Embedding-Based Talent Matching System
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Solution Overview
Problem
Current methods for evaluating a person's competencies for job roles within an organization are often manual, prone to subjective bias, and rely on outdated skills ontologies, leading to inefficient talent allocation and potential biases in career advancement.
Innovation Solution
A computing system that generates concept and job spaces using an embedding mechanism, allowing for a systematic and unbiased evaluation of competencies and job suitability by mapping individual learning histories and knowledge to job requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation by HR teams is used, then personalization and contextual understanding are improved, but time consumption and subjectivity increase
Solution Approach 1:
The patent introduces an embedding mechanism as an intermediary that transforms both job descriptions and learner profiles into a shared vector space. This mediator enables automated comparison while preserving the nuanced understanding that would otherwise require manual HR evaluation, thus reducing time loss without sacrificing measurement precision.
Solution Approach 2:
The system replaces the mechanical manual evaluation process with an automated computational approach. By substituting human reviewers with an embedding-based matching system, the patent eliminates time consumption and subjectivity while maintaining evaluation accuracy through systematic comparison of skill vectors.
2Productivity
If automated evaluation using static skills ontologies is used, then time efficiency is improved, but adaptability and measurement precision deteriorate
Solution Approach 1:
The patent transforms the static skills ontology into a dynamic embedding space where job descriptions and skill profiles can be continuously updated and compared. This dynamic representation allows the system to adapt to new roles and skills without requiring manual ontology updates, maintaining both efficiency and adaptability.
Solution Approach 2:
The system changes the parameter representation from fixed ontology categories to continuous embedding vectors. This parameter transformation enables flexible adaptation to new job roles and skills by simply adding new vector representations rather than restructuring the entire ontology, thus improving both efficiency and versatility.
3Extent of automation
If supervised machine learning with historical data is used, then automation is improved, but bias propagation increases
Solution Approach 1:
The patent applies preliminary action by pre-processing job descriptions and learner profiles through embedding mechanisms that focus on skill matching rather than historical outcomes. This preliminary transformation prevents bias from historical data from influencing the matching process, while still maintaining high automation levels.
Solution Approach 2:
The system extracts only the relevant skill and competency features from job descriptions and learner profiles, separating them from potentially biased historical evaluation data. By taking out and isolating the essential matching criteria, the patent achieves automation without propagating biases present in historical supervised learning data.
Data Source
AI summary
A system identifies a first path taken by a learner in a concept space relative to a plurality of concept nodes that are contextualized within the concept space according to an embedding dimension of an embedding mechanism, transforms the first path in the concept space into a second path in a job space comprising a plurality of job nodes that are contextualized within the job space according to the embedding dimension of the (same) embedding mechanism, determines a distance between a first point on the second path and a first job node of the plurality of job nodes, computes an affinity of the learner for a job of the first job node based on the distance, and reports the affinity. Mechanisms for a “reverse” process that transforms a desired affinity for a job into a learning plan with respect to the content mapped in the concept space are also discussed.