Elastic Rule Engine for Smart Home Anomaly Adaptation
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Solution Overview
Problem
Current smart home systems lack flexibility in rule management, as predefined rules often fail to adapt to real-life anomalies, limiting their ability to facilitate a relaxing and dynamic home environment.
Innovation Solution
An elastic rule engine that collects and learns from user override actions and anomaly contexts, using machine learning to recommend override actions for similar anomalies and allowing user input to enhance its decision-making process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined rules are used for smart home control, then system simplicity is maintained, but adaptability to real-life anomalies deteriorates
Solution Approach 1:
The system implements feedback by monitoring user override actions and anomaly contexts, then using machine learning to automatically adjust and refine override recommendations. This closed-loop feedback mechanism enables the system to adapt to anomalies while maintaining manageable complexity through automated learning rather than manual rule configuration.
Solution Approach 2:
The elastic rule engine performs self-service by automatically learning from crowd-sourced data and user interactions to generate override recommendations. This self-learning capability allows the system to improve its adaptability to anomalies without requiring increased user intervention or system complexity.
2Extent of automation
If machine learning is implemented for anomaly detection, then automation is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and storing anomaly contexts and override actions in a structured database during normal operation. When anomalies occur, the machine learning model can quickly retrieve and analyze pre-processed data, reducing real-time processing time while maintaining high automation levels.
3Adaptability or versatility
If crowd-sourced data is collected for override recommendations, then system intelligence is improved, but data privacy concerns increase
Solution Approach 1:
The system uses an intermediary approach by collecting and processing data through a centralized machine learning model that aggregates crowd-sourced information without exposing individual user data. The model learns from aggregated patterns while maintaining user privacy, enabling system intelligence improvement without direct exposure of sensitive personal information.
Data Source
AI summary
An embodiment of this disclosure provides an apparatus including a communications unit and at least one processor. The communications unit is configured to communicate with a client device in the system. The at least one processor is coupled to the communications unit. The at least one processor configured to monitor a rule in the system. The rule controls the client device. The at least one processor is also configured to, responsive to the rule for the client device being triggered, determine whether an anomaly exists in the system. The at least one processor is also configured to, response to the anomaly existing, determine whether an override action is available for the anomaly. The at least one processor is also configured to, responsive to the override action being available, override the rule.