Machine Learning Support Ticket Assignment System

The patent introduces an automated assignment system that acts as an intermediary between support tickets and agents. This system uses machine learning models to analyze ticket characteristics and agent skills, automatically making assignment decisions that would otherwise require manual human judgment. The intermediary handles the complex matching process efficiently while preserving the quality of discretion through algorithmic decision-making.

The patent replaces the mechanical manual assignment process with an automated computational system. Instead of human queue managers manually evaluating and assigning tickets, the system uses machine learning algorithms to automatically match tickets with appropriate agents based on multiple factors including skill sets, current workload, and ticket complexity. This substitution dramatically improves assignment efficiency while maintaining or enhancing matching quality.

The patent implements a self-service automated assignment system that monitors and manages ticket distribution without requiring human intervention. The machine learning model continuously analyzes ticket incoming patterns, agent availability, and skill matching requirements, automatically making assignment decisions. This self-service approach maintains reliable control over the assignment process while eliminating the need for complex human-managed centralized queue systems.

The patent transforms the assignment control mechanism by changing from manual parameter evaluation to automated computational analysis. The system evaluates multiple parameters simultaneously (ticket priority, agent skills, current workload, historical performance) using machine learning algorithms, making the control process more reliable but computationally simpler than human-managed systems. This parameter-based automated control reduces operational complexity while improving assignment quality.

The patent applies local quality by tailoring ticket assignment to the specific characteristics of each ticket and the unique skills of individual agents. Instead of a uniform first-come-first-served approach, the system analyzes the specific requirements of each ticket (problem type, complexity, priority) and matches it with the most suitable agent's specific skill set. This localized matching approach maintains fast assignment speeds while dramatically improving resolution quality by ensuring each ticket goes to the most competent agent.

The patent performs preliminary analysis of ticket characteristics and agent capabilities before making assignment decisions. The machine learning model pre-processes ticket information, identifies key requirements, and evaluates agent skill sets in advance, enabling rapid yet accurate assignments. This preliminary action allows the system to maintain the speed advantage of automated systems while achieving the quality improvement of expert-matched assignments, avoiding the pitfalls of both manual processes and simple first-come-first-served approaches.