Standardized Profile Matching for Non-Standard Data Sets
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Solution Overview
Problem
Conventional online recruiting systems are inefficient in matching job seekers' resumes with suitable job postings, requiring time-consuming manual review and evaluation, as they lack a standardized method to compare and rank non-standardized data sets.
Innovation Solution
A system and method that converts non-standardized data sets, such as resumes and job postings, into standardized profiles by parsing, attributing, and ranking them based on metrics like band position, occurrences, and support values, enabling efficient comparison and matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and evaluation of resumes is performed, then matching accuracy can be achieved, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary standardization of resumes into structured profiles with extracted attributes (education, experience, skills) before matching occurs. This preprocessing action enables automated comparison algorithms to efficiently evaluate candidates without manual review, maintaining accuracy while reducing time consumption.
Solution Approach 2:
The patent introduces standardized profiles as an intermediary representation between raw resumes and matching algorithms. These profiles contain structured attributes that serve as a common language for comparison, enabling automated systems to achieve human-level matching accuracy without the time cost of manual evaluation.
2Productivity
If keyword searches are conducted to reduce the number of resumes reviewed, then the search scope is narrowed, but the ability to find the most suitable match is limited
Solution Approach 1:
The system segments resumes into distinct attribute categories (education, work experience, skills, certifications) within standardized profiles. This segmentation enables multi-dimensional comparison beyond simple keyword matching, allowing the system to efficiently filter candidates while maintaining comprehensive evaluation of suitability.
Solution Approach 2:
The patent transforms unstructured resume text into structured parameters with defined formats and weightings. By changing the representation from free-text to parameterized data, the system enables sophisticated filtering and ranking algorithms that preserve matching quality while dramatically improving search efficiency.
3Reliability
If individual review of each resume is performed, then comprehensive evaluation is achieved, but the process becomes extremely time-consuming
Solution Approach 1:
The system creates standardized profile copies of original resumes that capture all essential information in a uniform format. These copies can be processed, compared, and analyzed automatically without requiring access to or review of the original unstructured documents, enabling high-speed processing while maintaining evaluation comprehensiveness.
Solution Approach 2:
The patent replaces the mechanical process of manual resume review with automated computational algorithms that compare standardized profiles. This substitution maintains comprehensive evaluation through systematic attribute comparison while increasing processing speed by orders of magnitude compared to human review.
Data Source
AI summary
A system and method is described for receiving a plurality of non-standardized data sets and generating respective plurality of standardized profiles that can be used for efficiently comparing and matching one profile against the other plurality of profiles. One application of this invention is to convert job seekers' resumes and job postings into respective profiles and then permitting either a job seeker to search for job postings that most closely match the job seeker's resume or, conversely, permitting an employer to search for job seekers whose resumes most closely match the employer's job posting.