Description:BACKGROUND
Technical Field
[0001] The embodiment herein generally relates to computing systems and data processing and more particularly, an Artificial Intelligence (AI)-driven interview system for N number of technical candidates screening and method thereof.
Description of Related Art
[0002] Generally, Human Resources (HR) management system or a HR team preforms technical employee screening by manually analysing each candidate’s resume and interviewing each candidate individually based on a job description. This way of screening candidates is time consuming and inefficient.
[0003] Accordingly, there remains a need for an AI-driven interview system for N number of technical candidates screening and method thereof.
SUMMARY
[0004] In view of the foregoing, embodiments herein an Artificial Intelligence (AI)-driven interview system for N number of technical candidates screening. One or more server is configured with the AI-driven interview system. The AI driven interview of the N number of candidates are performed by the system in the one or more server using a load balancing technique. The system includes a technical candidates screening controller which is configured with the memory and the at least one processor.
[0005] The controller is configured to generate one or more dynamic questions related to a job description and N number of candidates’ profile. The controller is further configured to perform real-time analysis of N number of candidates responses using a speech recognition and a text analysis. The controller is further configured to evaluate analysed N number of candidates based on a predefined metrics.
[0006] According to some embodiments herein, the predefined metrics comprises technical knowledge, problem-solving skills, and cultural fit of the N number of candidates.
[0007] According to some embodiments herein, the system provides one or more access levels for administrators, interviewers, and N number of candidates for secure and efficient management of the AI driven interview.
[0008] According to some embodiments herein, the system comprises Natural Language Processing (NLP), Machine Learning (ML), and Sentiment Analysis for preforming the speech recognition and the text analysis of the N number of candidates responses.
[0009] According to some embodiments herein, the system encrypts the N number of candidates data.
[00010] In an aspect the embodiments herein provide a method of performing an artificial Intelligence (AI)-driven interviews for N number of technical candidates screening. The method includes configuring, one or more server with the AI-driven interview system. The AI driven interview of the N number of candidates are performed by the system in the one or more server using a load balancing technique. The method further includes configuring, a technical candidates controller, with the memory and the at least one processor. The method further includes generating, by the controller one or more dynamic questions related to a job description and N number of candidates’ profile. The method further includes real-time analysing, by the controller of N number of candidates responses using a speech recognition and a text analysis. The method further includes evaluating, by the controller, analysed N number of candidates based on a predefined metrics.
[00011] According to some embodiments herein, the predefined metrics comprises technical knowledge, problem-solving skills, and cultural fit of the N number of candidates.
[00012] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein, and the embodiments herein include all such modifications.
BRIEF DESCRIPTION OF THE DRAWINGS
[00013] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[00014] FIG. 1 illustrates an AI driven interview environment, according to some embodiments herein;
[00015] FIG. 2 illustrates hardware components of an AI-driven interview system for N number of technical candidates screening, according to some embodiments herein;
[00016] FIG. 3 illustrates a plurality of modules stored in a memory according to some embodiments herein;
[00017] FIG. 4 illustrates a flow chart showing a method of performing an artificial Intelligence (AI)-driven interviews for N number of technical candidates screening, according to some embodiments herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[00018] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[00019] As mentioned, there remains a need for an AI-driven interview system for N number of technical candidates screening and method thereof. Referring now to the drawings, and more particularly to FIGS. 1 through 4, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.
[00020] FIG. 1 illustrates an AI driven interview environment 100, according to some embodiments herein. The AI driven interview environment 100 includes one or more server 102A-102N configured with an AI-driven interview system 104. N number of candidates login to the AI-driven interview system 104 through their login credentials. The AI driven interview of the N number of candidates are performed by the system 104 in the one or more server 102A-102N using a load balancing technique. The N number of candidates are authenticated using multi-factor authentication (MFA). In a non-limiting example, the one or more servers 102A-102N maybe cloud platforms such as AWS or Azure for high availability and reliability.
[00021] According to some embodiments, the system 104 can handle multiple interviews simultaneously, allowing the system 104 to interview for example over 20 candidates at a time. The system 104 utilizing cloud services ensures that the system 104 can be scale up or down based on demand without compromising performance. Further no technical Human Resource (HR) is required to perform a technical interview of the candidates.
[00022] According to some embodiments, the system 104 is built using modern web technologies such as Reacts or Angular for a responsive and user-friendly interface. Furthermore, the system 104 is developed using Python and Django, with APIs for NLP and ML provided by services like OpenAI and Google Cloud.
[00023] FIG. 2 illustrates hardware components of the AI-driven interview system 104 for N number of technical candidates screening, according to some embodiments herein. The AI-driven interview system 104 includes a camera 201, a memory 202, a processor 204, a technical candidates screening controller 206, and a communicator 208.
[00024] According to some embodiments herein, the camera 201 captures one or more images of the candidates during the interview process. The one or more images are processed by the system 104 to ensure that there is no proxy person attending the interview instead of the actual candidate. Further the one or images are analysed to check a confidence level of the candidate (for example the system evaluates the candidates as if an actual HR evaluates the candidate but in an efficient and faster manner).
[00025] According to some embodiments herein, the controller 206 is configured to generate one or more dynamic questions related to a job description and N number of candidates’ profile. The controller 206 is further configured to perform real-time analysis of N number of candidates responses using an image processing, a speech recognition, and a text analysis. The controller 206 is further configured to evaluate analysed N number of candidates based on a predefined metrics.
[00026] According to some embodiments herein, the predefined metrics includes technical knowledge, problem-solving skills, and cultural fit of the N number of candidates. Further the system 104 can provide detailed analysis report of all N candidates interviewed. Also, the system 104 provides a best fit candidate or candidates for a job role requirement.
[00027] The system 104 provides one or more access levels for administrators, interviewers, and N number of candidates for secure and efficient management of the AI driven interview. The system 104 includes Natural Language Processing (NLP), Machine Learning (ML), and Sentiment Analysis for preforming the speech recognition and the text analysis of the N number of candidates responses. The system 104 encrypts the N number of candidates data. The memory 202 includes a scalable SQL database such as PostgreSQL or MySQL to store candidate data and interview results. The system 104 utilizes caching mechanisms to reduce latency and improve response times during peak usage.
[00028] The system 104 includes intuitive dashboard for recruiters to monitor interview progress and review candidate performance. The system 104 provides detailed analytics and insights on candidate performance, interview trends, and system usage.
[00029] FIG. 3 illustrates a plurality of modules stored in a memory according to some embodiments herein. The plurality of modules include a question generating module 302, a response analysis module 304, an evaluation metrics module 310, and a load balancing module 312. The response analysis module 304 includes an image processing module 305, a natural language processing module 306, and a sentiment analysis module 308.
[00030] Further the question generating module 302 generates one or more dynamic questions related to a job description and N number of candidates’ profile. The image processing module 305 processes the one or more images of the N of number candidates. The response analysis module 304 performs real-time analysis of N number of candidates responses using one or more processed images of the N number of candidates, speech recognition of the N number of candidates, and text analysis of the N number of candidates answers. The evaluation metrics module 310 evaluates analysed N number of candidates based on a predefined metrics. The load balancing module 312 executes the load balancing techniques to divide the AI-drive interviews across the one or more servers 102A-102N
[00031] According to some embodiments, the system 104 uses NLP to understand and process candidate responses in real-time. The system 104 includes ML algorithms continuously learn from previous interviews to improve the accuracy of candidate evaluations. The system analyzes verbal and non-verbal cues to assess the candidate's confidence, communication skills, and emotional intelligence
[00032] FIG. 4 illustrates a flow chart showing a method 400 of performing an artificial Intelligence (AI)-driven interviews for N number of technical candidates screening, according to some embodiments herein. At step 402, the method 400 includes configuring, one or more server 102A-102N with the AI-driven interview system 104. The AI driven interview of the N number of candidates are performed by the system 104 in the one or more server 102A-102N using a load balancing technique. At step 404, the method 400 includes configuring, a technical candidates screening controller 206, with the memory 202 and the at least one processor 204. At step 406, the method 400 includes generating, by the controller 206, one or more dynamic questions related to a job description and N number of candidates’ profile. At step 408, the method 400 includes real-time analysing, by the controller 206, of N number of candidates responses using a speech recognition and a text analysis. At step 410, the method 400 includes evaluating, by the controller 206, analysed N number of candidates based on a predefined metrics.
[00033] An advantage of the embodiments herein is that the system 104 automates the initial screening process, reducing the time and resources required for hiring.
[00034] An advantage of the embodiments herein is that the system 104 provides a standardized evaluation process, minimizing human bias.
[00035] An advantage of the embodiments herein is that the system 104 offers detailed analytics and reports to aid in decision-making.
[00036] An advantage of the embodiments herein is that the system 104 can handle large volumes of candidates, making it suitable for mass recruitment drives.
[00037] An advantage of the embodiments herein is that the system 104 by leveraging AI technologies, this system ensures that only the most suitable candidates are shortlisted for further evaluation, streamlining the hiring process and improving overall efficiency.
[00038] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practised with modification within the scope of the appended claims.
, Claims:1. An Artificial Intelligence (AI)-driven interview system (104) for N number of technical candidates screening, the system comprising:
one or more server (102A-102N) that is configured with the AI-driven interview system (104), wherein the AI driven interview of the N number of candidates are performed by the system (104) in the one or more server (102A-102N) using a load balancing technique;
wherein the system (104) comprises:
a camera (201)
a memory (202);
at least one processor (204);
a technical candidates screening controller (206), configured with the memory (202) and the at least one processor (204), wherein the controller (206) is configured to:
generate one or more dynamic questions related to a job description and N number of candidates’ profile;
perform real-time analysis of N number of candidates responses using one or more processed images of the N number of candidates, speech of the N number of candidates, and text analysis of the N number of candidates answers; and
evaluate analysed N number of candidates based on a predefined metrics.
2. The system (104) as claimed in claim 1, wherein the predefined metrics comprises technical knowledge, problem-solving skills, and cultural fit of the N number of candidates.
3. The system (104) as claimed in claim 1, wherein the system (104) provides one or more access levels for administrators, interviewers, and N number of candidates for secure and efficient management of the AI driven interview.
4. The system (104) as claimed in claim 1, wherein the system (104) comprises Natural Language Processing (NLP), Machine Learning (ML), and Sentiment Analysis for preforming the speech recognition and the text analysis of the N number of candidates responses.
5. The system (104) as claimed in claim 1, wherein the system (104) encrypts the N number of candidates data.
6. A method (400) of performing an artificial Intelligence (AI)-driven interviews for N number of technical candidates screening, the method (400) comprising:
configuring, one or more server (102A-102N), with the AI-driven interview system (104), wherein the AI driven interview of the N number of candidates are performed by the system (104) in the one or more server (102A-102N) using a load balancing technique;
configuring, a technical candidates screening controller (206), with the memory (202) and the at least one processor (204);
generating, by the controller (206) one or more dynamic questions related to a job description and N number of candidates’ profile;
performing real-time analysis, by the controller (206), of N number of candidates responses using a speech recognition and a text analysis; and
evaluating, by the controller (206), analysed N number of candidates based on a predefined metrics.
7. The method (400) as claimed in claim 6, wherein the predefined metrics comprises technical knowledge, problem-solving skills, and cultural fit of the N number of candidates.