Configuration Table Error Detection With Active Learning

The system enables self-service by automatically detecting and correcting configuration errors without requiring manual intervention. The error detection model autonomously analyzes configuration tables, identifies erroneous entries, and suggests corrections, allowing the system to serve itself rather than relying on continuous manual monitoring and correction by operators.

The patent replaces the mechanical manual entry process with an automated AI-based system. Instead of humans manually entering and verifying configuration data, a machine learning model automatically processes configuration tables, detects errors, and generates corrections, substituting human cognitive and manual operations with automated computational processes.

The system implements feedback by continuously monitoring configuration tables for errors and providing automated corrections. When copy-pasting creates erroneous configurations, the error detection model identifies these errors and generates feedback in the form of correction suggestions, allowing the system to learn from and correct its own mistakes rather than propagating errors silently.

The patent applies preliminary action by detecting and correcting errors before they can propagate through the system. The error detection model analyzes configuration tables proactively, identifying potential errors in copy-pasted entries before they are finalized, thereby preventing error propagation rather than reacting to errors after they have occurred.

The system achieves universality by designing a multi-functional error detection model that can handle various types of configuration errors across different equipment and systems. Rather than creating separate specialized systems for different error types, the model universally analyzes configuration tables, identifies diverse error patterns, and provides appropriate corrections, reducing overall system complexity through consolidation.

The patent introduces an intermediary error detection model that acts as a mediator between the configuration data and the users. This intermediary layer automatically analyzes configuration tables, detects errors, and provides corrections, shielding users from the complexity of error detection algorithms while maintaining high data quality. The intermediary absorbs the system complexity rather than exposing it to users.

The system applies self-service in the training process by automatically generating training data and labels without requiring expert annotation. The error detection model analyzes configuration tables, identifies errors, and creates its own training datasets, eliminating the time-consuming manual annotation process while maintaining high accuracy through automated learning from real-world configuration data.

The patent uses copying by automatically replicating training data from existing configuration tables. Instead of manually creating training datasets through expert annotation, the system copies and transforms real configuration data into training samples, preserving the natural distribution and characteristics of actual configuration errors while dramatically reducing the time and resources required for training.