Job Matching Algorithm Using Elastic Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for filling job openings are inefficient due to limited access to talent pools and rigid evaluation methods that fail to handle complex multi-dimensional analyses of applicant qualifications and strengths. Additionally, prospective talents may miss opportunities due to lack of outreach or persuasive analytics.
Innovation Solution
A method and system for online job placement that utilizes a job matching algorithm executed by a processor to perform online job opening matching between a job-seeker profile and job opening profiles. The algorithm retrieves attributes from the job-seeker and employer databases, calculates weighted match and fit scores, and adjusts these scores to optimize matches, ultimately recommending actions for job-seekers to qualify for different job openings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional evaluation methods are used for job matching, then the process is simple and easy to operate, but the ability to handle complex multi-dimensional analysis of applicant qualifications and strengths is limited
Solution Approach 1:
The patent segments the job matching evaluation into multiple independent dimensions including skills matching, experience evaluation, cultural fit assessment, and potential analysis. Each dimension is evaluated separately using specialized algorithms, allowing complex multi-dimensional analysis while maintaining operational simplicity through modular processing.
Solution Approach 2:
The patent introduces additional evaluation dimensions beyond traditional resume screening, including quantitative skill matching scores, weighted experience relevance, cultural compatibility metrics, and predictive potential indicators. This multi-dimensional approach enables comprehensive analysis while the system manages complexity through automated scoring and ranking mechanisms.
2Productivity
If automated job matching algorithms are implemented, then matching efficiency and productivity are improved, but the system complexity and difficulty of implementation increase
Solution Approach 1:
The patent implements self-service mechanisms where the job matching system automatically learns from feedback, continuously refines matching algorithms, and adapts to new job roles and candidate profiles without requiring manual reconfiguration. The system performs self-optimization through machine learning techniques, maintaining high productivity while reducing long-term implementation complexity.
Solution Approach 2:
The patent performs preliminary actions by pre-processing job descriptions and candidate profiles into standardized formats, pre-calculating skill tags and experience metrics, and pre-establishing matching criteria templates. This preliminary preparation enables rapid automated matching while simplifying system implementation by reducing real-time computational complexity.
3Measurement precision
If comprehensive candidate analysis is performed, then matching precision and quality are improved, but the time required for evaluation increases
Solution Approach 1:
The patent applies partial action by initially evaluating only the most critical matching dimensions (skills and experience) to quickly identify promising candidates, then performing comprehensive analysis only on shortlisted applicants. This staged approach maintains high evaluation precision for final selections while significantly reducing average evaluation time across the entire candidate pool.
Solution Approach 2:
The patent replaces manual comprehensive evaluation with automated algorithmic assessment that simultaneously analyzes multiple candidate dimensions including skills, experience, cultural fit, and potential indicators. This computational substitution maintains high measurement precision through consistent algorithmic application while dramatically reducing evaluation time compared to human review processes.
Data Source
AI summary
An online job offer negotiation between a job-seeker and a plurality of prospective employers, including performing the following steps: negotiating to reach a preferred job offer bid between the job-seeker and the plurality of prospective employers; (a) submitting a respective job offer bid that includes a plurality of offer terms attributes; (b) determining a corresponding respective composite score from the plurality of offer terms attributes comprised in each respective job offer bid; (c) comparing all the corresponding respective composite scores of each respective job offer to display a winning job offer bid with a highest composite score; (d) iteratively adjusting a corresponding value to one or more offer terms attributes to at least one non-winning job offer bid; (e) repeating steps (b) to (d) to receive a final winning job offer bid; and (f) confirming acceptance of a final winning job offer bid from a winning bidding prospective employer.