Mass Attribute Modification via Statistical Grouping
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Solution Overview
Problem
Editing a large number of objects with multiple attributes is time-consuming and inefficient, especially when some groups have identical attributes, as existing methods require manual identification and modification of each object individually, leading to decreased system performance and response time.
Innovation Solution
A system and method for mass modification of attribute values, which involves receiving a selection of objects, determining statistical distributions of attribute values, identifying subsets with equal values, and enabling mass modification functionality in the user interface, allowing for simultaneous editing of attributes across identified groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and modification of each object individually is performed, then attribute values can be edited with precision, but the time consumption and system performance degradation increase significantly
Solution Approach 1:
The patent merges multiple individual object modifications into a single batch operation. Objects with identical attribute values are grouped together, and a single modification action applies to all objects in the group simultaneously, eliminating the need to edit each object individually while maintaining precision through the grouping criterion.
Solution Approach 2:
The system performs preliminary analysis to identify and group objects with identical attribute values before the actual modification takes place. This preliminary grouping action enables subsequent batch modifications to be executed efficiently on pre-identified sets of objects, reducing overall time consumption.
2Manufacturing precision
If individual object modification is performed, then each attribute can be edited accurately, but system response time decreases
Solution Approach 1:
Multiple individual modification operations are merged into a single batch modification operation. The system identifies groups of objects sharing identical attribute values and applies modifications to entire groups simultaneously, thereby maintaining accuracy while significantly improving system response time.
3Measurement precision
If manual identification of objects with identical attributes is performed, then grouping accuracy can be achieved, but the operational complexity and time required increase
Solution Approach 1:
The system performs automatic identification and grouping of objects with identical attribute values without requiring manual intervention. The automated grouping mechanism analyzes attribute data, identifies matching objects, and creates groups independently, maintaining high grouping accuracy while greatly simplifying the user operation.
Solution Approach 2:
The manual mechanical process of identifying and grouping objects is replaced with an automated computational system. The system uses algorithmic analysis to identify objects with identical attributes and automatically creates groups, substituting manual operations with automated processing that maintains accuracy while improving ease of operation.
4Productivity
If batch modification is enabled without statistical analysis, then productivity can be improved, but the accuracy of identifying objects with equal attributes decreases
Solution Approach 1:
The system performs preliminary statistical analysis on attribute values before enabling batch modification. This analysis determines the distribution of attribute values and identifies groups of objects with equal attributes, ensuring accurate matching before productivity-enhancing batch operations are executed.
Solution Approach 2:
The system uses statistical distribution analysis to provide feedback on attribute value patterns. This feedback mechanism identifies which attributes have equal values across objects, enabling the system to accurately determine appropriate batch modification groups while maintaining high attribute matching precision.
Data Source
AI summary
Various embodiments of systems and methods for mass modification of attribute values of objects are described. The methods include systematically analyzing attributes assigned to multiple objects, displaying the results to the user, enabling mass modification functionality in the user interface, and providing the user a comprehensive variety of options on how to proceed with mass editing.


