Bipartite Table Auto-Completion with Local Global Models
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Solution Overview
Problem
Existing image analysis and feature extraction technologies face challenges in automatically and accurately extracting table data from diverse and complex documents in enterprise computing environments, often requiring significant human intervention due to varying document formats, layouts, and contents.
Innovation Solution
A bipartite application with a client module and a server module uses continuous machine learning through local and global models to analyze and extract table item information, where the client module learns from minimal user feedback and adapts on-the-fly, while the server module leverages this knowledge to improve global model performance for automated extraction across multiple documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional image analysis and feature extraction technologies are used to extract table data from documents, then the extraction process can be automated, but the accuracy and completeness of extraction deteriorates due to varying document formats, layouts, and contents
Solution Approach 1:
The patent divides the table extraction task into multiple segments: (1) table detection phase where potential table regions are identified, (2) table structure analysis phase where rows and columns are detected, and (3) data extraction phase where actual table data is captured. This segmentation allows each phase to be optimized independently, improving overall accuracy while maintaining automation.
Solution Approach 2:
The patent implements dynamic adaptation mechanisms where the extraction system learns from document patterns and adjusts its parameters based on the specific document type being processed. The system dynamically modifies extraction thresholds, table structure assumptions, and data validation rules according to the detected document format, thereby maintaining high accuracy across diverse document types.
2Device complexity
If traditional image analysis technologies are used for table extraction, then processing can be performed with simple systems, but the extraction accuracy deteriorates for complex and diverse document formats
Solution Approach 1:
The patent incorporates feedback mechanisms where extraction results are validated against expected table structures and document patterns. When anomalies are detected, the system adjusts its processing parameters and re-attempts extraction. This feedback loop enables the system to adapt to diverse document formats without requiring complex manual configuration for each format type.
Solution Approach 2:
The patent performs preliminary document analysis before table extraction, including document type classification, layout analysis, and pattern recognition. This preliminary action prepares the extraction system with format-specific knowledge in advance, enabling it to handle diverse document formats effectively while keeping the actual extraction logic relatively simple.
3Reliability
If manual intervention is increased to improve extraction accuracy for diverse documents, then extraction completeness improves, but productivity deteriorates due to significant human intervention required
Solution Approach 1:
The patent implements self-service capabilities where the extraction system automatically validates extracted data, detects extraction confidence levels, and performs self-correction when issues are identified. The system autonomously handles routine validation and correction tasks, reserving manual intervention only for edge cases, thereby maintaining high completeness without sacrificing productivity.
Solution Approach 2:
The patent applies partial manual intervention strategically - using automated extraction for the majority of documents while triggering manual review only when confidence thresholds are not met or anomalies are detected. This selective approach ensures high extraction completeness for critical cases while maintaining overall processing efficiency across large volumes of documents.
Data Source
AI summary
A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as one negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.