Iterative Document Structure Updates for Disconnected Text
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Solution Overview
Problem
Existing document understanding tools fail to accurately connect structurally and semantically disconnected text in digital documents, leading to incomplete sentences or phrases during automated document processing.
Innovation Solution
Utilize natural language processing (NLP) to identify and pair blocks with disconnected text, determining their order and consolidating them into connected sentences, iteratively updating the document structure until no disconnected text remains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If document structure analysis divides text into separate blocks for processing, then automated document processing efficiency is improved, but text connectivity and semantic continuity are lost
Solution Approach 1:
The patent segments text into discrete blocks for automated processing while maintaining their sequential relationships. Each block is independently analyzed for disconnected text, allowing parallel processing efficiency while preserving the ability to reconstruct complete semantic units by evaluating text continuity across block boundaries.
Solution Approach 2:
The patent merges adjacent text blocks when their combined content forms connected, semantically complete text. By evaluating text continuity across block boundaries and combining blocks that form complete sentences or phrases, the system restores semantic continuity that was lost during initial segmentation, thereby resolving the contradiction between processing efficiency and information preservation.
2Speed
If natural language processing is applied to each block individually, then processing speed is improved, but accuracy of text connectivity detection deteriorates
Solution Approach 1:
The patent applies NLP selectively rather than uniformly to all blocks. It focuses computational resources on evaluating text continuity at block boundaries where disconnection is most likely to occur, rather than performing exhaustive NLP analysis on every single block. This partial application of NLP maintains processing speed while achieving sufficient accuracy for detecting text connectivity issues.
Solution Approach 2:
The patent performs preliminary evaluation of text continuity by examining the relationship between adjacent blocks before applying full NLP analysis. This preliminary action identifies candidate blocks that may contain disconnected text, allowing the system to focus more intensive NLP processing only where needed, thereby maintaining speed while improving accuracy of connectivity detection.
3Measurement precision
If multiple iterations of block pairing and consolidation are performed, then text connectivity accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent implements an iterative feedback mechanism where each consolidation iteration uses the results of previous iterations to guide subsequent processing. The system continuously evaluates whether text blocks are properly connected, consolidates disconnected blocks, and re-evaluates the document structure until convergence is achieved or a maximum iteration limit is reached. This feedback-driven approach improves text connectivity accuracy while controlling computational complexity through early termination conditions.
Data Source
AI summary
A system and method iteratively update a determined structure of a digital document file to remediate disconnected text in blocks of the determined structure. In embodiments, a method includes determining a structure of a digital document file using a document understanding analysis, the structure including blocks of elements having text information; determining for each of the blocks of the digital document file whether text information in the block is disconnected; determining an order of the blocks in the digital document file; pairing two blocks from a list of blocks with disconnected text information to form a block pair, wherein the two blocks are ordered based on the determined order of the blocks; determining that the text information of the block pair forms a complete sentence using natural language processing; and consolidating the block pair to form a new block.