Automated Feedstock Identification via Image Annotation

The patent replaces manual mechanical data entry with automated optical character recognition (OCR) technology. The system captures images of feedstock and automatically extracts and records data, eliminating the need for manual typing while maintaining recording flexibility through configurable data fields.

The system creates digital copies of physical feedstock through image capture and stores them in a database. These digital copies can be repeatedly analyzed, shared, and processed without additional time cost, unlike manual entry which must be repeated for each analysis.

The patent replaces error-prone manual data entry with automated image recognition and data extraction systems. The OCR technology and automated processing eliminate human errors such as typos and wrong entries, significantly improving data reliability.

The system implements automated feedback mechanisms where captured images are immediately processed and validated against predefined criteria. Any anomalies or errors are automatically flagged for review, creating a closed-loop system that continuously improves data accuracy.

The patent segments data into structured fields with predefined categories and data types. Each feedstock attribute is stored in a specific, standardized format rather than as unstructured free-text, enabling automated processing while maintaining full data accessibility through the digital storage system.

The system transforms unstructured free-field entries into structured data with defined parameters and constraints. By changing the data organization from flexible text fields to structured databases with predefined schemas, the system enables statistical evaluation and automated analysis while preserving data accessibility.

The patent replaces manual picture management with automated image processing and recognition systems. The system automatically detects, tags, and categorizes contaminants in feedstock images using computer vision algorithms, eliminating the need for manual annotation while preserving storage flexibility through digital file management.

The system creates annotated digital copies of original feedstock images with automatically generated metadata tags identifying contaminants. These enriched digital copies retain the original image quality while adding structured information layers that enable automated analysis without compromising storage flexibility.