Ensemble ML Framework for Automated Request Escalation
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Solution Overview
Problem
Conventional request processing systems face challenges in efficiently and accurately escalating user requests due to difficulties in tracking user sentiment, especially in textual interactions, leading to inefficient use of specialized agents and increased processing time.
Innovation Solution
An ensemble machine learning framework is implemented, aggregating interaction data to compute a weighted score based on keywords and sentiment prediction, using a boosting machine learning algorithm to determine anomalous requests and initiate automated actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional request processing systems manually track user sentiment to determine escalation needs, then accuracy in identifying escalation candidates may be maintained, but processing time increases and efficiency decreases
Solution Approach 1:
The patent replaces manual sentiment tracking with automated machine learning models that analyze interaction data. The ensemble framework uses multiple ML models to predict sentiment and escalation needs, substituting human analysis with computational algorithms that process data faster and more consistently.
Solution Approach 2:
The system enables automated self-assessment of escalation needs through ML models that independently analyze interaction patterns and predict escalation probability. The framework autonomously identifies requests requiring escalation without manual intervention, reducing processing time while maintaining accuracy.
2Reliability
If specialized agents are deployed to handle escalated requests, then request resolution quality improves, but system complexity and operational costs increase
Solution Approach 1:
The patent implements preliminary classification of requests using ML models that predict escalation needs before requests reach specialized agents. By pre-identifying and routing high-priority requests, the system reduces the burden on specialized agents and optimizes their utilization without requiring additional human resources.
Solution Approach 2:
The ensemble ML framework acts as an intermediary layer between general agents and specialized agents. It analyzes interaction data, predicts escalation probability, and automatically routes requests, serving as a mediator that reduces direct interaction between general agents and specialized agents, thereby simplifying system operations.
3Measurement precision
If manual review processes are used to determine request escalation, then control and accuracy are maintained, but productivity and response speed decrease
Solution Approach 1:
The patent transforms the escalation determination process by changing parameters from manual evaluation criteria to automated ML model predictions. The ensemble framework uses multiple prediction models that output escalation probabilities, enabling high-speed automated decision-making that maintains accuracy while dramatically increasing processing throughput.
Solution Approach 2:
Manual review processes are replaced with automated machine learning-based escalation determination. The system uses ML models to analyze interaction data and predict escalation needs, substituting human review with computational processes that operate faster and handle larger volumes of requests.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automated request processing using an ensemble machine learning framework are provided herein. An example computer-implemented method includes aggregating interaction data associated with a request; computing a weighted score for the request, wherein the weighted score comprises a first component that is based at least in part on a comparison of the aggregated interaction data to a set of keywords and a second component corresponding to a sentiment predicted by a first machine learning model for at least a portion of the aggregated interaction data; using a second machine learning model to determine whether the request is anomalous based at least in part on the weighted score; and in response to determining that the request is anomalous, initiating one or more automated actions for the request.