Hierarchical Machine Learning for Comment-Edit Relation Modeling
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Solution Overview
Problem
Managing collaborative document editing is challenging due to the complexity of tracking relationships between edits and comments made by multiple authors, which can lead to confusion and inefficiencies in document management.
Innovation Solution
A machine learning (ML) system is employed to model the relationship between edits and comments using a hierarchical neural network, incorporating an input embed layer, context embed layer, and comment-edit attention layer to determine relevance scores and associations between document content and comments.
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 introduces an intermediary system that automatically analyzes document revisions and comments using natural language processing and machine learning. This intermediary automatically links comments to relevant edits, tracks document evolution, and manages the complex relationships between multiple authors' contributions, thereby maintaining reliability while reducing management complexity
Solution Approach 2:
The patent replaces manual mechanical tracking methods with automated computational analysis. Machine learning models process document versions, identify semantic relationships between comments and edits, and automatically establish connections without human intervention, transforming the manual management process into an automated intelligent system
2Productivity
If automated relationship modeling between edits and comments is implemented, then tracking efficiency improves, but the system complexity and computational resources increase
Solution Approach 1:
The patent segments the complex relationship modeling task into distinct analytical components: document version comparison, comment interpretation, edit identification, and relationship matching. Each component is handled by specialized processing modules within the machine learning system, enabling efficient parallel processing while maintaining overall system manageability
Solution Approach 2:
The patent transforms the relationship modeling problem by changing parameters from manual metadata tagging to semantic vector representations. Machine learning models convert textual comments and edits into numerical vectors that capture semantic meaning, enabling efficient automated matching based on semantic similarity rather than manual categorization
3Measurement precision
If semantic analysis is used to understand comment-edit relationships, then association accuracy improves, but processing time and computational energy increase
Solution Approach 1:
The patent applies partial semantic analysis by focusing computational resources on the most relevant portions of comments and edits. The system identifies key phrases and semantically significant segments rather than processing entire documents uniformly, achieving high association accuracy while reducing overall computational energy requirements
Solution Approach 2:
The patent performs preliminary processing of document content by pre-computing semantic representations of frequently occurring patterns, common comment types, and typical edit structures. These pre-computed representations are stored and reused, reducing the computational energy needed for real-time analysis while maintaining high association accuracy
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.