Claims:WHAT WE CLAIM:
1. A system (10) for recruitment comprising:
a central database (20) comprises:
a first database (21) configured to store a set of candidate data;
a second database (25) configured to store a set of subject matter expert data;
a third database (28) configured to store a set of job requirement data;
wherein, the first database (22), the second database (25) and the third database (28) are operatively coupled with each other.
a refining module (30) communicatively coupled with the central database, configured to:
select one or more subject matter experts based on the set of job requirement data; and
assign one or more candidates based on the set of job requirement data to a one or more selected subject matter experts for recruitment.
2. The system (10) as claimed in claimed 1, wherein the set of candidate data comprises a set of skills, a plurality of years of work experience, a previous work record and an education qualification.
3. The system (10) as claimed in claim 1, wherein the set of subject matter expert data comprises a set of skills, a plurality of years of experience, a plurality of interviews performed, a plurality of positive feedback provided by the subject matter expert and the plurality of candidates selected by the subject matter expert.
4. The system (10) as claimed in claim 1, wherein the set of job requirement data comprises a required plurality of years of work experience, an industry relevance and a required set of skills.
5. The system (10) as claimed in claim 1, wherein the central database (20) is installed on a cloud or on a local server.
6. The system (10) as claimed in claim 1, further comprising a self-learning module configured to progressively learn from the received set of candidate data, the set of subject matter expert data and the set of job requirement data.
7. A method (500) for recruitment comprising:
receiving a set of candidate data, a set of subject matter expert data and a set of job requirement data;
selecting one or more subject matter experts based on the set of job requirement data; and
assigning one or more candidates based on the set of job requirement data and a one or more selected subject matter experts for recruitment.
8. The method (500) as claimed in claim 7, wherein selecting the one or more subject matter experts based on the set of job requirement data comprises selecting the one or more subject matter experts by comparing the set of subject matter expert data and the set of job requirement data.
9. The method (500) as claimed in claim 7, wherein assigning one or more candidates based on the set of job requirement data to the one or more selected subject matter experts for recruitment comprises conducting an online interview, a telephonic interview, a face to face interview.
, Description:BACKGROUND
[0001] Embodiments of the present invention relates to hiring, and more particularly to, a system and method for recruitment.
[0002] The process of interviewing applicants for job opening is used to separate the best fitting candidates for a given position from those who would not be an ideal selection. Various methods are used for screening of the plurality of candidates for the given position.
[0003] Conventionally, to access data specific to a subject matter, databases often utilize a keyword search function. However, such sorting method limits the searcher's access only to data which contains the specific keyword entered. By using a keyword filter, databases are able to narrow the scope of the material returned to the searcher.
[0004] Furthermore, the selection of an expert for a particular candidate is a manual process. Such expert is selected within an organization to initiate the interview process. However, such manual process leads to an extra effect and are error prone. Sometimes for a specific subject matter in house experts are not available. In such cases employers are left with no option but to shortlist candidates on the basis of superficial parameters such as tier of college, academic percentages and basic knowledge.
[0005] Hence, there is a need for an improved system and method for recruitment to address the aforementioned issues.
BRIEF DESCRIPTION
[0006] In accordance with one embodiment of the disclosure, a system and method for job recruitment are provided. The system for job recruitment includes a central database. The central database includes a first database configured to store a set of candidate data. The central database also includes a second database operatively coupled to the first database and is configured to store a set of subject matter expert data. The central database further includes a third database operatively coupled to the second database and is configured to store a set of job requirement data. The system also includes a refining module communicatively coupled with the central database and is configured to select one or more subject matter experts based on the set of job requirement data. The refining module is also configured to select one or more candidates based on the set of job requirement data and the one or more subject matter experts for recruitment.
[0007] The method for job recruitment includes receiving a set of candidate data, a set of subject matter expert data and a set of job requirement data. The method also includes selecting one or more subject matter experts based on the set of job requirement data. The method further includes selecting one or more candidates based on the set of job requirement data and the one or more subject matter experts for recruitment.
[0008] To further clarify the advantages and features of the present invention, a more particular description of the invention will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the invention and are therefore not to be considered limiting in scope. The invention will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0009] FIG. 1 is a block diagram of a system for recruitment in accordance with an embodiment of the present disclosure;
[0010] FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure;
[0011] FIG. 3 is a block diagram of an exemplary system for a system and method for recruitment of FIG. 1 in accordance with an embodiment of the present disclosure; and
[0012] FIG. 4 is a process flow for recruitment in accordance with an embodiment of the present disclosure.
[0013] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0014] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as would normally occur to those skilled in the art are to be construed as being within the scope of the present invention.
[0015] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
[0016] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0017] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0019] Embodiments of the present invention will be described below in detail with reference to the accompanying figures.
[0020] FIG. 1 is a block diagram of a system for recruitment in accordance with an embodiment of the present disclosure. The system (10) includes a central database (20) configured to store a plurality of information. The central database (20) includes a first database (21) configured to store a set of candidate data. As used herein, a candidate is a person who is searching for a job in a particular domain, and may apply for the job through an online portal of the exemplary embodiment. In another embodiment, the set of candidate data may be a set of skills, a plurality of years of work experience, a previous work record and an education qualification, a name, historical information, a candidate’s interest in leaving a previous job, at least one or more preference criteria.
[0021] The central database (20) also includes a second database (25) operatively coupled to the first database. The second database (25) is configured to store a set of subject matter expert data. In one embodiment, a subject matter expert may be a mentor. As used herein, the mentor may be an expertise in one or more domain. The mentor may be an employee from a company having a plurality of job openings. The mentor may be an expertise who may be hired by the company having the plurality of job openings to recruit a plurality of candidates for the offered job openings.
[0022] In another embodiment, the set of subject matter data may be a set of skills, a plurality of years of experience, a plurality of interviews performed, a plurality of positive feedback provided by the subject matter expert and the plurality of candidates selected by the subject matter expert.
[0023] The central database (20) further includes a third database (28) operatively coupled to the second database (25). The third database (28) is configured to store a set of job requirement data. A job requirement data is a plurality of data specified by the company having the plurality of job openings which has to be fulfilled by the plurality of candidate to get recruited in the company offering the plurality of job openings.
[0024] In one embodiment, the job requirement data may be a required plurality of years of work experience, an industry relevance and a required set of skills. In another embodiment, the central database may be installed on a cloud or a local server. In yet another embodiment, the server may provide a plurality of location details of a place of job after being recruited. Further, the server may facilitate scheduling of an interview of the candidate at the place of work.
[0025] The system (10) also includes a refining module (30) communicatively coupled with the first database (21), the second database (25) and the third database (28) of the central database (20). The refining module (30) is configured to select one or more subject matter experts based on the set of job requirement data. The refining module (30) is also configured to assign one or more candidates based on the set of job requirement data and the one or more subject matter experts for recruitment.
[0026] In one embodiment, the system (10) may further include a self-learning module which may be operatively coupled to the central database (20). As used herein, self-learning an unsupervised machine learning task. The self-learning module may be configured to progressively learn from the received set of candidate data, the set of subject matter expert data and the set of job requirement data. The self-learning module may use artificial intelligence,
[0027] FIG. 2 is a block diagram of an exemplary system for a system and method for recruitment of FIG. 1 in accordance with an embodiment of the present disclosure. For example there may be 10 different companies offering job opening for 100 candidates in different domains. The different domains may be an intern, a part time employee or a full time employee. The exemplary system (50) may be used by the 10 different companies for easy shortlisting of a plurality of applied candidates. The 10 different companies may upload 10 different job requirement data in the third database (28) which is substantially similar to the third database of FIG. 1, which is a part of the central database (20) which may be uploaded on the cloud or any other server. The central database (20) is substantially similar to the central database (20) of FIG. 1. The job recruitment data may be a plurality of years of experience, a set of skills like java advanced java and python, a specific education qualification like the master’s degree.
[0028] Further, there may be a plurality of candidates who have applied for the job openings offered by the 10 different companies in different domains. Further, the plurality of candidates may view the set of job requirement data from the third database (28) and may apply for the job openings offered by 10 different companies in different domains. The plurality of candidate may apply for 10 different jobs by uploading their profiles on to the first database (21), which is substantially similar to the first database (21) of FIG. 1, is operatively coupled to the third database (28), which is a part of a central database (20). Further, the central database also includes a second database (25), which is substantially similar to the second database (25) of FIG.1, is operatively coupled to the first database (21) and the third database (25). The second database may store a set of subject matter expert data such as a set of skills, a plurality of years of experience, a plurality of interviews performed, a plurality of positive feedback received by a higher subject matter expert.
[0029] The subject matter expert may be a mentor. Having the set of job requirement data, the set of candidate data and the set of subject matter data, the refining module (30), which is substantially similar to the refining module (30) of FIG. 1, which is operatively coupled to the central database (20) selects one or more mentors based on the job requirement data and the set of subject matter data. The refining module (30) may also assign one or more candidates to a one or more selected mentors for further interview. The subject matter expert data is responsible for conducting interviews for a plurality of shortlisted candidates. Also, the 10 different companies may have 50 mentors totally. Based on the set of requirement data and a profile of a plurality of applied candidates, the refining module (30) may compare and short list a plurality of eligible candidates for further rounds. Further, the plurality of shortlisted candidates may be 1000.
[0030] Further, the selected candidates may be assigned for the 50 mentors for the further rounds of interview. Further, the refining module (30) may compare the profile of the 1000 candidates with the set of subject matter expert data to allot the plurality of candidates to the mentors for further interview. The refining module (30) may assign approximately 20 candidates for each of the 50 mentors based on the set of candidate data and the set of subject matter data. Further, each of the 50 mentors conduct further recruitment rounds for the assigned 20 candidates. Further, the 50 mentors may select 100 candidates for the 100 job openings in 10 different companies.
[0031] The system (50) includes a self-learning module (40) which may be operatively coupled to the central database (20) and the refining module (30). The self-learning module (40) may be configured to progressively learn from the received set of candidate data, the set of subject matter expert data and the set of job requirement data. In one embodiment, the self-learning module (40) may use artificial intelligence operatively coupled to the central database (20).
[0032] Further, the central database (20) placed on the cloud platform, may be easily accessed by the refining module and the self-learning module for selecting and assigning the plurality of candidate to the plurality of subject matter experts for further rounds of recruitment.
[0033] For example, if the plurality of candidates applies for the job openings once the job recruitment is completed, the self-learning module may automatically refine the profile of the plurality of candidates applied after the job recruitment is completed and stores the same in the central databases for further references.
[0034] The further recruitment rounds may be one or more type of interviews like an online interview, a telephonic interview and a face to face interview for a final recruitment of the 1000 candidates for 100 job openings in 10 different companies.
[0035] FIG. 3 is an exemplary system (100) such as a computer or a server in accordance with an embodiment of the present disclosure. The exemplary system (100) for selecting the plurality of candidates for recruitment (10) includes a general-purpose computing device in the form of a computer (100) or a server or the like. The computer (100) includes including a processing unit (110) substantially similar to the refining module (30) of FIG. 1, and configured to select and assign a plurality of candidates and a plurality of subject matter expert, a system memory (120) substantially similar to the central database (20) of FIG. 1, and configured to store the set of job requirement data, the set of candidate data and the set of subject matter expert data. The computer (100) also includes a system bus (130) that couples various system components including the system memory (100) to the processing unit (110).
[0036] The system bus (130) may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory (120) includes read-only memory (ROM) (140) and random access memory (RAM) (150). A basic input/output system (BIOS) (160), containing the basic routines that help transfer information between elements within the computer (100), such as during start-up, is stored in ROM (140).
[0037] The computer (100) may further include a hard disk drive for reading from and writing to a hard disk, not shown, a magnetic disk drive for reading from or writing to a removable magnetic disk, and an optical disk drive for reading from or writing to a removable optical disk such as a CD-ROM, DVD-ROM or other optical media.
[0038] The hard disk drive, magnetic disk drive, and optical disk drive30 are connected to the system bus by a hard disk drive interface (220), a magnetic disk drive interface (230), and an optical drive interface (240), respectively. The drives and their associated computer-readable media provide non-volatile storage of computer readable instructions, data structures, program modules and other data for the computer (100) to the various results generated from the data processing unit (110).
[0039] Although the exemplary environment described herein employs a hard disk, a removable magnetic disk and a removable optical disk, it should be appreciated by those skilled in the art that other types of computer readable median that can store data that is accessible by a computer, Such as magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges, random access memories (RAMS), read-only memories (ROMs) and the like may also be used in the exemplary operating environment.
[0040] A number of program modules may be stored on the hard disk, magnetic disk, optical disk, ROM (140) or RAM (150), including an operating system (250). The computer (100) includes a file system (170) associated with or included within the operating system (250), one or more application programs (260), other program modules (270) and program data (280). A user may enter commands and information into the computer (100) through input devices (290) such as a keyboard and pointing device. Other input devices (not shown) may include a microphone, joystick, game pad, Satellite dish, Scanner or the like.
[0041] These and other input devices are often connected to the data processing unit (110) through a serial port interface (300) that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, game port or universal serial bus (USB). A monitor (310) or other type of display device is also connected to the system bus (130) via an interface. Such as a video adapter (320). In addition to the monitor (310), personal computers typically include other peripheral output devices (not shown), such as speakers and printers.
[0042] The computer (100) may operate in a networked environment using logical connections to one or more remote computers (330). The one or more remote computer (330) may be another computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer (100), although only a memory storage device (340) has been illustrated. The logical connections include a local area network (LAN) (350) and a wide area network (WAN) (360). Such networking environments are common place in offices, enterprise-wide computer networks, Intranets and the Internet.
[0043] When used in a LAN (350) networking environment, the computer (100) is connected to the local network (350) through a network interface or adapter (370). When used in a WAN (360) networking environment, the computer (100) typically includes a modem (380) or other means for establishing communications over the wide area network (360), such as the Internet.
[0044] The modem (380), which may be internal or external, is connected to the system bus (130) via the serial port interface (300). In a networked environment, program modules depicted relative to the computer (100), or portions thereof, may be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.
[0045] FIG. 4 is a process flow for recruitment in accordance with an embodiment of the present disclosure. The method (500) for recruitment includes receiving a set of candidate data, a set of subject matter expert data and a set of job requirement data (510). In one embodiment, the candidate data, the set of subject matter expert data and the set of job requirement data may be accessed by a computer recruitment medium. Further, the received set of candidate data, the set of subject matter expert data and the job requirement data may be stored in a computer readable medium. In another embodiment, the system may select one or more candidates based on an interview mentioned in the uploaded set of candidate data. The system may select the one or more candidates based on a plurality of skill sets and other metrics based on a received set of candidate data, the set of subject matter expert data and a set of requirement data.
[0046] The method (500) also includes selecting one or more subject matter experts based on the set of job requirement data (520). In one embodiment, selecting the one or more subject matter experts based on the set of job requirement data may be selecting the one or more subject matter experts which may be the set of subject matter expert data and the set of job requirement data.
[0047] The method (500) further includes assigning one or more candidates based on the set of job requirement data and the one or more subject matter experts for recruitment (530). In one embodiment, the subject matter experts may be a mentor. In another embodiment, assigning one or more candidates based on the set of job requirement data to the one or more selected subject matter experts for recruitment comprises conducting an online interview, a telephonic interview, a face to face interview.
[0048] In yet another embodiment, the recruited candidate may upload a plurality of documents on the server prior to a joining date for a job. The plurality of documents may an employee contract which may be signed and scanned before uploading to the server.
[0049] In one specific embodiment, a plurality of candidates may lack the set of job requirement data. Further the plurality of candidates may be placeable in the future when the plurality of candidates meets the required set of job requirement data and may be facilitated for future requirement.
[0050] In yet another embodiment, a selected one or more candidates may have an access to a plurality of additional information pertaining to a recruited job. Further, the selected one or more candidates may have access to the server for uploading additional documents or responding to a plurality of further inquiries. Further, the system may also schedule an interview for the selected one or more candidates. The scheduling interface may optionally be presented as a calendar.
[0051] Various embodiments of the present system enable the system to be faster. The system does not limit the searcher’s access to the specific key words. Since the system works automatically, the system is less prone to error. Also the employers are not restricted to shortlist the candidates on a limited parameters.
[0052] Also the system stores the set of candidate data, the set of subject matter expert data and the set of job requirement data in the central database which is installed on the cloud or any other local server, which improves the selection process as no manual access is required. Also the system enables an error free selection process.
[0053] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.