Automated Skill Gap Analysis Using Word Embeddings
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Solution Overview
Problem
Current approaches to reskilling the workforce rely on manual labor, making them time-consuming and expensive, and fail to provide sufficient explainability or rapid remedial actions for skill gaps, especially in fast-changing job markets.
Innovation Solution
An automated system using multiple word embedding models to identify skill adjacencies and gaps by extracting keywords from candidate and job descriptions, generating word embeddings, calculating cosine similarity scores, and providing reskilling recommendations with explainability statements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual labor approaches are used for reskilling assessment, then simplicity of implementation is maintained, but productivity and time efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical assessment processes with an automated computational system using word embedding models and cosine similarity algorithms. This substitution eliminates manual labor in skill gap analysis while maintaining assessment accuracy, directly resolving the contradiction between productivity improvement and system complexity.
2Productivity
If automated systems are implemented for skill gap analysis, then productivity and time efficiency improve, but measurement precision requirements increase
Solution Approach 1:
The patent transforms skill keywords into vector embeddings with specific dimensional parameters, enabling automated cosine similarity calculations. This parameter transformation allows the system to maintain high measurement precision in identifying skill adjacencies while achieving rapid automated processing, resolving the contradiction between productivity and precision.
3Measurement precision
If multiple word embedding models are used, then measurement precision and accuracy improve, but device complexity increases
Solution Approach 1:
The patent segments the skill assessment task into distinct computational components: keyword extraction, embedding generation, cosine similarity calculation, and gap identification. This segmentation allows multiple word embedding models to be applied systematically to different aspects of the problem, improving measurement precision while managing complexity through modular architecture.
4Loss of time
If automated extraction and comparison of skill keywords is performed, then loss of time is reduced, but loss of information may increase due to automated processing
Solution Approach 1:
The patent introduces word embedding vectors as intermediary representations between raw skill keywords and similarity comparisons. These embeddings capture semantic nuances and contextual relationships, serving as a faithful intermediary that preserves information while enabling rapid automated processing, thus resolving the contradiction between time efficiency and information retention.
Data Source
AI summary
An embodiment for identifying skill adjacencies and skill gaps to generate reskilling recommendations. The embodiment may receive input from a user including candidate details and a job description. The embodiment may automatically extract a first set of skill keywords from the candidate description and a second set of skill keywords from the job description. The embodiment may automatically input the first and second set of skill keywords into a first type of word embedding model and a second type of word embedding model to automatically generate word embeddings. The embodiment may automatically compare the generated word embeddings and calculate cosine similarity scores for the first and second set of skill keywords. The embodiment may automatically identify skill overlaps and skill gaps using the calculated similarity scores, and automatically generating and outputting corresponding explainability statements to the user, and generate and output corresponding reskilling recommendations for the identified skill gaps.