Adaptive Call Processing Control With Continuous ML Feedback
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Solution Overview
Problem
Conventional communication system metrics for call centers suffer from rarity and uniformity of input sampling, leading to sparse and noisy feedback, which limits the accuracy and responsiveness of system adaptations.
Innovation Solution
A communication system utilizing audio signal processing, natural language processing, and machine learning models generates adaptive metric controls through an ensemble of programmatic statistical models, providing continuous feedback for improved call processing efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional metric controls use random and low-frequency sampling of inputs, then the system complexity is reduced and ease of operation is improved, but the feedback signal becomes sparse and the measurement precision deteriorates
Solution Approach 1:
The system transitions from random, low-frequency sampling to continuous monitoring and analysis of all call inputs. The automated ensemble of programmatic statistical models processes every call in real-time, providing continuous feedback rather than intermittent measurements, thereby maintaining measurement precision while automating the operation
Solution Approach 2:
The patent replaces manual human listening and feedback provision with automated audio signal processing, natural language processing, and machine learning models. This substitution eliminates the need for human operators to manually sample calls, allowing continuous analysis of all inputs without increasing operational complexity
2Measurement precision
If all inputs are processed to generate adaptive controls, then the measurement precision and responsiveness are improved, but the device complexity and computational resources increase
Solution Approach 1:
The system segments the processing of call inputs by dividing them into distinct categories (e.g., customer service calls, technical support calls, sales calls) and applying specialized programmatic statistical models to each category. This segmentation allows precise analysis of all inputs while managing complexity through organized, modular processing approaches
Solution Approach 2:
The patent creates a universal automated feedback system that handles multiple types of calls and metrics through a single ensemble of programmatic statistical models. This multi-functional system processes diverse call types using the same core architecture, reducing overall device complexity compared to having separate systems for each call type
3Productivity
If conventional systems rely on human listening and manual feedback, then the device complexity is reduced, but the productivity and responsiveness of the system deteriorate
Solution Approach 1:
The system replaces manual human listening and feedback provision with automated audio signal processing, natural language processing, and machine learning models. This substitution eliminates the need for human operators to manually sample calls, allowing continuous analysis of all inputs without increasing operational complexity
Solution Approach 2:
The system enables self-service by having the call processing system automatically generate its own feedback and adaptive controls without requiring external human intervention. The ensemble of programmatic statistical models continuously monitors call outcomes and automatically adjusts processing parameters, improving productivity while managing complexity through automation
Data Source
AI summary
A communication system for processing a call includes control logic and at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor operated on the call. Heuristic logic transforms the call classifiers into weighted sub-metrics for the call, and aggregate normalized Gaussian logic transforms the weighted sub-metrics into a metric control that may be applied as a feedback signal to adapt the operation of the control logic. The control logic in turn may adapt the behavior of an agent, automated voice attendant, or a template utilized in a call flow.