Psychometric Prediction System for Recruitment Accuracy

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Solution Overview

Problem

Current recruitment processes often result in unsuitable job applicants being shortlisted due to a lack of awareness about required psychometric attributes, leading to false positives and false negatives, and involve significant manual overheads in screening resumes.

Innovation Solution

A system and method for predicting psychometric attributes relevant for job positions by analyzing job descriptions and answers to psychometric questions, using machine learning algorithms to generate predictor models that provide threshold scores and relevant skills for recruitment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual screening of resumes is performed by recruitment agencies, then job applicants can be evaluated for psychometric attributes, but the process involves significant manual overheads and results in false positives and false negatives due to lack of awareness about required attributes

Engineering Contradiction:

Improveaccuracy of identifying suitable job applicantsVSAvoidmanual overheads in screening resumes

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical screening process with an automated machine learning system. The system uses trained models to analyze resumes and predict psychometric attributes automatically, eliminating the need for manual review while improving accuracy through consistent application of trained criteria.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces machine learning models as an intermediary between the resume and the recruitment decision. These models act as a mediator that translates resume content into predicted psychometric scores, providing an objective bridge between applicant data and hiring decisions.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If recruitment agencies manually evaluate resumes without knowledge of required psychometric attributes, then the screening process can be completed, but unsuitable applicants are shortlisted and suitable applicants are rejected

Engineering Contradiction:

Improveefficiency of recruitment processVSAvoidaccuracy of identifying suitable job applicants

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent performs preliminary training of machine learning models using historical data before actual recruitment. This preliminary action establishes the foundation for accurate predictions by pre-learning the relationship between resume features and successful job performance, ensuring reliable screening from the start.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback loops where prediction results are continuously refined based on actual job performance outcomes. This feedback mechanism improves the reliability of predictions over time by adjusting model parameters based on real-world validation of which predictions were accurate.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11797938B2Prediction of psychometric attributes relevant for job positions

Publication Date: 2023.10.24 OPENSESAME

AI summary

Predicting psychometric attributes and relevant skills for a first job position includes generation of predictor models based on test data of tests users. The test data includes resumes of the test users, job descriptions of job positions of the test users, historical data of the test users, and answers provided by the test users to psychometric questions. The predictor models are then used to predict the psychometric attributes and the relevant skills based on target data, which is a first job description of the first job position.