AI Document Extraction With Classification and Data Normalization
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Solution Overview
Problem
Existing data extraction systems struggle with unstructured documents due to variability in format, mixed content types, and context-dependent information, leading to inefficiencies, inconsistencies, and computational intensity, particularly in managing large volumes of documents like contracts and invoices.
Innovation Solution
A system utilizing AI-driven document classification, NLP, and machine learning to classify documents, extract data elements, normalize them into a canonical format, and compare attributes across different client accounts, supporting integration with external systems and human review for accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction processes are used, then data extraction can be performed with human judgment, but substantial human effort is required and inconsistencies are introduced
Solution Approach 1:
The patent replaces manual mechanical extraction processes with an automated AI-based system that uses machine learning models to extract, classify, and normalize data from unstructured documents. This substitution eliminates human effort while maintaining high accuracy through intelligent algorithms that learn from training data and adapt to different document formats.
Solution Approach 2:
The system enables self-service data extraction by automatically processing documents without human intervention. The AI models independently identify, extract, and normalize data elements from various unstructured document formats, allowing the system to serve itself in handling large volumes of documents efficiently and consistently.
2Productivity
If rule-based extraction methods are used, then extraction processes can be automated, but flexibility to adapt to diverse document layouts is lost
Solution Approach 1:
The patent implements dynamic extraction methods where AI models can adapt their behavior based on the characteristics of each document. The system dynamically adjusts extraction strategies by analyzing document layouts, identifying patterns, and modifying extraction parameters in real-time, providing both automation and adaptability to diverse document formats.
Solution Approach 2:
The system changes extraction parameters dynamically based on document type and layout characteristics. Different AI models or model configurations are selected based on the detected document format, and extraction parameters such as field mappings, normalization rules, and processing depth are adjusted to optimize performance for each specific document type.
3Quantity of substance
If conventional extraction systems are used, then processing can be performed, but scalability for large volumes of documents is limited
Solution Approach 1:
The patent segments the document processing system into modular components including document ingestion, classification, extraction, normalization, and output generation. Each component can be independently scaled and optimized, allowing the system to handle large volumes of documents efficiently while managing complexity through modular architecture and distributed processing.
Data Source
AI summary
Systems and methods for automated data extraction of unstructured documents can include includes a processor coupled with memory, and configured to receive, from a client device, an electronic document corresponding to a client account and identify, from a plurality of classifications, a classification for at least a portion of the electronic document. Based on the classification, the processor can select a set of prompts for artificial intelligence models according to an extraction plan specifying the prompts and mapping rules to extract data elements from the document. The processor can transform the extracted data elements to map to predefined sets, establish relationships between normalized entities, and compare attributes of the electronic document with attributes of documents corresponding to different client accounts. The system can generate, based on comparisons, a parameter for an extracted data element and provide, for display on the client device, the parameter as a grade, score, or recommendation.