Security Gateway Camera Triggering for False Alarm Reduction
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Solution Overview
Problem
Traditional home security systems often trigger false alarms due to minor disturbances, leading to user disregard and delayed responses, and fail to capture event details like intruder identities.
Innovation Solution
A smart home security system with a security gateway that utilizes artificial intelligence to analyze data from various detection devices, determining the validity of triggering events and customizing models based on user behavior to reduce false alarms and enhance event capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the hub triggers an alarm based on a single detection device signal, then the response speed is fast, but the false alarm rate increases
Solution Approach 1:
The patent combines signals from multiple detection devices (cameras, motion sensors, smoke detectors) to trigger alarms. The hub aggregates data from these devices and uses AI analysis to determine whether an alarm is warranted, reducing false alarms while maintaining rapid response to genuine threats.
Solution Approach 2:
The system implements feedback loops where the hub continuously monitors detection device signals, analyzes them using AI models, and adjusts alarm triggering based on patterns and contextual information. This feedback mechanism helps distinguish between genuine threats and false alarms.
2Loss of information
If cameras are activated for all detection events, then event detail capture is complete, but energy consumption increases
Solution Approach 1:
Instead of activating cameras for all detection events, the system uses partial action by selectively activating cameras only when AI analysis of other sensor data indicates a potential threat requiring visual documentation. This reduces energy consumption while capturing essential event details.
Solution Approach 2:
The hub performs preliminary analysis using AI models on data from motion sensors and other detectors before activating cameras. This preliminary action determines whether camera activation is necessary, preventing unnecessary energy consumption while ensuring event details are captured when needed.
3Reliability
If multiple detection devices are integrated with AI models, then false alarms are reduced, but device complexity increases
Solution Approach 1:
The hub serves multiple functions: it receives signals from various detection devices, performs AI analysis, makes alarm decisions, and controls camera activation. This multi-functionality consolidates complexity into a single central device rather than distributing it across multiple components.
Solution Approach 2:
The hub acts as an intermediary between detection devices and the alarm system. It processes raw sensor data through AI models, translates this into meaningful threat assessments, and coordinates the alarm response. This intermediary role manages system complexity by centralizing the decision-making logic.
Data Source
AI summary
Techniques for improving alarm event detection for security systems are described herein. For example, a security system can receive a first set of data associated with an event in an environment from a first detection device. The security system can instruct a second detection device to detect a second set of data for the environment based on the first set of data. The security system can determine an alarm status for the event based at least in part on a condition of the event output by an artificial intelligence model. The artificial intelligence model can output the condition responsive to receiving an input including the first set of data and the second set of data. The alarm status can include a false alarm event or an alarm event. The security system can output an alarm indicating the event based on determining that the alarm status is an alarm event.