Semantic Document Comparison via Tokenization and Noise Filtering
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Solution Overview
Problem
Existing difference algorithms fail to provide humane and usable comparisons between documents, as they focus on low-level character edits rather than semantic differences, making it difficult for users to understand patterns and consistency of changes, and are not designed to handle multiple documents or ignore irrelevant changes.
Innovation Solution
A computer-implemented method that normalizes and tokenizes content from documents, compares the tokenized content to identify semantic differences, and generates a report that highlights meaningful changes while excluding predefined irrelevant differences, using a combination of parsers, lexers, regular expressions, and heuristics to facilitate logical comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing difference algorithms are used to compare documents, then character-level differences can be identified, but the comparison results are not humane and difficult to understand for users
Solution Approach 1:
The patent segments the comparison process into multiple stages: normalizing content to a common format, tokenizing into meaningful units (words, phrases, sentences), and then comparing tokens rather than individual characters. This segmentation transforms raw character differences into semantically meaningful comparisons that are easier to understand while preserving the essential information about what changed.
Solution Approach 2:
The patent introduces an intermediary processing layer between the raw documents and the comparison results. This layer includes normalization rules, tokenization algorithms, and semantic analysis components that translate character-level differences into meaningful semantic comparisons. The intermediary preserves semantic meaning while making the results more humane and usable.
2Measurement precision
If traditional comparison methods are used, then all differences including irrelevant ones are reported, but this creates noise and makes it difficult to identify meaningful changes
Solution Approach 1:
The patent applies local quality by treating different types of content differently during comparison. Specific sections or types of changes can be assigned different weights or levels of importance. This allows the system to focus on meaningful changes while downplaying or filtering out irrelevant noise, improving the signal-to-noise ratio in the results.
3Manufacturing precision
If character-level comparison is performed, then detailed differences are captured, but patterns and consistency of changes cannot be easily identified
Solution Approach 1:
The patent adds a new dimension to the comparison by organizing differences at multiple levels: character level, token level, and semantic level. This multi-dimensional approach allows detailed character-level differences to be captured while simultaneously enabling pattern recognition at higher semantic levels. The hierarchical structure preserves detail while adding contextual information for pattern analysis.
Data Source
AI summary
A technique relates comparing content. A first set of content in a first document and a second set of content in a second document are normalized. The first set of content in the first document and the second set of content in the second document are tokenized. The first set of content having been tokenized and the second set of content having been tokenized are compared in order to find differences in the second set of content with respect to the first set of content. The differences are compiled in a changes map. The differences in the changes map are analyzed to determine types of the differences in the first set of content and the second set of content, and predefined differences are to be excluded. A report of the differences is generated, and the report delineates the types of the differences while excluding the predefined differences.