Security Event Graphs with Natural Language Descriptors
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Solution Overview
Problem
Current home security systems face challenges in accurately identifying and reporting security events due to misidentification of objects and threats, overlapping security perimeters, and inadequate alert systems that fail to convey complex security situations effectively.
Innovation Solution
A system that monitors and captures data from various sources, classifies moving objects, builds event graphs, and generates natural language descriptors for security events using sensors, camera vehicles, and smart home devices, allowing for verbal, visual, or conversational notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors and camera vehicles are deployed to monitor security perimeters, then detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The system divides the security monitoring function into separate sensor types (motion sensors, camera vehicles, smart home devices) and locations (perimeter, interior, external areas). Each sensor type handles specific detection tasks, and the system integrates their data through centralized processing that classifies events and builds comprehensive security pictures without requiring every sensor to do everything.
Solution Approach 2:
The system creates a universal security monitoring platform where different sensor types (PIR, vibration, light, laser, ultrasonic, seismic, radar) and device categories (camera vehicles, smart home devices) all feed into a common event processing architecture. The event graph and classification system serve as universal interfaces that handle diverse data types uniformly, reducing overall system complexity.
2Loss of information
If traditional alert tones are used to notify security events, then notification delivery is simple, but the ability to convey complex security situations is insufficient
Solution Approach 1:
The system transitions from one-dimensional alert tones to multi-dimensional notification delivery including visual displays, text messages, voice announcements, and conversational interfaces. The natural language generation component adds a linguistic dimension that can describe complex security situations in detail while remaining easily consumable by users through various output channels.
Solution Approach 2:
The system introduces natural language generation as an intermediary between the complex event data and the end user. This intermediary translates detailed security event information into human-friendly descriptions that convey complete situational context while maintaining ease of consumption through various notification channels.
3Area of stationary object
If security perimeters are expanded to cover larger areas, then security coverage is improved, but overlapping perimeters with neighboring properties cause misidentification
Solution Approach 1:
The system applies different detection strategies and classification criteria to different local areas within the security perimeter. Each zone (exterior, interior, perimeter boundaries) has tailored event processing that considers local context, and the system can distinguish between events in different locations even when perimeters overlap with neighboring properties.
Solution Approach 2:
The system dynamically adjusts security perimeter definitions and event classification based on real-time context and spatial relationships. When perimeters overlap with neighboring properties, the system can adapt its detection algorithms to distinguish between legitimate intrusions and false alarms by analyzing movement patterns, timing, and spatial trajectories.
4Loss of information
If multiple alert tones are used to notify different security events, then event differentiation is improved, but the multiplicity and complex relations between diverse security situations cannot be adequately reflected
Solution Approach 1:
Instead of creating multiple distinct alert tones for different security events, the system uses a single unified notification channel that delivers differentiated information through natural language descriptions. Each security event type receives the same notification format but with customized content that clearly identifies the event type, location, and contextual details, eliminating the need for complex multi-tone systems.
Data Source
AI summary
Detecting security events and generating corresponding natural language descriptors includes monitoring an area to capture data corresponding to moving objects in the area, classifying the moving objects, generating events based on classifying the moving objects, building an event graph by connecting related ones of the events, using the event graph to detect security events, and building natural language activity descriptors for the security events of the event graph using natural language templates to convert the security events to natural language. The natural language security descriptors may be presented using a verbal request to a voice-enabled assistant, a mandatory notification by the voice-enabled assistant, periodic reports and/or conversational style notifications in a visual format. Data may be captured using sensors, video streams from at least one camera vehicle, smart home devices, presence detection mechanisms, and/or weather data/forecasts. The sensors may include PIR, vibration, light, laser, ultrasonic, seismic, and/or radar sensors.