Graphic Object Format Conversion via Checksum Normalization
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Solution Overview
Problem
Existing technologies face challenges in modifying graphic objects across different software applications that use distinct data formats, as they struggle to detect and propagate changes between main and private datasets effectively, often failing to distinguish between meaningful and inconsequential changes.
Innovation Solution
A method involving normalization of the main dataset, computation of checksums, and comparison with stored checksums to identify changes, allowing for the propagation of changes from the main dataset to the private dataset, ensuring that both datasets remain consistent and up-to-date, even when used by software applications with different data formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple software applications use distinct data formats to represent graphic objects, then each application can interpret and edit graphic objects according to its own format, but changes made in one application cannot be detected or propagated to other applications using different formats
Solution Approach 1:
The patent introduces a format-agnostic checksum as an intermediary mechanism that bridges different data formats. The checksum is computed from the normalized main dataset and stored alongside format-specific private datasets, serving as a universal change detector that works across applications using different formats without requiring each application to understand other formats
Solution Approach 2:
The patent segments the graphic object representation into a format-independent main dataset and format-specific private datasets. This segmentation allows the checksum to operate on the normalized main dataset independently of any specific format, enabling change detection to work universally across different software applications while preserving format-specific capabilities
2Stability of the object's composition
If all changes in the main dataset are propagated to the private dataset, then the private dataset remains consistent with the main dataset, but inconsequential and non-substantive changes are also propagated unnecessarily
Solution Approach 1:
The patent implements feedback through checksum comparison: after computing a new checksum from the modified main dataset, the system compares it with the stored checksum to determine whether substantive changes occurred. This feedback mechanism enables intelligent propagation decisions - only when the checksum changes indicating substantive modifications does the system propagate changes to the private dataset, avoiding unnecessary propagation of inconsequential changes
3Measurement precision
If the main dataset is normalized before computing the checksum, then rounding errors and inconsequential variations are eliminated, but the normalization process adds computational overhead
Solution Approach 1:
The patent applies preliminary action by normalizing the main dataset before computing the checksum. This pre-processing step eliminates rounding errors and inconsequential variations in advance, ensuring that the checksum reflects only substantive changes. The normalization is performed once during the change detection process, and the computational overhead is justified by the improved precision in detecting meaningful changes versus noise
Data Source
AI summary
Methods, systems, apparatus, and computer program products involve modifying graphic objects. An electronic document can be obtained in a first format. The electronic document can include a graphic object represented using a main dataset and a private dataset. The main dataset can be normalized while stored in an object model in memory. A checksum can be computed for the normalized main dataset of the graphic object. The computed checksum can be compared with a stored checksum located in the electronic document. The electronic document can be converted from the first format to a second format based in part on a result of the comparison.