Dynamic Threshold Image Set for Recruiter-Specific Applicant Matching
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Solution Overview
Problem
Existing systems for introducing applicants to recruiters face challenges in determining appropriate criteria for similarity between face images, leading to incorrect matches and increased labor for recruiters, as they struggle to set uniform threshold values that align with human perception.
Innovation Solution
An introduction system that uses a threshold image set generation system to determine a set of reference vectors based on recruiter feedback, calculating a satisfaction level for applicants by comparing their images to these vectors, and notifying recruiters of suitable matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed threshold value is used to determine similarity between face images, then the matching process is simple and fast, but the accuracy of identification decreases because it cannot adapt to individual recruiter preferences
Solution Approach 1:
The patent implements dynamic threshold adjustment by allowing the system to learn and adapt similarity thresholds based on recruiter feedback. Instead of using fixed threshold values, the system dynamically modifies thresholds according to actual recruiter preferences and decision patterns, thereby resolving the contradiction between simple fast matching and accurate identification.
Solution Approach 2:
The patent incorporates feedback mechanisms where recruiter decisions (accept/reject) on introduced applicants are used to adjust and refine the similarity threshold values. This feedback loop enables the system to continuously improve identification accuracy while maintaining efficient matching operations, addressing both productivity and precision requirements.
2Quantity of substance
If the threshold for similarity is set low to include more applicants, then more applicants are introduced to recruiters, but the number of incorrect matches increases leading to more labor for recruiters
Solution Approach 1:
The patent dynamically changes the similarity threshold parameter based on learned recruiter preferences and feedback data. By adjusting this critical parameter, the system optimizes the balance between introducing enough applicants to maintain quantity while filtering out incorrect matches to reduce recruiter labor time, thus resolving the contradiction between these two objectives.
Solution Approach 2:
The patent performs preliminary learning and threshold adjustment based on initial recruiter feedback before full-scale applicant introduction. This preliminary action allows the system to pre-establish accurate threshold values that prevent incorrect matches from being introduced, thereby reducing recruiter labor hours while maintaining adequate applicant quantity.
3Reliability
If the threshold for similarity is set high to ensure quality matches, then fewer incorrect matches are introduced, but the number of suitable applicants decreases
Solution Approach 1:
The patent implements dynamic threshold adjustment that adapts to different recruiters and different applicant contexts. Rather than using a uniformly high threshold that would reduce applicant quantity, the system dynamically optimizes thresholds to maintain high match quality while preserving an adequate pool of suitable applicants through continuous learning from feedback.
Solution Approach 2:
The patent uses recruiter feedback on introduced applicants to continuously refine threshold settings. This feedback mechanism allows the system to maintain high match quality by learning from actual recruiter decisions while adjusting thresholds to ensure sufficient suitable applicants remain in the pool, resolving the contradiction between reliability and quantity.
4Measurement precision
If individualized threshold values are created for each recruiter to match their specific preferences, then identification accuracy improves, but the system complexity increases
Solution Approach 1:
The patent implements self-service automation where the system automatically learns and creates individualized threshold values for each recruiter through machine learning algorithms. Instead of requiring manual configuration of complex individualized thresholds, the system autonomously adapts to each recruiter's preferences through feedback, thereby achieving high identification accuracy without proportionally increasing system complexity.
Solution Approach 2:
The patent automatically adjusts and optimizes threshold parameters for each recruiter based on learned preferences and feedback data. This automated parameter change process enables individualized accurate matching while avoiding the manual complexity that would otherwise be required to create and maintain custom threshold values for each recruiter.
Data Source
AI summary
An introduction system is capable of identifying, with a high degree of precision, applicants who fulfill recruiter's requirements. An applicant identification unit 81 identifies applicants who satisfy minimum criteria on the basis of images input by applicants and a threshold image set for use in discriminating the minimum criteria for determining whether a recruiter is satisfied. A notification unit 82 notifies the recruiter of the applicants identified by the applicant identification unit 81. The introduction system may further include a threshold image set determination unit that determines the threshold image set on the basis of a result of a determination made by the recruiter on whether a sample image set prepared as samples fulfill the recruiter's requirements.