Dynamic Automation Threshold Adjustment for Accuracy Control
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Solution Overview
Problem
Automation systems face challenges in maintaining accuracy without upfront manually labeled data and preset automation thresholds that may be too high, leading to reduced automated responses and inefficiency.
Innovation Solution
A computer-implemented method that adjusts automation thresholds dynamically based on a target accuracy level by computing classification scores, generating a suggestion list, and monitoring feedback to create a historical performance dataset, allowing for continuous adjustment of automation levels without additional human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preset automation thresholds are set high to ensure accuracy, then automation reliability is improved, but productivity decreases due to reduced automated responses
Solution Approach 1:
The patent implements dynamic threshold adjustment where automation thresholds are no longer fixed but adapt continuously based on real-time performance feedback. The system monitors actual accuracy metrics and automatically modifies threshold levels to optimize the balance between maintaining reliability and maximizing productivity, allowing thresholds to be higher when accuracy is strong and lower when performance dips.
Solution Approach 2:
The system incorporates a feedback loop that continuously monitors automation performance and uses this information to adjust thresholds. By tracking actual accuracy against target accuracy levels and automatically modifying thresholds in response to performance variations, the system maintains reliability while capturing additional automated response opportunities that would otherwise be missed with static high thresholds.
2Productivity
If preset automation thresholds are set low to increase automated responses, then productivity is improved, but automation reliability deteriorates with more errors
Solution Approach 1:
Rather than using fixed low thresholds that sacrifice accuracy, the system employs dynamic thresholds that adapt to current performance conditions. This allows the system to operate with more aggressive (lower) thresholds when performance is strong, capturing additional automated responses, while automatically raising thresholds when accuracy dips, thus preventing error accumulation.
Solution Approach 2:
The feedback mechanism continuously monitors accuracy metrics and adjusts thresholds to prevent reliability deterioration. When the system detects accuracy falling below target levels, it automatically increases thresholds to reduce false positives, thereby maintaining productivity gains while preventing error rates from escalating.
3Reliability
If manually labeled data is collected upfront to improve accuracy, then automation reliability is improved, but loss of time increases due to preparatory training phase
Solution Approach 1:
The system performs preliminary actions by collecting performance feedback data during normal operation rather than requiring a separate upfront training phase. By gathering labeled data from actual automated responses and their outcomes, the system builds its accuracy foundation concurrently with deployment, eliminating the time-consuming preparatory training stage while maintaining reliability improvement.
Solution Approach 2:
The system maintains continuous useful action by collecting performance data and improving accuracy throughout operation rather than pausing for an initial training phase. The feedback collection and threshold adjustment processes run continuously, allowing the system to improve reliability incrementally over time without interrupting productivity or requiring a separate training period.
4Productivity
If automation threshold is adjusted frequently to optimize performance, then productivity is improved, but device complexity increases
Solution Approach 1:
The system implements self-service by automatically adjusting thresholds based on its own performance feedback without requiring external intervention or complex manual configuration. The automated threshold adjustment mechanism monitors its own accuracy metrics and modifies thresholds autonomously, reducing the perceived complexity for users while maintaining high productivity through continuous optimization.
Data Source
AI summary
A computer-implemented method, system and computer program product for maintaining a target accuracy level. A target accuracy level is received. Thresholds including ongoing adjustable automation thresholds for categories are computed based on the target accuracy level. Data is received and a classification score for the categories is generated with respect to the data based on a category knowledgebase. Furthermore, a classification score is detected for a category with a higher classification score than other categories of the plurality of categories that exceeds an ongoing adjustable automation threshold. A reply to the data is automatically sent out based on the category with the higher classification score. The action, the suggestion list, and corresponding received feedback are monitored to generate a historical performance dataset. An actual accuracy level is then determined based on the historical performance dataset. The ongoing adjustable automation threshold is then adjusted based on the actual accuracy level.