Context-Based Object Clustering via Tagged Attribute Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data processing technologies face challenges in clustering objects from documents and images, as they primarily rely on keyword-based approaches and fail to account for dynamic attributes and non-physical characteristics, limiting their ability to identify context-based similarities.
Innovation Solution
A method and system for context-based clustering of objects, which involves receiving objects with both physical and non-physical attributes, tagging non-physical attributes to physical attributes, identifying a common context, and mapping these attributes to cluster objects effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If keyword-based clustering is used, then the clustering process is simple, but the clustering precision is low
Solution Approach 1:
The patent segments the clustering process into multiple stages: initial keyword-based clustering followed by secondary context-based clustering. This segmentation allows the system to first group objects using simple keywords, then refine these groups using contextual parameters, thereby improving precision without entirely replacing the simple initial method
Solution Approach 2:
The patent introduces contextual parameters as an intermediary layer between keyword matching and final clustering. These contextual parameters (such as location, time, device information) act as mediators that enhance the precision of clustering by providing additional dimensions for comparison beyond simple keyword similarity
2Productivity
If only physical attributes are considered, then the clustering is computationally efficient, but the adaptability is limited
Solution Approach 1:
The patent creates a universal clustering framework that can handle both physical attributes (color, shape, size) and non-physical attributes (contextual parameters like location, time, device information). This multi-functional approach allows the same clustering system to adapt to different object types and clustering requirements without requiring separate specialized systems
3Stability of the object's composition
If dynamic attributes are not considered, then the clustering stability is high, but the reliability decreases
Solution Approach 1:
The patent introduces dynamics into the clustering process by incorporating contextual parameters that can change over time (such as location, time stamps, device information). This allows the clustering to adapt to changing conditions while maintaining stability through the structured framework of context-based grouping, resolving the contradiction between stability and reliability
4Measurement precision
If context-based clustering is implemented, then the clustering precision is improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex context-based clustering into manageable components: extracting contextual parameters, comparing objects based on these parameters, and forming clusters. This segmentation makes the complex process more implementable while maintaining the precision benefits of context-based clustering
Data Source
AI summary
A method and a system are described for context based clustering of one or more objects. The method comprises receiving, by the object clustering system, receiving, by an object clustering system, an object clustering request for one or more objects associated with a plurality of contextual parameters, where the plurality of contextual parameters comprises one or more physical attributes and one or more non-physical attributes. It further includes tagging the one or more non-physical attributes respectively to the one or more physical attributes. It further includes identifying a common context from the one or more physical attributes associated with the one or more objects based on the tagging. It further includes mapping the one or more physical attributes to the one or more objects based on the common context. It then includes clustering the one or more objects based on the mapping.