system

Abstract

The system according to this embodiment aims to enhance home and community security and to quickly detect and report anomalies. [Solution] The system according to the embodiment comprises an analysis unit, a management unit, and a notification unit. The analysis unit analyzes fixed camera footage. The management unit manages drone patrols based on the information analyzed by the analysis unit. The notification unit issues an alarm and notifies when a drone managed by the management unit detects an abnormality.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, a monitoring system for strengthening the security of homes and regions is not sufficiently autonomous, and there is room for improvement in the speed of anomaly detection and reporting.

[0005] The system according to the embodiment aims to strengthen the security of homes and regions and quickly detect and report anomalies.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a management unit, and a reporting unit. The analysis unit analyzes fixed camera images. The management unit manages drone patrols based on the information analyzed by the analysis unit. The reporting unit issues an alarm and reports when an anomaly is detected by the drone managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can enhance home and community security and quickly detect and report anomalies. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 Home Protect AI Security Agent according to an embodiment of the present invention is a network system that enhances home and community security by utilizing generative AI and AI agent technology. This Home Protect AI Security Agent uses generative AI to analyze fixed camera footage and detect suspicious individuals in real time. Next, the AI ​​agent autonomously manages drone patrols, immediately issuing an alarm when an anomaly is detected and notifying nearby residents, security companies, and the police. Furthermore, weather-resistant equipment and ground robots are used during inclement weather to maintain a constant monitoring system. This aims to deter crime, enable rapid response, build a safe and secure community, and improve the overall security level of the region. For example, the generative AI analyzes fixed camera footage. During this process, the generative AI analyzes the footage in real time and detects suspicious individuals or unusual movements. For example, if a suspicious person enters the property at night, the generative AI detects their movements and immediately issues an alarm. Next, the AI ​​agent autonomously manages drone patrols. The drones fly autonomously and patrol a wide area. When an anomaly is detected, the drone transmits live video footage from the site and issues an alarm. For example, if an intruder enters the property, the drone tracks their movements and transmits video in real time. Furthermore, weather-resistant equipment and ground robots are used to maintain a constant monitoring system during inclement weather. For example, in rainy weather, weather-resistant fixed cameras acquire video footage, which is then analyzed by generating AI. Ground robots also patrol the area and issue alarms when an anomaly is detected. This system enables crime deterrence and rapid response. For example, if an intruder enters the property, the generating AI detects their movements, the drone tracks them, and an alarm is issued, allowing for a swift response. In addition, by notifying neighbors, security companies, and the police, the overall security level of the area is improved. In this way, the Home Protect AI Security Agent, which utilizes generating AI and AI agent technology, aims to build a safe and secure community and improve the overall security level of the area. As a result, the Home Protect AI Security Agent can strengthen home and community security and build a safe and secure community.

[0029] The home protect AI security agent according to this embodiment comprises an analysis unit, a management unit, and a notification unit. The analysis unit analyzes fixed camera footage. The analysis unit, for example, analyzes fixed camera footage in real time to detect suspicious persons or abnormal movements. The analysis unit can, for example, use generative AI to analyze the footage and detect the movements of suspicious persons. The analysis unit can also, for example, use video analysis technology to perform motion detection and facial recognition. The management unit manages drone patrols based on the information analyzed by the analysis unit. The management unit, for example, manages the autonomous flight of drones to patrol a wide area. The management unit can, for example, use AI to optimize the drone's flight route and achieve efficient patrols. The management unit can also, for example, set the drone's flight altitude and patrol route to cover the monitored area. The notification unit issues an alarm and makes a notification when a drone managed by the management unit detects an anomaly. The notification unit, for example, issues an alarm when it detects an anomaly and notifies nearby residents, security companies, and the police. The notification unit can, for example, use AI to improve the accuracy of anomaly detection and provide prompt notifications. Furthermore, the notification unit can customize the notification content according to the type of anomaly, providing appropriate information. As a result, the Home Protect AI Security Agent according to this embodiment can enhance home and community security and build a safe and secure community.

[0030] The analysis unit analyzes fixed camera footage. For example, the analysis unit analyzes fixed camera footage in real time to detect suspicious individuals and unusual movements. Specifically, the analysis unit can use generative AI to analyze the footage and detect the movements of suspicious individuals. The generative AI has been trained on a large amount of video data in advance and has the ability to distinguish between normal and abnormal movements. For example, the generative AI analyzes walking patterns, movement speed, direction, etc., to detect movements that are different from the norm. The analysis unit can also perform motion detection and face recognition using video analysis technology. In motion detection, it tracks moving objects in the camera footage in real time and analyzes their movement patterns. In face recognition, it detects the faces of people in the camera footage and identifies suspicious individuals by comparing them with a pre-registered database. As a result, the analysis unit can analyze fixed camera footage with high accuracy and enhance home and community security. Furthermore, the analysis unit can periodically update its AI model to improve the accuracy of anomaly detection and respond to new threats and suspicious movements. For example, if new methods or behavioral patterns are discovered, this information can be incorporated into the AI ​​model, enabling more advanced analysis. This allows the analysis department to constantly incorporate the latest technologies to perform highly accurate analysis and contribute to improved security.

[0031] The management department manages drone patrols based on information analyzed by the analysis department. For example, the management department manages the autonomous flight of drones to patrol wide areas. Specifically, the management department can use AI to optimize drone flight routes, enabling efficient patrols. The AI ​​calculates the optimal flight route considering terrain data, building layouts, and past anomaly detection data. This allows drones to cover a wide area with efficient routes and conduct patrols effectively. The management department can also set the drone's flight altitude and patrol routes to cover the monitored area. For example, it can adjust the drone's flight altitude according to specific time periods or weather conditions for optimal monitoring. Furthermore, the management department manages the drone's battery level and flight time, and performs charging and maintenance as needed, ensuring that the drones are always in optimal condition for patrolling. This allows the management department to maximize the efficiency and effectiveness of drone patrols and strengthen home and community security. Additionally, the management department can coordinate multiple drones for coordinated operation. For example, multiple drones can work together to patrol a wide area, and if an anomaly is detected, a specific drone can rush to the scene to respond quickly. In this way, the management department can oversee the entire drone patrol, enabling efficient and effective security measures.

[0032] The reporting unit issues an alarm and reports when a drone managed by the control unit detects an anomaly. Specifically, it issues an alarm when an anomaly is detected and notifies nearby residents, security companies, and the police. The reporting unit uses AI to improve the accuracy of anomaly detection and enable rapid reporting. The AI ​​determines the type and urgency of the anomaly and generates appropriate report content. For example, if an intruder is detected, it will issue an alarm to nearby residents and provide detailed information to security companies and the police. The reporting unit can also customize the report content according to the type of anomaly and provide appropriate information. For example, in the event of a fire, it will notify the fire department of the location and scale of the fire to encourage a rapid response. Furthermore, the reporting unit can reliably transmit information by using multiple communication methods when making a report. For example, it can use smartphone notifications, voice calls, SMS, and email in combination to ensure that important information is delivered reliably. This allows the reporting unit to report anomalies quickly and reliably and support appropriate responses. In addition, the reporting unit also follows up after the report to confirm whether the anomaly has been resolved. For example, after receiving reports from security companies or the police and confirming that the anomaly has been resolved, the system provides reassuring information to nearby residents. This allows the reporting department to manage the entire process from the occurrence of an anomaly to its resolution, thereby strengthening home and community security.

[0033] The weatherproof section performs monitoring using weatherproof equipment during adverse weather conditions. For example, the weatherproof section can perform monitoring in rainy weather using a waterproof camera. It can also perform monitoring during sandstorms using a dustproof camera. Furthermore, the weatherproof section can perform monitoring during strong winds using a windproof camera. This allows for the maintenance of a continuous monitoring system by using weatherproof equipment even during adverse weather conditions. Some or all of the above-described processes in the weatherproof section may be performed using, for example, a generation AI, or without a generation AI. For example, the weatherproof section can input video data acquired by a waterproof camera into a generation AI, which can then analyze the video data to detect anomalies.

[0034] The ground robot unit performs monitoring using ground robots. For example, the ground robot unit can monitor a wide area using ground robots with high mobility. It can also perform efficient monitoring using ground robots with a wide monitoring range. Furthermore, the ground robot unit can perform safe monitoring using ground robots equipped with obstacle avoidance capabilities. This allows for the maintenance of a continuous monitoring system even in adverse weather conditions by using ground robots for monitoring. Some or all of the above-described processes in the ground robot unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ground robot unit can input data acquired by the ground robots into a generative AI, which can then analyze the data to detect anomalies.

[0035] The analysis unit can analyze video in real time and detect suspicious individuals or abnormal movements. For example, the analysis unit can use a generating AI to analyze video in real time and detect the movements of suspicious individuals. The analysis unit can also use video analysis technology to perform motion detection or face recognition. For example, the analysis unit receives a prompt from the generating AI to analyze the video and detect the movements of suspicious individuals, and outputs the analysis results. This enables a rapid response by analyzing video in real time and detecting suspicious individuals or abnormal movements. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input video data into a generating AI, and the generating AI can analyze the video data and detect anomalies.

[0036] The control unit can manage the autonomous flight of drones and patrol a wide area. For example, the control unit can use AI to optimize the drone's flight route and achieve efficient patrols. The control unit can also set the drone's flight altitude and patrol route to cover the area to be monitored. For example, the control unit can receive prompts from the generative AI to optimize the drone's flight route and output the optimal route. This enables efficient surveillance by managing the autonomous flight of drones and patrolling a wide area. Some or all of the above processes in the control unit may be performed using generative AI, or not. For example, the control unit can input drone flight data into the generative AI, which can analyze the flight data and generate the optimal route.

[0037] The notification unit can issue an alarm when it detects an anomaly and notify nearby residents, security companies, and the police. The notification unit can improve the accuracy of anomaly detection using AI, for example, and make notifications quickly. The notification unit can also customize the content of the notification according to the type of anomaly and provide appropriate information. For example, the notification unit receives a prompt from a generating AI that detects an anomaly and generates notification content, and outputs the notification content. This enables a quick response by issuing an alarm when an anomaly is detected and notifying nearby residents, security companies, and the police. Some or all of the above processing in the notification unit may be performed using a generating AI, for example, or without a generating AI. For example, the notification unit can input anomaly detection data into a generating AI, and the generating AI can generate and output notification content.

[0038] The analysis unit can optimize its analysis algorithm by referring to past suspicious person data during video analysis. For example, the analysis unit can use a generative AI to refer to past suspicious person data and optimize the analysis algorithm. For example, the analysis unit can learn specific movements and behavioral patterns based on past suspicious person data and optimize the analysis algorithm. For example, the analysis unit can also improve the accuracy of suspicious person detection at specific times and locations by referring to past suspicious person data. For example, the analysis unit can analyze past suspicious person data, predict new suspicious person behavioral patterns, and adjust the analysis algorithm. This improves analysis accuracy by optimizing the analysis algorithm by referring to past suspicious person data. Some or all of the above processes in the analysis unit may be performed using a generative AI, for example, or without using a generative AI. For example, the analysis unit can input past suspicious person data into a generative AI, and the generative AI can analyze the data and optimize the analysis algorithm.

[0039] The analysis unit can change its analysis method depending on the time of day and weather conditions during video analysis. For example, the analysis unit can use a generative AI to change the analysis method according to the time of day and weather conditions. For example, the analysis unit can use an infrared camera at night to detect suspicious persons even in darkness. For example, the analysis unit can use a weather-resistant camera during rainy weather to minimize image distortion caused by raindrops. For example, the analysis unit can use a regular camera during the day to improve analysis accuracy in bright environments. In this way, analysis accuracy is improved by changing the analysis method according to the time of day and weather conditions. Some or all of the above processing in the analysis unit may be performed using a generative AI, or without a generative AI. For example, the analysis unit can input data on time of day and weather conditions into a generative AI, which can analyze the data and change the analysis method.

[0040] The analysis unit can improve analysis accuracy by integrating multiple camera images during video analysis. For example, the analysis unit can improve analysis accuracy by integrating multiple camera images using a generation AI. For example, the analysis unit can integrate multiple camera images in real time to achieve wide-area surveillance. For example, the analysis unit can also detect suspicious persons by analyzing multiple camera images and integrating information from different angles. For example, the analysis unit can improve surveillance accuracy by eliminating blind spots using multiple camera images. This improves analysis accuracy by integrating multiple camera images. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input multiple camera image data into a generation AI, which can then analyze and integrate the data.

[0041] The analysis unit can improve the accuracy of detecting suspicious individuals by using audio data in conjunction with video analysis. For example, the analysis unit can improve the accuracy of detecting suspicious individuals by using a generation AI in conjunction with audio data. For example, the analysis unit can integrate video and audio data to simultaneously analyze the movements and sounds of suspicious individuals. For example, the analysis unit can use audio data to detect specific sounds (such as the sound of glass breaking) and determine if an anomaly has occurred. For example, the analysis unit can use video and audio data in conjunction to more accurately grasp the behavioral patterns of suspicious individuals. As a result, the accuracy of detecting suspicious individuals is improved by using audio data in conjunction. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input audio data into a generation AI, and the generation AI can analyze the data to detect suspicious individuals.

[0042] The management unit can generate optimal patrol routes by referring to past patrol data when managing drones. For example, the management unit can use a generation AI to refer to past patrol data and generate optimal patrol routes. For example, the management unit can generate the most effective patrol routes based on past patrol data. The management unit can also analyze past patrol data to optimize patrol routes for specific time periods and locations. For example, the management unit can refer to past patrol data to propose new patrol routes. This improves monitoring accuracy by generating optimal patrol routes by referring to past patrol data. Some or all of the above processes in the management unit may be performed using a generation AI, for example, or without a generation AI. For example, the management unit can input past patrol data into a generation AI, which can then analyze the data to generate optimal patrol routes.

[0043] The management department can change patrol routes depending on the weather and time of day when managing drones. For example, the management department can use generative AI to change patrol routes according to the weather and time of day. For example, in rainy weather, the management department can change the drone's flight route and prioritize patrolling areas with high weather resistance. For example, at night, the management department can use drones equipped with infrared cameras to conduct surveillance even in darkness. For example, during the day, the management department can maintain the normal patrol route and monitor a wide area. By changing patrol routes according to the weather and time of day, surveillance accuracy is improved. Some or all of the above processes in the management department may be performed using generative AI, for example, or without generative AI. For example, the management department can input weather and time-of-day data into the generative AI, which can analyze the data and change the patrol route.

[0044] The management department can expand the monitoring range by strengthening cooperation with ground robots when managing drones. For example, the management department can use generative AI to strengthen cooperation with ground robots and expand the monitoring range. For example, the management department can have drones and ground robots work together to simultaneously monitor a wide area. For example, the management department can eliminate blind spots by having drones monitor from the air and ground robots monitor from the ground. For example, the management department can have drones and ground robots work together to respond quickly when an anomaly is detected. This expands the monitoring range by strengthening cooperation with ground robots. Some or all of the above processes in the management department may be performed using generative AI, for example, or without generative AI. For example, the management department can input data from drones and ground robots into a generative AI, which can then analyze the data to strengthen cooperation.

[0045] The management unit can optimize patrol routes by considering the drone's battery level. For example, the management unit can use a generative AI to consider the drone's battery level and optimize patrol routes. For example, the management unit can monitor the drone's battery level in real time and adjust patrol routes according to the remaining level. For example, if the battery level is low, the management unit can prioritize a route that returns to the charging station. For example, if the battery level is sufficient, the management unit can set a route that patrols a wide area. This makes efficient patrols possible by considering the drone's battery level. Some or all of the above processing in the management unit may be performed using a generative AI, or not. For example, the management unit can input drone battery level data into a generative AI, which can then analyze the data to optimize patrol routes.

[0046] The reporting unit can optimize its reporting algorithm by referring to past reporting history when a report is made. For example, the reporting unit can use a generative AI to refer to past reporting history and optimize the reporting algorithm. For example, the reporting unit can propose the most effective reporting method based on past reporting history. The reporting unit can also analyze past reporting history and optimize the reporting algorithm for specific time periods or locations. For example, the reporting unit can propose a new reporting algorithm by referring to past reporting history. This improves reporting accuracy by optimizing the reporting algorithm by referring to past reporting history. Some or all of the above processes in the reporting unit may be performed using a generative AI, or not. For example, the reporting unit can input past reporting history data into a generative AI, which can then analyze the data and optimize the reporting algorithm.

[0047] The notification unit can customize the content of the notification depending on the type of anomaly. For example, the notification unit can use a generation AI to customize the content of the notification depending on the type of anomaly. For example, when an intruder enters the building, the notification unit can create a notification that includes detailed information about the person. For example, when a fire breaks out, the notification unit can also create a notification that describes the scale and location of the fire in detail. For example, when an abnormal sound is detected, the notification unit can also create a notification that describes the type of sound and the location where it occurred in detail. By customizing the notification content according to the type of anomaly, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using a generation AI, for example, or without a generation AI. For example, the notification unit can input anomaly detection data into a generation AI, and the generation AI can analyze the data to customize the content of the notification.

[0048] The reporting unit can optimize the reporting scope by considering the contact information of neighboring residents when reporting. For example, the reporting unit can use a generation AI to consider the contact information of neighboring residents and optimize the reporting scope. For example, the reporting unit can set the optimal reporting scope based on the contact information of neighboring residents. The reporting unit can also, for example, refer to the contact information of neighboring residents to make a report quickly. The reporting unit can also, for example, use the contact information of neighboring residents to customize the content of the report. This optimizes the reporting scope and enables a quick response by considering the contact information of neighboring residents. Some or all of the above processing in the reporting unit may be performed using a generation AI, for example, or without a generation AI. For example, the reporting unit can input the contact information of neighboring residents into a generation AI, and the generation AI can analyze the data to optimize the reporting scope.

[0049] The reporting department can strengthen cooperation with security companies and the police to enable a rapid response when a report is made. The reporting department can, for example, use a generation AI to strengthen cooperation with security companies and the police to enable a rapid response. The reporting department can, for example, strengthen cooperation with security companies and the police to make a rapid report. The reporting department can, for example, strengthen cooperation with security companies and the police to respond quickly when an anomaly occurs. The reporting department can, for example, strengthen cooperation with security companies and the police to share the content of the report. This makes a rapid response possible by strengthening cooperation with security companies and the police. Some or all of the above processes in the reporting department may be performed using a generation AI, for example, or without a generation AI. For example, the reporting department can input cooperation data with security companies and the police into a generation AI, and the generation AI can analyze the data to strengthen cooperation.

[0050] The weatherproof unit can select the optimal equipment by referring to past weather data when using weatherproof equipment. For example, the weatherproof unit can use a generating AI to refer to past weather data and select the optimal equipment. For example, based on past weather data, the weatherproof unit can use a waterproof camera in rainy weather. For example, the weatherproof unit can also refer to past weather data to select equipment with high cold resistance on snowy days. For example, the weatherproof unit can analyze past weather data to use equipment with high wind resistance on windy days. By selecting the optimal equipment by referring to past weather data, monitoring accuracy is improved. Some or all of the above processing in the weatherproof unit may be performed using a generating AI, for example, or without a generating AI. For example, the weatherproof unit can input past weather data into a generating AI, and the generating AI can analyze the data to select the optimal equipment.

[0051] The weatherproof unit can enhance cooperation with ground robots and expand the monitoring range when using weatherproof equipment. For example, the weatherproof unit can use generative AI to enhance cooperation with ground robots and expand the monitoring range. For example, the weatherproof unit can have weatherproof equipment and ground robots cooperate to simultaneously monitor a wide area. For example, the weatherproof unit can eliminate blind spots by having weatherproof equipment monitor from the air and ground robots monitor from the ground. For example, the weatherproof unit can have weatherproof equipment and ground robots cooperate to respond quickly when an anomaly is detected. This expands the monitoring range by enhancing cooperation with ground robots. Some or all of the above processing in the weatherproof unit may be performed using generative AI, for example, or without generative AI. For example, the weatherproof unit can input data from weatherproof equipment and ground robots into a generative AI, which can then analyze the data to enhance cooperation.

[0052] The ground robot unit can generate an optimal patrol route by referring to past patrol data when the ground robot is in use. For example, the ground robot unit can use a generation AI to refer to past patrol data and generate an optimal patrol route. For example, the ground robot unit can generate the most effective patrol route based on past patrol data. The ground robot unit can also analyze past patrol data to optimize patrol routes for specific time periods and locations. For example, the ground robot unit can refer to past patrol data to propose new patrol routes. This improves monitoring accuracy by generating an optimal patrol route by referring to past patrol data. Some or all of the above processing in the ground robot unit may be performed using a generation AI, for example, or without a generation AI. For example, the ground robot unit can input past patrol data into a generation AI, which can then analyze the data to generate an optimal patrol route.

[0053] The ground robot unit can enhance cooperation with drones and expand the monitoring range when using the ground robot. For example, the ground robot unit can use generative AI to enhance cooperation with drones and expand the monitoring range. For example, the ground robot unit and drone can work together to simultaneously monitor a wide area. For example, the ground robot unit can eliminate blind spots by having the ground robot monitor from the ground and the drone monitor from the air. For example, the ground robot unit and drone can work together to respond quickly when an anomaly is detected. This expands the monitoring range by enhancing cooperation with drones. Some or all of the above processing in the ground robot unit may be performed using generative AI, for example, or without generative AI. For example, the ground robot unit can input data from the ground robot and drone into the generative AI, which can analyze the data to enhance cooperation.

[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 analysis unit can improve the accuracy of detecting suspicious individuals by using audio data in conjunction with video analysis. For example, the analysis unit can improve the accuracy of detecting suspicious individuals by using a generation AI in conjunction with audio data. For example, the analysis unit can integrate video and audio data to simultaneously analyze the movements and sounds of suspicious individuals. For example, the analysis unit can use audio data to detect specific sounds (such as the sound of glass breaking) and determine if an anomaly has occurred. For example, the analysis unit can use video and audio data in conjunction to more accurately grasp the behavioral patterns of suspicious individuals. As a result, the accuracy of detecting suspicious individuals is improved by using audio data in conjunction. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input audio data into a generation AI, and the generation AI can analyze the data to detect suspicious individuals.

[0056] The management unit can optimize patrol routes by considering the drone's battery level. For example, the management unit can use a generative AI to consider the drone's battery level and optimize patrol routes. For example, the management unit can monitor the drone's battery level in real time and adjust patrol routes according to the remaining level. For example, if the battery level is low, the management unit can prioritize a route that returns to the charging station. For example, if the battery level is sufficient, the management unit can set a route that patrols a wide area. This makes efficient patrols possible by considering the drone's battery level. Some or all of the above processing in the management unit may be performed using a generative AI, or not. For example, the management unit can input drone battery level data into a generative AI, which can then analyze the data to optimize patrol routes.

[0057] The weatherproof unit can select the optimal equipment by referring to past weather data when using weatherproof equipment. For example, the weatherproof unit can use a generating AI to refer to past weather data and select the optimal equipment. For example, based on past weather data, the weatherproof unit can use a waterproof camera in rainy weather. For example, the weatherproof unit can also refer to past weather data to select equipment with high cold resistance on snowy days. For example, the weatherproof unit can analyze past weather data to use equipment with high wind resistance on windy days. By selecting the optimal equipment by referring to past weather data, monitoring accuracy is improved. Some or all of the above processing in the weatherproof unit may be performed using a generating AI, for example, or without a generating AI. For example, the weatherproof unit can input past weather data into a generating AI, and the generating AI can analyze the data to select the optimal equipment.

[0058] The analysis unit can optimize its analysis algorithm by referring to past suspicious person data during video analysis. For example, the analysis unit can use a generative AI to refer to past suspicious person data and optimize the analysis algorithm. For example, the analysis unit can learn specific movements and behavioral patterns based on past suspicious person data and optimize the analysis algorithm. For example, the analysis unit can also improve the accuracy of suspicious person detection at specific times and locations by referring to past suspicious person data. For example, the analysis unit can analyze past suspicious person data, predict new suspicious person behavioral patterns, and adjust the analysis algorithm. This improves analysis accuracy by optimizing the analysis algorithm by referring to past suspicious person data. Some or all of the above processes in the analysis unit may be performed using a generative AI, for example, or without using a generative AI. For example, the analysis unit can input past suspicious person data into a generative AI, and the generative AI can analyze the data and optimize the analysis algorithm.

[0059] The reporting unit can optimize its reporting algorithm by referring to past reporting history when a report is made. For example, the reporting unit can use a generative AI to refer to past reporting history and optimize the reporting algorithm. For example, the reporting unit can propose the most effective reporting method based on past reporting history. The reporting unit can also analyze past reporting history and optimize the reporting algorithm for specific time periods or locations. For example, the reporting unit can propose a new reporting algorithm by referring to past reporting history. This improves reporting accuracy by optimizing the reporting algorithm by referring to past reporting history. Some or all of the above processes in the reporting unit may be performed using a generative AI, or not. For example, the reporting unit can input past reporting history data into a generative AI, which can then analyze the data and optimize the reporting algorithm.

[0060] The ground robot unit can enhance cooperation with drones and expand the monitoring range when using the ground robot. For example, the ground robot unit can use generative AI to enhance cooperation with drones and expand the monitoring range. For example, the ground robot unit and drone can work together to simultaneously monitor a wide area. For example, the ground robot unit can eliminate blind spots by having the ground robot monitor from the ground and the drone monitor from the air. For example, the ground robot unit and drone can work together to respond quickly when an anomaly is detected. This expands the monitoring range by enhancing cooperation with drones. Some or all of the above processing in the ground robot unit may be performed using generative AI, for example, or without generative AI. For example, the ground robot unit can input data from the ground robot and drone into the generative AI, which can analyze the data to enhance cooperation.

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

[0062] Step 1: The analysis unit analyzes the fixed camera footage. For example, the analysis unit analyzes the fixed camera footage in real time to detect suspicious individuals or unusual movements. The analysis unit can analyze the footage using generated AI to detect the movements of suspicious individuals. It can also perform motion detection and facial recognition using video analysis technology. Step 2: The management unit manages drone patrols based on the information analyzed by the analysis unit. For example, the management unit manages the autonomous flight of drones and patrols a wide area. The management unit can use AI to optimize the drone's flight route and achieve efficient patrols. It can also set the drone's flight altitude and patrol route to cover the area to be monitored. Step 3: The notification unit issues an alarm and reports when a drone managed by the control unit detects an anomaly. For example, when the notification unit detects an anomaly, it issues an alarm and notifies nearby residents, security companies, and the police. The notification unit uses AI to improve the accuracy of anomaly detection and enables rapid reporting. It can also customize the content of the report according to the type of anomaly and provide appropriate information.

[0063] (Example of form 2) The Home Protect AI Security Agent according to an embodiment of the present invention is a network system that enhances home and community security by utilizing generative AI and AI agent technology. This Home Protect AI Security Agent uses generative AI to analyze fixed camera footage and detect suspicious individuals in real time. Next, the AI ​​agent autonomously manages drone patrols, immediately issuing an alarm when an anomaly is detected and notifying nearby residents, security companies, and the police. Furthermore, weather-resistant equipment and ground robots are used during inclement weather to maintain a constant monitoring system. This aims to deter crime, enable rapid response, build a safe and secure community, and improve the overall security level of the region. For example, the generative AI analyzes fixed camera footage. During this process, the generative AI analyzes the footage in real time and detects suspicious individuals or unusual movements. For example, if a suspicious person enters the property at night, the generative AI detects their movements and immediately issues an alarm. Next, the AI ​​agent autonomously manages drone patrols. The drones fly autonomously and patrol a wide area. When an anomaly is detected, the drone transmits live video footage from the site and issues an alarm. For example, if an intruder enters the property, the drone tracks their movements and transmits video in real time. Furthermore, weather-resistant equipment and ground robots are used to maintain a constant monitoring system during inclement weather. For example, in rainy weather, weather-resistant fixed cameras acquire video footage, which is then analyzed by generating AI. Ground robots also patrol the area and issue alarms when an anomaly is detected. This system enables crime deterrence and rapid response. For example, if an intruder enters the property, the generating AI detects their movements, the drone tracks them, and an alarm is issued, allowing for a swift response. In addition, by notifying neighbors, security companies, and the police, the overall security level of the area is improved. In this way, the Home Protect AI Security Agent, which utilizes generating AI and AI agent technology, aims to build a safe and secure community and improve the overall security level of the area. As a result, the Home Protect AI Security Agent can strengthen home and community security and build a safe and secure community.

[0064] The home protect AI security agent according to this embodiment comprises an analysis unit, a management unit, and a notification unit. The analysis unit analyzes fixed camera footage. The analysis unit, for example, analyzes fixed camera footage in real time to detect suspicious persons or abnormal movements. The analysis unit can, for example, use generative AI to analyze the footage and detect the movements of suspicious persons. The analysis unit can also, for example, use video analysis technology to perform motion detection and facial recognition. The management unit manages drone patrols based on the information analyzed by the analysis unit. The management unit, for example, manages the autonomous flight of drones to patrol a wide area. The management unit can, for example, use AI to optimize the drone's flight route and achieve efficient patrols. The management unit can also, for example, set the drone's flight altitude and patrol route to cover the monitored area. The notification unit issues an alarm and makes a notification when a drone managed by the management unit detects an anomaly. The notification unit, for example, issues an alarm when it detects an anomaly and notifies nearby residents, security companies, and the police. The notification unit can, for example, use AI to improve the accuracy of anomaly detection and provide prompt notifications. Furthermore, the notification unit can customize the notification content according to the type of anomaly, providing appropriate information. As a result, the Home Protect AI Security Agent according to this embodiment can enhance home and community security and build a safe and secure community.

[0065] The analysis unit analyzes fixed camera footage. For example, the analysis unit analyzes fixed camera footage in real time to detect suspicious individuals and unusual movements. Specifically, the analysis unit can use generative AI to analyze the footage and detect the movements of suspicious individuals. The generative AI has been trained on a large amount of video data in advance and has the ability to distinguish between normal and abnormal movements. For example, the generative AI analyzes walking patterns, movement speed, direction, etc., to detect movements that are different from the norm. The analysis unit can also perform motion detection and face recognition using video analysis technology. In motion detection, it tracks moving objects in the camera footage in real time and analyzes their movement patterns. In face recognition, it detects the faces of people in the camera footage and identifies suspicious individuals by comparing them with a pre-registered database. As a result, the analysis unit can analyze fixed camera footage with high accuracy and enhance home and community security. Furthermore, the analysis unit can periodically update its AI model to improve the accuracy of anomaly detection and respond to new threats and suspicious movements. For example, if new methods or behavioral patterns are discovered, this information can be incorporated into the AI ​​model, enabling more advanced analysis. This allows the analysis department to constantly incorporate the latest technologies to perform highly accurate analysis and contribute to improved security.

[0066] The management department manages drone patrols based on information analyzed by the analysis department. For example, the management department manages the autonomous flight of drones to patrol wide areas. Specifically, the management department can use AI to optimize drone flight routes, enabling efficient patrols. The AI ​​calculates the optimal flight route considering terrain data, building layouts, and past anomaly detection data. This allows drones to cover a wide area with efficient routes and conduct patrols effectively. The management department can also set the drone's flight altitude and patrol routes to cover the monitored area. For example, it can adjust the drone's flight altitude according to specific time periods or weather conditions for optimal monitoring. Furthermore, the management department manages the drone's battery level and flight time, and performs charging and maintenance as needed, ensuring that the drones are always in optimal condition for patrolling. This allows the management department to maximize the efficiency and effectiveness of drone patrols and strengthen home and community security. Additionally, the management department can coordinate multiple drones for coordinated operation. For example, multiple drones can work together to patrol a wide area, and if an anomaly is detected, a specific drone can rush to the scene to respond quickly. In this way, the management department can oversee the entire drone patrol, enabling efficient and effective security measures.

[0067] The reporting unit issues an alarm and reports when a drone managed by the control unit detects an anomaly. Specifically, it issues an alarm when an anomaly is detected and notifies nearby residents, security companies, and the police. The reporting unit uses AI to improve the accuracy of anomaly detection and enable rapid reporting. The AI ​​determines the type and urgency of the anomaly and generates appropriate report content. For example, if an intruder is detected, it will issue an alarm to nearby residents and provide detailed information to security companies and the police. The reporting unit can also customize the report content according to the type of anomaly and provide appropriate information. For example, in the event of a fire, it will notify the fire department of the location and scale of the fire to encourage a rapid response. Furthermore, the reporting unit can reliably transmit information by using multiple communication methods when making a report. For example, it can use smartphone notifications, voice calls, SMS, and email in combination to ensure that important information is delivered reliably. This allows the reporting unit to report anomalies quickly and reliably and support appropriate responses. In addition, the reporting unit also follows up after the report to confirm whether the anomaly has been resolved. For example, after receiving reports from security companies or the police and confirming that the anomaly has been resolved, the system provides reassuring information to nearby residents. This allows the reporting department to manage the entire process from the occurrence of an anomaly to its resolution, thereby strengthening home and community security.

[0068] The weatherproof section performs monitoring using weatherproof equipment during adverse weather conditions. For example, the weatherproof section can perform monitoring in rainy weather using a waterproof camera. It can also perform monitoring during sandstorms using a dustproof camera. Furthermore, the weatherproof section can perform monitoring during strong winds using a windproof camera. This allows for the maintenance of a continuous monitoring system by using weatherproof equipment even during adverse weather conditions. Some or all of the above-described processes in the weatherproof section may be performed using, for example, a generation AI, or without a generation AI. For example, the weatherproof section can input video data acquired by a waterproof camera into a generation AI, which can then analyze the video data to detect anomalies.

[0069] The ground robot unit performs monitoring using ground robots. For example, the ground robot unit can monitor a wide area using ground robots with high mobility. It can also perform efficient monitoring using ground robots with a wide monitoring range. Furthermore, the ground robot unit can perform safe monitoring using ground robots equipped with obstacle avoidance capabilities. This allows for the maintenance of a continuous monitoring system even in adverse weather conditions by using ground robots for monitoring. Some or all of the above-described processes in the ground robot unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ground robot unit can input data acquired by the ground robots into a generative AI, which can then analyze the data to detect anomalies.

[0070] The analysis unit can analyze video in real time and detect suspicious individuals or abnormal movements. For example, the analysis unit can use a generating AI to analyze video in real time and detect the movements of suspicious individuals. The analysis unit can also use video analysis technology to perform motion detection or face recognition. For example, the analysis unit receives a prompt from the generating AI to analyze the video and detect the movements of suspicious individuals, and outputs the analysis results. This enables a rapid response by analyzing video in real time and detecting suspicious individuals or abnormal movements. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input video data into a generating AI, and the generating AI can analyze the video data and detect anomalies.

[0071] The control unit can manage the autonomous flight of drones and patrol a wide area. For example, the control unit can use AI to optimize the drone's flight route and achieve efficient patrols. The control unit can also set the drone's flight altitude and patrol route to cover the area to be monitored. For example, the control unit can receive prompts from the generative AI to optimize the drone's flight route and output the optimal route. This enables efficient surveillance by managing the autonomous flight of drones and patrolling a wide area. Some or all of the above processes in the control unit may be performed using generative AI, or not. For example, the control unit can input drone flight data into the generative AI, which can analyze the flight data and generate the optimal route.

[0072] The notification unit can issue an alarm when it detects an anomaly and notify nearby residents, security companies, and the police. The notification unit can improve the accuracy of anomaly detection using AI, for example, and make notifications quickly. The notification unit can also customize the content of the notification according to the type of anomaly and provide appropriate information. For example, the notification unit receives a prompt from a generating AI that detects an anomaly and generates notification content, and outputs the notification content. This enables a quick response by issuing an alarm when an anomaly is detected and notifying nearby residents, security companies, and the police. Some or all of the above processing in the notification unit may be performed using a generating AI, for example, or without a generating AI. For example, the notification unit can input anomaly detection data into a generating AI, and the generating AI can generate and output notification content.

[0073] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit can use a generative AI to estimate the user's emotions and adjust the accuracy of the analysis. For example, if the user is feeling anxious, the analysis unit can increase the accuracy of the analysis to enhance the detection of suspicious individuals. For example, if the user is relaxed, the analysis unit can maintain the accuracy of the analysis at a normal level. For example, if the user is tense, the analysis unit can maximize the accuracy of the analysis to perform anomaly detection. This allows for more appropriate analysis by adjusting the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then analyze the emotion data and adjust the accuracy of the analysis.

[0074] The analysis unit can optimize its analysis algorithm by referring to past suspicious person data during video analysis. For example, the analysis unit can use a generative AI to refer to past suspicious person data and optimize the analysis algorithm. For example, the analysis unit can learn specific movements and behavioral patterns based on past suspicious person data and optimize the analysis algorithm. For example, the analysis unit can also improve the accuracy of suspicious person detection at specific times and locations by referring to past suspicious person data. For example, the analysis unit can analyze past suspicious person data, predict new suspicious person behavioral patterns, and adjust the analysis algorithm. This improves analysis accuracy by optimizing the analysis algorithm by referring to past suspicious person data. Some or all of the above processes in the analysis unit may be performed using a generative AI, for example, or without using a generative AI. For example, the analysis unit can input past suspicious person data into a generative AI, and the generative AI can analyze the data and optimize the analysis algorithm.

[0075] The analysis unit can change its analysis method depending on the time of day and weather conditions during video analysis. For example, the analysis unit can use a generative AI to change the analysis method according to the time of day and weather conditions. For example, the analysis unit can use an infrared camera at night to detect suspicious persons even in darkness. For example, the analysis unit can use a weather-resistant camera during rainy weather to minimize image distortion caused by raindrops. For example, the analysis unit can use a regular camera during the day to improve analysis accuracy in bright environments. In this way, analysis accuracy is improved by changing the analysis method according to the time of day and weather conditions. Some or all of the above processing in the analysis unit may be performed using a generative AI, or without a generative AI. For example, the analysis unit can input data on time of day and weather conditions into a generative AI, which can analyze the data and change the analysis method.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using a generative AI and adjust the display method of the analysis results. For example, if the user is feeling anxious, the analysis unit can display detailed analysis results to provide reassurance. For example, if the user is relaxed, the analysis unit can also display concise analysis results to reduce stress. For example, if the user is tense, the analysis unit can highlight important information to encourage a quick response. This makes it possible to provide more appropriate information by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can analyze the emotion data and adjust the display method of the analysis results.

[0077] The analysis unit can improve analysis accuracy by integrating multiple camera images during video analysis. For example, the analysis unit can improve analysis accuracy by integrating multiple camera images using a generation AI. For example, the analysis unit can integrate multiple camera images in real time to achieve wide-area surveillance. For example, the analysis unit can also detect suspicious persons by analyzing multiple camera images and integrating information from different angles. For example, the analysis unit can improve surveillance accuracy by eliminating blind spots using multiple camera images. This improves analysis accuracy by integrating multiple camera images. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input multiple camera image data into a generation AI, which can then analyze and integrate the data.

[0078] The analysis unit can improve the accuracy of detecting suspicious individuals by using audio data in conjunction with video analysis. For example, the analysis unit can improve the accuracy of detecting suspicious individuals by using a generation AI in conjunction with audio data. For example, the analysis unit can integrate video and audio data to simultaneously analyze the movements and sounds of suspicious individuals. For example, the analysis unit can use audio data to detect specific sounds (such as the sound of glass breaking) and determine if an anomaly has occurred. For example, the analysis unit can use video and audio data in conjunction to more accurately grasp the behavioral patterns of suspicious individuals. As a result, the accuracy of detecting suspicious individuals is improved by using audio data in conjunction. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input audio data into a generation AI, and the generation AI can analyze the data to detect suspicious individuals.

[0079] The control unit can estimate the user's emotions and adjust the drone's patrol route based on the estimated emotions. For example, the control unit can use generative AI to estimate the user's emotions and adjust the drone's patrol route. For example, if the user is feeling anxious, the control unit can frequently change the drone's patrol route and expand the surveillance area. For example, if the user is relaxed, the control unit can maintain the normal patrol route. For example, if the user is stressed, the control unit can set a route that focuses on patrolling important areas. This allows for more appropriate surveillance by adjusting the drone's patrol route based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 control unit may be performed using generative AI, for example, or without generative AI. For example, the management department can input user emotion data into a generating AI, which can then analyze the emotion data and adjust patrol routes.

[0080] The management unit can generate optimal patrol routes by referring to past patrol data when managing drones. For example, the management unit can use a generation AI to refer to past patrol data and generate optimal patrol routes. For example, the management unit can generate the most effective patrol routes based on past patrol data. The management unit can also analyze past patrol data to optimize patrol routes for specific time periods and locations. For example, the management unit can refer to past patrol data to propose new patrol routes. This improves monitoring accuracy by generating optimal patrol routes by referring to past patrol data. Some or all of the above processes in the management unit may be performed using a generation AI, for example, or without a generation AI. For example, the management unit can input past patrol data into a generation AI, which can then analyze the data to generate optimal patrol routes.

[0081] The management department can change patrol routes depending on the weather and time of day when managing drones. For example, the management department can use generative AI to change patrol routes according to the weather and time of day. For example, in rainy weather, the management department can change the drone's flight route and prioritize patrolling areas with high weather resistance. For example, at night, the management department can use drones equipped with infrared cameras to conduct surveillance even in darkness. For example, during the day, the management department can maintain the normal patrol route and monitor a wide area. By changing patrol routes according to the weather and time of day, surveillance accuracy is improved. Some or all of the above processes in the management department may be performed using generative AI, for example, or without generative AI. For example, the management department can input weather and time-of-day data into the generative AI, which can analyze the data and change the patrol route.

[0082] The control unit can estimate the user's emotions and adjust the drone patrol frequency based on the estimated emotions. For example, the control unit can use generative AI to estimate the user's emotions and adjust the drone patrol frequency. For example, if the user is feeling anxious, the control unit can increase the drone patrol frequency. For example, if the user is relaxed, the control unit can maintain the normal patrol frequency. For example, if the user is stressed, the control unit can set the frequency to focus patrols on important areas. This allows for more appropriate monitoring by adjusting the drone patrol frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 control unit may be performed using generative AI or not. For example, the control unit can input user emotion data into a generative AI, which can analyze the emotion data and adjust the patrol frequency.

[0083] The management department can expand the monitoring range by strengthening cooperation with ground robots when managing drones. For example, the management department can use generative AI to strengthen cooperation with ground robots and expand the monitoring range. For example, the management department can have drones and ground robots work together to simultaneously monitor a wide area. For example, the management department can eliminate blind spots by having drones monitor from the air and ground robots monitor from the ground. For example, the management department can have drones and ground robots work together to respond quickly when an anomaly is detected. This expands the monitoring range by strengthening cooperation with ground robots. Some or all of the above processes in the management department may be performed using generative AI, for example, or without generative AI. For example, the management department can input data from drones and ground robots into a generative AI, which can then analyze the data to strengthen cooperation.

[0084] The management unit can optimize patrol routes by considering the drone's battery level. For example, the management unit can use a generative AI to consider the drone's battery level and optimize patrol routes. For example, the management unit can monitor the drone's battery level in real time and adjust patrol routes according to the remaining level. For example, if the battery level is low, the management unit can prioritize a route that returns to the charging station. For example, if the battery level is sufficient, the management unit can set a route that patrols a wide area. This makes efficient patrols possible by considering the drone's battery level. Some or all of the above processing in the management unit may be performed using a generative AI, or not. For example, the management unit can input drone battery level data into a generative AI, which can then analyze the data to optimize patrol routes.

[0085] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, the reporting unit can use generative AI to estimate the user's emotions and determine the priority of reports. For example, the reporting unit may increase the priority of reports if the user is feeling anxious. For example, the reporting unit may maintain the normal reporting priority if the user is relaxed. For example, the reporting unit may prioritize important reports if the user is stressed. 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, such as 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 generative AI or not. For example, the reporting unit can input user emotion data into a generative AI, which can analyze the emotion data to determine the priority of reports.

[0086] The reporting unit can optimize its reporting algorithm by referring to past reporting history when a report is made. For example, the reporting unit can use a generative AI to refer to past reporting history and optimize the reporting algorithm. For example, the reporting unit can propose the most effective reporting method based on past reporting history. The reporting unit can also analyze past reporting history and optimize the reporting algorithm for specific time periods or locations. For example, the reporting unit can propose a new reporting algorithm by referring to past reporting history. This improves reporting accuracy by optimizing the reporting algorithm by referring to past reporting history. Some or all of the above processes in the reporting unit may be performed using a generative AI, or not. For example, the reporting unit can input past reporting history data into a generative AI, which can then analyze the data and optimize the reporting algorithm.

[0087] The notification unit can customize the content of the notification depending on the type of anomaly. For example, the notification unit can use a generation AI to customize the content of the notification depending on the type of anomaly. For example, when an intruder enters the building, the notification unit can create a notification that includes detailed information about the person. For example, when a fire breaks out, the notification unit can also create a notification that describes the scale and location of the fire in detail. For example, when an abnormal sound is detected, the notification unit can also create a notification that describes the type of sound and the location where it occurred in detail. By customizing the notification content according to the type of anomaly, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using a generation AI, for example, or without a generation AI. For example, the notification unit can input anomaly detection data into a generation AI, and the generation AI can analyze the data to customize the content of the notification.

[0088] The reporting unit can estimate the user's emotions and adjust the reporting method based on the estimated emotions. For example, the reporting unit can use generative AI to estimate the user's emotions and adjust the reporting method. For example, if the user is feeling anxious, the reporting unit may select a rapid reporting method. For example, if the user is relaxed, the reporting unit may select a normal reporting method. For example, if the user is stressed, the reporting unit may prioritize important reporting. 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, such as 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 generative AI or not. For example, the reporting unit can input user emotion data into a generative AI, which can analyze the emotion data and adjust the reporting method.

[0089] The reporting unit can optimize the reporting scope by considering the contact information of neighboring residents when reporting. For example, the reporting unit can use a generation AI to consider the contact information of neighboring residents and optimize the reporting scope. For example, the reporting unit can set the optimal reporting scope based on the contact information of neighboring residents. The reporting unit can also, for example, refer to the contact information of neighboring residents to make a report quickly. The reporting unit can also, for example, use the contact information of neighboring residents to customize the content of the report. This optimizes the reporting scope and enables a quick response by considering the contact information of neighboring residents. Some or all of the above processing in the reporting unit may be performed using a generation AI, for example, or without a generation AI. For example, the reporting unit can input the contact information of neighboring residents into a generation AI, and the generation AI can analyze the data to optimize the reporting scope.

[0090] The reporting department can strengthen cooperation with security companies and the police to enable a rapid response when a report is made. The reporting department can, for example, use a generation AI to strengthen cooperation with security companies and the police to enable a rapid response. The reporting department can, for example, strengthen cooperation with security companies and the police to make a rapid report. The reporting department can, for example, strengthen cooperation with security companies and the police to respond quickly when an anomaly occurs. The reporting department can, for example, strengthen cooperation with security companies and the police to share the content of the report. This makes a rapid response possible by strengthening cooperation with security companies and the police. Some or all of the above processes in the reporting department may be performed using a generation AI, for example, or without a generation AI. For example, the reporting department can input cooperation data with security companies and the police into a generation AI, and the generation AI can analyze the data to strengthen cooperation.

[0091] The weatherproof unit can estimate the user's emotions and adjust the frequency of use of weatherproof equipment based on the estimated user emotions. For example, the weatherproof unit can use generative AI to estimate the user's emotions and adjust the frequency of use of weatherproof equipment. For example, if the user is feeling anxious, the weatherproof unit may increase the frequency of use of weatherproof equipment. For example, if the user is relaxed, the weatherproof unit may maintain the normal frequency of use. For example, if the user is stressed, the weatherproof unit may adjust the frequency of use of weatherproof equipment to focus on monitoring important areas. This allows for more appropriate monitoring by adjusting the frequency of use of weatherproof equipment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 weatherproof unit may be performed using generative AI, for example, or without generative AI. For example, the weather-resistant component can input user emotion data into a generating AI, which then analyzes the emotion data to adjust the frequency of use of the weather-resistant equipment.

[0092] The weatherproof unit can select the optimal equipment by referring to past weather data when using weatherproof equipment. For example, the weatherproof unit can use a generating AI to refer to past weather data and select the optimal equipment. For example, based on past weather data, the weatherproof unit can use a waterproof camera in rainy weather. For example, the weatherproof unit can also refer to past weather data to select equipment with high cold resistance on snowy days. For example, the weatherproof unit can analyze past weather data to use equipment with high wind resistance on windy days. By selecting the optimal equipment by referring to past weather data, monitoring accuracy is improved. Some or all of the above processing in the weatherproof unit may be performed using a generating AI, for example, or without a generating AI. For example, the weatherproof unit can input past weather data into a generating AI, and the generating AI can analyze the data to select the optimal equipment.

[0093] The weatherproof unit can estimate the user's emotions and adjust the placement of weatherproof equipment based on the estimated user emotions. For example, the weatherproof unit can use generative AI to estimate the user's emotions and adjust the placement of weatherproof equipment. For example, if the user is feeling anxious, the weatherproof unit will concentrate the placement of weatherproof equipment. For example, if the user is relaxed, the weatherproof unit can maintain the normal placement. For example, if the user is stressed, the weatherproof unit can concentrate the placement of weatherproof equipment in important areas. This allows for more appropriate monitoring by adjusting the placement of weatherproof equipment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 weatherproof unit may be performed using generative AI, for example, or without generative AI. For example, the weather-resistant section can input user emotion data into a generating AI, which then analyzes the emotion data to adjust the placement of weather-resistant equipment.

[0094] The weatherproof unit can enhance cooperation with ground robots and expand the monitoring range when using weatherproof equipment. For example, the weatherproof unit can use generative AI to enhance cooperation with ground robots and expand the monitoring range. For example, the weatherproof unit can have weatherproof equipment and ground robots cooperate to simultaneously monitor a wide area. For example, the weatherproof unit can eliminate blind spots by having weatherproof equipment monitor from the air and ground robots monitor from the ground. For example, the weatherproof unit can have weatherproof equipment and ground robots cooperate to respond quickly when an anomaly is detected. This expands the monitoring range by enhancing cooperation with ground robots. Some or all of the above processing in the weatherproof unit may be performed using generative AI, for example, or without generative AI. For example, the weatherproof unit can input data from weatherproof equipment and ground robots into a generative AI, which can then analyze the data to enhance cooperation.

[0095] The ground robot unit can estimate the user's emotions and adjust its patrol route based on the estimated emotions. For example, the ground robot unit can use generative AI to estimate the user's emotions and adjust its patrol route. For example, if the user is feeling anxious, the ground robot unit can frequently change its patrol route to expand its monitoring range. For example, if the user is relaxed, the ground robot unit can maintain its normal patrol route. For example, if the user is stressed, the ground robot unit can set a route that focuses on patrolling important areas. This allows for more appropriate monitoring by adjusting the ground robot's patrol route based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 ground robot unit may be performed using generative AI, or not using generative AI. For example, the ground robot unit can input user emotion data into a generating AI, which can then analyze the emotion data and adjust the patrol route.

[0096] The ground robot unit can generate an optimal patrol route by referring to past patrol data when the ground robot is in use. For example, the ground robot unit can use a generation AI to refer to past patrol data and generate an optimal patrol route. For example, the ground robot unit can generate the most effective patrol route based on past patrol data. The ground robot unit can also analyze past patrol data to optimize patrol routes for specific time periods and locations. For example, the ground robot unit can refer to past patrol data to propose new patrol routes. This improves monitoring accuracy by generating an optimal patrol route by referring to past patrol data. Some or all of the above processing in the ground robot unit may be performed using a generation AI, for example, or without a generation AI. For example, the ground robot unit can input past patrol data into a generation AI, which can then analyze the data to generate an optimal patrol route.

[0097] The ground robot unit can estimate the user's emotions and adjust the patrol frequency of the ground robot based on the estimated user emotions. For example, the ground robot unit can estimate the user's emotions using generative AI and adjust the patrol frequency of the ground robot. For example, if the user is feeling anxious, the ground robot unit can increase the patrol frequency of the ground robot. For example, if the user is relaxed, the ground robot unit can maintain the normal patrol frequency. For example, if the user is stressed, the ground robot unit can set the frequency to focus patrolling on important areas. This allows for more appropriate monitoring by adjusting the patrol frequency of the ground robot 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 ground robot unit may be performed using generative AI, for example, or without generative AI. For example, the ground robot unit can input user emotion data into a generating AI, which can then analyze the emotion data and adjust the patrol frequency.

[0098] The ground robot unit can enhance cooperation with drones and expand the monitoring range when using the ground robot. For example, the ground robot unit can use generative AI to enhance cooperation with drones and expand the monitoring range. For example, the ground robot unit and drone can work together to simultaneously monitor a wide area. For example, the ground robot unit can eliminate blind spots by having the ground robot monitor from the ground and the drone monitor from the air. For example, the ground robot unit and drone can work together to respond quickly when an anomaly is detected. This expands the monitoring range by enhancing cooperation with drones. Some or all of the above processing in the ground robot unit may be performed using generative AI, for example, or without generative AI. For example, the ground robot unit can input data from the ground robot and drone into the generative AI, which can analyze the data to enhance cooperation.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The analysis unit can improve the accuracy of detecting suspicious individuals by using audio data in conjunction with video analysis. For example, the analysis unit can improve the accuracy of detecting suspicious individuals by using a generation AI in conjunction with audio data. For example, the analysis unit can integrate video and audio data to simultaneously analyze the movements and sounds of suspicious individuals. For example, the analysis unit can use audio data to detect specific sounds (such as the sound of glass breaking) and determine if an anomaly has occurred. For example, the analysis unit can use video and audio data in conjunction to more accurately grasp the behavioral patterns of suspicious individuals. As a result, the accuracy of detecting suspicious individuals is improved by using audio data in conjunction. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input audio data into a generation AI, and the generation AI can analyze the data to detect suspicious individuals.

[0101] The management unit can optimize patrol routes by considering the drone's battery level. For example, the management unit can use a generative AI to consider the drone's battery level and optimize patrol routes. For example, the management unit can monitor the drone's battery level in real time and adjust patrol routes according to the remaining level. For example, if the battery level is low, the management unit can prioritize a route that returns to the charging station. For example, if the battery level is sufficient, the management unit can set a route that patrols a wide area. This makes efficient patrols possible by considering the drone's battery level. Some or all of the above processing in the management unit may be performed using a generative AI, or not. For example, the management unit can input drone battery level data into a generative AI, which can then analyze the data to optimize patrol routes.

[0102] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, the reporting unit can use generative AI to estimate the user's emotions and determine the priority of reports. For example, the reporting unit may increase the priority of reports if the user is feeling anxious. For example, the reporting unit may maintain the normal reporting priority if the user is relaxed. For example, the reporting unit may prioritize important reports if the user is stressed. 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, such as 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 generative AI or not. For example, the reporting unit can input user emotion data into a generative AI, which can analyze the emotion data to determine the priority of reports.

[0103] The weatherproof unit can select the optimal equipment by referring to past weather data when using weatherproof equipment. For example, the weatherproof unit can use a generating AI to refer to past weather data and select the optimal equipment. For example, based on past weather data, the weatherproof unit can use a waterproof camera in rainy weather. For example, the weatherproof unit can also refer to past weather data to select equipment with high cold resistance on snowy days. For example, the weatherproof unit can analyze past weather data to use equipment with high wind resistance on windy days. By selecting the optimal equipment by referring to past weather data, monitoring accuracy is improved. Some or all of the above processing in the weatherproof unit may be performed using a generating AI, for example, or without a generating AI. For example, the weatherproof unit can input past weather data into a generating AI, and the generating AI can analyze the data to select the optimal equipment.

[0104] The ground robot unit can estimate the user's emotions and adjust its patrol route based on the estimated emotions. For example, the ground robot unit can use generative AI to estimate the user's emotions and adjust its patrol route. For example, if the user is feeling anxious, the ground robot unit can frequently change its patrol route to expand its monitoring range. For example, if the user is relaxed, the ground robot unit can maintain its normal patrol route. For example, if the user is stressed, the ground robot unit can set a route that focuses on patrolling important areas. This allows for more appropriate monitoring by adjusting the ground robot's patrol route based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 ground robot unit may be performed using generative AI, or not using generative AI. For example, the ground robot unit can input user emotion data into a generating AI, which can then analyze the emotion data and adjust the patrol route.

[0105] The analysis unit can optimize its analysis algorithm by referring to past suspicious person data during video analysis. For example, the analysis unit can use a generative AI to refer to past suspicious person data and optimize the analysis algorithm. For example, the analysis unit can learn specific movements and behavioral patterns based on past suspicious person data and optimize the analysis algorithm. For example, the analysis unit can also improve the accuracy of suspicious person detection at specific times and locations by referring to past suspicious person data. For example, the analysis unit can analyze past suspicious person data, predict new suspicious person behavioral patterns, and adjust the analysis algorithm. This improves analysis accuracy by optimizing the analysis algorithm by referring to past suspicious person data. Some or all of the above processes in the analysis unit may be performed using a generative AI, for example, or without using a generative AI. For example, the analysis unit can input past suspicious person data into a generative AI, and the generative AI can analyze the data and optimize the analysis algorithm.

[0106] The control unit can estimate the user's emotions and adjust the drone's patrol route based on the estimated emotions. For example, the control unit can use generative AI to estimate the user's emotions and adjust the drone's patrol route. For example, if the user is feeling anxious, the control unit can frequently change the drone's patrol route and expand the surveillance area. For example, if the user is relaxed, the control unit can maintain the normal patrol route. For example, if the user is stressed, the control unit can set a route that focuses on patrolling important areas. This allows for more appropriate surveillance by adjusting the drone's patrol route based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 control unit may be performed using generative AI, for example, or without generative AI. For example, the management department can input user emotion data into a generating AI, which can then analyze the emotion data and adjust patrol routes.

[0107] The reporting unit can optimize its reporting algorithm by referring to past reporting history when a report is made. For example, the reporting unit can use a generative AI to refer to past reporting history and optimize the reporting algorithm. For example, the reporting unit can propose the most effective reporting method based on past reporting history. The reporting unit can also analyze past reporting history and optimize the reporting algorithm for specific time periods or locations. For example, the reporting unit can propose a new reporting algorithm by referring to past reporting history. This improves reporting accuracy by optimizing the reporting algorithm by referring to past reporting history. Some or all of the above processes in the reporting unit may be performed using a generative AI, or not. For example, the reporting unit can input past reporting history data into a generative AI, which can then analyze the data and optimize the reporting algorithm.

[0108] The weatherproof unit can estimate the user's emotions and adjust the frequency of use of weatherproof equipment based on the estimated user emotions. For example, the weatherproof unit can use generative AI to estimate the user's emotions and adjust the frequency of use of weatherproof equipment. For example, if the user is feeling anxious, the weatherproof unit may increase the frequency of use of weatherproof equipment. For example, if the user is relaxed, the weatherproof unit may maintain the normal frequency of use. For example, if the user is stressed, the weatherproof unit may adjust the frequency of use of weatherproof equipment to focus on monitoring important areas. This allows for more appropriate monitoring by adjusting the frequency of use of weatherproof equipment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 weatherproof unit may be performed using generative AI, for example, or without generative AI. For example, the weather-resistant component can input user emotion data into a generating AI, which then analyzes the emotion data to adjust the frequency of use of the weather-resistant equipment.

[0109] The ground robot unit can enhance cooperation with drones and expand the monitoring range when using the ground robot. For example, the ground robot unit can use generative AI to enhance cooperation with drones and expand the monitoring range. For example, the ground robot unit and drone can work together to simultaneously monitor a wide area. For example, the ground robot unit can eliminate blind spots by having the ground robot monitor from the ground and the drone monitor from the air. For example, the ground robot unit and drone can work together to respond quickly when an anomaly is detected. This expands the monitoring range by enhancing cooperation with drones. Some or all of the above processing in the ground robot unit may be performed using generative AI, for example, or without generative AI. For example, the ground robot unit can input data from the ground robot and drone into the generative AI, which can analyze the data to enhance cooperation.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The analysis unit analyzes the fixed camera footage. For example, the analysis unit analyzes the fixed camera footage in real time to detect suspicious individuals or unusual movements. The analysis unit can analyze the footage using generated AI to detect the movements of suspicious individuals. It can also perform motion detection and facial recognition using video analysis technology. Step 2: The management unit manages drone patrols based on the information analyzed by the analysis unit. For example, the management unit manages the autonomous flight of drones and patrols a wide area. The management unit can use AI to optimize the drone's flight route and achieve efficient patrols. It can also set the drone's flight altitude and patrol route to cover the area to be monitored. Step 3: The notification unit issues an alarm and reports when a drone managed by the control unit detects an anomaly. For example, when the notification unit detects an anomaly, it issues an alarm and notifies nearby residents, security companies, and the police. The notification unit uses AI to improve the accuracy of anomaly detection and enables rapid reporting. It can also customize the content of the report according to the type of anomaly and provide appropriate information.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the analysis unit, management unit, notification unit, weather-resistant unit, and ground robot unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the camera 42 of the smart device 14 and the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the communication I / F 44 of the smart device 14 and the communication I / F 26 of the data processing unit 12. The weather-resistant unit is implemented by the camera 42 of the smart device 14 and the specific processing unit 290 of the data processing unit 12. The ground robot unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the analysis unit, management unit, notification unit, weather-resistant unit, and ground robot unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the camera 42 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the communication I / F 44 of the smart glasses 214 and the communication I / F 26 of the data processing unit 12. The weather-resistant unit is implemented by the camera 42 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12. The ground robot unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the analysis unit, management unit, notification unit, weather-resistant unit, and ground robot unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the camera 42 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the communication I / F 44 of the headset terminal 314 and the communication I / F 26 of the data processing unit 12. The weather-resistant unit is implemented by the camera 42 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The ground robot unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the analysis unit, management unit, notification unit, weather-resistant unit, and ground robot unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the camera 42 of the robot 414 and the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the communication I / F 44 of the robot 414 and the communication I / F 26 of the data processing unit 12. The weather-resistant unit is implemented by the camera 42 of the robot 414 and the specific processing unit 290 of the data processing unit 12. The ground robot unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) An analysis unit that analyzes fixed camera footage, A management unit manages drone patrols based on the information analyzed by the aforementioned analysis unit, The aforementioned control unit issues an alarm and reports when it detects an abnormality in a drone managed by the control unit, Equipped with A system characterized by the following features. (Note 2) It is equipped with a weather-resistant section that uses weather-resistant equipment for monitoring during inclement weather. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a ground robot unit that uses ground robots for monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The system analyzes video footage in real time to detect suspicious individuals and unusual movements. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Manage autonomous drone flights and patrol wide areas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reporting unit, When an anomaly is detected, an alarm is issued, and the system notifies nearby residents, security companies, and the police. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During video analysis, the analysis algorithm is optimized by referring to past data on suspicious individuals. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing video footage, the analysis method is changed depending on the time of day and weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing video, integrating footage from multiple cameras improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, By using audio data in conjunction with video analysis, the accuracy of detecting suspicious individuals can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned management department, It estimates the user's emotions and adjusts the drone's patrol route based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, When managing drones, the system generates optimal patrol routes by referencing past patrol data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, When managing drones, patrol routes are changed depending on the weather and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, The system estimates the user's emotions and adjusts the drone's patrol frequency based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, When managing drones, enhance coordination with ground robots to expand the monitoring range. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, Optimize patrol routes while considering the drone's battery level. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned reporting unit, When a report is submitted, the reporting algorithm is optimized by referring to past reporting history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting unit, When reporting an issue, the content of the report will be customized according to the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned reporting unit, When reporting an incident, the scope of the report is optimized by considering the contact information of nearby residents. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, To ensure a swift response when a report is made, we will strengthen cooperation with security companies and the police. The system described in Appendix 1, characterized by the features described herein. (Note 25) The weather-resistant part is The system estimates the user's emotions and adjusts the frequency of use of weather-resistant equipment based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The weather-resistant part is When using weather-resistant equipment, select the optimal equipment by referring to past weather data. The system described in Appendix 2, characterized by the features described herein. (Note 27) The weather-resistant part is The system estimates the user's emotions and adjusts the placement of weather-resistant equipment based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The weather-resistant part is When using weather-resistant equipment, enhance coordination with ground robots to expand the monitoring range. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned ground robot unit is It estimates the user's emotions and adjusts the ground robot's patrol route based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned ground robot unit is When using ground robots, the system generates the optimal patrol route by referring to past patrol data. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned ground robot unit is The system estimates the user's emotions and adjusts the patrol frequency of the ground robots based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned ground robot unit is When using ground robots, enhance coordination with drones to expand the monitoring range. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0184] 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. An analysis unit that analyzes fixed camera footage, A management unit manages drone patrols based on the information analyzed by the aforementioned analysis unit, The aforementioned control unit issues an alarm and reports when it detects an abnormality in a drone managed by the control unit, Equipped with A system characterized by the following features.

2. It is equipped with a weather-resistant section that uses weather-resistant equipment for monitoring during inclement weather. The system according to feature 1.

3. It includes a ground robot unit that uses ground robots for monitoring. The system according to feature 1.

4. The aforementioned analysis unit, The system analyzes video footage in real time to detect suspicious individuals and unusual movements. The system according to feature 1.

5. The aforementioned management department, Manage autonomous drone flights and patrol wide areas. The system according to feature 1.

6. The aforementioned reporting unit, When an anomaly is detected, an alarm is issued, and the system notifies nearby residents, security companies, and the police. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, During video analysis, the analysis algorithm is optimized by referring to past data on suspicious individuals. The system according to feature 1.

Images