Document Signature Matching for Accurate Parameter Extraction
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Solution Overview
Problem
Extracting data from unstructured documents is resource-intensive and often results in inaccurate and inconsistent outcomes due to the lack of standardized formats and manual intervention.
Innovation Solution
A system utilizing a data storage device with predefined forms and a machine-learning model to generate signatures for document analysis, enabling efficient matching and extraction of parameters through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis is used to extract data from documents, then accuracy can be maintained, but resource consumption increases and efficiency decreases
Solution Approach 1:
The system enables automatic self-service document processing by using machine-learning models to autonomously extract parameters without requiring manual analysis. The model learns from training data and automatically identifies and extracts relevant information from documents, eliminating the need for human resources while maintaining processing accuracy.
Solution Approach 2:
The patent replaces manual mechanical analysis with an automated machine-learning-based system. The machine-learning model substitutes human cognitive processing with computational algorithms that can automatically analyze document structures, identify patterns, and extract parameters efficiently at scale.
2Productivity
If automatic extraction is implemented, then productivity increases, but accuracy and consistency deteriorate
Solution Approach 1:
The system performs preliminary training of the machine-learning model on labeled training data before actual document processing. This preliminary learning phase enables the model to understand document patterns, formats, and parameter relationships, ensuring accurate and consistent extraction when processing new documents without requiring manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where extracted parameters can be validated and corrected, allowing the machine-learning model to learn from errors and improve its accuracy over time. This feedback loop ensures continuous improvement of extraction reliability while maintaining high processing speeds.
3Productivity
If machine-learning models are used for extraction, then efficiency improves, but device complexity increases
Solution Approach 1:
The patent extracts and isolates the machine-learning modeling complexity into a separate, dedicated component, while the rest of the system uses simpler processing logic. By separating the complex model inference from the overall system architecture, the system achieves efficient parameter extraction without requiring every component to handle complex computations, thus managing overall system complexity.
Data Source
AI summary
Provided are systems, methods, and computer program products for extracting parameters from documents. The system includes at least one processor programmed or configured to receive at least one document from a user device, generate at least one signature based on the at least one document, compare the at least one signature to the plurality of forms to identify at least one matching form, input the at least one document and the at least one instruction corresponding to the at least one matching form into a machine-learning model configured to extract values from the at least one document, and generate at least one user interface based on the values extracted from the at least one document.