Conflict Detection for Modular Intent Classifiers

The patent applies preliminary action by performing conflict detection and resolution analysis before deploying multiple intent classifiers into production. The system evaluates potential overlaps between classifiers using test data and confusion matrices, identifying conflicting intent pairs in advance. This allows developers to resolve conflicts through retraining or intent redefinition before the classifiers are combined, preventing instability from occurring during actual operation rather than reacting to it afterward.

The patent implements self-service by enabling the system to automatically evaluate intent classifier conflicts without requiring manual intervention. The conflict detection mechanism automatically feeds test data through multiple classifiers, collects their predictions, builds confusion matrices, and identifies conflicting intent pairs autonomously. Resolution recommendations are generated automatically based on the detected conflicts, eliminating the need for developers to manually test each classifier combination and significantly reducing the time required for conflict resolution while maintaining high precision through systematic evaluation.

The patent introduces an intermediary conflict detection and resolution layer between the individual intent classifiers and the final decision-making process. This intermediary mechanism uses confusion matrices to identify which intent pairs are likely to conflict when classifiers compete. By detecting these conflicts in advance and providing resolution recommendations (such as retraining classifiers or redefining intents), the system prevents unstable competition from occurring, allowing confidence scores to remain reliable indicators of individual classifier performance without being undermined by inter-classifier conflicts.