Stochastic Detection Threshold Adjustment
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Solution Overview
Problem
Detection systems for computing devices face challenges in minimizing false acceptance and rejection errors, which negatively impact user experience due to operational thresholds that are not optimized for varying environmental conditions and user interactions.
Innovation Solution
A stochastic dynamical model is used to adjust detection thresholds based on probabilistic states and costs associated with false acceptance and rejection errors, incorporating user expectations and environmental conditions to optimize detection system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed operational threshold is used for detection, then the system operation is simple, but false acceptance and false rejection errors increase under varying environmental conditions
Solution Approach 1:
The patent implements dynamic threshold adjustment by transitioning from a fixed operational threshold to a time-varying threshold that adapts to changing environmental conditions and user interaction patterns. The detection threshold becomes a dynamic parameter that evolves over time based on observed false acceptance and false rejection rates, allowing the system to maintain high detection accuracy across varying conditions without manual intervention.
Solution Approach 2:
The patent changes the detection threshold parameter from a static value to a dynamic value that varies with environmental conditions and system performance metrics. By continuously adjusting the threshold parameter based on observed error rates and contextual factors, the system optimizes detection accuracy while adapting to changing operating conditions.
2Reliability
If the detection threshold is lowered to reduce false rejections, then user communication responsiveness improves, but false acceptances increase
Solution Approach 1:
The patent dynamically adjusts the detection threshold parameter based on the relative costs of false acceptances versus false rejections. When false rejection rates are high, the threshold is lowered to improve responsiveness; when false acceptance rates increase, the threshold is raised to reduce errors. This continuous parameter adjustment optimizes the trade-off between the two error types.
Solution Approach 2:
The patent implements feedback mechanisms that monitor false acceptance and false rejection rates in real-time and use this information to adjust the detection threshold. The system continuously learns from its performance and adapts the threshold to minimize overall error rates, creating a closed-loop control system that responds to actual system behavior.
3Reliability
If the detection threshold is raised to reduce false acceptances, then detection precision improves, but false rejections increase
Solution Approach 1:
The patent dynamically adjusts the detection threshold parameter based on the relative costs of false acceptances versus false rejections. When false acceptance rates are high, the threshold is raised to improve precision; when false rejection rates increase, the threshold is lowered to improve responsiveness. This continuous parameter adjustment optimizes the trade-off between the two error types.
4Reliability
If environmental conditions are not considered in threshold setting, then system operation is simple, but detection performance degrades under varying conditions
Solution Approach 1:
The patent adjusts the detection threshold parameter based on environmental conditions such as background noise levels, lighting conditions, and user behavior patterns. By incorporating environmental factors into threshold determination, the system maintains high detection performance across varying conditions without requiring complex manual configuration.
Solution Approach 2:
The patent implements self-service functionality where the detection system automatically adapts to environmental conditions and adjusts its own threshold parameters without external intervention. The system monitors environmental factors and autonomously optimizes its detection performance, reducing the need for manual configuration and maintenance.
Data Source
AI summary
Features are disclosed for modeling user interaction with a detection system using a stochastic dynamical model in order to determine or adjust detection thresholds. The model may incorporate numerous features, such as the probability of false rejection and false acceptance of a user utterance and the cost associated with each potential action. The model may determine or adjust detection thresholds so as to minimize the occurrence of false acceptances and false rejections while preserving other desirable characteristics. The model may further incorporate background and speaker statistics. Adjustments to the model or other operation parameters can be implemented based on the model, user statistics, and/or additional data.