Table Data Extraction and Mapping to Bookkeeping Fields
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Solution Overview
Problem
Existing technologies fail to accurately extract information from tables, particularly line item tables, in documents, leading to inaccuracies in calculations and resource wastage, and require extensive manual user input.
Innovation Solution
A system that automatically detects and parses structural elements like rows and columns from tables, uses similarity scores to map data objects, and performs operations only when similarity thresholds are met, reducing manual input and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning models process natural language characters in documents, then semantic meaning of words can be determined, but information extraction from tables fails and extensive manual user input is required
Solution Approach 1:
The patent segments the document processing task by specifically targeting table structures within documents. Instead of treating the entire document as unstructured text, the system identifies and extracts tabular data as a distinct component, applying specialized processing to map table cells to bookkeeping document fields, thereby improving information extraction accuracy while reducing manual intervention.
Solution Approach 2:
The patent introduces an intermediary mapping layer between table data and bookkeeping documents. This intermediary process automatically translates tabular information into structured bookkeeping entries using field mapping techniques, eliminating the need for manual user input while maintaining high extraction accuracy.
2Reliability
If existing technologies process all document data through natural language processing, then comprehensive text analysis is achieved, but computational resources are unnecessarily consumed
Solution Approach 1:
The patent extracts table structures from documents as a separate processing target. By identifying and isolating tabular data, the system applies specialized extraction techniques only to relevant portions rather than processing entire documents through general NLP pipelines, thereby reducing computational resource consumption while maintaining processing completeness for critical data.
Solution Approach 2:
The patent applies different processing qualities to different document portions. Table data receives specialized structural extraction and mapping processing, while other document portions use standard NLP approaches. This localized quality differentiation optimizes resource allocation by applying intensive processing only where necessary for accurate bookkeeping data extraction.
3Measurement precision
If existing machine learning models process entire documents, then all text is analyzed, but latency increases and resource consumption rises
Solution Approach 1:
The patent extracts and prioritizes table data from documents for immediate processing. By identifying tabular structures and extracting their content separately, the system focuses computational resources on the most critical data elements that require high extraction accuracy, thereby reducing overall processing latency while maintaining precision for bookkeeping-relevant information.
Data Source
AI summary
The accuracy of existing machine learning models, software technologies, and computers are improved by using one or more machine learning models to map data inside structural elements, such as rows or columns, as found within a document to data objects of other documents, where the data objects are at least partially indicative of candidate categories that the data can belong to.