Semantic Automerge for Structured Data Files
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Solution Overview
Problem
Existing methods for merging structured data files, such as XML files, often fail to resolve conflicts effectively due to their lack of semantic understanding, leading to incomplete or incorrect merges, especially when changes are made in distributed environments.
Innovation Solution
A semantic automerge method using a fully extensible rule engine that determines file types and applies specific merge rules, including support for custom formats and multi-file merges, to resolve conflicts while preserving human context and whitespace structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If line-based merge is performed on structured data files, then the merge process is simple and fast, but the merge accuracy is poor and conflicts cannot be resolved effectively
Solution Approach 1:
The patent segments the structured data file into multiple elements based on semantic tags (e.g., <person>, <organization>, <location>). Each element can be independently identified, compared, and merged. This segmentation allows the system to process specific data elements rather than entire lines, improving both accuracy and conflict resolution capability while maintaining efficiency through targeted processing.
Solution Approach 2:
The patent changes the parameter of comparison from line-based text matching to element-based semantic matching. By introducing semantic understanding through tag identification and element extraction, the system transforms the merge process from simple string comparison to intelligent structural comparison, resolving conflicts based on data meaning rather than textual similarity.
2Manufacturing precision
If semantic automerge with rule engine is applied, then merge accuracy and conflict resolution improve, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary rule engine that mediates between the raw structured data and the merge operation. The rule engine parses XML/JSON files, identifies semantic elements, applies merge rules, and resolves conflicts automatically. This intermediary layer handles the complexity internally while presenting a simple interface to users, effectively managing system complexity through modular architecture.
Solution Approach 2:
The system implements self-service through automatic conflict resolution using predefined merge rules. The rule engine autonomously processes conflicting elements by applying semantic understanding and configured rules without requiring manual intervention. This self-service capability reduces the need for complex user interfaces and manual conflict management, balancing accuracy with operational simplicity.
3Ease of operation
If distributed copies of structured data are maintained, then accessibility and collaboration improve, but conflict frequency increases
Solution Approach 1:
The patent implements feedback mechanisms where the merge system continuously monitors changes across distributed copies, identifies conflicts, and applies resolution rules. The system provides feedback about merge status, resolved conflicts, and any remaining issues, enabling automated synchronization while maintaining data reliability across distributed environments.
Data Source
AI summary
Resolving conflicting changes to structured data files. A method includes for a structured data file which has both a generic structure and dialects built on top of the generic structure, for which conflicting changes have been made, and where an original version of the structured data, a first version of the structured data and a second version of the structured data exist, determining a dialect of the structured data. The method further includes, based on the determined dialect performing at least one of merge or diff operations on the original version of the structured data, the first version of the structured data and the second version of the structured data.