It can also provide instructions to the security guard via voice using speech synthesis technology. Step 3: Based on the instructions provided by the dialogue unit, the deployment command unit instructs the agent to autonomously extract suspicious behavior and deploy staff to provide focused care. If suspicious activity is detected in a specific area, the deployment command unit instructs staff to be deployed to that area. Using AI, it analyzes patterns of suspicious behavior, identifies areas that require focused care, and monitors staff deployment status in real time to provide optimal deployment instructions. Step 4: The reporting unit automatically records images and notifies the police when a problem occurs. The reporting unit automatically records surveillance camera footage when a problem occurs and notifies the police. Using AI, it identifies the type of problem, such as theft or fire, and makes the appropriate report. It can also automatically generate the content of the report and quickly notify the police.

[0062] (Example of form 2) The next-generation security system according to an embodiment of the present invention is an AI-powered "Guardian Agent." This Guardian Agent is a system that integrates and manages surveillance cameras and various sensors to instantly detect anomalies. The Guardian Agent analyzes the situation through voice interaction with security guards and provides optimal instructions. This integrates with human judgment to achieve a swift and accurate response. Furthermore, the agent autonomously extracts suspicious behavior and instructs staff deployment to provide focused care. In the event of trouble, it automatically records images and notifies the police. This system dramatically improves security efficiency and safety. For example, the Guardian Agent integrates and manages surveillance cameras and various sensors. This enables wide-area monitoring, preventing suspicious activity from being overlooked. For example, it can monitor a wide area within a store while instantly detecting anomalies in a specific area. Next, the Guardian Agent analyzes the situation through voice interaction with security guards. The AI ​​analyzes reports from security guards and on-site audio to provide optimal instructions. For example, in the event of a sudden incident, it may be difficult for on-site staff to respond to the report, but the AI ​​can grasp the situation and provide appropriate instructions, enabling a swift response. Furthermore, the Guardian Agent autonomously identifies suspicious activity and directs staff deployment to provide focused care. This helps prevent crimes from occurring. For example, if suspicious activity is detected in a specific area, staff can be deployed to that area to enhance security. Finally, in the event of trouble, the Guardian Agent automatically records images and notifies the police. This secures evidence and enables a swift response. For example, if a theft occurs in a store, the AI ​​automatically records images and notifies the police, enabling a rapid response. In this way, the Guardian Agent, a next-generation security system utilizing AI, dramatically improves security efficiency and safety.

[0063] The guardian agent according to this embodiment comprises a monitoring unit, a dialogue unit, a deployment instruction unit, and a notification unit. The monitoring unit integrates and manages surveillance cameras and various sensors to immediately detect abnormalities. For example, the monitoring unit coordinates surveillance cameras and various sensors to perform wide-area monitoring. The monitoring unit can use AI to perform image analysis and voice analysis to detect abnormalities. For example, the monitoring unit uses AI to analyze surveillance camera footage in real time and detect suspicious movements. The monitoring unit can also use AI to analyze sensor data and detect abnormal temperature changes or abnormal sounds. Based on the abnormalities detected by the monitoring unit, the dialogue unit analyzes the situation through voice dialogue with security guards and provides optimal instructions. For example, the dialogue unit analyzes reports from security guards and on-site audio and generates optimal instructions using AI. The dialogue unit can use AI and speech recognition technology to convert the content of security guard reports into text data. For example, the dialogue unit analyzes the content reported by security guards in real time and generates appropriate instructions. Furthermore, the dialogue unit can use AI-powered speech synthesis technology to provide voice instructions to security guards. For example, the dialogue unit can use AI-generated instructions to communicate them to security guards using speech synthesis technology. Based on the instructions provided by the dialogue unit, the deployment instruction unit has agents autonomously extract suspicious behavior and instruct staff deployment to provide focused care. For example, if suspicious activity is detected in a specific area, the deployment instruction unit will instruct staff to be deployed in that area. The deployment instruction unit can use AI to extract suspicious behavior and instruct appropriate staff deployment. For example, the deployment instruction unit can use AI to analyze patterns of suspicious behavior and identify areas that require focused care. The deployment instruction unit can also use AI to monitor staff deployment status in real time and provide optimal deployment instructions. The reporting unit automatically records images and reports to the police when a problem occurs. For example, the reporting unit automatically records surveillance camera footage when a problem occurs and reports to the police. The reporting unit can use AI to determine the type of problem and make appropriate reports. For example, the reporting department uses AI to analyze video footage of incidents and determine the type of incident, such as theft or fire.Furthermore, the reporting unit can use AI to automatically generate report content and quickly report it to the police. As a result, the Guardian Agent according to this embodiment can dramatically improve security efficiency and safety.

[0064] The monitoring unit integrates and manages surveillance cameras and various sensors to immediately detect anomalies. For example, the monitoring unit coordinates surveillance cameras and various sensors to perform wide-area monitoring. Specifically, surveillance cameras capture high-resolution video in real time, and various sensors collect environmental data such as temperature, humidity, vibration, and sound. This data is transmitted to a central database and analyzed by AI. The monitoring unit can use AI to perform image analysis and sound analysis to detect anomalies. For example, the monitoring unit uses AI to analyze surveillance camera footage in real time and detect suspicious movements. The AI ​​uses image recognition technology to analyze people's movements and behavior patterns and identify abnormal behavior. The monitoring unit can also use AI to analyze sensor data and detect abnormal temperature changes or unusual sounds. For example, if a temperature sensor detects a rapid rise in temperature, the AI ​​analyzes the data and determines the possibility of a fire. If a sound sensor detects an unusual sound, the AI ​​analyzes the sound and identifies abnormal sounds such as sounds of destruction or screams. This allows the monitoring unit to perform wide-area monitoring and detect anomalies immediately. Furthermore, the monitoring unit can centrally manage this data and collaborate with other systems and departments as needed. For example, the monitoring unit can store collected data on a cloud server, making it accessible to the dialogue unit and deployment instruction unit. In addition, the monitoring unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. As a result, the monitoring unit can collect data efficiently and effectively, improving the overall performance of the system.

[0065] The dialogue unit analyzes the situation through voice dialogue with security guards based on anomalies detected by the monitoring unit and provides optimal instructions. For example, the dialogue unit analyzes reports from security guards and on-site audio, and generates optimal instructions using AI. Specifically, the dialogue unit utilizes speech recognition technology to analyze the content reported by security guards in real time and generate appropriate instructions. The AI ​​converts the security guard's voice into text data and analyzes its content. For example, if a security guard reports that they have spotted a suspicious person, the AI ​​analyzes the content and generates instructions to identify the suspicious person's location and actions. The dialogue unit can also use AI and speech synthesis technology to provide instructions to security guards via voice. For example, the dialogue unit can transmit instructions generated using AI to security guards using speech synthesis technology. This allows security guards to receive instructions quickly and take appropriate action. Furthermore, the dialogue unit can record the content of conversations with security guards and analyze them later. This allows the dialogue unit to understand the security guard's response and improve the instructions as needed. For example, by analyzing past conversations, it's possible to identify problems with security guards' responses and revise instructions. This allows the dialogue unit to achieve more effective communication with security guards and improve the overall reliability and security of the system.

[0066] The deployment command unit, based on instructions provided by the dialogue unit, autonomously extracts suspicious behavior from agents and instructs staff deployment to provide focused care. For example, if suspicious activity is detected in a specific area, the deployment command unit will instruct staff to be deployed to that area. Specifically, the deployment command unit uses AI to analyze patterns of suspicious behavior and identify areas that require focused care. Based on past data and statistical information, the AI ​​evaluates the frequency and risk level of suspicious behavior and calculates the optimal staff deployment. For example, if suspicious behavior is frequent in a specific area, the deployment command unit will instruct additional staff to be deployed to that area. The deployment command unit can also use AI to monitor staff deployment status in real time and provide optimal deployment instructions. For example, it can calculate the most efficient deployment based on the current staff deployment status and instruct staff to move as needed. This allows the deployment command unit to deploy staff efficiently and effectively, improving the overall system performance. Furthermore, the deployment command unit can collect staff feedback and continuously improve the accuracy and effectiveness of deployment instructions. For example, it can review and improve deployment instructions based on feedback from staff. This allows the staffing control unit to consistently achieve optimal staffing, improving the overall reliability and safety of the system.

[0067] The reporting unit automatically records images and notifies the police when a problem occurs. For example, when a problem occurs, the reporting unit automatically records surveillance camera footage and notifies the police. Specifically, the reporting unit can use AI to identify the type of problem and make an appropriate report. The AI ​​analyzes surveillance camera footage to identify the type of problem. For example, the AI ​​analyzes people's movements and actions from the footage to identify problems such as theft or fire. The reporting unit can also use AI to automatically generate report content and quickly notify the police. For example, the AI ​​automatically generates report content based on information such as the type of problem, location, and time of occurrence, and sends it to the police. This allows the reporting unit to report problems quickly and accurately, supporting the police response. Furthermore, the reporting unit can record the content of reports and analyze them later. This allows the reporting unit to understand the circumstances of the problem and improve the content of reports as needed. For example, it can analyze past reports to evaluate the accuracy and effectiveness of reports and identify areas for improvement. This allows the reporting unit to always provide optimal reports and improve the reliability and security of the entire system.

[0068] The monitoring unit can perform wide-area monitoring and immediately detect suspicious activity. For example, to perform wide-area monitoring, the monitoring unit installs multiple surveillance cameras and manages them in an integrated manner. The monitoring unit can use AI to analyze the surveillance camera footage in real time and detect suspicious activity. For example, the monitoring unit can use AI to analyze the surveillance camera footage and immediately detect abnormal activity in a specific area. The monitoring unit can also place multiple sensors and manage them in an integrated manner to perform wide-area monitoring. For example, the monitoring unit can use AI to analyze sensor data and immediately detect abnormal temperature changes or unusual sounds. This ensures that suspicious activity is not overlooked by performing wide-area monitoring. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input surveillance camera footage into a generating AI, which can analyze the footage and detect suspicious activity.

[0069] The dialogue unit can analyze reports from security guards and on-site audio to provide optimal instructions. For example, the dialogue unit can convert reports from security guards into text data using speech recognition technology and analyze it using AI. The dialogue unit can analyze the content of security guards' reports using AI and generate optimal instructions. For example, the dialogue unit can analyze the content of reports from security guards in real time and generate appropriate instructions. The dialogue unit can also convert audio data into text data using speech recognition technology in order to analyze on-site audio. For example, the dialogue unit can analyze on-site audio in real time and detect abnormal sounds. This allows the dialogue unit to provide optimal instructions by analyzing reports from security guards and on-site audio. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input the content of reports from security guards into a generating AI, which can analyze the content of the reports and generate optimal instructions.

[0070] The deployment command unit can strengthen security by deploying staff to a specific area if suspicious activity is detected in that area. For example, if suspicious activity is detected in a specific area, the deployment command unit will issue an instruction to deploy staff to that area. The deployment command unit can use AI to extract suspicious behavior and issue instructions for appropriate staff deployment. For example, the deployment command unit can use AI to analyze patterns of suspicious behavior and identify areas that require special attention. The deployment command unit can also use AI to grasp the staff deployment status in real time and issue optimal deployment instructions. This allows for strengthened security by deploying staff to an area if suspicious activity is detected in that area. Some or all of the above processing in the deployment command unit may be performed using AI, or not using AI. For example, the deployment command unit can input patterns of suspicious behavior into a generating AI, which can then identify areas that require special attention.

[0071] The reporting unit can automatically record images and report to the police when a problem occurs. For example, the reporting unit can automatically record surveillance camera footage when a problem occurs and report it to the police. The reporting unit can use AI to determine the type of problem and make an appropriate report. For example, the reporting unit can use AI to analyze the video of the problem and determine the type, such as theft or fire. The reporting unit can also use AI to automatically generate the content of the report and quickly report it to the police. This enables a quick response by automatically recording images and reporting to the police when a problem occurs. Some or all of the above processes in the reporting unit may be performed using AI, or they may not. For example, the reporting unit can input the video of the problem into a generating AI, which can determine the type of problem and generate the content of the report.

[0072] The monitoring unit can estimate the user's emotions and adjust the viewpoint and angle of the surveillance camera based on the estimated emotions. For example, if the user is tense, the monitoring unit can widen the viewpoint of the surveillance camera to make it easier to grasp the whole picture. If the user is relaxed, the monitoring unit can also focus on a specific area for detailed monitoring. If the user is excited, the monitoring unit can frequently switch the camera viewpoint to respond to movement. This allows for more appropriate monitoring by adjusting the viewpoint and angle of the surveillance camera based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user facial expression data into the generative AI, which can estimate the user's emotions and adjust the viewpoint and angle of the surveillance camera.

[0073] The monitoring unit can improve detection accuracy by referring to past anomaly detection data when detecting anomalies. For example, the monitoring unit can analyze past anomaly detection data, learn similar patterns, and improve detection accuracy. The monitoring unit can also improve detection accuracy by saving anomaly detection data to the cloud and sharing it with other monitoring systems. The monitoring unit can also update anomaly detection data in real time and improve detection accuracy based on the latest information. This improves detection accuracy by referring to past anomaly detection data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past anomaly detection data into a generating AI, which can then analyze the data to improve detection accuracy.

[0074] The monitoring unit can apply different detection algorithms depending on the type of anomaly when it detects one. For example, the monitoring unit can use different algorithms for detecting suspicious activity and fire. It can also use different algorithms for detecting abnormal sounds and abnormal temperature changes. It can also use different algorithms for detecting abnormal vibrations and abnormal light changes. By applying different detection algorithms depending on the type of anomaly, detection accuracy is improved. 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 corresponding to the type of anomaly into a generating AI, and the generating AI can detect the anomaly by applying an appropriate detection algorithm.

[0075] The monitoring unit can estimate the user's emotions and determine the priority of monitoring areas based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit will prioritize monitoring areas where the user is feeling particularly anxious. If the user is feeling at ease, the monitoring unit can also prioritize monitoring normal monitoring areas. If the user is excited, the monitoring unit can also prioritize monitoring areas where a specific event is taking place. This allows for more appropriate monitoring by prioritizing monitoring areas based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI, which can estimate the user's emotions and determine the priority of monitoring areas.

[0076] The monitoring unit can improve detection accuracy by considering the geographical location information of the monitored object when detecting an anomaly. For example, the monitoring unit can improve detection accuracy by identifying areas where anomalies are likely to occur based on the geographical location information of the monitored object. The monitoring unit can also improve detection accuracy by identifying areas where anomalies are less likely to occur based on the geographical location information of the monitored object. The monitoring unit can also improve detection accuracy by identifying time periods when anomalies are likely to occur based on the geographical location information of the monitored object. In this way, detection accuracy is improved by considering the geographical location information of the monitored object. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the geographical location information of the monitored object into a generating AI, and the generating AI can analyze the data to improve detection accuracy.

[0077] The monitoring unit can detect related anomalies by analyzing the social media activity of the monitored system when detecting an anomaly. For example, the monitoring unit can analyze the social media activity of the monitored system, detect abnormal posts, and detect related anomalies. The monitoring unit can also analyze the social media activity of the monitored system, detect abnormal comments, and detect related anomalies. The monitoring unit can also analyze the social media activity of the monitored system, detect abnormal images, and detect related anomalies. In this way, related anomalies can be detected by analyzing the social media activity of the monitored system. 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 social media activity data of the monitored system into a generating AI, and the generating AI can analyze the data and detect related anomalies.

[0078] The dialogue unit can estimate the user's emotions and adjust the way the dialogue is expressed based on the estimated emotions. For example, if the user is nervous, the dialogue unit will speak in a calm voice. If the user is relaxed, the dialogue unit may speak in a cheerful voice. If the user is excited, the dialogue unit may speak quickly and concisely. By adjusting the way the dialogue is expressed based on the user's emotions, a more appropriate dialogue becomes possible. 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 dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input the user's voice data into a generative AI, which can estimate the user's emotions and adjust the way the dialogue is expressed.

[0079] The dialogue unit can improve the accuracy of its analysis by referring to past dialogue data when analyzing a situation. For example, the dialogue unit can improve its analysis accuracy by analyzing past dialogue data and learning similar situations. The dialogue unit can also improve its analysis accuracy by saving dialogue data to the cloud and sharing it with other dialogue systems. The dialogue unit can also improve its analysis accuracy by updating dialogue data in real time and based on the latest information. This improves the accuracy of the analysis by referring to past dialogue data. Some or all of the above processes in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input past dialogue data into a generating AI, and the generating AI can analyze the data to improve the analysis accuracy.

[0080] The dialogue unit can apply different analysis algorithms depending on the type of anomaly when analyzing a situation. For example, the dialogue unit can use different algorithms to analyze the movements of a suspicious person and different algorithms to analyze a fire. The dialogue unit can also use different algorithms to analyze abnormal sounds and abnormal temperature changes. The dialogue unit can also use different algorithms to analyze abnormal vibrations and abnormal light changes. By applying different analysis algorithms depending on the type of anomaly, the accuracy of the analysis is improved. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data corresponding to the type of anomaly into a generating AI, and the generating AI can apply an appropriate analysis algorithm to analyze the situation.

[0081] The dialogue unit can estimate the user's emotions and adjust the length of the dialogue based on the estimated emotions. For example, if the user is in a hurry, the dialogue unit can shorten the conversation to get straight to the point. If the user is relaxed, the dialogue unit can also lengthen the conversation to provide more detailed information. If the user is excited, the dialogue unit can also shorten the conversation to respond quickly. By adjusting the length of the dialogue based on the user's emotions, a more appropriate dialogue becomes possible. 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 dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the user's voice data into a generative AI, which can estimate the user's emotions and adjust the length of the dialogue.

[0082] The dialogue unit can determine the priority of analysis based on when the anomaly occurred when analyzing a situation. For example, the dialogue unit may prioritize analyzing the situation immediately after the anomaly occurred. It may also prioritize analyzing the situation after a certain period of time has elapsed since the anomaly occurred. It may also prioritize analyzing the situation during time periods when the anomaly is likely to occur. By determining the priority of analysis based on when the anomaly occurred, a faster response becomes possible. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data regarding the timing of the anomaly into a generating AI, which can then analyze the data and determine the priority of analysis.

[0083] The dialogue unit can adjust the order of analysis based on the correlation of anomalies when analyzing a situation. For example, the dialogue unit can adjust the order of analysis based on the correlation of the area where the anomaly occurred. The dialogue unit can also adjust the order of analysis based on the correlation of the time period in which the anomaly occurred. The dialogue unit can also adjust the order of analysis based on the correlation of the circumstances in which the anomaly occurred. This allows for more efficient analysis by adjusting the order of analysis based on the correlation of anomalies. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data on the correlation of anomalies into a generating AI, and the generating AI can analyze the data and adjust the order of analysis.

[0084] The staffing instruction unit can estimate the user's emotions and adjust the staffing instruction method based on the estimated user emotions. For example, if the user is tense, the staffing instruction unit can issue instructions to quickly assign staff. If the user is relaxed, the staffing instruction unit can also issue normal staffing instructions. If the user is excited, the staffing instruction unit can also issue quick and detailed staffing instructions. This allows for more appropriate staffing by adjusting the staffing instruction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the staffing instruction unit may be performed using AI, for example, or not using AI. For example, the staffing instruction unit can input user emotion data into a generative AI, which can estimate the user's emotions and adjust the staffing instruction method.

[0085] The deployment instruction unit can improve extraction accuracy by referring to past suspicious behavior data when extracting suspicious behavior. For example, the deployment instruction unit can analyze past suspicious behavior data, learn similar patterns, and improve extraction accuracy. The deployment instruction unit can also save suspicious behavior data to the cloud and share it with other deployment instruction systems to improve extraction accuracy. The deployment instruction unit can also update suspicious behavior data in real time and improve extraction accuracy based on the latest information. This improves extraction accuracy by referring to past suspicious behavior data. Some or all of the above processes in the deployment instruction unit may be performed using AI, for example, or without AI. For example, the deployment instruction unit can input past suspicious behavior data into a generating AI, which can then analyze the data to improve extraction accuracy.

[0086] The placement instruction unit can apply different extraction algorithms depending on the type of suspicious behavior when extracting suspicious behavior. For example, the placement instruction unit can use different algorithms for extracting suspicious person movements and for extracting fires. It can also use different algorithms for extracting abnormal sounds and for extracting abnormal temperature changes. It can also use different algorithms for extracting abnormal vibrations and for extracting abnormal light changes. By applying different extraction algorithms depending on the type of suspicious behavior, the extraction accuracy is improved. Some or all of the above processing in the placement instruction unit may be performed using AI, for example, or without AI. For example, the placement instruction unit can input data corresponding to the type of suspicious behavior into a generating AI, and the generating AI can apply an appropriate extraction algorithm to extract the suspicious behavior.

[0087] The staffing instruction unit can estimate the user's emotions and determine staffing priorities based on the estimated emotions. For example, if the user is feeling anxious, the staffing instruction unit will prioritize placing staff in areas where the user is feeling particularly anxious. If the user is feeling at ease, the staffing instruction unit can also perform normal staffing. If the user is excited, the staffing instruction unit can also prioritize placing staff in areas where a specific event is taking place. This allows for more appropriate staffing by prioritizing staff placement based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the staffing instruction unit may be performed using AI or not using AI. For example, the staffing instruction unit can input user emotion data into a generative AI, which can estimate the user's emotions and determine staffing priorities.

[0088] The deployment instruction unit can improve the accuracy of extracting suspicious behavior by considering the geographical location information of the suspicious behavior. For example, the deployment instruction unit can improve the accuracy of extraction by identifying areas where anomalies are likely to occur based on the geographical location information of the suspicious behavior. The deployment instruction unit can also improve the accuracy of extraction by identifying areas where anomalies are less likely to occur based on the geographical location information of the suspicious behavior. The deployment instruction unit can also improve the accuracy of extraction by identifying time periods when anomalies are likely to occur based on the geographical location information of the suspicious behavior. In this way, the accuracy of extraction is improved by considering the geographical location information of the suspicious behavior. Some or all of the above processing in the deployment instruction unit may be performed using AI, for example, or without using AI. For example, the deployment instruction unit can input the geographical location information of the suspicious behavior into a generating AI, and the generating AI can analyze the data to improve the accuracy of extraction.

[0089] The deployment instruction unit can improve the accuracy of extracting suspicious behavior by referring to relevant literature on suspicious behavior. For example, the deployment instruction unit can improve the accuracy of extraction based on the latest research findings by referring to relevant literature on suspicious behavior. The deployment instruction unit can also improve the accuracy of extraction based on past cases by referring to relevant literature on suspicious behavior. The deployment instruction unit can also improve the accuracy of extraction based on analysis from different perspectives by referring to relevant literature on suspicious behavior. In this way, the accuracy of extraction is improved by referring to relevant literature on suspicious behavior. Some or all of the above processing in the deployment instruction unit may be performed using AI, for example, or without using AI. For example, the deployment instruction unit can input data on relevant literature on suspicious behavior into a generating AI, and the generating AI can analyze the data to improve the accuracy of extraction.

[0090] The reporting unit can estimate the user's emotions and adjust the reporting method based on the estimated emotions. For example, if the user is nervous, the reporting unit will make the report in a calm voice. If the user is relaxed, the reporting unit may also make the report in a cheerful voice. If the user is excited, the reporting unit may also make a quick and concise report. This allows for more appropriate reporting by adjusting the reporting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 or not using AI. For example, the reporting unit can input the user's voice data into a generative AI, which can estimate the user's emotions and adjust the reporting method.

[0091] The reporting unit can improve recording accuracy by referring to past trouble data when recording troubles. For example, the reporting unit can analyze past trouble data, learn similar patterns, and improve recording accuracy. The reporting unit can also improve recording accuracy by saving trouble data to the cloud and sharing it with other reporting systems. The reporting unit can also update trouble data in real time and improve recording accuracy based on the latest information. This improves recording accuracy by referring to past trouble data. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past trouble data into a generating AI, which can then analyze the data to improve recording accuracy.

[0092] The reporting unit can apply different recording algorithms depending on the type of trouble when recording it. For example, the reporting unit can use different algorithms for recording theft and fire. It can also use different algorithms for recording abnormal noise and abnormal temperature changes. It can also use different algorithms for recording abnormal vibration and abnormal light changes. By applying different recording algorithms depending on the type of trouble, the recording accuracy is improved. 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 data corresponding to the type of trouble into a generating AI, and the generating AI can apply an appropriate recording algorithm to record the trouble.

[0093] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit will prioritize reporting issues that cause particular anxiety. If the user is feeling at ease, the reporting unit can apply the normal reporting priority. If the user is agitated, the reporting unit can also prioritize reporting specific issues. This allows for more appropriate reporting by determining the priority of reports based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 or not using AI. For example, the reporting unit can input user emotion data into a generative AI, which can estimate the user's emotions and determine the priority of reports.

[0094] The reporting unit can improve recording accuracy by considering the geographical location of the trouble when recording it. For example, the reporting unit can improve recording accuracy by identifying areas where anomalies are likely to occur based on the geographical location of the trouble. The reporting unit can also improve recording accuracy by identifying areas where anomalies are less likely to occur based on the geographical location of the trouble. The reporting unit can also improve recording accuracy by identifying time periods when anomalies are likely to occur based on the geographical location of the trouble. In this way, recording accuracy is improved by considering the geographical location of the trouble. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without using AI. For example, the reporting unit can input the geographical location of the trouble into a generating AI, and the generating AI can analyze the data to improve recording accuracy.

[0095] The reporting department can improve the accuracy of its recordings by referring to relevant literature on the trouble when recording it. For example, the reporting department can improve the accuracy of its recordings by referring to relevant literature on the trouble and based on the latest research findings. The reporting department can also improve the accuracy of its recordings by referring to relevant literature on the trouble and based on past cases. The reporting department can also improve the accuracy of its recordings by referring to relevant literature on the trouble and based on analysis from different perspectives. In this way, the accuracy of recordings is improved by referring to relevant literature on the trouble. Some or all of the above processing in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input data on relevant literature on the trouble into a generating AI, and the generating AI can analyze the data to improve the accuracy of the recordings.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The monitoring unit can determine detection priorities based on the frequency of anomalies. For example, by prioritizing monitoring areas where anomalies have frequently occurred in the past, recurrence can be prevented. Furthermore, resources can be used more efficiently by reducing the monitoring frequency in areas with a low frequency of anomalies. It is also possible to optimize the placement of surveillance cameras based on the frequency of anomalies. This allows for more efficient monitoring by prioritizing monitoring based on the frequency of anomalies.

[0098] The dialogue unit can estimate the user's emotions and adjust the content of the conversation based on those emotions. For example, if the user is feeling anxious, it can provide reassuring content. If the user is excited, it can offer advice to help them regain their composure. Furthermore, if the user is relaxed, it can provide detailed information to promote a deeper understanding. In this way, adjusting the content of the conversation based on the user's emotions enables more appropriate dialogue.

[0099] The deployment control unit can optimize staff assignments based on their skill sets. For example, it can prioritize the deployment of staff with the relevant skills to address suspicious behavior in a specific area. Furthermore, deploying staff with multiple skill sets allows for responses to different types of suspicious behavior. The unit can also determine the need for training based on staff skill sets and provide appropriate training. This optimization of staff assignments based on skill sets enables more effective responses.

[0100] The reporting system can estimate the user's emotions and adjust the timing of the report based on those emotions. For example, if the user is anxious, a quick report can provide reassurance. If the user is relaxed, the system can wait to fully assess the situation before reporting. Furthermore, if the user is agitated, the system can give them time to calm down before reporting. By adjusting the timing of the report based on the user's emotions, more appropriate reports can be made.

[0101] The monitoring unit can utilize additional sensors to identify the cause of an anomaly when it detects one. For example, if an abnormal temperature change is detected, not only a temperature sensor but also humidity and gas sensors can be added to identify the cause. Similarly, if an abnormal sound is detected, not only a sound sensor but also vibration and light sensors can be added to identify the cause. Furthermore, if abnormal movement is detected, not only a motion detection sensor but also infrared and pressure sensors can be added to identify the cause. By utilizing additional sensors to identify the cause of an anomaly, more accurate anomaly detection becomes possible.

[0102] The dialogue unit can estimate the user's emotions and adjust the pace of the conversation based on those emotions. For example, if the user is nervous, a slower pace of dialogue can provide reassurance. If the user is relaxed, the dialogue can proceed at a normal pace. Furthermore, if the user is excited, a faster pace of dialogue allows for a more appropriate response. In this way, adjusting the pace of the dialogue based on the user's emotions enables more appropriate conversations.

[0103] The staffing system can optimize staff assignments based on their health status. For example, it can assign other staff to allow a fatigued staff member to rest. Prioritizing the assignment of healthy staff members enables more efficient response. Furthermore, assigning staff with specific health risks to specific areas minimizes those risks. This allows for more effective responses by optimizing staff assignments based on their health status.

[0104] The reporting system can estimate the user's emotions and adjust the level of detail in the report based on that estimation. For example, if the user is stressed, it can provide a concise and to-the-point report. If the user is relaxed, it can provide a more detailed report. Furthermore, if the user is agitated, it can provide a quick and concise report, enabling a situation-appropriate response. In this way, adjusting the level of detail in the report based on the user's emotions makes it possible to provide more appropriate reports.

[0105] The monitoring unit can determine detection priorities based on the time of day the anomaly occurs. For example, by prioritizing monitoring of areas where anomalies are likely to occur at night, safety during nighttime hours can be ensured. Similarly, for areas where anomalies are likely to occur during the day, daytime monitoring can be strengthened for more efficient monitoring. Furthermore, areas with a high probability of anomalies occurring during specific time periods can be monitored intensively during those times. This allows for more efficient monitoring by determining detection priorities based on the time of day the anomaly occurs.

[0106] The dialogue unit can estimate the user's emotions and adjust the tone of the conversation based on those emotions. For example, if the user is nervous, a calm tone of voice can be used to provide reassurance. If the user is relaxed, a friendly tone of voice can be used. Furthermore, if the user is excited, a calm tone of voice can be used to respond appropriately to the situation. In this way, adjusting the tone of the conversation based on the user's emotions enables more appropriate communication.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The monitoring unit integrates and manages surveillance cameras and various sensors to immediately detect anomalies. The monitoring unit coordinates surveillance cameras and various sensors to perform wide-area monitoring. Using AI, it performs image and sound analysis to analyze surveillance camera footage in real time and detect suspicious activity. It can also analyze sensor data to detect abnormal temperature changes and unusual sounds. Step 2: Based on the anomalies detected by the monitoring unit, the dialogue unit analyzes the situation through voice interaction with the security guard and provides optimal instructions. The dialogue unit analyzes reports from the security guard and on-site audio, and generates optimal instructions using AI. It utilizes speech recognition technology to convert the security guard's reports into text data, analyzes it in real time, and generates appropriate instructions. It can also provide instructions to the security guard via voice using speech synthesis technology. Step 3: Based on the instructions provided by the dialogue unit, the deployment command unit instructs the agent to autonomously extract suspicious behavior and deploy staff to provide focused care. If suspicious activity is detected in a specific area, the deployment command unit instructs staff to be deployed to that area. Using AI, it analyzes patterns of suspicious behavior, identifies areas that require focused care, and monitors staff deployment status in real time to provide optimal deployment instructions. Step 4: The reporting unit automatically records images and notifies the police when a problem occurs. The reporting unit automatically records surveillance camera footage when a problem occurs and notifies the police. Using AI, it identifies the type of problem, such as theft or fire, and makes the appropriate report. It can also automatically generate the content of the report and quickly notify the police.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the monitoring unit, dialogue unit, deployment instruction unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit detects anomalies using the camera 42 and various sensors of the smart device 14, and the control unit 46A performs image analysis and voice analysis. The dialogue unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the situation through voice dialogue with security guards and provide optimal instructions. The deployment instruction unit is implemented in the specific processing unit 46A of the smart device 14, for example, to extract suspicious behavior and instruct staff deployment. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to record images when trouble occurs and notify the police. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the monitoring unit, dialogue unit, deployment instruction unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit detects anomalies using the camera 42 and various sensors of the smart glasses 214 and performs image and voice analysis using the control unit 46A. The dialogue unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the situation through voice dialogue with security guards and provides optimal instructions. The deployment instruction unit is implemented, for example, in the control unit 46A of the smart glasses 214, which extracts suspicious behavior and instructs staff deployment. The notification unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which records images when trouble occurs and notifies the police. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the monitoring unit, dialogue unit, deployment instruction unit, and notification 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 detects abnormalities using the camera 42 and various sensors of the headset terminal 314 and performs image and voice analysis using the control unit 46A. The dialogue unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the situation through voice dialogue with security guards and provides optimal instructions. The deployment instruction unit is implemented by, for example, the control unit 46A of the headset terminal 314, which extracts suspicious behavior and instructs staff deployment. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which records images when trouble occurs and notifies the police. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the monitoring unit, dialogue unit, deployment instruction unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit detects abnormalities using the camera 42 and various sensors of the robot 414, and the control unit 46A performs image analysis and voice analysis. The dialogue unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the situation through voice dialogue with security guards and provide optimal instructions. The deployment instruction unit is implemented in the specific processing unit 46A of the robot 414, for example, to extract suspicious behavior and instruct staff deployment. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to record images when trouble occurs and notify the police. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A monitoring unit that integrates and manages surveillance cameras and various sensors to immediately detect abnormalities, Based on the anomaly detected by the aforementioned monitoring unit, the dialogue unit analyzes the situation through voice interaction with the security guard and provides optimal instructions. Based on instructions provided by the aforementioned dialogue unit, the deployment instruction unit autonomously identifies suspicious behaviors and instructs staff to be assigned to provide focused care. It includes a reporting unit that automatically records images and notifies the police in the event of a problem. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Conduct wide-area surveillance and immediately detect suspicious activity. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned dialogue unit, It analyzes reports from security guards and on-site audio to provide optimal instructions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned placement instruction unit is, If suspicious activity is detected in a specific area, staff will be deployed to that area and security will be heightened. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting unit, If a problem occurs, it will automatically record images and report them to the police. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the viewpoint and angle of the surveillance camera based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, When detecting anomalies, past anomaly detection data is referenced to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, When detecting anomalies, different detection algorithms are applied depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, The system estimates user sentiment and prioritizes monitoring areas based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, When detecting anomalies, the detection accuracy is improved by taking into account the geographical location information of the monitored target. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, When detecting anomalies, the system analyzes the social media activity of the monitored target to identify related anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned dialogue unit, When analyzing a situation, we refer to past conversation data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned dialogue unit, When analyzing a situation, different analytical algorithms are applied depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the length of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned dialogue unit, When analyzing a situation, prioritize the analysis based on when the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned dialogue unit, When analyzing the situation, adjust the order of analysis based on the correlation of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned placement instruction unit is, The system estimates user emotions and adjusts staff allocation instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned placement instruction unit is, When identifying suspicious behavior, we improve the accuracy of the identification process by referring to past data on suspicious behavior. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned placement instruction unit is, When extracting suspicious behavior, different extraction algorithms are applied depending on the type of suspicious behavior. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned placement instruction unit is, The system estimates user emotions and prioritizes staff allocation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned placement instruction unit is, When identifying suspicious behavior, consider the geographical location information of the suspicious behavior to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned placement instruction unit is, When identifying suspicious behavior, we improve the accuracy of the identification by referring to relevant literature on suspicious behavior. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, We estimate the user's emotions and adjust the reporting method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, When recording problems, refer to past problem data to improve recording accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, When recording problems, different recording algorithms are applied depending on the type of problem. The system described in Appendix 1, characterized by the features described herein. (Note 27) 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 28) The aforementioned reporting unit, When recording problems, consider the geographical location of the problem to improve recording accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reporting unit, When recording problems, refer to relevant literature related to the problem to improve the accuracy of the recording. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that integrates and manages surveillance cameras and various sensors to immediately detect abnormalities, Based on the anomaly detected by the aforementioned monitoring unit, the dialogue unit analyzes the situation through voice interaction with the security guard and provides optimal instructions. Based on instructions provided by the aforementioned dialogue unit, the deployment instruction unit autonomously identifies suspicious behaviors and instructs staff to be assigned to provide focused care. It includes a reporting unit that automatically records images and notifies the police in the event of a problem. A system characterized by the following features.

2. The aforementioned monitoring unit, Conduct wide-area surveillance and immediately detect suspicious activity. The system according to feature 1.

3. The aforementioned dialogue unit, It analyzes reports from security guards and on-site audio to provide optimal instructions. The system according to feature 1.

4. The aforementioned placement instruction unit is, If suspicious activity is detected in a specific area, staff will be deployed to that area and security will be heightened. The system according to feature 1.

5. The aforementioned reporting unit, If a problem occurs, it will automatically record images and report them to the police. The system according to feature 1.

6. The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the viewpoint and angle of the surveillance camera based on those estimated emotions. The system according to feature 1.

7. The aforementioned monitoring unit, When detecting anomalies, past anomaly detection data is referenced to improve detection accuracy. The system according to feature 1.

8. The aforementioned monitoring unit, When detecting anomalies, different detection algorithms are applied depending on the type of anomaly. The system according to feature 1.

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