Inadvertent Edit Detection in Collaborative Document Editing
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Solution Overview
Problem
Collaborative document editing often results in unintentional edits due to accidental changes, which can diminish the quality of the document and reduce efficiency, as users may not detect these changes in a timely manner.
Innovation Solution
A system that uses machine learning algorithms to analyze usage history and detect inadvertent edits, presenting users with options to revert changes and providing tools for managing edits, including user interface elements to confirm intentionality and automatically undo unwanted modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If collaborative document editing is enabled with autosave functionality, then document collaboration efficiency is improved, but inadvertent edits occur more frequently
Solution Approach 1:
The system implements feedback by analyzing usage history and edit patterns to detect inadvertent edits, then notifying users of potential mistakes. This feedback loop allows the system to monitor editing behavior and alert users when accidental changes are detected, resolving the contradiction between enabling collaborative editing and preventing inadvertent changes.
Solution Approach 2:
The system performs preliminary analysis of edit patterns and usage history before finalizing document changes. By detecting inadvertent edits through pattern recognition and usage analysis prior to saving, the system can prevent unwanted changes from being committed to the document, thus maintaining both collaboration efficiency and edit accuracy.
2Reliability
If machine learning algorithms are used to detect inadvertent edits, then edit accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses machine learning algorithms to automatically analyze usage history and detect inadvertent edits without requiring manual intervention or complex configuration. The system serves itself by learning from patterns in the data, reducing the need for manual rule-setting and simplifying the overall system architecture while maintaining high detection accuracy.
Solution Approach 2:
The machine learning model acts as an intermediary layer between user actions and document changes. Instead of directly implementing complex detection logic in the document editing core, the patent introduces an ML-based intermediary that analyzes usage patterns and flags potential inadvertent edits, thereby isolating the complexity from the main document editing system.
3Reliability
If users are prompted to confirm edits, then inadvertent edits are reduced, but workflow efficiency decreases
Solution Approach 1:
Instead of prompting users to confirm every single edit, the system applies partial action by only requesting confirmation when inadvertent edits are detected through usage pattern analysis. This selective approach reduces the number of confirmation prompts compared to universal confirmation requirements, thereby maintaining workflow efficiency while still preventing inadvertent changes.
Solution Approach 2:
The system dynamically changes the confirmation parameter based on detected edit patterns. When the machine learning model determines an edit is likely inadvertent based on usage history analysis, it adjusts the confirmation requirement accordingly. This parameter-based approach allows the system to maintain high reliability for suspicious edits while preserving workflow efficiency for normal editing operations.
Data Source
AI summary
A mechanism and model by which to manage inadvertent edits of electronic content. The model determines the likelihood of whether a modification made to a document is intentional or was inadvertent and assist users in identifying these edits. The proposed devices and method can significantly improve workflow efficiency and allow users to feel more comfortable in the development and use of their electronic content, and/or reduce the possibility of other users causing accidental or unintentional errors in a document.