Composite Table Header Hierarchy for Data Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automated data extraction from tables can be inaccurate due to misidentification of data lines and headers, leading to incorrect calculations and unusable results, especially in composite tables with non-uniform headers.
Innovation Solution
A computer-implemented method that transforms composite table content into structured data objects by identifying data and header zones, generating a hierarchical representation of header cells, and creating structured data objects keyed to the header cells, using cell attribute metadata and natural language processing to differentiate table data types and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated data extraction methods are used on composite tables, then data processing time is reduced, but identification accuracy of data lines and headers deteriorates
Solution Approach 1:
The patent segments the table identification process into multiple specialized components: a table detector for locating tables, a header analyzer for processing header rows, and a data extractor for retrieving data. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while maintaining automation efficiency.
Solution Approach 2:
The patent introduces an intermediary processing layer between the automated extraction system and the composite table data. This intermediary analyzes the table structure, identifies header rows through multiple criteria (formatting, content patterns, position), and creates a structured representation that guides the data extraction process, thereby improving identification accuracy without sacrificing processing speed.
2Speed
If simple automated extraction is used, then processing speed is improved, but data extraction accuracy deteriorates
Solution Approach 1:
The patent performs preliminary actions by first detecting and analyzing the table structure before extracting data. The system pre-processes the composite table to identify table boundaries, header rows, and data regions, creating a structured framework that guides subsequent extraction. This preliminary analysis ensures accurate data extraction while maintaining processing efficiency through automated preprocessing routines.
3Adaptability or versatility
If complex table structures are processed, then versatility is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal table processing system that can handle various composite table structures through a single integrated framework. The table detector and header analyzer use multiple detection criteria (formatting patterns, content analysis, positional relationships) that can adapt to different table types without requiring separate processing logic for each structure, thereby managing complexity while maintaining versatility.
Data Source
AI summary
A computer transforms content of a composite table into structured data objects. The computer receives a composite table and identifying a data zone characterized by data columns, and a header zone. The computer identifies first header cells arranged coextensive with a single data column and second header cells arranged coextensive with a set of data columns. The computer generates a hierarchical representation of said header cells, based at least in part, on the header cell arrangements. The computer generates a revised table based on the hierarchical representation, with the first header cells identifying a data column and the second header cells identify a first header cell. The computer generates structured data objects representing the zones and being arranged based, at least in part, on the revised table and where the structured data objects are keyed to the first header cells.