ANALYTICAL MODEL FOR RECRUITMENT THROUGH AI

Abstract

This analytical model leverages artificial intelligence (Al) to revolutionise recruitment processes by integrating a range of cutting-edge technologies. Machine Learning (ML) algorithms are deployed to analyse vast candidate databases, accurately identifying the best-fit candidates. At the same time, Natural Language Processing (NLP) extracts and matches relevant information from candidate profiles and job descriptions to improve the precision of talent matching. Robotic Process Automation (RPA) automates repetitive tasks like interview scheduling, freeing recruiters to focus on more strategic and value-added aspects of hiring. The ■ mode! further enhances candidate profiling through deep learning, which recognises intricate patterns in hiring data and trends. Meanwhile, NLP-based tools automate resume screening using sentiment analysis and keyword extraction, reducing human bias and expediting the process. Video interview assessments utilise computer vision to assess emotional intelligence and communication skills, analysing non-verbal cues such as facial expressions and body language. Integrated Al chatbots automate early-stage candidate interactions, providing continuous engagement and 24/7 support throughout the application process. Blockchain technology ensures secure, verifiable credential verification, enhancing trust in candidate qualifications. Predictive analytics enables recruiters to forecast future talent needs, while Explainable Al clarifies the reasoning behind Al-driven candidate recommendations, offering transparency and better decision-making for recruiters.

Specification

FIELD OF THE INVENTION
The field of the invention relates to the integration of artificial intelligence (Al) in recruitment and talent acquisition processes. Specifically, it encompasses the application of machine learning (ML), natural language processing (NLP), robotic process automation (RPA), blockchain technology, computer vision, and predictive analytics to streamline and optimise the hiring lifecycle. This invention focuses on automating key recruitment tasks, improving candidate-job matching, reducing biases, enhancing decision-making transparency, and enabling secure verification of credentials. It addresses the growing need for data-driven, efficient, and ethical recruitment methods in modem, competitive job markets.


Background of the proposed invention:
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Recruitment has long been a labour-intensive and time-consuming process, requiring extensive manual effort to sift through vast numbers of resumes, screen candidates, schedule interviews, and make hiring decisions. Traditional methods often suffer from inefficiencies, subjective biases, and difficulty matching candidates to the right job roles. As the job market becomes increasingly competitive and candidate pools grow, organisations are pressured to make faster, more precise, and informed hiring decisions. In recent years, the emergence of artificial intelligence (Al) has offered promising solutions to many of these challenges. Technologies like Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA) have the potential to automate and enhance various stages of the recruitment process. Al can help identify the best-fit candidates from massive data sets, automate repetitive

tasks like interview scheduling, and even analyse candidates' non-verbal communication during video interviews through computer vision. Blockchain can further enhance the security and reliability of the hiring process by providing verifiable credentials for candidates. Despite the potential benefits, organisations remain hesitant to fully adopt Al in recruitment due to concerns about data privacy, ethical considerations, and transparency in Al-driven decision­making. These challenges are compounded by fears of algorithmic bias and the difficulty in ensuring that Al systems treat all candidates fairly and equitably. The proposed invention addresses these challenges by developing a comprehensive Al-driven recruitment model. This model integrates various Al technologies to improve hiring efficiency, accuracy, and


transparency while addressing ethical concerns. This model also focuses on reducing human
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bias, safeguarding candidate data, and providing predictive analytics to help organisations
anticipate future talent needs.
Summary of the proposed invention:
The proposed invention is an Al-driven analytical model designed to transform the recruitment process by integrating advanced technologies such as Machine Learning (ML), Natural Language Processing (NLP), Robotic Process Automation (RPA), Blockchain, and Computer Vision. This model automates and optimises key recruitment tasks, making hiring faster, more
accurate, and less biased.

Key components of the invention include:
1. Al-Driven Candidate Matching: Using ML algorithms to analyse large candidate databases and job descriptions, the model identifies the best-fit candidates based on
qualifications, skills, and experiences.
2. Natural Language Processing (NLP) for Resume Screening: NLP automates the extraction and analysis of information from resumes, matching it with job descriptions through keyword extraction, sentiment analysis, and relevance scoring. This reduces
human bias and increases precision.


Robotic Process Automation (RPA): The model leverages RPA to handle repetitive tasks such as interview scheduling, sending follow-up emails, and initial candidate .outreach, freeing recruiters to focus on strategic decision-making.
4. Video Interview Assessment with Computer Vision: The model utilises computer vision to analyse non-verbal cues such as facial expressions, body language, and emotional intelligence during video interviews. This provides an additional layer of
candidate assessment beyond verbal responses.
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5. Al Chatbots for Candidate Engagement: Al-powered chatbots automate early-stage candidate interactions, offering 24/7 support, answering frequently asked questions, and keeping candidates engaged throughout the recruitment process.
6. Blockchain for Credential Verification: To enhance the trustworthiness of candidate qualifications, the model integrates blockchain technology for secure and tamper-proof verification of educational credentials, work experience, and certifications.
7. Predictive Analytics: The model applies predictive analytics to forecast future talent needs based on hiring trends, industry changes, and organisational goals, enabling
recruiters to stay ahead of talent demands.

Brief description of the proposed invention:
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The proposed invention is a comprehensive Al-based analytical model designed to optimise and automate various stages of the recruitment process by leveraging several advanced
technologies, each serving specific functions.
1. Machine Learning (ML) for Candidate Matching:
The model employs supervised and unsupervised ML algorithms to process large candidate resumes and job description datasets. It uses feature extraction techniques such as term frequency-inverse document frequency (TF-IDF) and word embeddings (e.g., Word2Vec, BERT) to identify relevant skills, experiences, and qualifications. The model continuously learns from past hiring decisions to improve the accuracy of candidate-job fit predictions.
2. Natural Language Processing (NLP) for Resume Parsing and Matching:
o NLP techniques, including Named Entity Recognition (NER) and Part-of- Speech (POS) tagging, are used to parse candidate resumes and extract structured data such as work history, education, and skill sets. NLP models are also employed to match these parsed profiles to job descriptions using algorithms like cosine similarity and Latent Dirichlet Allocation (LDA) for
keyword and topic-based relevance matching

Sentiment analysis further analyses the tone of cover letters or written assessments to gauge candidates' motivations and cultural fit.
3. Robotic Process Automation (RPA) for Administrative Tasks:
o The model incorporates RPA bots to automate repetitive and manual tasks in the recruitment process. These bots are configured with triggers and workflows using platforms like UiPath or Automation Anywhere to handle tasks such as:
■ Automatically scheduling interviews based on candidate and recruiter
availability.
■ Sending routine follow-up communications to candidates.
■ Tracking and updating candidate status in Applicant Tracking Systems
(ATS).
4. Computer Vision for Video Interview Analysis:
o Video interviews are processed using computer vision algorithms, such as OpenCV or Tensor Flow-based facial recognition models, to analyse candidates' non-verbal cues. Using facial expression recognition software, the model extracts facial features and tracks emotions (e.g., happiness, sadness, surprise).
Body posture and hand gestures are also analysed to evaluate candidates' emotional intelligence, communication skills, and overall demeanour.

Al Chatbots for Candidate Engagement:
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o The invention includes Al-powered chatbots, built using NLP frameworks like Rasa or Google Dialogflow, to handle initial candidate queries and provide real­time responses. These bots guide candidates through the application process, answer frequently asked questions, and conduct pre-screening by asking qualification-related questions. The chatbot remains active 24/7, ensuring
continuous engagement.

Blockchain for Credential Verification:
o Blockchain technology is integrated into the model to create a decentralised, tamper-proof ledger for verifying candidate credentials. Using platforms like Ethereum or Hyperledger, the system enables candidates to store their educational qualifications, certifications, and work history in a secure digital format, ensuring that recruiters can easily verify their authenticity.
7. Predictive Analytics for Talent Forecasting:
o Predictive analytics tools are embedded in the system to forecast future hiring • needs. Historical hiring data, industry trends, and economic indicators are analysed using time series models like ARIMA or decision tree-based algorithms like Random Forest to predict talent demands and skill gaps. These insights help organisations plan their recruitment strategies proactively.

We Claim:
1) An Al-driven recruitment model that integrates Machine Learning (ML), Natural Language Processing (NLP), Robotic Process Automation (RPA), Blockchain, and Computer Vision to automate and optimise recruitment processes, including candidate matching, resume screening, interview assessments, and credential verification, providing a comprehensive
system for efficient, transparent, and bias-free hiring.
2) The Al-driven recruitment model of claim 1, wherein Machine Learning algorithms are employed to analyse candidate data and job descriptions, using feature extraction techniques such as term frequency-inverse document frequency (TF-IDF) and word embeddings.
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3) The Al-driven recruitment model of claim 1, wherein Natural Language Processing (NLP) is used for resume parsing, named entity recognition, and part-of-speech tagging to extract structured information from unstructured candidate resumes, automatically matching them to relevant job descriptions through keyword and topic modelling algorithm.
4) The Al-driven recruitment model of claim 1, wherein Robotic Process Automation (RPA) automates repetitive recruitment tasks, including scheduling interviews, sending follow-up emails, and tracking candidate status in Applicant Tracking Systems (ATS), thereby improving
operational efficiency and reducing administrative overhead.
5) The Al-driven recruitment model of claim 1, wherein computer vision technology is used in video interview assessments to analyse non-verbal cues, including facial expressions, body

language, and emotional intelligence, using models like OpenCV and TensorFlow, thereby providing deeper insights into candidate behaviour and communication skills.
6) The Al-driven recruitment model of claim 1, wherein Al-powered chatbots built on NLP frameworks engage candidates in real-time, answering queries, pre-screening applicants based on qualifications, and guiding them through the application process, available 24/7 to ensure
continuous candidate interaction and support.
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7) The Al-driven recruitment model of claim 1, wherein blockchain technology is integrated to securely verify and authenticate candidate credentials, including educational degrees and professional certifications, through a decentralised ledger system, ensuring the integrity and
trustworthiness of candidate qualifications.
8) The Al-driven recruitment model of claim 1, wherein predictive analytics tools are used to forecast future talent needs by analysing historical recruitment data, industry trends, and economic indicators using time series models and decision tree-based algorithms, allowing organisations to anticipate skill shortages and plan hiring strategies.

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