Credential Processing System Using ML for Job Matching
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Solution Overview
Problem
Current recruitment systems lack efficiency in matching user credentials with relevant job requisitions, leading to wasted time for users and evaluators, as they often apply to unsuitable positions, resulting in inefficient evaluation processes.
Innovation Solution
A computer-implemented method using machine learning models to process user credentials in real-time, assigning classifications and tags to identify suitable job requisitions, dynamically trained with feedback to improve matching accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual credential review and requisition matching is used, then users can apply to jobs, but it leads to wasted time for users and evaluators applying to unsuitable positions
Solution Approach 1:
The patent replaces manual credential review and job matching (mechanical human evaluation process) with an automated machine learning system that processes credentials, extracts skills, and matches them to requisitions using NLP and classification algorithms, thereby eliminating time waste while maintaining or improving matching quality
Solution Approach 2:
The system performs preliminary credential analysis and skill extraction before the matching process, pre-processing user credentials to identify relevant skills and qualifications in advance, which enables faster and more accurate requisition recommendations without compromising evaluation thoroughness
2Adaptability or versatility
If users apply to multiple requisitions without filtering, then more application opportunities are created, but the evaluation process becomes inefficient
Solution Approach 1:
The system implements feedback mechanisms where evaluation outcomes and matcher performance are continuously monitored and used to refine the matching algorithm, allowing the system to learn from successful matches and improve future recommendations while maintaining user flexibility in applying to multiple positions
3Measurement precision
If automated machine learning models are implemented, then matching accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the credential matching system into distinct modular components: credential parsing module, skill extraction module, requisition analysis module, and matching algorithm module. Each module performs a specific function and can be independently trained and optimized, reducing overall system complexity while maintaining high matching accuracy through specialized processing at each stage
Data Source
AI summary
Disclosed embodiments provide a framework for processing sets of credentials in real-time using machine learning models to identify requisitions that can be recommended to users. In response to receiving a set of credentials from a user, a system assigns a classification to the set of credentials based on a set of characteristics associated with the set of credentials. A machine learning model is used to assign tags to the text of the set of credentials. These tags correspond to the set of characteristics. Using these tags, a set of open requisitions are identified and provided to the user.