Localized Content Edit Propagation and Substantive Change Filtering
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Solution Overview
Problem
In collaborative document editing, it is challenging to determine whether edits made to localized versions of electronic content should be propagated to the original base version, as edits may be specific to the translated version and not substantive for the original version, leading to unnecessary changes.
Innovation Solution
A system that uses a processor and machine-readable media to determine the likelihood of an edit being substantive based on content characteristics, generating a score and automatically propagating substantive edits to the base electronic content while preventing non-substantive edits from being applied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all edits from localized versions are propagated to the base version, then the base version receives comprehensive updates, but unnecessary changes are introduced that are specific to the translated version
Solution Approach 1:
The system evaluates each edit individually using content characteristics (such as whether the edit is in a translated section vs. original content) to determine if it should be propagated. This selective approach ensures that only edits relevant to the base version are propagated, while language-specific or localization-specific edits are filtered out, thus maintaining accuracy without introducing unnecessary changes.
2Measurement precision
If manual review of each edit is performed to determine substantiveness, then accurate filtering of edits is achieved, but the process becomes time-consuming and complex
Solution Approach 1:
The system automatically evaluates edits using machine learning models that analyze content characteristics of the edits and determine their substantiveness without requiring manual review. The model processes edits autonomously, classifying them as substantive or non-substantive based on patterns learned from training data, thus achieving accurate filtering while eliminating time-consuming manual intervention.
3Extent of automation
If a scoring system is implemented to evaluate edit substantiveness, then automated filtering becomes possible, but the system complexity increases
Solution Approach 1:
The system uses a unified machine learning model that performs multiple functions: it evaluates content characteristics, generates substantiveness scores, and makes propagation decisions all in one automated process. This multi-functional approach consolidates what could be separate complex systems into a single coherent framework, achieving high automation while managing complexity through integration rather than multiplication of components.
Data Source
AI summary
A system and model by which to manage edits to localized versions of base-language electronic content. The model determines the likelihood of whether a modification made to a document is substantive and should be propagated back to the base electronic content and assists users in identifying these edits. The proposed method can significantly improve workflow efficiency and allow users to feel more comfortable in the development and use of their electronic content across multiple authoring platforms.