Digital Document Update via Change Propagation
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Solution Overview
Problem
Conventional digital document techniques focus on creation and publication but fail to address the maintenance of digital documents over time, leading to staleness and reduced relevance, especially in networked environments like the Internet.
Innovation Solution
A document management system that detects triggering changes in digital documents and generates similarity scores for candidate document portions using natural language processing and machine learning, enabling automatic or user-controlled updates to maintain consistency and accuracy across document portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques are used for digital document creation and publication, then documents can be created and made available, but the documents become stale and outdated over time without automatic maintenance
Solution Approach 1:
The system enables digital documents to maintain themselves automatically by detecting changes in source data and propagating updates to affected document portions without requiring manual intervention. The document management system continuously monitors for triggering changes and automatically identifies and updates trailing changes, allowing the document to self-maintain its accuracy over time.
Solution Approach 2:
The system implements a feedback mechanism where the document management system continuously monitors document portions for changes, compares updated content against existing document portions, and automatically propagates updates when changes are detected. This closed-loop feedback ensures documents remain current with source data without manual intervention.
2Reliability
If manual updates are performed for each document portion, then accuracy can be maintained, but the process becomes extremely time-consuming and inefficient
Solution Approach 1:
The document management system acts as an intermediary between source data and document portions. It automatically detects triggering changes in source data, identifies affected document portions through similarity comparison, and propagates updates accordingly. This intermediary system eliminates the need for manual review and update of each document portion while maintaining consistency.
Solution Approach 2:
The system replaces the manual mechanical process of reviewing and updating each document portion with an automated computational system. Machine learning models and natural language processing automatically detect changes, determine relevance, and propagate updates, substituting human manual work with automated algorithms that process documents much faster and more efficiently.
3Reliability
If all document portions are reviewed for updates, then no outdated information remains, but the computational resources and time required become prohibitive
Solution Approach 1:
The system applies local quality by focusing update efforts only on specific document portions that are actually affected by source data changes. Instead of reviewing all document portions uniformly, the system uses similarity comparison and machine learning to identify only those portions that need updating, allocating computational resources efficiently to where they are most needed.
Solution Approach 2:
The system performs partial action by updating only the necessary subset of document portions rather than all portions. It detects triggering changes and propagates updates only to affected areas, avoiding the excessive computational effort of reviewing and potentially updating every document portion in the system.
4Reliability
If users manually identify and update all affected document portions, then consistency can be maintained, but the cognitive load and complexity of the task increases significantly
Solution Approach 1:
The system enables documents to self-maintain consistency by automatically detecting changes and propagating updates without requiring user intervention. The document management system handles the entire process of identifying triggering changes, determining affected portions, and applying updates, freeing users from the cognitively demanding task of manual document maintenance.
Solution Approach 2:
The document management system serves as an intermediary that handles the complex task of maintaining document consistency. It automatically monitors source data, identifies triggering changes, determines which document portions are affected through similarity comparison, and propagates updates accordingly, shielding users from the complexity of these operations.
Data Source
AI summary
Techniques and systems are described in which a document management system is configured to update content of document portions of digital documents. In one example, an update to the digital document is initially triggered by a document management system by detecting a triggering change applied to an initial portion of the digital document. The document management system, in response to the triggering change, then determines whether trailing changes are to be made to other document portions, such as to other document portions in the same digital document or another digital document. To do so, triggering and trailing change representations are generated and compared to determine similarity of candidate document portions with an initial document portion.