The present disclosure relates generally to a method for ranking profiles of candidates, on the basis of a predefined list of parameters; and particularly, to a method and a system for dynamic ranking of profiles of the candidates.
BACKGROUND
An organization (such as a business that offers products or services or both) seeks talent, such as people, that have the skills to assist the organization in providing its products or services. The organization can include a Human Resources (HR) department dedicated to searching for, evaluating, and acquiring such talent. Alternatively, or even more and more in addition, individuals or other departments in the organization can search for, evaluate, and acquire talent as the need arises for those individuals or departments, respectively. To acquire talent for a job in the organization, a job profile, which specifies criteria associated with the job, can be used as the basis. The job profile can be circulated and responses from persons interested in the job can be solicited. Responses, including resumes, from one or more persons can be received and evaluated to identify candidates for the job.
Generally, the process for filling an open position includes: 1) preparing a job description and listing the requirements for the opening, 2) screening the profiles and shortlisting the candidates, 3) conducting interviews with candidates, 4) selecting the suitable candidate(s), 5) making an offer to the selected candidate, 6) acceptance of the offer made by the candidate, and 7) joining of the candidate.
A most common practice among the recruiting entities is to prepare a job description for each job opening, and then to publish the job opening along with the job description on some Internet® web sites. The job seekers also provide their personal records to these Internet® web sites, for recruiters to view their profile. Moreover, for recruiters various networking websites have become a

source to contact and screen candidates of interest for job opening in an organization.
Due to the competitive nature of the industry, it is necessary to make accurate assessment of the candidates' profiles prior to make sure correct and smooth hiring. Further, shortlisting of the candidates' profiles is the most important step of the whole recruitment process.
Commonly, the skill set, job experience and any other information provided by the candidate are matched with the job description and requirements enlisted by the recruiter, accordingly a match and/or relevancy score is assigned to each profile, and subsequently profiles of the candidates are ranked. However, such ranking mechanism for shortlisting the suitable candidate lacks in including profile history related factors, such as rate of job change, additional certifications and so forth, for calculating the match and/or relevancy score. Thus, providing inaccurately ranked profiles to the recruiter.
Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with shortlisting profile(s) of candidate(s).
SUMMARY
The present disclosure seeks to provide a system and method for dynamic shortlisting of profile(s) of candidate(s) of interest for a recruiter for a relevant job opening. The present disclosure seeks to provide a solution to the existing problems associated with assessing the profile of the candidate of interest in a secured way. An aim of the present disclosure is to provide a solution that overcomes at least partially the problems encountered in prior art.
In an aspect, an embodiment of the present disclosure provides a computer implemented method of shortlisting of profile(s) of candidate(s) of interest for a recruiter for a relevant job opening, the method comprising:

- identifying the profiles of candidates of interest, using a data processing
arrangement, based on criteria of the job opening and candidate parameters;
- assigning a match and/or relevancy scores to each of the profiles of candidates of interest, using the data processing arrangement;
- employing at least one external factor using the data processing arrangement, wherein the at least one external factor is received from at least one external source;
- updating the match and/or relevancy scores to the profiles of candidates of interest, using the data processing arrangement, based on the at least one external factor;
- ranking the profiles of candidates of interest based on the updated match and/or relevancy scores, using the data processing arrangement;
- identifying the profiles of candidates of interest having similarities between the criteria of the job position and the candidate parameters, using the data processing arrangement, wherein a candidate with an exact match is assigned a highest rank; and
- displaying the ranked profiles of candidates of interest, using a display module,
in a desired layout to the recruiter.
The aforementioned method enables recruiter to rank the profiles of candidates of interest in a dynamic manner such that recruiter may include various factors in the ranking mechanism, which may affect the rack of the candidates of interest and improve the relevancy of the profile for the job opening. The Table 1 explained below illustrates the ranking of the profiles of candidates of interest in the dynamic manner.

In another aspect, an embodiment of the present disclosure provides a system for shortlisting of profiles of candidates of interest for a recruiter for a relevant job opening, the system comprising:
- a data processing arrangement, using one of a scoring algorithm or an artificial
intelligence algorithm, is configured to:
- identify the profiles of candidates of interest based on criteria of the job opening and candidate parameters;
- assign a match and/or relevancy score(s) to each of the profiles of candidates of interest;
- employ at least one external factor , wherein the at least one external
factor is received from at least one external source;
- update the match and/or relevancy scores to the profiles of candidates of interest based on the at least one external factor; - rank the profiles of candidates of interest based on the updated match and/or relevancy scores; and
- identify the profiles of candidates of interest having similarities between the criteria of the job position and the candidate parameters, wherein a candidate with an exact match is assigned a highest rank;

- a database arrangement to store the rank of the profiles of candidates of interest;
- a display module for displaying the ranked profiles profiles of candidates of interest in a desired layout to the recruiter; and
- a communication module coupled with processing arrangement, the database arrangement and the display module.
Embodiments of the present disclosure substantially eliminate or at least partially address the aforementioned problems in the prior art and enable secured

transaction of information shared by the job seeker, after the authentication and verification of his/her own identity.
Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments construed in conjunction with the appended claims that follow.
It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the different aspects of the disclosure.
BRIEF DESCRIPTION OF DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 is a flow chart illustrating of shortlisting of profile(s) of candidate(s) of interest for a recruiter for a relevant job opening, in accordance with an embodiment of the present disclosure; and
FIG. 2 is a schematic illustration of a network environment in which a system for shortlisting of profiles of candidates of interest for a recruiter for a relevant job opening can be implemented, in accordance with an embodiment of the present disclosure;

In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION OF EMBODIMENTS
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
In an aspect, an embodiment of the present disclosure provides a computer implemented method of shortlisting of profiles of candidates of interest for a recruiter for a relevant job opening, the method comprising:
- identifying the profiles of candidates of interest, using a data processing
arrangement, based on criteria of the job opening and candidate parameters;
- assigning a match and/or relevancy scores to each of the profiles of candidates of interest, using the data processing arrangement;
- employing at least one external factor using the data processing arrangement, wherein the at least one external factor is received from at least one external source;
- updating the match and/or relevancy scores to the profiles of candidates of interest, using the data processing arrangement, based on the at least one external factor;

- ranking the profiles of candidates of interest based on the updated match and/or relevancy scores, using the data processing arrangement;
- identifying the profiles of candidates of interest having similarities between the criteria of the job position and the candidate parameters, using the data processing arrangement, wherein a candidate with an exact match is assigned a highest rank; and
- displaying the ranked profiles of candidates of interest, using a display module,
in a desired layout to the recruiter.
In another aspect, an embodiment of the present disclosure provides a system for shortlisting of profiles of candidates of interest for a recruiter for a relevant job opening, the system comprising:
- a data processing arrangement, using one of a scoring algorithm or an artificial
intelligence algorithm, is configured to:
- identify the profiles of candidates of interest based on criteria of the job opening and candidate parameters;
- assign a match and/or relevancy score(s) to each of the profiles of candidates of interest;
- employ at least one external factor , wherein the at least one external
factor is received from at least one external source;
- update the match and/or relevancy scores to the profiles of candidates of interest based on the at least one external factor; - rank the profiles of candidates of interest based on the updated match and/or relevancy scores; and
- identify the profiles of candidates of interest having similarities between the criteria of the job position and the candidate parameters, wherein a candidate with an exact match is assigned a highest rank;

- a database arrangement to store the rank of the profiles of candidates of interest;
- a display module for displaying the ranked profiles profiles of candidates of interest in a desired layout to the recruiter; and
- a communication module coupled with processing arrangement, the database arrangement and the display module.
The present disclosure provides the aforesaid system and method for shortlisting of profile(s) of candidate(s) of interest for a recruiter for a relevant job opening. In this method the recruiter already has the access to a database having list of profiles of all the job seekers related to the job opening. The system based on criteria for the job opening, selects a few profiles of candidates of interest from the list of all the job seekers. Further, the system assigns a match and/or relevancy score to the profiles of interests and shorts the profiles based on the match and/or relevancy score. Specifically, in the aforementioned method and system enables the recruiter to get dynamic ranking of profiles based on at least one external factor, such as certifications obtained by the candidate, preferred joining date/period of the recruiter and so forth. Moreover, the aforesaid method and system ensures accurate shortlisting of the candidates of interest. In other words, the system and method enable the recruiter to shortlist profiles of candidates of interest that are highly matched with the job requirements.
In an embodiment, the present disclosure relates to system and method for shortlisting of profile(s) of candidate(s) of interest for a recruiter for a relevant job opening based on the exact match. In other words, the computer implemented method provides a perfect /exact match to the employers searching for candidates, and the candidates searching for job. This means that as a candidate who is looking for a specific job with specific salary in a specific city, the method finds the exact match of a job for the candidate. Further, as an employer who is searching for this particular candidate but also requires the right educated candidate, with the right experience, with specializations required for that job, the

method finds the exact match of candidates from the profiles of candidates of interest and ranks them as highest.
In accordance with the above embodiment, the computer implemented method comprises identifying the profiles of candidates of interest based on criteria of the job opening and candidate parameters. If match is exact, for example, if the candidate lives in the same city or the right range from job location, has the same requested gender, is looking for the same position, has a minimum of 2 matching specializations as needed, has the same requested education and experience, the method identifies the profile of this candidate as perfect match, and ranks the profile of this candidate as highest.
Throughout the description, the term "recruiter" refers to an individual who finds qualified candidates for a job opening and works to meet the demands of both the employer and the employee throughout the hiring process. The recruiter posts the job opening and looks for eligible individual suitable for a job opening. The recruiter, based on the information provided by the job seekers and suitability of the job seeker according to the requirement of a job position, selects a candidate of interest. In an embodiment, the recruiter finds the candidate of interest based on the profile summary of the candidate of interest. For example, the profile summary includes characteristics collected from the profile of candidates, like: city, gender, DL, position, specializations, education, experience and salary.
Throughout the description, the term "job seeker" refers to an individual or candidate who is actively looking for an employment opportunity/job and/or has applied to a job posting.
Throughout the description, the term "candidate of interest" refers to eligible job seekers sorted out by the recruiters based on a profile summary of the job seekers. The recruiters typically have many job seekers reaching out to them with resumes and/or job application. For making the task to select an eligible candidate, the

recruiters check the profile summary of the job seekers and based on the profile summary make a list of candidates of interest.
Throughout the description, the term "criteria of the job opening" refers to criteria required by the recruiter/ employer for hiring the candidate for a particular job opening. The criteria of the job opening comprises at least one of: city of job location, range of job location, gender of the candidate required, job position, job specialization required by the recruiter, job description provided by the recruiter, education requirements for the job, experience requirements for the job, suggested salary offered by the recruiter and driving license (DL) requirements by the job.
Throughout the description, the term "candidate parameters" refers to credentials of candidates and preferences chosen by the candidates when looking for a job. The candidate parameters comprise at least one of: city of the candidate, address of the candidate, gender of the candidate, desired designation of the candidate, specialization of the candidate, education of the candidate, experience of the candidate, expected salary by the candidate and driving license details of the candidate.
In accordance with the present disclosure, the computer implemented method comprises assigning a match and/or relevancy score(s) to each of the profile(s) of candidate(s) of interest. The term "Relevancy score" refers to the rank attached for each Candidate for Job match. In other words, relevancy from employers point of view refers to how much a candidate is relevant for the job, and from the candidate point of view how much is the job relevant for the job seeker himself. In an example, top score/rank is 14, therefore 14 means most relevant. Optionally, lower score/rank is 2, and 2 means most likely not relevant at all. Relevancy is based on all key factors explained with reference to Table 1 below.
In accordance with the present disclosure, the computer implemented method comprises employing at least one external factor, wherein the at least one external factor is received from at least one external source.

In accordance with the present disclosure, the computer implemented method comprises updating the match and/or relevancy scores to the profiles of candidates of interest based on the at least one external factor. In other words, based on the external factor the score of each of the profiles of candidates is updated by the method, i.e., the algorithm used by the method.
Throughout the description, the term "external factor" refers to a parameter considered to have and affect for matching candidates with jobs, in accordance with the system and method disclosed. In the present disclosure, the at least one external factor includes at least one of: market trends, average salaries for a job position, supply and demand of jobs, supply and demand of candidates, job positions per city, certifications obtained by the candidate, and preferred joining date as per the recruiter. Throughout the description, the term "external source" refers to a external database or storage device or publicly available data from websites.
In accordance with the present disclosure, the computer implemented method comprises ranking the profiles of candidates of interest based on the updated match and/or relevancy scores. Optionally, the ranking in this method starts from 2 and ends with 14. In an example, the method represents these ranks as playing card ranks: 2, 3,4, 5, 6, 7, 8, 9, 10, Jack, Queen, King, Ace and Joker. In another example, the method represents the ranks as 5.0/10, 5.5/10, 6.0/10, 6.5/10 7.0/10, 7.5/10, 8.0/10, 8.5/10, 9.0/10, 9.5/10, 10/10 and 9*/10.
According to the above embodiment, a perfect match is designed to score 14, which is the highest ranking, and equivalent to Royal Ace. In other words, perfect match means, that all criteria are fulfilled perfectly. The method compares the criteria of the job opening versus the candidates' parameters.
In an example, the criteria's used by the method are: City or Range from Job Location, Candidate vs Job Gender, Driving license requirements, Job required position vs. candidate desired designation, Specializations the employer requested

vs. what the candidate has to offer, Education-job vs. candidate, Experience-job vs. candidate and Salary range offered by employer vs. candidate minimum desired salary. Accordingly, in an example, if criteria are perfectly matched, the method rank this candidate as 14 (Ace or 10/10) for the job and rank this job 14 (Ace or 10/10) for the candidate. Accordingly, in an example, if criteria are not perfectly matched, the method starts with shifting the ranks according to the match it finds. For example, in Table 1 it can be seen how method finds the ranking for each match. The abbreviations used in the table, refer to as follows: Job Education = Jed, Candidate Education = CEd, Job Experience = JEx, Candidate Experience = CEx. The table indicates the mathematical equalities of ">" greater than, "<" less than, ">=" greater or equal to and "<=" less than or equal to.

Factor 14 13 12 11 10 9 8 7 6 5 4 3 2 Joker
Location • • • • • • • • • • • • • •
Designation • • • • • • • • • X • • • •
Position

Specialization Min
2 Min 1 Min
2 Min 1 Min
2 X
Oor 1 X Min 1 X
None Min 1 X
0 or 1 X
0 or 1 X
0 or 1 Mini
Experience • • • • X
CEx
< JEx then
10, CEx
> JEx then
11 X
CEx
< JEx then
9, CEx
> JEx then
10 • X
CEx
< JEx then
7, CEx
> JEx then
8 • X • X X •
Education CEd
=> JEd CEd
=> JEd X
CEd
< JEd X
CEd
< JEd then
11, CEd
> JEd then
12 • • X
CEd
< JEd then
8, CEd
> JEd then
9 X
CEd
< JEd then
7, CEd
> JEd then
8 • • X • X •

Salary • • • • • • • • X X X X • X
Gender • • • • • • • • • • • • • •
DL • • • • • • • • • • • • • •

In an exemplary embodiment, when posting a new job, the employer controls the requirements for the job. Accordingly, the major requirement is the position the employer is seeking a candidate for, but after stating that, the employer has other characteristics to control which candidate has to match for that job. The key factors as per the Table 1 are Specializations, Education, Experience and Salary. By controlling these factors, the employer can change the way matching of the candidates happen and can change the way ranking of the candidates is done in general.
In accordance with the present disclosure, the computer implemented method comprises identifying the profiles of candidates of interest having similarities between the criteria of the job position and the candidate parameters, wherein a candidate with an exact match is assigned a highest rank.
Throughout the description, the term "exact match" or "perfect match" refers to a condition of exact similarity between the criteria of the job opening and the candidate parameters.
In accordance with the present disclosure, the computer implemented method comprises displaying the ranked profiles of candidates of interest in a desired layout to the recruiter.
Throughout the present disclosure, the term "display module" as used herein, relates to a portable or fixed communication device selected from at least one of a mobile phone, a kindle, PDA (Personal Digital Assistant), a tablet, a computer, an electronic notebook or a smartphone. The display module is configured to display the ranking of candidates in the pre-defined layouts. All display devices can show the result of matches. The method at backend has all the logic and algorithm to do the matches and gives the scores and rank those matches. In an example, if the algorithm just matched a specific candidate to a job and ranked it as 10, the display device will show this rank on a predefined layout. Optionally, the layout can be either card layouts (ranking of Ace, King, Queen, Jack, Joker, 10, 9, 8, 7,

6, 5, 4, 3 and 2), numbers layout (5.0/10, 5.5/10, 6.0/10, 6.5/10 7.0/10, 7.5/10, 8.0/10, 8.5/10, 9.0/10, 9.5/10, 10/10 and 9*/10) or other layouts like pool layout (ranking as billiar balls), chess layout (ranking as the game tools) or other layouts.
In an embodiment, the disclosed method works in 4 synchronic phases. Phase 1: matching jobs per candidate, with highest ranking job on top. Phase 2: Allowing the candidates apply for those jobs (or reject them). Phase 3: Matching applied candidates per job of employer, with highest ranking candidate on top. Phase 4: allowing the employer and the candidate take actions and communicate on that list through the platform. The method matches candidates and posted job, inserting the profiles of candidates of interest into the matched list while sorting them with highest rank on top. Beneficially, this ensures that the employer will see best matched candidates on top and the candidates will see best matched jobs on top (matched candidates-jobs). Additionally, the scoring algorithm makes sure the employer will have a list of highest matched candidate to work with, while adding more and more best matched candidates-jobs as both candidates and employers make the necessary actions on the list. Optionally, phase 1 and phase 3 are automatic and generated by the system of the present disclosure. Optionally, phase 2 and phase 4 are manual and need the candidate and later the employer to take actions and respond. The scoring mechanism of the present disclosure matches the candidate-job and ask the candidate to take an action and decide whether to apply for the job or reject it. After and only if the candidate applies for the job, the employer needs to take an action and shortlist/request to interview the candidate.
Beneficially, the match of the disclosed method aims for prioritising high match of Candidate-Job. This way, the candidate can see and can apply for high ranked jobs. This ensures that the recruiters can also see high ranked candidates. The method provides the candidates and recruiters communicate through the platform disclosed by the system and ensures a high probability to hire the candidate.

In an embodiment, one other parameter that controls how the method matches candidates for this new job posted by the employer, is the setting parameter of how to run the search for this employer. If the parameter is set to "Criteria Search", the mechanism will match and rank using "scoring algorithm". If the setting parameter is set to "Smart AI agent search", the method uses "artificial intelligence algorithm" for matching and ranking. The method employs this AI algorithm that will take under consideration the key factors but will expand the fine tune the match based on the ML/AI algorithm.
Throughout the description, the term "scoring algorithm" or "scoring algorithms"
refers to an algorithm or set of steps used for calculating a match and/or relevance score of each candidate of interest by evaluating at least one of the skillset, preferences, experiences of the candidate with respect to the requirements of the recruiter.
Throughout the description, the term "artificial intelligence algorithm" or "artificial intelligence algorithms" refers to algorithm that is trained based on a plurality of data points and the external factors. Optionally, the artificial intelligence algorithm, is further operable for receiving a feed of a plurality of data points related to the candidates and the recruiters, for identifying matches of the candidates to the jobs and matches of the jobs to the candidates. Optionally, the plurality of data points includes at least one of: activity of the candidates and the employers, degree of involvement of the candidates and the employers, data collected from the candidates and the employers, data related to the external factors, analysis of the data collected by the system.
Optionally, the activity of the candidate or employer comprises: acceptance of suggestions (the system just matched a candidate with a job, if the user applied, this is considered acceptance, and the same for an employer, if the user applied and the employer shortlisted, this is also considered acceptance), time to accept, time to respond, total time app was active, time in the day app was active,

response to interviews, respond in feedback, response in WhatsApp and response for interactive voice response IVR.
Optionally, the degree of involvement of a user (candidate or employer) in the system is collected as a data and sorted as high involvement, medium and low involvement.
Optionally, the data collected from the user by the AI algorithm comprises feedbacks, comments, CV, all proofs, recordings of interviews, all images.
Optionally, the data collected from the publicly available sources by the AI algorithm comprises data collected from open websites, as per the job market in globally, and as per users.
In an embodiment, the method employs artificial intelligence algorithm. The method keeps a collection of all job-candidates match with the result of Hired or Rejected category. This collection of data is inserted this into trained file for the ML/AI. The method keeps a collection of durability and frequency of activity of each candidate, for example, how often the candidate logged in, times the candidates responded to events, the timings, the level on involvement (how many times responded, how many times check status etc., high medium and low involvement) and then inserts this collection of data into trained file for the machine learning/artificial intelligence algorithm. The method keeps a collection of durability and frequency of activity of each employer, for example, the method keeps all feedbacks of employers on candidates (reject feedback, interview feedback, overall feedback) and then inserts this data into trained file for the machine learning/artificial intelligence algorithm.
Optionally, the method performs analysis on data, such as average salaries per position, per category, per business type or business size, average time to respond per position or per city, total jobs per city, per category, per total candidates (supply demand), comparison of requested salaries vs suggested salaries vs past salaries vs salaries agreed while hired, and the like, and then inserts the data into

trained file for the machine learning/artificial intelligence algorithm. Optionally, the artificial intelligence algorithm analyses the data collected by system, to create many types of data points like trends, hot points, volumes, averages, deviation, standards, peaks and such analytical data.
Optionally, the artificial intelligence algorithm analyses the interview recording voices and the video calls images to understand the tone and emotions of the candidates and employers and the corresponding data is inserted by the system into the trained file for the machine learning/artificial intelligence algorithm. In an embodiment, the artificial intelligence algorithm collects information from external sources, to understand the external factors such as market trends, salaries and supply demand.
Optionally, the result of the machine learning/artificial intelligence algorithm provides new clusters of matched Candidates-Jobs, and new scoring based not only on criteria's, but also on probability of candidate to be hired, and probability of candidate to retain the job and succeed not only to hold the job, but also the make the employer satisfied from the process. The term high probability means high rank/score. Herein, the ranks are the same, 14 down to 2. The higher score in the method corresponds to a match that is closer to the requirements of the job. Accordingly, all matches in the cluster are acceptable, but matches that are closer to job requirements is given higher score.
Optionally, the artificial intelligence algorithm employs Random Forest, Naive Bayes and clustering methods to create those clusters and scores. Optionally, the clustering unsupervised model is employed by the AI algorithm to predict trends and future matches.
Optionally, whether the candidates accept and apply for the job, the artificial intelligence algorithm pushes this piece of information back to the AI model to train it for next loop, and after the employer also shortlist/interview the candidate and after hiring the candidate the method inserts this information into the model.

The method also keeps training the AI model if candidates or recruiters reject the match.
In another embodiment, if the method does not get any perfect match, the method starts expanding the match with different education, different experience, other specialization, higher salary. Accordingly, the ranking of such profiles of candidates of interest has lower values.
In accordance with another embodiment, the computer implemented method comprises to find match based on job path or job chain. The word "job chain" or "job path" here refers to the series of job designations holded by a candidate. The method comprises identifying the profiles of candidates of interest having the experience of the candidate or education of the candidate higher than the experience requirements or education requirements respectively. The method then matches the identified profiles of candidates of interest with higher job position. The word "higher job position" here refers to higher designation in a job. For example, the next job of experienced Sales Executive can be Senior Executive and then Sales Manager and Area Manager and Sales Head. Accordingly, all these positions are grouped as Sales and the sorting is according to the above example. In such cases, the profiles of candidates of interest matched are ranked with a jump of one rank. For example, in case the candidate is more experienced than required by the criteria of the job position, then instead of ranking 10 the method ranks the same candidate as 11.
In accordance with another embodiment, the computer implemented method comprises to find match based on cluster matching. The method comprises identifying the profiles of candidates who have less experience. For example, when the experience of the candidate is zero and is a fresher. The method then finds the match of the identified profiles of candidates of interest with the job positions from each of a plurality of clusters, wherein the plurality of clusters comprises the job positions that are similar to the desired position of the candidates. Therefore, in such cases instead of exact job position, the method

finds match of profiles of candidates with positions from each of the clusters. Accordingly, the profiles of candidates of interest is ranked.
Throughout the description, the term "cluster" or "cluster matching" refers to the task of grouping a set of data in such a way that data in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). The clusters here relate to groups that relate to job positions for a specific field. The different cluster relate to jobs in different fields.
In accordance with another embodiment, the computer implemented method comprises to find match when the candidates are looking for any job. In such cases the method identifies candidates who do not have any preference for finding a job and are looking for any suitable job. The method comprises identifying the profiles of candidates having the experience of candidate equal to zero and/or the education of candidate is up to senior secondary. The method then finds the match for the identified profiles of candidates of interest with the job positions from each of the plurality of clusters. In such cases, the method provides job suggestions for those who are unemployed and are ready to do any job that match their credentials.
In accordance with another embodiment, the computer implemented method finds match based on previous job match. The method comprises identifying the profiles of candidates of interest based on the previous employment details of the candidate. The method then finds a match for the identified profiles of candidates of interest with the job positions identical to the job position of the previous employment details of the candidate.
In an embodiment, the method employs artificial intelligence algorithm. The method keeps a collection of all job-candidates match with the result of Hired or Rejected category. This collection of data is inserted this into trained file for the ML/AI. The method keeps a collection of durability and frequency of activity of each candidate, for example, how often the candidate logged in, times the

candidates responded to events, the timings, the level on involvement (how many times responded, how many times check status etc., high medium and low involvement) and then inserts this collection of data into trained file for the machine learning/artificial intelligence algorithm. The method keeps a collection of durability and frequency of activity of each employer, for example, the method keeps all feedbacks of employers on candidates (reject feedback, interview feedback, overall feedback) and then inserts this data into trained file for the machine learning/artificial intelligence algorithm.
Optionally, the method performs analysis on data, such as average salaries per position, per category, per business type or business size, average time to respond per position or per city, total jobs per city, per category, per total candidates (supply demand), comparison of requested salaries vs suggested salaries vs past salaries vs salaries agreed while hired, and the like, and then inserts the data into trained file for the machine learning/artificial intelligence algorithm.
Optionally, the artificial intelligence algorithm analyses the interview recording voices and the video calls images to understand the tone and emotions of the candidates and employers and the corresponding data is inserted by the system into the trained file for the machine learning/artificial intelligence algorithm. In an embodiment, the artificial intelligence algorithm collects information from external sources, to understand the external factors such as market trends, salaries and supply demand.
Optionally, the result of the machine learning/artificial intelligence algorithm provides new clusters of matched Candidates-Jobs, and new scoring based not only on criteria's, but also on probability of candidate to be hired, and probability of candidate to retain the job and succeed not only to hold the job, but also the make the employer satisfied from the process. The term high probability means high rank/score. Herein, the ranks are the same, 14 down to 2. Optionally, the artificial intelligence algorithm employs Random Forest and Clustering methods to create those clusters and scores. The higher score in the method corresponds to

a match that is closer to the requirements of the job. Accordingly, all matches in the cluster are acceptable, but matches that are closer to job requirements is given higher score.
Optionally, whether the candidates accept and apply for the job, the artificial intelligence algorithm pushes this piece of information back to the AI model to train it for next loop, and after the employer also shortlist/interview the candidate and after hiring the candidate the method inserts this information into the model. The method also keeps training the AI model if candidates or recruiters reject the match.
Throughout the present disclosure, the term "data processing arrangement" as used herein, relates to a computational element that is operable to respond to and process instructions. Optionally, the processing module includes, but is not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing circuit. Furthermore, the term processing arrangement may refer to one or more individual processors, processing devices and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices and elements are arranged in various architectures for responding to and processing the instructions.
Optionally, the data processing arrangement is operable to implement the at least one scoring algorithm or the at least on artificial intelligence algorithm. Optionally, the candidate and the employer can choose/decide whether to use criteria match that uses scoring algorithm or smart AI match that uses artificial intelligence algorithm^
Further, the processing arrangement is communicably coupled to a database arrangement, wherein the database arrangement is operable to store the

information related to the jobseekers. In an example, the processing arrangement is coupled to the database arrangement using a communication network. Examples of the communication network include, but are not limited to, a cellular network, short range radio (for example, such as Bluetooth®), Internet, a wireless local area network, and an Infrared Local Area Network, or any combination thereof.
Throughout the present disclosure, the term "database arrangement" as used herein, relates to an organized body of digital information regardless of a manner in which the data or the organized body thereof is represented. Optionally, the database arrangement may be hardware, software, firmware and/or any combination thereof. For example, the organized body of digital information may be in a form of a table, a map, a grid, a packet, a datagram, a file, a document, a list or in any other form. The database arrangement includes any data storage software and system, such as, for example, a relational database like IBM 082 and Oracle 9. Furthermore, the data storage software and system may include MongoDB, HBase, Elastic Search, Neo4J, ArangoDB and so forth. Additionally, the database arrangement refers to a software program for creating and managing one or more databases. Optionally, the database arrangement may be operable to support relational operations, regardless of whether it enforces strict adherence to the relational model, as understood by those of ordinary skill in the art. Moreover, the data base arrangement may be operable to store the match and/or relevancy score, candidate ranking and pre-defined layouts for representing the ranking of the candidates.
Throughout the present disclosure, the term "communication module" as used herein, relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices, software modules and/or databases, whether available or known at the time of filing or as later developed. Additionally, the communication module employs communication network that can be carried out via any number of known protocols. In an embodiment, the

communication network is a wired network. In another embodiment, the network is a wireless network. In yet another embodiment, the communication network is a combination of the wired network and the wireless network. In yet another embodiment, the communication network is the Internet. In an embodiment, the present method is implemented in a software or a hardware or a combination thereof.
In an embodiment the database arrangement is communicatively coupled to the processing arrangement using the communication module. Moreover, the communication module is operable to access the database arrangement and communicate the accessed data to the processing module. Consequently, the coupling of processing arrangement and communication module enables exchange of data between the database arrangement and the processing arrangement. For example, the information of the candidate of interest stored in the database arrangement is accessible to the processing arrangement via the communication module.
In an embodiment, the pre-defined layouts for displaying the ranking of the candidates of interest may include but not limited to a card game layout, a chess game layout, a billiards game layout and so forth.
Specifically, in the card game layout the candidate with maximum match score or highest ranking is represented as Ace, the candidate with second highest rank is represented as King and so forth.
Further, in the chess game layout the candidate with maximum match score or highest ranking is represented as King, the candidate with second highest rank is represented as Queen and so forth.
Furthermore, in the billiards game layout the candidate with maximum match score or highest ranking is represented as 'a ball with number 1', the candidate with second highest rank is represented as 'a ball with number 2' and so forth.

Throughout the present disclosure, the term "information" refers to the information related to candidates' profile. In an embodiment, the candidate's profile comprises at least one of a personal information, an information regarding a specific job assignment, or an input associated with a recruitment process, personal records. In an embodiment, the at least one candidate may provide the personal information. The personal information comprises at least one of, but not limited to, full name, birth date, age, gender, email Id, short bio, profile summary, country, pin code, province, contact number, Facebook details, Linkedln details or any other social networking profile details. The personal records comprise Resume or CV and other certificates of achievement of the candidates of interest. In an embodiment, the information from profile of candidates is available on social networking sites, job portals, Internet websites and so forth.
Moreover, the present disclosure also relates to the system as described above. Various embodiments and variants disclosed above apply mutatis mutandis to the system.
DETAILED DESCRIPTION OF DRAWINGS
Referring to FIG. 1 there is an illustration steps of a method 100 of shortlisting of profile(s) of candidate(s) of interest for a recruiter for a relevant job opening, in accordance with an embodiment of the present disclosure. At step 102 the method 100 initiates. At the step 102 the profiles of candidates of interest are identified based on criteria of the job opening and candidate parameters. At step 104 a match and/or relevancy score(s) to each of the profile(s) of candidate(s) of interest is assigned. At step 106 at least one external factor is employed. At step 108 the match and/or relevancy score(s) to the profile(s) of candidate(s) of interest based on the at least one external factor is updated. At step 110 the profile(s) of candidate(s) of interest are ranked based on the updated match and/or relevancy scores. At step 112 the profiles of candidates of interest having similarities between the criteria of the job position and the candidate parameters are identified, wherein a candidate with an exact match is assigned a highest rank.

And at step 114 the ranked profiles profile(s) of candidate(s) of interest are displayed in a desired layout to the recruiter. The method 100 ends at the step 114.
Referring to FIG. 2 there is a schematic illustration of a network environment in which a system for shortlisting of profiles of candidates of interest for a recruiter for a relevant job opening can be implemented, in accordance with an embodiment of the present disclosure. The system 200 comprises a data processing arrangement 202, a database arrangement 204, a display module 206, a communication module (not shown). The communication module is coupled with the data processing arrangement 202, the database arrangement 204 and the display module 206 via a communication network.
FIG. 1 and FIG. 2 are merely examples, which should not unduly limit the scope of the claims herein. A person skilled in the art will recognize many variations, alternatives and modifications of embodiments of the present disclosure.
Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural.

WE CLAIM:

1. A computer implemented method of shortlisting of profiles of candidates of
interest for a recruiter for a relevant job opening, the method comprising:
- identifying the profiles of candidates of interest, using a data processing
arrangement, based on criteria of the job opening and candidate parameters;
- assigning a match and/or relevancy scores to each of the profiles of candidates of interest, using the data processing arrangement;
- employing at least one external factor using the data processing arrangement, wherein the at least one external factor is received from at least one external source;
- updating the match and/or relevancy scores to the profiles of candidates of interest, using the data processing arrangement, based on the at least one external factor;
- ranking the profiles of candidates of interest based on the updated match and/or relevancy scores, using the data processing arrangement;
- identifying the profiles of candidates of interest having similarities between the criteria of the job position and the candidate parameters, using the data processing arrangement, wherein a candidate with an exact match is assigned a highest rank; and
- displaying the ranked profiles of candidates of interest, using a display module,
in a desired layout to the recruiter.
2. The method of claim 1, wherein the criteria of the job opening includes at least
one of: city of job location, range of job location, gender of the candidate
required, job position, job specialization required by the recruiter, job description
provided by the recruiter, education requirements, experience requirements,
suggested salary offered by the recruiter and driving license requirements.

3. The method of claim 1, wherein the candidate parameters include at least one of: city of candidate, address of the candidate, gender of the candidate, desired designation of the candidate, specialization of the candidate, education of the candidate, experience of the candidate, expected salary by the candidate and driving license details of the candidate.
4. The method of claim 1, wherein the at least one external factor includes at least one of: market trends, average salaries for a job position, supply and demand of jobs, supply and demand of candidates, job positions per city, certifications obtained by the candidate, and preferred joining date as per the recruiter.
5. The method of claim 1, wherein the exact match of the profiles of candidates of interest comprises a condition of exact similarity between the criteria of the job opening and the candidate parameters.
6. The method of claim 1, wherein the artificial intelligence algorithm, is further operable for receiving a feed of a plurality of data points related to the candidates and the recruiters, for identifying matches of the candidates to the jobs and matches of the jobs to the candidates.
7. The method of claim 6, wherein the plurality of data points includes at least one of: activity of the candidates and the employers, degree of involvement of the candidates and the employers, data collected from the candidates and the employers, data related to the external factors, analysis of the data collected by the system.
8. The method of claim 1, wherein the artificial intelligence algorithm is trained using the plurality of data points and the external factors.
9. A system for shortlisting of profiles of candidates of interest for a recruiter for a relevant job opening, the system comprising:
- a data processing arrangement, using one of a scoring algorithm or an artificial intelligence algorithm, is configured to:

- identify the profiles of candidates of interest based on criteria of the job opening and candidate parameters;
- assign a match and/or relevancy score(s) to each of the profiles of candidates of interest;
- employ at least one external factor , wherein the at least one external
factor is received from at least one external source;
- update the match and/or relevancy scores to the profiles of candidates of interest based on the at least one external factor; - rank the profiles of candidates of interest based on the updated match and/or relevancy scores; and
- identify the profiles of candidates of interest having similarities between the criteria of the job position and the candidate parameters, wherein a candidate with an exact match is assigned a highest rank;

- a database arrangement to store the rank of the profiles of candidates of interest;
- a display module for displaying the ranked profiles profiles of candidates of interest in a desired layout to the recruiter; and
- a communication module coupled with processing arrangement, the database arrangement and the display module.

10. The system of claim 9, wherein the criteria of the job opening includes at least one of: city of job location, range of job location, gender of the candidate required, job position, job specialization required by the recruiter, job description provided by the recruiter, education requirements, experience requirements, suggested salary offered by the recruiter and driving license requirements.
11. The system of claim 9, wherein the candidate parameters include at least one of: city of candidate, address of the candidate, gender of the candidate, desired designation of the candidate, specialization of the candidate, education of the

candidate, experience of the candidate, expected salary by the candidate and driving license details of the candidate.
12. The system of claim 9, wherein the at least one external factor includes at least one of: market trends, average salaries for a job position, supply and demand of jobs, supply and demand of candidates, job positions per city, certifications obtained by the candidate, and preferred joining date as per the recruiter.
13. The system of claim 9, wherein the exact match of the profiles of candidates of interest comprises a condition of exact similarity between the criteria of the job opening and the candidate parameters.
14. The system of claim 9, wherein the artificial intelligence algorithm, is further operable to receive a feed of a plurality of data points related to the candidates and employers, to identify matches of the candidates to the jobs and matches of the jobs to the candidates.
15. The system of claim 14, wherein the plurality of data points includes at least one of: activity of the candidates and the employers, degree of involvement of the candidates and the employers, data collected from the candidates and the employers, data related to the external factors, analysis of the data collected by the system.
16. The system of claim 9, wherein the artificial intelligence algorithm is trained using the plurality of data points and the external factors.