Adaptive Metric Control for Call Center Feedback
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Solution Overview
Problem
Conventional communication system metric controls suffer from rarity and uniformity issues, leading to sparse and noisy feedback, which limits their effectiveness in adapting agent and system behavior, particularly in call centers where efficient resource allocation and quality assurance are critical.
Innovation Solution
A communication system utilizing a combination of audio signal processing, natural language processing, machine learning models, and heuristic algorithms to generate adaptive metric controls, providing continuous feedback and improving call processing efficiency by transforming call classifiers into weighted sub-metrics and applying them as feedback signals to adapt call flow and agent behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional metric controls use random and low-frequency sampling of inputs, then the system complexity is reduced, but the feedback precision and reliability deteriorate due to sparse signals
Solution Approach 1:
The patent implements continuous feedback by processing every call input through the metric control system rather than random sampling. The system continuously transforms call classifiers into metric controls that provide ongoing feedback for adapting agent and system behavior, eliminating the sparsity problem while maintaining manageable complexity through automated processing.
Solution Approach 2:
The patent replaces manual quality assurance audits with automated machine learning models that process call data. This substitution of mechanical human review with automated computational systems enables high-volume continuous processing without proportionally increasing system complexity, thereby improving feedback precision while keeping the system manageable.
2Measurement precision
If conventional metric controls rely on manual quality assurance audits and human survey results, then the measurement accuracy is improved, but the productivity and response time deteriorate due to cost ineffectiveness and time consumption
Solution Approach 1:
The patent replaces manual quality assurance processes with automated machine learning models that analyze call data. This substitution maintains measurement accuracy by using sophisticated algorithms to evaluate call quality while dramatically improving productivity by processing all calls automatically without human time constraints.
Solution Approach 2:
The system performs self-evaluation by automatically generating metric controls from call data without requiring external human auditing. The machine learning models autonomously process calls, transform classifiers into metrics, and provide feedback, eliminating the need for separate manual quality assurance operations.
3Device complexity
If traditional metric controls use singular measurements, then the device complexity is reduced, but the reliability and accuracy deteriorate due to noisy and bimodal distributions
Solution Approach 1:
The patent segments the metric control generation process into multiple components: call classification, transformation to sub-metrics, aggregation, and normalization. This segmentation allows the system to process multiple dimensions of call data and combine them into a comprehensive metric control, improving reliability while keeping each individual processing step manageable in complexity.
Data Source
AI summary
An alert generator in a communication system for processing a call includes at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configure to operate on the call. Heuristic logic is configured to transform the call classifiers into a plurality of weighted sub-metrics for the call, and aggregate normalized Gaussian logic is configured to transform the weighted sub-metrics into a metric control. A threshold analyzer is configured to generate an alert signal to the communication system based on the metric control meeting a condition.