Property Event Detection via Activity Rhythm Analysis
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Solution Overview
Problem
Traditional property monitoring systems evaluate sensor data in isolation, failing to detect events that require patterns of related activities over time or in specific locations, leading to missed detections of expected or unexpected events.
Innovation Solution
A monitoring system that collects and analyzes activity data from various sensors, cameras, and listening devices to identify patterns of related activities, determining events based on the rhythm and flow of a property, including expected and unexpected events, and performs operations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is evaluated in isolation, then the system is simple to operate and quick to process, but event detection accuracy deteriorates due to inability to identify patterns of related activities
Solution Approach 1:
The patent combines multiple sensor data streams and activity information into a unified event detection process. The system merges data from motion sensors, contact sensors, glass-break sensors, and other monitoring components to identify patterns of related activities, thereby improving event detection accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The patent adds a temporal and contextual dimension to traditional sensor evaluation by analyzing sequences of activities over time and across different locations. Instead of evaluating single sensor events in isolation, the system examines patterns of related activities occurring in specific time periods and geographic zones, transforming the detection approach from point-in-time to pattern-based analysis.
2Reliability
If the system analyzes patterns of related activities over time and location, then event detection capability improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining patterns of related activities and their expected temporal-spatial relationships. Activity patterns, geographic zones, and time period configurations are established in advance, allowing the system to quickly match incoming sensor data against predefined patterns rather than performing complex analysis from scratch, thereby reducing processing time while maintaining detection reliability.
3Measurement precision
If the system monitors multiple sensors and activities continuously, then detection coverage improves, but energy consumption increases
Solution Approach 1:
The system implements periodic action by monitoring activities within defined time periods and geographic zones rather than continuous uninterrupted monitoring. The system evaluates sensor data in discrete time windows and spatial zones, activating full pattern analysis only when activities occur within these predefined parameters, thereby reducing overall energy consumption while maintaining comprehensive detection coverage during active monitoring periods.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a storage device, for a monitoring system that is configured to detect an event at a property. The monitoring system may include a processor and a storage device storing instructions that, when executed by the processor, cause the processor to perform operations. The operations include obtaining current activity data that (i) is generated by monitoring system components and (ii) represents two or more activities that have occurred at the property between a first time and a second time, accessing historical activity data that represents historical activities that have been learned by the monitoring system, determining, by the monitoring system and based on (i) the current activity data and (ii) the historical activity data, whether an event has been detected, and based on determining that an event has been detected, performing one or more operations based on the detected event.