Skill Profiling System Using Multi-Tier Taxonomy
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Solution Overview
Problem
Traditional skill profiling and recruitment methods are subjective and prone to bias, and existing AI-based systems rely heavily on self-declared skills from resumes, which may not accurately reflect a candidate's true abilities.
Innovation Solution
A system and method for skill profiling using a multi-tier skill taxonomy, which involves obtaining normalized skill proficiency scores through multiple-choice questions, correlating these scores with predefined skills, and generating accurate skill profiles and job recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual skill profiling and resume screening methods are used, then the process is simple and easy to operate, but the assessment is subjective and prone to bias
Solution Approach 1:
The patent replaces manual, subjective assessment processes with an automated AI-based system that uses machine learning models to objectively evaluate candidate skills. The system substitutes human interviewers and resume screeners with computational algorithms that process MCQ responses and generate standardized skill profiles, thereby eliminating personal bias while maintaining operational efficiency.
Solution Approach 2:
The patent introduces an intermediary AI-based assessment platform that mediates between the candidate and the recruitment process. This intermediary system administers standardized MCQs, automatically scores responses, and generates skill profiles without direct human intervention in the evaluation phase, thus removing subjective bias while preserving the recruitment workflow.
2Measurement precision
If AI-based systems use self-declared skills from resumes, then data acquisition is automated, but the skill assessment is inaccurate due to over-rating by candidates
Solution Approach 1:
Instead of relying on candidates to declare their own skills (self-assessment), the patent inverts the approach by having candidates demonstrate their skills through standardized MCQs administered by the system. The assessment shifts from candidate-driven self-declaration to system-driven objective testing, thereby improving measurement precision while maintaining automation.
Solution Approach 2:
The system enables self-service automated assessment where candidates independently complete MCQs without human intervention. The AI system automatically administers the test, scores responses, and generates skill profiles, maintaining full automation while ensuring accurate, bias-free skill measurement through standardized questioning.
3Reliability
If multiple-choice questions are used for skill assessment, then objective quantifiable data is obtained, but the system complexity increases due to MCQ repository and correlation matrices
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing correlation matrices and inter-dependence relationships in the system before actual assessments. These pre-computed structures are built once and reused across multiple assessments, thereby managing system complexity through advance preparation rather than real-time computation during candidate evaluation.
Solution Approach 2:
The patent manages complexity by changing parameters from raw MCQ responses to normalized skill proficiency score vectors through standardized transformation processes. The system converts diverse MCQ data into unified skill profiles using predefined correlation matrices, thereby simplifying the assessment output while maintaining objective measurement through standardized parameter transformation.
Data Source
AI summary
Disclosed is disclosure provide a system and a method for skill profiling based on a multi-tier skill taxonomy. The method for skill profiling includes obtaining a first normalized skill proficiency score vector associated with a user by way of a first engine and mapping the first normalized skill proficiency score vector with a pre-set multi-tier skill taxonomy, by way of a second engine. The method for skill profiling further includes obtaining a first normalized skill score associated with the user by way of a first skill score engine and generating a first skill profile associated with the user by way of a skill profile engine. Furthermore, the method for skill profiling includes generating a second skill profile associated with the user, through update in the first skill profile associated with the user, by way of a profile update engine.