NLP Skill Forecasting via Engagement Similarity
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Solution Overview
Problem
Current methods fail to systematically identify future skill needs and determine appropriate training for employees, often relying on unstructured data and human intuition, which can lead to inadequate or mismatched training opportunities.
Innovation Solution
A natural language processing-based system that analyzes unstructured data from past and future engagements to forecast required skillsets and generate learning paths, using techniques like convolutional neural networks and clustering algorithms to identify similar engagements and skillsets, thereby determining necessary skill upgrades or reskilling needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing models and clustering algorithms are used to systematically analyze unstructured data, then measurement precision of skill requirements is improved, but device complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct components: natural language processing models extract characteristics from engagement data, clustering algorithms identify similar engagements, and skill requirement forecasting generates predictions. This modular segmentation enables high measurement precision while managing system complexity through organized functional blocks.
Solution Approach 2:
The patent introduces intermediate processing layers including characteristic extraction modules and similarity computation mechanisms that mediate between raw unstructured data and final skill requirement predictions. These intermediaries transform complex unstructured data into structured features, improving measurement precision without requiring the entire system to handle full complexity simultaneously.
2Loss of information
If comprehensive unstructured data from past and future engagements is analyzed, then information completeness is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing engagement data to extract key characteristics and storing them in structured formats. This preliminary extraction of features from unstructured data allows the system to maintain information completeness while reducing the time required for actual skill requirement forecasting, as the heavy lifting of data transformation is done in advance.
Solution Approach 2:
The patent employs techniques to skip through large volumes of unstructured data efficiently using natural language processing that rapidly identifies and extracts relevant characteristics without manually processing every detail. This enables comprehensive data analysis while minimizing time loss through automated, high-speed text processing and feature extraction.
Data Source
AI summary
By executing a natural language processing model on a set of natural language text describing a first engagement, a set of characteristics of the first engagement is generated. By executing the natural language processing model on a set of natural language text describing a future engagement, a set of characteristics of the future engagement is generated. The first engagement is determined to be above a threshold similarity with the future engagement. Using the skillset used in performing the first engagement, a required skillset of the future engagement is forecasted. By executing the natural language processing model on a set of natural language text describing a current skillset, a set of characteristics of the current skillset is generated. Using the required skillset of the future engagement and the set of characteristics of the current skillset, a learning path is generated.