Automated Document Ingestion System for Data Extraction Accuracy
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Solution Overview
Problem
Document ingestion is a manual and time-consuming process, especially in dynamic environments with varying document formats, requiring an efficient automation solution to streamline data extraction and processing.
Innovation Solution
The Automated Document Ingestion (ADI) system integrates machine learning models and tools to automate document ingestion, comprising an Annotation machine for generating labeled data, Document enhancement for preprocessing, an Augmented data entry UI for efficient data entry, and a Machine learning operations pipeline for model training and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual document ingestion is used, then data extraction accuracy can be maintained through human judgment, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical data extraction with an automated system combining optical character recognition (OCR) technology and machine learning models. The OCR engine converts document images to text, and trained models extract specific data fields automatically, eliminating the need for manual reading and entry while maintaining high accuracy through algorithmic pattern recognition.
Solution Approach 2:
The system enables documents to be processed autonomously without human intervention. The machine learning models self-adjust and improve through continuous training on annotated data, and the automated pipeline independently performs extraction, validation, and loading operations, reducing dependency on manual labor while sustaining extraction precision.
2Productivity
If automated document ingestion is implemented, then processing speed increases, but difficulty arises with varying document formats
Solution Approach 1:
The patent creates a universal document ingestion system that handles multiple document formats through a single automated pipeline. The machine learning models are trained on diverse document types and can adapt to various layouts, making the system multi-functional capable of processing different formats while maintaining high processing speed through automation.
Solution Approach 2:
The system dynamically adapts to different document formats through flexible machine learning models that can adjust to varying layouts and structures. The automated pipeline dynamically routes documents based on format detection, and the models learn from annotated data to handle format variations, enabling both high speed and format versatility.
3Extent of automation
If machine learning models are used for automation, then manual effort is reduced, but system complexity increases
Solution Approach 1:
The patent segments the document ingestion system into distinct modular components: OCR engine for text extraction, machine learning models for data extraction, annotated data for training, and an automated pipeline for orchestration. This segmentation enables high automation while managing complexity through clear separation of concerns and independent trainable modules.
Data Source
AI summary
Automated document ingestion (ADI) provides a comprehensive system and method to streamline document ingestion automation through developing, deploying, and monitoring machine learning models and tools. The system is designed to integrate alongside existing manual entry pipelines within a company. ADI has multiple components to accomplish each step of this task, namely document enhancements, an augmented data entry user interface, and a machine learning operations (ML Ops) pipeline.