Edit Control for Unconstrained Electronic Documents
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Solution Overview
Problem
Existing document comparison tools, such as redline functionality in text editing software, fail to indicate the significance of text-based differences between documents, making it difficult to determine whether edits are substantive or non-substantive, especially when documents are edited unconstrainedly by multiple entities, leading to a mismatch between structured and unstructured data.
Innovation Solution
The system uses a stored template to map text-based edits to structured data, predicting the significance of edits by measuring similarity and applying domain-specific logic, which can alter the processing flow based on edit significance, such as triggering additional reviews or constraining edits, to ensure data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If document comparison tools highlight text differences visually, then differences between documents are made visible, but the significance of these differences cannot be determined
Solution Approach 1:
The patent introduces an intermediary system that acts as a bridge between the visual comparison tool and the document metadata. This intermediary analyzes the highlighted differences, maps them to structured data fields, and determines their significance based on domain-specific rules and logic, thereby providing the missing information about edit importance without requiring changes to the original comparison tool
Solution Approach 2:
The system implements feedback by analyzing document differences and providing information about their significance back to the user interface. The feedback mechanism uses domain logic and structured data mapping to evaluate whether a difference is substantive or non-substantive, then communicates this evaluation to guide user attention and decision-making in the document review process
2Adaptability or versatility
If documents are edited unconstrainedly by multiple entities, then editing flexibility is improved, but data consistency between structured and unstructured data deteriorates
Solution Approach 1:
The system performs preliminary action by establishing domain-specific logic and structured data mappings before the unconstrained editing process begins. These pre-configured rules and field mappings serve as a framework that guides the analysis of edits afterward, enabling the system to detect and flag consistency issues even though the editing itself remains flexible and unconstrained
Solution Approach 2:
The system implements feedback by continuously monitoring edits made during unconstrained collaboration and comparing them against the structured data and domain logic. When inconsistencies are detected between unstructured text changes and structured data fields, the system provides feedback to alert users and trigger resolution processes, thereby maintaining data consistency despite flexible editing
3Reliability
If all text differences are flagged for review, then data integrity is maintained, but review time and process complexity increase significantly
Solution Approach 1:
The patent applies local quality by differentiating between various types of edits and applying different levels of review scrutiny accordingly. Non-substantive edits (such as formatting changes or minor word substitutions) are flagged with lower priority or automatically approved, while substantive edits (such as changes to key terms, amounts, or contractual obligations) are flagged for mandatory review. This localized quality approach maintains data integrity for critical fields while reducing unnecessary review overhead
Solution Approach 2:
The system changes parameters by dynamically adjusting the significance threshold and review requirements based on the type of document, section, and specific edit being analyzed. Domain logic evaluates multiple parameters (edit location, field importance, user role, document type) to determine the appropriate review level, thereby optimizing the balance between data integrity and review efficiency for different contexts
Data Source
AI summary
Embodiments of the disclosed technologies are capable of detecting an edit in an edited document, mapping the edit to stored structured data, executing stored logic associated with the stored structured data to alter a stored template and/or alter the stored structured data and/or insert a candidate edit from a stored set of candidate edits into a reference document.


