Cross-Domain Feature Correlation Index
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Solution Overview
Problem
Existing methods for correlating information artifacts across distinct domains are ineffective when there is a lack of feature co-occurrence due to differences in language, culture, purpose, format, or natural shifts in terminology, leading to difficulties in determining relevant relationships between artifacts.
Innovation Solution
A method that creates a correlation index between features from disparate information artifact collections, extracts and selects relevant features, and computes a correlation score to rank matching features, allowing for correlation without requiring feature co-occurrence or domain-specific knowledge, using techniques like TF-IDF and mutual information for natural language documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior art methods use feature co-occurrence to determine similarity between artifacts, then correlation accuracy is improved for artifacts with shared features, but the method becomes ineffective when there is a lack of feature co-occurrence across distinct domains
Solution Approach 1:
The patent introduces a correlation index as an intermediary mechanism that bridges disparate feature sets across domains. Instead of requiring direct feature co-occurrence between artifacts, the system uses the correlation index to establish indirect relationships, enabling similarity measurement even when artifacts lack shared features. This mediator allows the system to traverse across domain boundaries effectively.
Solution Approach 2:
The patent transforms the correlation measurement from a direct two-dimensional feature comparison to a three-dimensional approach by introducing the correlation index as an additional dimension. The correlation index serves as a mapping layer that connects features from different domains, creating a multi-dimensional feature space where artifacts can be correlated despite lacking direct feature overlap.
2Reliability
If the system requires building and maintaining large ontologies to handle domain disparities, then domain knowledge is improved, but system complexity increases significantly
Solution Approach 1:
The patent extracts the essential correlation information directly from the data itself rather than relying on pre-built ontologies. By computing the correlation index from actual feature co-occurrence patterns in the data, the system eliminates the need for complex ontology maintenance while still capturing domain relationships. This data-driven extraction replaces the knowledge-driven ontology approach.
Solution Approach 2:
The system performs self-learning by automatically computing correlation indices from the data without requiring external domain knowledge or manual ontology construction. The correlation index is derived autonomously from feature co-occurrence patterns, allowing the system to adapt to new domains automatically without human intervention or pre-maintained ontologies.
3Measurement precision
If manual encoding of relationships between artifacts is used, then relationship accuracy is improved, but the process becomes difficult or impossible in environments with large numbers of artifacts
Solution Approach 1:
The patent replaces the manual mechanical process of encoding relationships with an automated computational system. Instead of human experts manually analyzing and encoding relationships between artifacts, the system uses algorithmic computation to automatically calculate correlation indices and identify relationships, dramatically improving processing efficiency while maintaining accuracy through rigorous mathematical computations.
Data Source
AI summary
A method for correlating information across distinct domains without requiring feature co-occurrence. The disparate information collections are broken down into features, and a correlation index with correlation score is created. To determine the correlation between distinct domains, an information artifact collection is reduced to a representational set of features, these features are replaced with correlated features using the correlation index, and the new set of features is matched against the second information artifact collection using an appropriate comparison technique. The correlation method allows a single input artifact to be matched against an existing collection, resulting in a set of correlated artifacts from the disparate collection, each ranked by correlation score.