ML-Based Help Desk Channel Routing and Bias Reduction
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Solution Overview
Problem
Current help desk systems fail to provide a personalized and efficient support experience across multiple channels, leading to inconsistent user satisfaction and increased resolution times due to inadequate channel selection and bias in routing algorithms.
Innovation Solution
A computer-implemented method that trains machine learning models to analyze customer and IT feedback, cluster users and issues, and employ genetic algorithms to dynamically route users to the best-suited attendance channels, reducing bias through random routing and feedback-based reinforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional routing algorithms are used to route users to help desk channels, then the system structure is simple, but user satisfaction is inconsistent and resolution times increase due to bias in routing algorithms
Solution Approach 1:
The patent replaces traditional mechanical routing algorithms with machine learning models that analyze customer profiles, issue types, and historical data to dynamically determine optimal help desk channels. This substitution eliminates bias in traditional algorithms and provides consistent, data-driven routing decisions that improve user satisfaction while accepting increased system complexity through ML infrastructure.
Solution Approach 2:
The system changes routing parameters from static, rule-based criteria to dynamic parameters derived from machine learning predictions. These parameters include customer sentiment scores, issue complexity assessments, and channel performance metrics that continuously adapt based on feedback data, enabling consistent and optimized routing decisions across diverse scenarios.
2Productivity
If machine learning models are trained to optimize channel selection, then user experience is enhanced and resolution rates improve, but data processing requirements and model training complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on historical help desk data before deployment. Customer profiles, issue classifications, and channel performance data are processed in advance to build trained models that can quickly make routing decisions during operation. This preliminary training phase, while complex, enables fast, accurate predictions that improve resolution rates without adding complexity to real-time operations.
Solution Approach 2:
The machine learning models perform self-service by automatically learning optimal routing patterns from historical data without requiring manual configuration or tuning. The models self-train on feedback loops from resolved issues, continuously improving their predictions while reducing the need for manual model maintenance and simplifying long-term operational complexity.
3Measurement precision
If feedback metrics are analyzed and datasets are augmented with cluster centroids, then model accuracy improves, but data preprocessing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by performing data augmentation with cluster centroids during the offline model training phase rather than during real-time operations. Customer feedback data is collected, clustered, and transformed into centroid representations in advance, creating pre-processed feature sets that accelerate real-time predictions. This upfront investment in data preparation reduces preprocessing time during actual help desk operations.
Solution Approach 2:
The system uses copying by creating simplified representations (cluster centroids) that capture the essential characteristics of large groups of similar customers or issues. Instead of processing individual detailed records during real-time routing, the system copies and uses aggregated centroid data that preserves predictive accuracy while dramatically reducing computational requirements and preprocessing time for individual routing decisions.
4Reliability
If random routing is implemented to avoid bias, then model bias is reduced, but the efficiency of directing users to optimal channels decreases
Solution Approach 1:
The patent applies local quality by implementing different routing strategies for different user contexts. Rather than uniform random routing or uniform optimized routing, the system uses machine learning to determine the appropriate routing approach for each specific customer-issue combination. For well-understood patterns, optimized routing is used; for uncertain cases, more exploratory approaches including elements of random routing are applied, achieving both fairness and efficiency locally adapted to each situation.
Data Source
AI summary
Computer-implemented methods of training machine learning models and using the machine learning models for tailoring a multi-channel help desk environment. One or more computers train a machine learning model of selecting best attendance channels for respective customer clusters and for respective issue clusters. One or more computers train machine learning models of tailoring respective attendance channel types. One or more computers employ the machine learning models to determine a best attendance channel for resolving an information technology problem of a user and to predict channel tailoring characteristics for the best attendance channel. One or more computers employ genetic algorithm operators to determine a random attendance channel with random tailoring characteristics. One or more computer use random routing to route the user to one of the best attendance channel and the random attendance channel, avoiding undesired bias favorable toward the best attendance channel.