Job Matching Algorithm Using Normalized Assessment Quotients
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Solution Overview
Problem
Traditional methods of matching job seekers with employers are hindered by non-standardized assessments and disparate data formats, leading to difficulties in comparing suitability data and identifying the most compatible candidates, especially with the increased volume of applicants due to electronic communications.
Innovation Solution
A method that normalizes assessment data and suitability information to create a performance quotient for job seekers, which is then compared to a position quotient set by employers, allowing for the ranking of candidates and reducing the number of applicants presented to employers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If employers review all applicants from online databases and resumes, then they can access a large pool of candidates, but it becomes difficult to focus on the best candidates and the hiring process becomes inefficient
Solution Approach 1:
The patent replaces manual review processes with automated electronic assessment systems that use algorithms to evaluate candidates. The system automatically compares candidate profiles against employer requirements, substitutes human screening with computer-based matching, and ranks candidates based on compatibility scores, thereby maintaining ability to review many applicants while dramatically improving hiring efficiency
Solution Approach 2:
The patent introduces an intermediary assessment system that acts as a mediator between employers and job seekers. This system standardizes candidate evaluation through uniform assessments, creates comparable compatibility metrics, and facilitates efficient matching without requiring employers to manually review all applicants, thus resolving the contradiction between accessing large applicant pools and maintaining hiring efficiency
2Loss of information
If various assessments are used to evaluate job seekers, then employers gain additional understanding of candidate suitability, but the assessments are not standardized and produce dissimilar results that are difficult to compare
Solution Approach 1:
The patent transforms diverse assessment results into a standardized parameter format - a compatibility score or ranking metric that all candidates receive. This parameter transformation allows employers to compare candidates across different assessments on a common scale, maintaining comprehensive suitability evaluation while achieving precise, comparable measurements through uniform scoring parameters
Solution Approach 2:
The patent creates a universal assessment framework that can accommodate multiple types of evaluations (personality tests, skill assessments, background checks) while producing results in a single standardized format. This multi-functional system accepts various assessment inputs but outputs unified compatibility metrics, enabling both comprehensive evaluation and precise comparison simultaneously
3Loss of information
If employers require multiple types of suitability data including resumes and assessments, then they can make more informed hiring decisions, but the elements are in different formats that are difficult to compare and evaluate
Solution Approach 1:
The patent merges multiple disparate data elements (resumes, assessment results, references, work history) into a single integrated candidate profile. This consolidation combines unstructured and structured data from different sources into a unified format that can be systematically evaluated against job requirements, maintaining information quality while reducing the complexity of data integration through centralized profile management
Data Source
AI summary
A method of facilitating a match between an employer with at least one job opening and job seekers is provided. The employer has a set of position preferences related to the job opening. The job seekers have suitability data, resumes, etc., that are provided to the employer. The suitability data includes normalized assessment data. The method includes the steps of: determining a position quotient based on the position preferences; deriving a performance quotient for each job seeker, the performance quotient including normalized assessment data; comparing each the performance quotient to the position quotient; and ranking each the job seeker based on the comparison of the performance quotient to the position quotient.