Interpreting presence signals using historical data
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Solution Overview
Problem
Monitoring systems face challenges in accurately determining user presence at home based on presence signals, as they struggle to differentiate between users leaving or staying at home, leading to potential false alerts and inefficient resource management.
Innovation Solution
The implementation of a monitoring system that interprets presence signals using historical data patterns, analyzing sensor data from various sources to determine user activity and adjust responses accordingly, such as sending alerts or controlling HVAC systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If monitoring systems use presence signals to determine user presence, then security alert functionality is provided, but false alerts occur due to inability to differentiate between users leaving or staying at home
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical presence data before making presence determination decisions. It establishes baseline patterns of user behavior through prior observations, then uses these pre-established patterns to accurately interpret current presence signals and differentiate between users leaving versus staying at home, thereby reducing false alerts.
2Reliability
If monitoring systems send alerts based on presence signals, then security monitoring is provided, but resource management becomes inefficient due to inaccurate presence determination
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring presence signals and comparing them against historical patterns. It uses this feedback loop to refine its understanding of user behavior, adjust its presence determination logic, and optimize resource management decisions. The system learns from past outcomes and adjusts its alerting and resource allocation strategies accordingly, improving both accuracy and efficiency over time.
Data Source
AI summary
A method includes obtaining historical event data for events detected over a past period of time by sensors within a property, receiving a set of current event data for one or more events detected by one or more of the sensors within the property, determining that the set of current event data matches a pattern of events indicated by the historical event data, generating, based on the pattern of events, a confidence score for the set of current event data, wherein the confidence scores reflects a confidence that a person is not within the property, determining that the confidence score satisfies a confidence threshold associated with an action to be performed when a person is not within the property, and triggering execution of the action.