Comment-Edit Relation Modeling for Collaborative Document Revisions
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Solution Overview
Problem
Managing collaborative documents is challenging due to the complexity of edits and comments, making it difficult to track and associate comments with their corresponding edits, especially in multi-author environments.
Innovation Solution
A machine learning (ML) system utilizing a hierarchical neural network (NN) to model the relationship between edits and comments, employing techniques like supervised learning, comment ranking, and edit anchoring to automatically associate and resolve comments with document revisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tracking of edits and comments is used in collaborative documents, then users can maintain control over document revisions, but the complexity of managing multiple authors' edits and comments increases significantly
Solution Approach 1:
The patent replaces manual mechanical tracking of comment-edit relationships with an automated machine learning system. The ML model automatically analyzes document content, identifies edits, and associates them with relevant comments without human intervention, thereby reducing management complexity while maintaining association accuracy.
Solution Approach 2:
The system enables self-service by allowing the document management system to automatically track and associate comments with edits without requiring user intervention. The ML model autonomously processes document revisions and maintains the comment-edit relationship database, freeing users from manual tracking tasks.
2Productivity
If automated systems are introduced to track comment-edit relationships, then management efficiency improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the document management system into distinct functional modules: an ML model for analyzing content and identifying edits, a processing circuitry for executing the model, and a database for storing comment-edit associations. This modular architecture improves management efficiency while containing system complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces an intermediary ML model that acts as a bridge between raw document edits and comment associations. This intermediary layer processes edits automatically and generates associations without requiring complex direct integration between all system components, thereby improving efficiency while managing complexity.
3Measurement precision
If machine learning models are used to determine comment-edit relationships, then association accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training the ML model on document data before actual use. The model learns patterns of comment-edit relationships in advance, enabling it to quickly and accurately determine associations during actual document management without requiring extensive processing time during operation.
Solution Approach 2:
The patent optimizes the balance between accuracy and processing time by adjusting ML model parameters such as model architecture depth, training data size, and processing thresholds. These parameter changes allow the system to achieve high association accuracy while maintaining acceptable processing speeds for practical document management.
Data Source
AI summary
Generally discussed herein are devices, systems, and methods for determining a relationship between an edit and a comment. A system can include a memory to store parameters defining a machine learning (ML) model, the ML model to determine a relationship between an edit, by an author or reviewer, of content of a document and a comment, by a same or different author or reviewer, regarding the content of the document, and processing circuitry to provide the comment and the edit as input to the ML model, and receive, from the ML model, data indicating a relationship between the comment and the edit, the relationship including whether the edit addresses the comment or a location of the content that is a target of the comment.