Adaptive Call Routing via ML-Generated Metric Feedback
Find Innovative SolutionsGenerate Solutions
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
1Measurement precision
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 precision and reliability are worsened due to sparse signals
Solution Approach 1:
The patent implements continuous feedback by processing all or a much larger percentage of call inputs through audio signal processing and machine learning models, transforming call classifiers into weighted sub-metrics that provide dense, actionable feedback signals for adapting agent and system behavior, eliminating the sparse feedback problem of conventional sampling
Solution Approach 2:
The patent replaces manual quality assurance audits and human survey results with automated audio signal processing, natural language processing, and machine learning models that continuously analyze call data, transforming the feedback generation mechanism from labor-intensive sampling to automated continuous processing
2Productivity
If conventional metric controls rely on human listening and manual feedback, then the measurement process is simple and easy to operate, but the productivity and time efficiency are worsened
Solution Approach 1:
The system performs self-service by automatically processing call audio through signal processing and machine learning models to generate metric controls and performance comparisons without human intervention, enabling continuous automated feedback generation that significantly increases productivity and eliminates time-consuming manual analysis
Solution Approach 2:
The patent substitutes manual human listening and feedback provision with automated audio signal processing, natural language processing, and machine learning-based call classification systems that continuously generate performance metrics, transforming the feedback process from slow manual operations to rapid automated processing
3Reliability
If conventional metric controls use singular measurements, then the device complexity is reduced, but the reliability and accuracy are worsened due to noisy and bimodal data
Solution Approach 1:
The patent merges multiple call classifiers and performance metrics into a unified metric control framework that processes audio signal data, natural language data, and machine learning outputs simultaneously, combining multiple measurement dimensions to produce reliable, non-bimodal performance comparisons that eliminate the noise of singular measurements
Solution Approach 2:
The patent creates composite metric controls by integrating results from audio signal processing, natural language processing, and multiple machine learning models into a unified performance assessment, forming a composite measurement that is more reliable and less noisy than any single measurement source
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.