AUTOMATED RESUME SCREENING USING AI

Abstract

AUTOMATED RESUME SCREENING USING Al ABSTRACT Automated Resume Screening using Al is a transformative approach to streamlining the recruitment process by leveraging artificial intelligence (Al) and machine learning (ML) technologies. This system automates the initial screening ofjob applicants' resumes, allowing organizations to efficiently sift through large volumes of applications. Al-powered tools extract and analyze key data such as skills, education, and work experience, matching candidates to job descriptions through natural language processing and keyword matching. The system ranks and filters resumes based on predefined criteria, improving accuracy, reducing hiring time, and minimizing human bias. While offering significant efficiency, challenges such as potential data bias and overreliance on keyword matching require careful consideration. As Al evolves, the technology promises enhanced resume screening with deeper context understanding and scalability, making it an essential tool in modem talent acquisition strategies. Automated Resume Screening using Al revolutionizes recruitment by leveraging artificial intelligence and machine learning to efficiently handle large volumes of job applications. This technology employs Natural Language Processing to extract and analyse resume data, matching candidates to job descriptions based on skills, experience, and qualifications. By automating the screening process, Al significantly reduces time-to-hire, increases accuracy, and minimizes human bias. Despite its advantages, challenges such as data bias and over-reliance on keyword matching must be addressed. As Al continues to advance, it promises further enhancements in contextual understanding and predictive analytics, reshaping the future of talent acquisition.

Specification

FORM - 2
THE PATENTS ACT, 1970
(39 OF 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(see Section 10 & rule 13)

I. title OF THE invention : AUTOMATED RESUME SCREENING USING Al
2. APPLICANT:
Saveetha Institute of Medical and
Technical Sciences
NAME NATIONALITY ADDRESS
INDIAN Saveetha Nagar, Thandalam,
Chennai - 602 105,Tamil Nadu,
India
^^PREAMDbE'TO-THE-DESCKTPTIONr
20-Nov-2024/138526/202441089935/Form 2(Title Page)
The following specification describes the invention and how it is to be performed.
4. COMPLETE SPECIFICATION
The following specification particularly describes the invention and the manner in which it is tobe
performed. .......................Separate sheet is attached......................................
5. DESCRIPTION
Separate sheet is attached______
6. CLAIMS
Separate sheet is attached______
DATE:
SIGNATURE:
NAME
1. ABSTRACT OF THE INVENTION
Separate sheet is attached-

Dr. B.RAMESH
PRINCIPAL
SI MATS SCHOOL OF ENGINEERING
SAVEETHA INSTITUTE OF MEDICAL AND
TECHNIAL SCIENCES

Note:-
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*Tic (V)/cross(x) whichever is applicable/not applicable in declaration inpara-9.
*Name of the Inventor and applicant should be given in full, family name in the beginning.
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Bank : INDUSIND BANK
Branch : NUNGAMBAKKAM
( SAVEETHA UNIVERSITY )
DD No : 402543
DD Date: 14/11/2024
DD Amount: 8,900/-

AUTOMATED RESUME SCREENING USING Al
PREAMBLE TO THE DESORPTION

FIELD OF INVENTION:
Al techniques like machine learning enable the development of models that can learn from historical hiring data, identify patterns in resumes, and predict candidate suitability. NLP is a subfield of Al that helps in processing and understanding human language. In resume screening, NLP is used to extract relevant skills, experience, and qualifications from unstructured text (resumes) and match them against job requirements. This is the broader domain in which Al-driven resume screening tools are developed. HR Tech involves the automation of recruitment processes, including candidate tracking, interview scheduling, and performance evaluation. Data science is applied to analyse vast amounts of candidate data efficiently. It enables models to
make data-driven decisions about the suitability of a resume, filtering large candidate pools based on predefined criteria. The invention automates the labour-intensive task of manually screening resumes. Al systems are trained to shortlist candidates by analysing qualifications, work experience, and other relevant factors with
high speed and accuracy.

BACKGROUND OF THE AUTOMATED RESUME SCREENING
Before the advent of modem technology, resume screening was a labor-intensive process reliant on manual review by human recruiters. This traditional method involved sorting through physical resumes or job applications, which required significant time and effort. Recruiters or hiring managers would manually assess
each resume to determine candidates qualifications, experience, and suitability for the role, often leading to long processing times and potential for oversight or bias.

SUMMARY OF THE AUTOMATED RESUME SCREENING:
By utilizing artificial intelligence and machine learning to expedite and improve the assessment of job applications, Automated Resume Screening with Al transforms the hiring procedure. This system uses Natural Language Processing to automatically collect and analyse resume data, matching applicants skills to open positions. As Al continues to advance, it promises further improvements in contextual understanding and predictive analytics, shaping the future of talent acquisition. Artificial intelligence algorithms are adept at managing massive amounts of resumes, which results in quicker, more precise screening and less prejudice from humans.

AUTOMATED RESUME SCREENING USING Al
COMPLETE SPECIFICATION
Specifications
* Experience & Skills Evaluation: Assess candidates experience and skill sets to determine suitability for the role.

* Scoring System: Assign scores based on predefined criteria such as qualifications, years of experience, skills, certifications, etc.

* Bias Reduction: Implement measures to reduce bias based on gender, age, or ethnicity by anonymizing specific details if necessary.

* Recommendation System: Suggest top candidates based on overall score and match with job requirements.

AUTOMATED RESUME SCREENING USING Al
DESCRIPTION
Automated Resume Screening with Al leverages cutting-edge artificial intelligence to revolutionize the hiring process. This system automates the tedious task of manually reviewing resumes, providing a faster, more accurate, and efficient way to screen job applicants. Using Machine Learning algorithms, the system extracts and analyse key information from resumes, such as education, skills, and work experience. By
comparing candidates qualifications against job requirements, the Al-powered solution ranks applicants based on their suitability for the role. This process eliminates bias by focusing on objective data and can anonymize certain personal details to promote diversity. The result is a list of top candidates, saving recruiters significant time and effort while ensuring the most qualified individuals are considered. The system is highly scalable and adaptable, capable of integrating into existing HR workflows. It significantly reduces the time-to-hire and improves the quality of hires by focusing on relevant qualifications and experience.

AUTOMATED RESUME SCREENING USING Al
CLAIM
We Claim
1. Claim: This Al-driven solution will reduce time-to-hire, ensure high-quality candidate shortlists, and improve overall recruitment outcomes by providing an unbiased, data-driven approach to screening applicants.

2. Claim: It will seamlessly integrate into existing hiring workflows, providing greater
scalability, consistency, and fairness in the recruitment process.

3. Claim: By quickly filtering out unqualified candidates, the system helps recruiters focus on
interviewing the best-suited applicants, accelerating the entire hiring process and reducing the time it takes to move from application to interview stage.

4. Claim: The Al screening system is versatile and can be adapted for a wide range of industries and
job types, from technical and managerial roles to creative and entry-level positions.

5. Claim: Automated resume screening helps eliminate unnecessary delays in the hiring process, providing a more streamlined and transparent experience for candidates, which can improve employer branding and candidate satisfaction.

6. Claim: The system generates detailed reports and insights, such as candidate trends, common
qualifications, and skill gaps, helping companies make informed decisions not only for individual
hires but also for future workforce planning.

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