ML Clustering for IT Ticket Categorization

The system enables self-service automated categorization where the machine learning model independently processes and categorizes issue tickets without human intervention. The model learns from historical data and automatically assigns categories, eliminating the need for manual categorization while maintaining high accuracy through continuous learning and feedback mechanisms.

The patent replaces the mechanical manual categorization process with an automated machine learning system. The ML model processes tickets using algorithms and patterns learned from data, substituting human cognitive processes with computational processes that can handle large volumes of tickets rapidly and consistently.

The system performs preliminary actions by pre-processing and cleaning issue ticket data before training the machine learning model. Historical data is prepared in advance with standardized formats, removed duplicates, and organized structures that reduce complexity during the actual categorization process. This upfront preparation simplifies the runtime operations.

The patent introduces intermediary components such as data pre-processing layers, feature extraction modules, and model training pipelines that mediate between raw input data and the core categorization function. These intermediaries break down complex tasks into manageable stages, reducing overall system complexity by organizing the flow of data and operations.

The system applies local quality by implementing data cleaning and normalization specific to each data source and ticket type. Different preprocessing rules are applied locally to different sections of the data pipeline, addressing inconsistencies at their source rather than attempting uniform processing. This allows the system to handle diverse data formats and quality levels effectively.

The patent employs parameter changes by transforming raw data into standardized formats through feature extraction and normalization. The system adjusts data parameters such as text encoding, category mappings, and data structures to create consistent input for the machine learning model, converting unreliable raw data into reliable processed features.

The system implements periodic action by performing full re-clusterization at scheduled intervals rather than continuously. Between these periodic full updates, the system applies incremental updates that only process new or changed data. This approach maintains dynamic adaptability while significantly reducing average computational processing time compared to continuous re-clusterization.

The patent applies dynamics by making the clustering system adaptable to changing conditions through incremental learning and adaptive parameter adjustment. The model can dynamically adjust to new issue types and patterns without requiring complete retraining, allowing real-time adaptability with minimal computational overhead through selective updates based on data drift detection.