AI-Based Ticket Routing System for Dynamic Endpoint Matching

The patent implements dynamic routing by using machine learning models that continuously learn from communication patterns and adapt routing decisions in real-time. The system transitions from static pre-defined routing rules to dynamic adaptive routing that responds to changing conditions such as agent availability, communication topic trends, and channel capabilities, thereby improving routing accuracy without requiring proportional increases in system complexity.

The patent introduces an intelligent routing intermediary layer that sits between the communication endpoint and the agent. This intermediary uses trained machine learning models to analyze communication content, determine user intent, and select optimal routing targets. The intermediary handles the complexity of adaptive routing algorithms while presenting a simplified interface to both users and agents, effectively decoupling routing intelligence from system complexity.

The patent applies preliminary action by pre-training machine learning models offline using historical communication data before deploying them to the routing system. The models are trained in advance to recognize communication patterns, user intents, and optimal routing decisions. This pre-training phase separates the complex learning process from the real-time routing operation, allowing the system to achieve high adaptability during runtime without the complexity of real-time model training and retraining.

The patent uses copying by deploying replicated instances of trained machine learning models across the routing infrastructure. Instead of implementing a single complex centralized routing brain, the system creates multiple copies of the routing intelligence that can independently make decisions. This distribution of routing logic through model copies improves adaptability across different communication channels while managing system complexity through modular replication.

The patent implements self-service by enabling the routing system to automatically analyze communication content, determine user intent, and select routing targets without human intervention. The machine learning models process communications autonomously, extracting relevant features and making routing decisions based on learned patterns. This self-service capability eliminates manual routing overhead and significantly improves response time, while the efficient algorithms keep resource consumption manageable.

The patent replaces manual mechanical routing processes with automated electronic machine learning-based routing. Instead of human operators manually analyzing communications and making routing decisions (a slow, resource-intensive process), the system uses trained ML models that process communications electronically at high speed. This substitution dramatically improves productivity and response time while the automated nature of ML inference actually reduces operational resource consumption compared to manual processes.