Multi-dimensional Data Grouping via Simultaneous Commonality Derivation
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Solution Overview
Problem
Conventional data grouping techniques only identify associations based on a single layer of commonality, failing to recognize further common characteristics among data entries, which limits the identification of complex relationships within data sets.
Innovation Solution
Implementing a method that uses a similarity algorithm to derive multiple commonalities among data entries, allowing for the identification of associations between various characteristics by filtering and analyzing data entries with predefined aspects, and generating profiles indicative of these associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional data grouping techniques are used, then data entries can be grouped according to a single common characteristic, but the ability to identify associations between various similarities is lost
Solution Approach 1:
The patent transitions from single-dimensional grouping (one commonality at a time) to multi-dimensional analysis by deriving multiple commonalities simultaneously. The system examines data entries across multiple aspects (fields, values, patterns) concurrently, enabling identification of associations between different characteristics while maintaining manageable complexity through systematic dimension expansion.
Solution Approach 2:
The patent segments the analysis process into distinct phases: first identifying a primary commonality among data entries, then separately deriving secondary and tertiary commonalities from those groupings. This segmentation allows the system to handle complex multi-layered relationships by breaking them into manageable analytical steps, preventing information loss while avoiding overwhelming complexity.
2Loss of information
If multiple commonalities are derived from data entries, then associations between characteristics can be identified, but the complexity of the analysis process increases
Solution Approach 1:
The patent applies preliminary action by first establishing groupings based on primary commonalities before deriving secondary and tertiary commonalities. This sequential preparation creates a structured foundation that simplifies subsequent analysis, allowing the system to identify complex associations without overwhelming computational complexity at each step.
Solution Approach 2:
The patent implements nesting by deriving commonalities in hierarchical layers: primary commonalities form the outer layer, secondary commonalities are nested within those groupings, and tertiary commonalities are nested within secondary groups. This nested structure enables the system to manage complexity by organizing analysis at multiple levels, where each layer builds upon the previous without requiring simultaneous processing of all dimensions.
Data Source
AI summary
In accordance with embodiments, there are provided mechanisms and methods for deriving commonalities among data entries. These mechanisms and methods for deriving commonalities among data entries can identify characteristics that are known to be common to at least some data entries in addition to unknown characteristics that are common to data entries. The ability to identify common known and unknown characteristics among data entries may allow data entries to be grouped according to the identified common known and unknown characteristics.