Automated Document Information Extraction System
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Solution Overview
Problem
Organizations face inefficiencies and inaccuracies in extracting information from received documents, as manual data entry is often required and different teams use varying processes.
Innovation Solution
A method and system for extracting information from documents, involving receiving a document, extracting data, categorizing the document based on the extracted data, and generating a structured output, which can include using optical character readers and machine learning algorithms for data extraction and categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry is used to extract information from documents, then flexibility in handling different document types is maintained, but productivity is reduced and errors increase
Solution Approach 1:
The system enables self-service by allowing documents to automatically undergo data extraction, categorization, and routing without human intervention. The machine learning models autonomously process documents, extract relevant information, determine categories, and route to appropriate queues, eliminating the need for manual data entry while maintaining high accuracy through automated validation mechanisms.
Solution Approach 2:
The patent replaces the mechanical manual data entry process with an automated information extraction system using optical character recognition (OCR), machine learning algorithms, and natural language processing. This substitution transforms the manual typing and copying process into an automated digital extraction and classification workflow, significantly improving productivity while reducing errors.
2Manufacturing precision
If different teams use their own processes to review and extract information from documents, then each team can optimize for their specific needs, but manufacturing precision deteriorates due to inconsistent processes
Solution Approach 1:
The system provides a universal document processing platform that can handle multiple document types and serve multiple teams simultaneously. The machine learning models are trained on diverse document categories and can be configured to meet different team requirements through parameter adjustments rather than process changes, ensuring consistent accuracy across all teams while maintaining adaptability to specific needs.
Solution Approach 2:
The patent enables teams to optimize extraction accuracy by adjusting parameters such as confidence thresholds, extraction fields, and categorization criteria within the unified system. Rather than using different processes, teams can modify system parameters to suit their specific requirements, maintaining manufacturing precision through consistent underlying technology while achieving team-specific optimization.
3Ease of operation
If multiple document formats are accepted for submission, then ease of operation is improved for customers, but device complexity increases for the processing system
Solution Approach 1:
The system introduces an intermediary preprocessing layer that receives documents in various formats (PDF, images, Word documents) and automatically converts them into a standardized internal representation. This intermediary step handles format-specific processing requirements, allowing the core extraction engine to work with a uniform data structure, thereby maintaining ease of operation for customers while managing system complexity through abstraction.
4Productivity
If automated information extraction is implemented, then productivity is improved, but reliability may worsen due to potential extraction errors
Solution Approach 1:
The system implements feedback mechanisms where extracted data is validated against expected patterns, document structure, and business rules. Confidence scores are generated for each extraction, and low-confidence results are automatically routed for review or re-extraction. This feedback loop continuously improves reliability by identifying and correcting extraction errors while maintaining high productivity through automated processing of high-confidence cases.
Data Source
AI summary
A method and computing apparatus for extracting information from a document are provided. The method includes receiving a document, extracting data from the document, assigning the document to a category from among a predetermined plurality of categories based on a result of the extracted data, and generating a structured output by formatting the extracted data based on the assigned category.