US-12627685-B1 - Method and system for AI-based autonomous security agent

US12627685B1US 12627685 B1US12627685 B1US 12627685B1US-12627685-B1

Abstract

A system for an automated processing of security data including a processor of a Home Security Agent (HAS) node configured to host a machine learning (ML) module and connected to security data capture array and to at least one controller of a drone and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire security data from the security data capture array entity reflecting a threat subject extract a set of classifying features reflecting positioning and movements of the threat subject relative to the protected home; generate a feature vector based on the set of classifying features; receive a plurality of security parameters from a security predictive model generated by the ML module using outputs of the ANN based on the classifier feature vector; and generate a threat subject verification verdict based on the classifier feature vector.

Inventors

Joseph Dominic Natoli, III
Samantha Marie Sutterley-Natoli

Assignees

Joseph Dominic Natoli, III
Samantha Marie Sutterley-Natoli

Dates

Publication Date: 20260512
Application Date: 20251003

Claims (20)

1 . A system for an automated processing of security data, comprising: a processor of a Home Security Agent (HAS) node configured to host a machine learning (ML) module and connected to security data capture array and to at least one controller of a drone over a wireless network connection; and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire security data from the security data capture array entity reflecting a threat subject in a vicinity of a protected home, the security data comprising a plurality of signals; normalize the plurality of signals for depth consistency; extract a set of classifying features reflecting positioning and movements of the threat subject relative to the protected home from the normalized signal; generate at least one classifier feature vector based on the set of classifying features; provide the at least one classifier feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of security parameters from a security predictive model generated by the ML module using outputs of the ANN based on the at least one classifier feature vector; generate a threat subject verification verdict based on the at least one classifier feature vector; generate a control command for operation of the drone based on the threat subject verification verdict; and send the control command to a target controller of the drone.
2 . The system of claim 1 , wherein the machine-readable instructions that when executed by the processor, cause the processor to record the plurality of security parameters along with the threat subject verification verdict on a permissioned blockchain for creation of a security audit log.
3 . The system of claim 1 , wherein the machine-readable instructions that when executed by the processor, cause the processor to derive the set of classifying features comprising any of: presence of the threat subject within a range from the protected home, a speed of movement of the threat subject, a trajectory of the movement of the threat subject, a distance from the of the threat subject to at least one entry point of the protected home, and an orientation of the threat subject relative to the at least one entry point of the protected home.
4 . The system of claim 2 , wherein the machine-readable instructions that when executed by the processor, cause the processor to continually track the distance from the threat subject to the at least one entry point to produce the threat subject verification verdict responsive to the distance reaching a pre-set threshold distance value.
5 . The system of claim 3 , wherein the machine-readable instructions that when executed by the processor, cause the processor to send the control command for activation of the drone responsive to the distance reaching a pre-set threshold distance value.
6 . The system of claim 4 , wherein the machine-readable instructions that when executed by the processor, cause the processor to acquire additional security data from the drone.
7 . The system of claim 5 , wherein the machine-readable instructions that when executed by the processor, cause the processor to generate the at least one classifier feature vector based on the set of classifying features and the additional sensory data acquired from the drone.
8 . The system of claim 6 , wherein the machine-readable instructions that when executed by the processor, cause the processor to adjust the threshold distance based on outputs of the security predictive model generated based on the speed of movement of the threat subject and the trajectory of movement of the threat subject.
9 . The system of claim 6 , wherein the machine-readable instructions that when executed by the processor, cause the processor to increase the threshold distance value based on at least one security parameters generated by the ML module based on the speed of movement of the threat subject and the trajectory of the movement of the threat subject combined with the orientation of the threat subject relative to the at least one entry point.
10 . The system of claim 1 , wherein the machine-readable instructions that when executed by the processor, cause the processor to continuously monitor the threat subject by the security data capture array to provide an updated current set of classifying features to generate the at least one classifier feature vector to be ingested into the ML module configured to generate an updated set of security parameters for generation of an updated threat subject verification verdict in real-time.
11 . The system of claim 10 , wherein the machine-readable instructions that when executed by the processor, cause the processor to generate the at least one classifier feature vector based on the set of classifying features and the local remote historical property security data combined with the remote historical property security data.
12 . The system of claim 1 , wherein the machine-readable instructions that when executed by the processor, cause the processor to retrieve local and remote historical property security data from at least one local and at least one remote database based on the set of classifying features, wherein the remote historical data is collected at remote locations employing the security data capture array for collecting security data.
13 . A method for an automated processing of security data, comprising: acquiring, by a Home Security Agent (HAS) node configured to host a machine learning (ML) module, security data from a security data capture array entity reflecting a threat subject in a vicinity of a protected home, the security data comprising a plurality of signals; normalizing, by the HAS node, the plurality of signals for depth consistency; extracting, by the HAS node, a set of classifying features reflecting positioning and movements of the threat subject relative to a protected home from the normalized signal; generating, by the HAS node, at least one classifier feature vector based on the set of classifying features; providing, by the HAS node, the at least one classifier feature vector to the ML module coupled to an Artificial Neural Network (ANN); receiving, by the HAS node, a plurality of security parameters from a security predictive model generated by the ML module using outputs of the ANN based on the at least one classifier feature vector; generating, by the HAS node, a threat subject verification verdict based on the at least one classifier feature vector; generating, by the HAS node, a control command for operation of the drone based on the threat subject verification verdict; and sending, by the HAS node, the control command to a target controller of the drone.
14 . The method of claim 13 , further comprising recording the plurality of security parameters along with the threat subject verification verdict on a permissioned d blockchain for creation of a security audit log.
15 . The method of claim 14 , further comprising sending the control command for activation of the drone responsive to the updated threat subject verification verdict.
16 . The method of claim 13 , further comprising deriving the set of classifying features comprising any of: presence of the threat subject within a range from the protected home, a speed of movement of the threat subject, a trajectory of the movement of the threat subject, a distance from the of the threat subject to at least one entry point of the protected home, and an orientation of the threat subject relative to the at least one entry point of the protected home.
17 . The method of claim 16 , further comprising acquiring additional security data from the drone.
18 . The method of claim 13 , further comprising continually tracking the distance from the threat subject to the at least one entry point to produce an updated threat subject verification verdict responsive to the distance reaching a pre-set threshold distance value.
19 . The method of claim 18 , further comprising continuously monitoring the threat subject by the security data capture array to provide an updated current set of classifying features to generate the at least one classifier feature vector to be ingested into the ML module configured to generate an updated set of security parameters for generation of an updated threat subject verification verdict in real-time.
20 . A non-transitory computer-readable medium comprising instructions, that when read by a processor, cause the processor to perform: acquiring security data from a security data capture array entity reflecting a threat subject in a vicinity of a house, the security data comprising a plurality of signals; normalizing the plurality of signals for depth consistency; extracting a set of classifying features reflecting positioning and movements of the threat subject relative to a protected home from the normalized signal; generating at least one classifier feature vector based on the set of classifying features; providing the at least one classifier feature vector to a machine learning (ML) module coupled to an Artificial Neural Network (ANN); receiving a plurality of security parameters from a security predictive model generated by the ML module using outputs of the ANN based on the at least one classifier feature vector; generating a threat subject verification verdict based on the at least one classifier feature vector; and generating a control command for operation of the drone based on the threat subject verification verdict; and sending the control command to a target controller of the drone.

Description

FIELD OF DISCLOSURE The present disclosure generally relates to security data processing, and more particularly, to an AI-based automated autonomous security system for processing of sensory data for activation of security measures. BACKGROUND The process of remote controlling of connected equipment such as automatic doors by analysis of security video and/or motion detection data is commonly used in homes and Building Management Systems (BMSs). The existing BMS systems have very limited operational ranges and heavily depend on a single camera or motion detector locations. Thus, these systems provided for a limited security within a smart home, commercial building and/or broader living environments, including amenity spaces. For example, US 2009/0121861 A1 discloses a security system used to detect, deter, and/or document security events and information from a secured area. The security system may include an intelligence system for distinguishing between false alarms and actual alarm events. The security system may also include multiple deter devices actionable upon detection of an actual alarm event. The security system may further include a security event data collection system that collects event or alarm data pertaining to security events either onsite or remote and a video/camera system that records images of security events. As another example, US 2007/0120978 A1 discloses an intelligent surveillance platform is provided with wireless two-way sensory surveillance in environments wherein the entire surveillance unit and its supporting structure fit within a standard light switch junction box and otherwise also functions as a light switch. The platform serves as a sensory ‘edge-of-network’ subsystem for use with a local controller, a central data center engine, and a central viewing platform. The edge-of-network sensory element includes sensors, intelligence and transceivers housed in a wall enclosure under a modified light switch faceplate that incorporates a built-in antenna. The sensors may include an embedded camera, microphone, passive infrared heat detector and odor detector. The intelligence includes memory and logic controller. The transceiver has two-way audio and at least one video channel for broadcast. Light switch wiring supplies power to the device, obviating the need for any specialist installation. Are-chargeable battery device built into the unit enables the device to operate maintenance free without the need to replace any parts over the expected life of the battery. U.S. Pat. No. 11,109,229 B2 discloses implementing security for a network environment using a centralized smart security system. For example, a method includes implementing a network comprising a plurality of network devices which collectively generate data that is utilized by a computing system to execute an application, and implementing a centralized security system as a computing node within the network to manage security operations within the network and to establish secured and trusted communications between the network devices and the computing system. The network devices may comprise wireless sensor devices operating in a wireless sensor network, wherein the computing system executes an IoT (Internet of Things) application which processes the data that is generated by the wireless sensor devices. However, these references do not use sensors to operate or initiate drones, smart lights, additional video recording, etc. based on predictive analytics performed by AI/ML learning module. This reference also does not use AI models running on the autonomous security device. Accordingly, a system and method for an AI-based automated autonomous security system for processing of sensory data for activation of security measures are desired. BRIEF OVERVIEW This brief overview is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This brief overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this brief overview intended to be used to limit the claimed subject matter's scope. One embodiment of the present disclosure provides a system for an automated processing of security data including a processor of a Home Security Agent (HAS) node configured to host a machine learning (ML) module and connected to security data capture array and to at least one controller of a drone over a wireless network connection and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire security data from the security data capture array entity reflecting a threat subject in a vicinity of a protected home, the security data including a plurality of signals; normalize the plurality of signals for depth consistency; extract a set of classifying features reflecting positioning and movements of the threat subject relative to the protected home from the nor