ML Classification Model for Service Request Routing
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Solution Overview
Problem
Traditional service request routing systems are slow and prone to errors, requiring front-end agents to manually direct requests to back-end agents, which increases costs and handling time while consuming computing resources.
Innovation Solution
Implementing a machine learning-based classification model using Natural Language Processing to automatically categorize and route service requests, reducing the need for manual intervention and improving routing accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual routing by front-end agents is used, then service requests can be directed to back-end agents, but routing errors increase and handling time increases
Solution Approach 1:
The system enables self-service routing where the classification model automatically categorizes and routes service requests without requiring front-end agents to manually direct requests. The model analyzes request content, determines appropriate categories, and routes to suitable back-end agents autonomously, eliminating manual intervention errors and reducing handling time.
Solution Approach 2:
The patent replaces the mechanical manual routing process with an automated machine learning-based classification system. The classification model uses natural language processing and machine learning algorithms to substitute human agents in the routing decision-making process, improving both accuracy and speed of request routing.
2Reliability
If manual routing by front-end agents is used, then requests can be directed to appropriate agents, but computing resources are consumed
Solution Approach 1:
The classification model performs self-service routing by automatically analyzing request content and determining appropriate categories and target agents. This eliminates the need for front-end agents to consume computing resources during manual routing, while the automated model efficiently processes requests using optimized machine learning algorithms.
Solution Approach 2:
The system performs preliminary classification and categorization of service requests before they reach back-end agents. The classification model pre-processes requests by analyzing content, determining categories, and identifying appropriate agents in advance, reducing the computational burden on downstream systems and agents.
3Productivity
If traditional routing systems are used, then service requests can be processed, but routing errors require recovery processes that consume additional resources
Solution Approach 1:
The classification model incorporates feedback mechanisms where routing decisions and their outcomes are continuously monitored. Successful routings reinforce the model's learning, while any routing errors or corrections provide feedback for model refinement. This feedback loop reduces future routing errors and eliminates the need for resource-intensive error recovery processes.
Solution Approach 2:
The automated classification system performs self-correction and continuous improvement by learning from routing outcomes. The model autonomously refines its classification accuracy over time, reducing routing errors without requiring manual intervention or resource-intensive recovery processes that would otherwise be needed to correct errors.
Data Source
AI summary
Techniques are described for routing service requests in a computer-implemented service environment. A received service request may be initially analyzed to determine a priority of the request. In some implementations, one or more actions may be automatically performed to provide an initial response to the requester. The text of the request may be analyzed to automatically determine a category of the request. In some implementations, a classification engine may determine the category of the request through use of a classification model that has been trained using one or more machine learning (ML) techniques and/or that employs Natural Language Processing (NLP). Based on the category, the request may be routed to agent(s) for handling. Routing may include generating a ticket that includes the request, the category, the priority, and/or other information, and the ticket may be provided to the appropriate agent(s) through a ticketing service.