Electronic Loan Document Tagging via AI Object Detection
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Solution Overview
Problem
Existing systems face challenges in electronically enabling loan and mortgage documents, particularly with PDFs, as they often require manual intervention to add metadata and configure for electronic signing, and image-based PDFs restrict content extraction and insertion.
Innovation Solution
A method involving OCR, object detection, and metadata tagging to convert documents into images, detect keywords, and associate electronic tags with object fields, enabling electronic transactions by determining object fields and creating metadata for interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to add metadata and configure documents for electronic signing, then document preparation accuracy is improved, but processing time and labor requirements increase
Solution Approach 1:
The system performs self-service by automatically detecting document types, extracting text using OCR, identifying object fields, and adding metadata without requiring manual intervention. The automated pipeline processes documents independently, eliminating the need for manual configuration while maintaining accuracy through algorithmic decision-making.
Solution Approach 2:
The patent replaces manual mechanical operations with automated computational processes. Instead of manually adding metadata and configuring documents, the system uses optical character recognition, machine learning models, and automated scripting to perform these tasks, substituting human labor with digital processing mechanisms.
2Loss of information
If documents are converted to images for processing, then content extraction capability is improved, but document interactivity and editability deteriorate
Solution Approach 1:
The system segments the document processing into distinct stages: converting to image format for analysis, extracting text and identifying fields, then separately creating an interactive PDF output. This segmentation allows each stage to optimize for its specific function without compromising the others.
Solution Approach 2:
The patent creates a copy of the original document in image format for processing and analysis, while preserving the original interactive PDF. The image copy enables content extraction and field identification without affecting the interactivity of the original document, which remains editable and signable.
3Productivity
If automated detection algorithms are used to identify object fields, then processing speed is improved, but detection accuracy may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the automated detection algorithms continuously learn from and adapt to document patterns. The machine learning models are trained on labeled data and provide feedback during the detection process, improving accuracy over time while maintaining high processing speeds through automated iterations.
Solution Approach 2:
The patent employs parameter changes by adjusting detection thresholds, confidence levels, and algorithm parameters to optimize both speed and accuracy. The system can modify detection sensitivity and processing parameters dynamically to balance between rapid processing and high accuracy based on document complexity and requirements.
Data Source
AI summary
The system prepares PDF documents to be digitally populated or signed. The method may comprise converting, by a processor, a document into an image; detecting, by the processor using an artificial intelligence engine, words on the document; searching, by the processor, the words for keywords; searching, by the processor using the artificial intelligence engine, for an object on the document; determining, by the processor, an object field based on the keywords and the object; creating, by the processor, a tag with metadata about a type of the tag and the object field; associating, by the processor, the tag with the object field; and enabling, by the processor using the metadata, interaction with the object field.