TORQ Occupational Transferability Metric Using Statistical Correlation
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Solution Overview
Problem
Current methods for assessing the transferability of skills between occupations are limited by the complexity of occupational data sets, such as O*NET and WORKKEYS, which often focus on single dimensions or repackaged data, making it difficult to create comprehensive career lattices or pathways that accurately reflect the feasibility of job transitions.
Innovation Solution
The Transferable Occupation Relationship Quotient (TORQ) uses statistical correlation and normalization to calculate a single number representing the relationship between occupations based on skills, abilities, and knowledge, incorporating gap analysis to account for asymmetry and spurious correlations, thereby providing a precise measure of transferability between occupations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive occupational data from multiple dimensions (O*NET, WORKKEYS) is used to assess transferability, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the comprehensive occupational data into distinct dimensions (skills, abilities, knowledge, education, experience) and processes each dimension separately through correlation analysis. This segmentation allows the system to handle complex multi-dimensional data by breaking it down into manageable components while maintaining overall measurement precision.
Solution Approach 2:
The patent introduces statistical correlation coefficients as an intermediary mechanism to bridge the complex occupational data and the transferability assessment. The correlation analysis serves as a mediator that transforms raw multi-dimensional data into meaningful transferability scores, reducing processing complexity while preserving measurement accuracy.
2Measurement precision
If statistical correlation analysis is applied to occupational attributes, then measurement precision improves, but loss of information increases due to data normalization
Solution Approach 1:
The patent extracts the essential correlation relationships from the comprehensive occupational data through statistical analysis. By taking out the core correlation patterns and representing them as normalized scores, the system achieves precise measurement of transferability while managing information complexity. The extraction process identifies and preserves the most significant relationships between occupational attributes.
Solution Approach 2:
The patent transforms the original occupational attribute data into normalized correlation parameters (scores from 0 to 100). This parameter change allows the system to maintain measurement precision through standardized correlation coefficients while reducing information loss by capturing the essential relationships in a condensed format that preserves comparative accuracy.
3Measurement precision
If gap analysis is incorporated to account for asymmetry, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies asymmetry by incorporating gap analysis that specifically measures the difference between source and destination occupation requirements in a directional manner. The gap calculation accounts for the specific context of transfer from one occupation to another, recognizing that transferability is not symmetric. This approach improves measurement precision by capturing directional nuances while adding manageable complexity through focused analysis rather than comprehensive re-evaluation.
Data Source
AI summary
A method and system for measuring transferability of workers between and among occupations by means of the mathematical relationships between those occupations' key attributes, as defined by publicly available data on the competencies required as specified by a complete catalog of U.S. occupations known as O*NET. This method provides a concise, informative measurement for comparing the relative requirements of abilities, skills, knowledge, and other relevant attributes of occupations, enabling users to gauge the feasibility of transferring workers from one occupation to another.