Automated Document Feature Modeling and Extraction
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Solution Overview
Problem
Current electronic document processing systems face challenges in accurately extracting and analyzing information from scanned documents, with typical accuracy around 50%, leading to difficulties in fulfilling contractual obligations and generating business insights, due to manual and time-intensive processes involving human analysts and specialized programming.
Innovation Solution
A user-customizable system for automated document feature modeling and extraction allows users to create and edit data models and extraction rules through a graphical interface, streamlining the process and eliminating the need for human analysts and programming skills, enabling faster template adoption and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes involving human analysts and specialized programming are used, then document feature extraction can be performed, but the process is time-intensive and slow
Solution Approach 1:
The system enables automated self-service document feature extraction using machine learning models that automatically identify and extract relevant information from documents without requiring manual human analysts or specialized programming, thereby significantly reducing processing time and increasing productivity
Solution Approach 2:
The patent replaces manual mechanical processes (human analysts reviewing documents) with automated electronic systems using machine learning and natural language processing algorithms, transforming the extraction process from labor-intensive to computation-intensive, which dramatically improves speed and efficiency
2Productivity
If automated feature extraction is implemented, then processing speed improves, but accuracy remains limited to around 50%
Solution Approach 1:
The system dynamically adjusts extraction parameters and model configurations based on document characteristics and feedback from verification processes, optimizing the balance between processing speed and extraction accuracy for different document types and contexts
Solution Approach 2:
The patent incorporates feedback loops where extraction results are verified and validated, with performance metrics fed back into the system to continuously improve model accuracy and reduce errors while maintaining high processing speeds through iterative optimization
3Adaptability or versatility
If custom data models are created to improve extraction accuracy, then the system becomes more adaptable, but the complexity of model creation and maintenance increases
Solution Approach 1:
The system employs universal data model templates and pre-configured extraction patterns that can be applied across multiple document types and use cases, reducing the need for complex custom models while maintaining adaptability through parameter customization rather than structural complexity
Data Source
AI summary
Provided herein are systems and methods for user-defined automated document feature modeling, extraction and optimization. In the present disclosure, an end user of an automated document review system can customize and create new data models applicable to a set of focus documents. In addition, an end user of the automated document review system can customize and create new extraction rules applicable to text extraction from the set of focus documents. The user-defined edits to the data model and extraction rules can be further tested in a staging environment, and tested against a ground truth set of documents, before being widely applied to other relevant documents.