Adaptive Similarity Scoring for Electronic Document Revisions
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Solution Overview
Problem
Existing methods for suggesting edits to electronic documents often result in suboptimal similarity scores and alignment methods, leading to ineffective application of edits, particularly when many words are deleted or when different alignment techniques yield varying results.
Innovation Solution
A method and system that select specific similarity metrics and alignment methods based on the type of edit operation applied to the original text, allowing for improved calculation of similarity scores and alignment of statements-under-analysis with original and final texts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a one-size-fits-all approach is used to calculate similarity scores for all original texts, then the calculation process is simple and consistent, but the similarity scores become inaccurate particularly when many words are deleted
Solution Approach 1:
The patent applies different similarity calculation methods based on the specific characteristics of each original text. When an original text has many words deleted, a different calculation method is used compared to when few words are deleted. This local adaptation of calculation quality resolves the contradiction by improving accuracy for specific cases without requiring complete system complexity.
Solution Approach 2:
The patent changes the parameters of the similarity calculation based on the edit type. Different weights, metrics, or calculation formulas are applied depending on whether the edit involves deletions, insertions, or substitutions. This parameter adaptation allows accurate similarity scoring across diverse edit scenarios while maintaining a unified system framework.
2Reliability
If the same alignment method is used for all SUA and original text pairs, then the alignment process is consistent and simple, but the alignment quality varies and edit application effectiveness decreases
Solution Approach 1:
The patent selects different alignment methods based on the specific characteristics of each SUA-original text pair. When edits involve many deletions, a different alignment strategy is employed compared to cases with minimal deletions. This localized adaptation of alignment quality improves edit application reliability without requiring complete system complexity.
Solution Approach 2:
The alignment method is made dynamic rather than static. The system adapts the alignment approach based on the edit type and text characteristics, allowing the alignment strategy to change according to the specific task at hand. This dynamic adaptation resolves the contradiction between consistency and effectiveness.
3Measurement precision
If similarity scores are calculated using a uniform method for all text pairs, then the processing is efficient and consistent, but the scores are suboptimal particularly when many words have been deleted
Solution Approach 1:
The patent segments the similarity calculation process into different pathways based on edit characteristics. Text pairs are divided into categories (e.g., many deletions vs. few deletions), and each category receives an optimized calculation method. This segmentation improves accuracy for specific cases while maintaining overall processing efficiency through organized parallel processing.
Data Source
AI summary
A method for suggesting revisions to a document-under-analysis from a seed database, the seed database including a plurality of original texts each respectively associated with one of a plurality of final texts, the method for suggesting revisions including selecting a statement-under-analysis (“SUA”), selecting a first original text of the plurality of original texts, determining a first edit-type classification of the first original text with respect to its associated final text, generating a first similarity score for the first original text based on the first edit-type classification, the first similarity score representing a degree of similarity between the SUA and the first original text, selecting a second original text of the plurality of original texts, determining a second edit-type classification of the second original text with respect to its associated final text, generating a second similarity score for the second original text based on the second edit-type classification, the second similarity score representing a degree of similarity between the SUA and the second original text, selecting a candidate original text from one of the first original text and the second original text, and creating an edited SUA (“ESUA”) by modifying a copy of the first SUA consistent with a first candidate final text associated with the first candidate original text.