Automated Complexity Model for Support Ticket Prioritization
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Solution Overview
Problem
Existing customer support systems lack the ability to accurately assess the complexity of support tickets, leading to inefficient prioritization and increased response times, which can impact customer satisfaction.
Innovation Solution
An automated method and system that extracts data from historical support tickets to generate training data, trains a complexity model to predict task complexity, and adjusts initial complexity values based on similarity and reporting time of similar tickets, enabling more accurate prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional support ticket systems are used without complexity assessment, then the system structure remains simple, but prioritization accuracy deteriorates and response times increase
Solution Approach 1:
The patent replaces manual complexity assessment with an automated machine learning model that processes support ticket data. The complexity model uses natural language processing and vectorization techniques to automatically evaluate ticket complexity, substituting human judgment with computational analysis to improve measurement precision while managing system complexity through automation.
Solution Approach 2:
The patent introduces a complexity model as an intermediary component between support ticket intake and prioritization. This model acts as a mediator that analyzes ticket characteristics, extracts features, and generates complexity scores that inform prioritization decisions, thereby improving assessment accuracy without requiring complete system redesign.
2Productivity
If manual complexity assessment is used, then system implementation remains simple, but productivity deteriorates due to increased response times
Solution Approach 1:
The patent implements a self-service complexity assessment system where the machine learning model automatically evaluates support tickets without requiring manual intervention. The system extracts features, generates complexity scores, and supports automated prioritization independently, eliminating time-consuming manual assessment and improving overall processing efficiency.
Solution Approach 2:
The patent performs complexity assessment as a preliminary action immediately when support tickets are created or received. By evaluating complexity upfront and using these scores for prioritization before tickets enter the full processing queue, the system reduces waiting time and ensures complex issues are addressed promptly, thereby improving productivity and reducing response time loss.
3Measurement precision
If no complexity model is implemented, then data processing requirements remain low, but prioritization accuracy deteriorates leading to inefficient resource allocation
Solution Approach 1:
The patent transforms support ticket data into structured features through vectorization and normalization, changing the parameters from unstructured text to quantifiable numerical representations. This parameter transformation enables the complexity model to process and compare tickets systematically, improving prioritization accuracy by converting qualitative assessment needs into quantitative measurements that can be automatically evaluated.
Data Source
AI summary
An automated method for determining a complexity of a task. The method includes extracting data from the plurality of historical support tickets to generate training data. The method trains a complexity model to predict a complexity value of a task associated with a support ticket using the training data. The method predicts, using the complexity model, the complexity value of a new task associated with a new support ticket.