AI Data Quality Filtering via Graph Analysis

The patent applies preliminary action by performing quality assessment and filtering of training data before it is used to train the AI model. The system evaluates data quality metrics and removes low-quality or malicious data points in advance, ensuring that only high-quality data reaches the training process. This prevents contamination of the model with poor-quality data while still allowing extensive use of open-sourced and crowd-sourced data sources.

The patent introduces an intermediary quality assessment mechanism that acts as a mediator between raw training data and the AI model training process. This intermediary layer evaluates data quality using multiple metrics and filters data accordingly, allowing the system to maintain both large data quantity and high data quality by selectively passing only approved data points to the training process.

The patent applies segmentation by dividing the quality assessment process into distinct modular components: data collection, quality metric evaluation, filtering decisions, and training execution. Each component handles a specific aspect of quality control independently, making the overall complex process more manageable and maintainable while effectively improving data quality.

The patent uses parameter changes by implementing multiple adjustable quality metrics and thresholds that can be configured based on specific training requirements. The system evaluates data against various parameters (completeness, consistency, source reliability) and applies filtering based on configurable threshold values, allowing flexible control over quality standards without hardcoding complex filtering logic.