Abstract
A reinforcement learning-based anti-false triggering onlooker detection system and method for intelligent laptops are disclosed to mitigate alert fatigue and accidental lockouts caused by dynamic environments. The system continuously captures multi-modal environmental status information via a sensor module. When the status satisfies a low-power threshold, a primary trigger processor awakens a backend reinforcement learning decision engine, which outputs a graded defense action based on a dynamic confidence threshold. Within a predefined observation window following the action, the system captures implicit user feedback to calculate a reward signal, updating policy network parameters in real time. The invention significantly improves triggering accuracy, minimizes false alarm rates, and optimizes user experience.
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Recommended Citation
INC, HP, "Reinforcement Learning-Based Anti-False Triggering Onlooker Detection System and Method", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11115