Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
TITLE:
SYSTEM AND METHOD FOR SCREENING CANDIDATES AND INCLUDING A
PROCESS FOR AUTOBUCKETING CANDIDATE ROLES
FIELD OF THE INVENTION
[0001] The
present invention relates to computer systems and more particularly, to a
system and method for screening potential candidates for an employment
position or other role
and including an auto-bucketing process for grouping similar jobs or roles.
BACKGROUND OF THE INVENTION
[0002] A
business organization typically has multiple roles or job positions. For a
large
enterprise, such as a corporation or multi-national corporation, the roles or
positions can
number in the thousands.
[0003]
Applicant Tracking Systems (ATS) provide a mechanism for tracking applicants
for an
organization.
[0004]
However, to provide the capability to group roles for applicants and/or
scaling at
the enterprise level, there remains a need for improvements in the art.
BRIEF SUMMARY OF THE INVENTION
[0005] The
present invention is directed to a method and system for screening potential
candidates for an employment position, role, or other function and including
an autobucketing
process for grouping similar jobs or roles in a database system, for example,
an Applicant
Tracking System (ATS).
[0006]
According to an embodiment, the present invention comprises a computer system
for determining suitability of a candidate for a selected role in an
organization, the system
comprises: a processor operatively coupled to a database and including an
input component
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configured to retrieve data associated with an ideal candidate, the data
including historical data;
the processor including a component configured to generate an ideal candidate
profile based on
the ideal candidate data and the historical data associated with the ideal
candidate; the
processor including another input component configured to input application
data associated
with the candidate, the application data including a job description; the
processor including a
component configured to determine if the job description fits one of a
plurality of pre-defined
job buckets associated with the organization, and the processor including
another component
for generating a new job bucket if the job description fails to match one of
the plurality of pre-
defined job buckets; the processor including a component configured to
generate a profile for
the candidate based on the inputted data; and the processor including a
comparison component
configured to compare the candidate profile to the ideal candidate profile,
and a component
configured to generate a suitability rating for the selected role based on the
comparison.
[0007] According to an embodiment, the present invention comprises a
computer-
implemented method for categorizing a selected job role in an organization,
the computer-
implemented method comprising the steps of: inputting data for the selected
job role from a
database associated with the organization; comparing the inputted job data
with one or more
pre-determined job buckets existing in the organization; if the inputted job
role matches one of
said pre-determined job buckets, then adding the inputted job role to the
matching pre-
determined job bucket; creating a new job bucket if said inputted job role
fails to match one of
the pre-determined job buckets; and saving the new job bucket to the database.
[0008] According to an embodiment, the present invention comprises a
computer program
product for categorizing a selected job role in an organization, said computer
program product
comprising: a non-transitory storage medium configured to store computer
readable
instructions; the computer readable instructions including instructions for,
inputting data for the
selected job role from a database associated with the organization; comparing
the inputted job
data with one or more pre-determined job buckets existing in the organization;
if the inputted
job role matches one of the pre-determined job buckets, then adding the
inputted job role to
the matching pre-determined job bucket; creating a new job bucket if the
inputted job role fails
to match one of the pre-determined job buckets; and saving the new job bucket
to the
database.
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[0009] Other aspects and features of the present invention will become
apparent to those
ordinarily skilled in the art upon review of the following description of
embodiments of the
invention in conjunction with the accompanying figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[00010] Reference will now be made to the accompanying drawings which
show, by way of
example, embodiments of the present invention, and in which:
[00011] Fig. 1 shows in diagrammatic form an exemplary network-based
configuration
suitable for implementing a system and a method according to embodiments of
the present
invention;
[00012] Fig. 2 shows in block diagram form an exemplary implementation of
a system
according to an embodiment of the present invention;
[00013] Fig. 3 shows in flowchart form a process for training the system
according to an
embodiment of the present invention;
1000141 Fig. 4 shows in flowchart form a process executed by the system
for evaluating or
selecting a candidate according to an embodiment of the present invention;
[00015] Fig. 5A shows in flowchart form a process executed by the system
for training the
system further based on a selected candidate;
[00016] Fig. 56 shows in flowchart form a process executed by the system
for training the
system based on a candidate that has been dismissed;
[00017] Fig. 6A shows a process for parsing and contextualizing an
exemplary resume to
generate a set of tokens according to an embodiment of the present invention;
[00018] Fig. 66 shows a process for parsing and contextualizing another
exemplary resume
to generate a set of tokens according to an embodiment of the present
invention;
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[00019] Fig. 6C shows a process for evaluating a candidate based on a
token set associated
with an ideal candidate and a token set generated for the candidate in
accordance with an
embodiment of the present invention;
[00020] Fig. 7 shows in block diagram form an exemplary hardware
configuration for a client
or server of Fig. 1 suitable for implementing a system and a method according
to embodiments
of the present invention; and
[00021] Fig. 8 shows in flowchart form an auto-bucketing process
according to an
embodiment of the present invention.
[00022] Like reference numerals indicate like or corresponding elements
or components in
the drawings.
DETAILED DESCRIPTION OF THE EMBODIMENTS OF THE INVENTION
[00023] Reference is first made to Fig. 1, which shows an exemplary
network-based
implementation of the system for screening potential candidates for an
employment position or
other function based, in part, on historical data or information, and
indicated generally by
reference 100. The system 100 comprises a server (or one or more servers)
indicated generally
by reference 110 coupled to one or more client machines or computers 130,
indicated
individually by references 130a and 130b in Fig. 1, operatively coupled
through a network
indicated generally by reference 102.
[00024] The client machine or appliance 130 may include a device, such
as a personal
computer, a wireless communication device or smart phone, a portable digital
device such as an
iPad or tablet, a laptop or notebook computer, or another type of computation
or
communication device, a thread or process running on one of those devices,
and/or an object
executable by one of these devices. The server 110 may include a server
application or module
120 configured to gather, process, search, and/or maintain a graphical user
interface (GUI) and
functionality (e.g. web pages) in a manner consistent with the embodiments as
described in
more detail below.
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[00025] The network 102 may comprise a local area network (LAN), a wide
area network
(WAN), a telecommunication network, such as the Public Switched Telephone
Network (PSTN),
an Intranet, the Internet, or a combination of networks. According to another
aspect, the
system 100 may be implemented as a cloud-based system or service utilizing the
Internet 102.
[00026] Reference is next made to Fig. 7, which shows an exemplary
implementation for a
client or server entity (i.e. a "client/server entity"), which may correspond
to one or more of the
servers (e.g. computers) 110 and/or client machines or appliances (e.g.
computers) 130, in
accordance with the functionality and features of the embodiments as described
in more detail
below. The client/server entity is indicated generally by reference 700 and
comprises a
processor (e.g. a central processing unit or CPU) 710, a bus 720, a main
memory 730, a read only
memory or ROM 740, a mass storage device 750, an input device 760, an output
device 770, and
a communication interface 780. The bus 720 comprises a configuration (e.g.
communication
paths or channels) that permits communication among the elements or components
comprising
the client/server entity 700.
[00027] The processor 710 may comprise a hardware-based processor,
microprocessor, or
processing logic that is configured, e.g. programmed, to interpret and/or
execute instructions.
The main memory 730 may comprise a random-access memory (RAM) or other type of
dynamic
storage device that is configured to store information and/or instructions for
execution by the
processor 710. The read only memory (ROM) may comprise a conventional ROM
device or
another type of static or non-volatile storage device configured to store
static information
and/or instructions for user by the processor 710. The storage device 750 may
comprise a disk
drive, solid state memory or other mass storage device such an optical
recording medium and
its corresponding drive or controller.
[00028] The input device 760 may comprise a device or mechanism
configured to permit an
operator or user to input information to the client/server entity, such as a
keyboard, a mouse, a
touchpad, voice recognition and/or biometric mechanisms, and the like. The
output device 770
may comprise a device or mechanism that outputs information to the user or
operator,
including a display, a printer, a speaker, etc. The communication interface
780 may comprise a
transceiver device or mechanism, and the like, configured to enable the
client/server entity 700
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to communicate with other devices and/or systems. For instance, the
communication interface
780 may comprise mechanisms or devices for communicating with another machine,
appliance
or system via a network, for example, the Internet 102 (Fig. 1).
[00029] As will be described in more detail below, the client/server
entity 700, in accordance
with embodiments according to the present invention, may be configured to
perform
operations or functions relating to the process of selecting a suitable
candidate, to the process
of generating a candidate model or template, and the other functions as
described or depicted
herein. The client/server 700 may be configured to perform these operations
and/or functions
in response to the processor 710 executing software instructions or computer
code contained in
a machine or computer-readable medium, such as the memory 730. The computer-
readable
medium may comprise a physical or a logical memory device or medium.
[00030] The software instructions or computer code may be read into the
memory 730 from
another computer-readable medium, such as a data storage device 750, or from
another device
or machine via the communication interface 780. The software instructions or
computer code
contained or stored in the memory 730 instruct or cause the processor 710 to
perform or
execute processes and/or functions as described in more detail herein. In the
alternative,
hardwired circuitry, logic arrays, and the like, may be used in place of or in
combination with
software instructions to implement the processes and/or functions in
accordance with the
embodiments of the present invention. Therefore, implementations consistent
with the
principles of the embodiments according to the present invention are not
limited to any specific
combination of hardware and/or software.
[00031] Referring back to Fig. 1, each of the client machines 130
includes a client interface
module 132 which is configured to provide an interface with the server 110.
According to an
embodiment, the client interface module 132 comprises a user interface or GUI
(Graphical User
Interface) which is configured to display and run one or more web or browser
pages that are
downloaded from the server 110 (i.e. the application module 120 running on or
being executed
by the server 110) and may be implemented utilizing a web browser, such
Internet ExplorerTM
browser or the SafariTM browser. According to an embodiment, the client
interface module 132
and the web pages comprise logic and processes configured to provide a user
with the
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functionality as described in more detail herein. The particular
implementation details,
hardware and software, will be readily with the understanding of those skilled
in the art.
[00032] Reference is next made to Fig. 2, which shows an exemplary
implementation of the
system according to an embodiment of the present invention and indicated
generally by
reference 200. The system 200 comprises a learning machine 210, a database 220
and an
interface or connector indicated by reference 230. The interface 230 is
configured to couple or
communicate with an external data source or repository indicated generally by
reference 240.
According to an exemplary embodiment, the external data source 240 comprises
an Applicant
Tracking System or ATS, which is configured with data and information
associated with a client
or customer comprising a list of applicants that have applied or have been
considered for a
particular job opening at a company. The applicant data includes name, contact
information,
resume, any questions answered during the application process, and any other
information
stored in the ATS 240. According to an exemplary implementation, the ATS 240
comprises
historical data and historical actions. The historical data includes, for
example, data/information
about positions, employees and the like. The historical actions include, for
example,
data/information about hiring, dismissal, review, interview events or actions,
and the like. For
each decision, there is associated application data. The application data
comprises: a
candidate's resume; screening question(s) (e.g. "have you ever been convicted
of a crime?");
other application questions (e.g. "are you available to work weekends?");
location information;
assessment data (e.g. psychometric test data); additional information provided
by the applicant,
such as, the applicant's cover letter. According to another aspect, the system
200 can be
configured to optionally import certain information, such as, the candidate's
name and
demographics, specifically for the purpose of controlling bias in the
candidate model or
template.
[00033] As will be described in more detail below, the system 200 is
configured to generate
or build an ideal candidate template or an ideal candidate model. The ideal
candidate template
is based on historical data imported from the ATS 240, for example, historical
candidate and job
data, historical action data (e.g. dismissal, interview, hiring data). The
system 200 is further
configured to augment the imported data from the ATS 240 with data extracted
or imported
from external services as described in more detail below. A machine profile or
template is
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generated or built for a new candidate, i.e. potential hire, and compared to
the ideal candidate
profile and a comparison result or score is generated. The score(s) are sent
or transmitted back
to the ATS 240, i.e. client, and utilized in a hiring decision. The scores at
the ATS 240 can be used
to trigger manual or automated workflow processes, for example, contacting
high score (i.e.
high-grade) candidates to schedule interviews. According to another aspect,
the system 200 is
configured with a further learning mode or feedback mechanism. In the learning
mode, the
system 200 utilizes data on candidate decisions, e.g. interviews, hires, to
further refine and
teach the machine learning processes, as described in more detail below.
[00034] According to an exemplary embodiment and as shown in Fig. 2, the
learning
machine 210 is configured with a resume parsing module 250, a people or social
data services
module 252, a company data services module 254 and/or an artificial
intelligence services
module 256.
[00035] The resume parsing module 250 is configured to parse or break
down a candidate's
resume into useful or relevant data or information components. For example,
the resume
parsing module 250 is configured to break a candidate's resume down into the
following parts:
previous positions/companies, school(s) attended, degrees completed, skills,
etc., and as
described in more detail below with reference to Fig. 6A.
[00036] The people/social services module 252 is configured to search
public directories or
services for additional information on the candidate. The public services may
comprise social
media and other publicly available sources. The information obtained from such
sources or
services is utilized to gain additional insight on the candidate and/or
provide context about the
person.
[00037] The company data services module 254 is configured to examine a
company or
companies and other keywords appearing on a candidate's resume and provide
additional
information or context for the candidate. For example, if the candidate's
resume lists "Oracle,
Inc.", the system is configured to interpret Oracle as a B2B software company,
and other
keywords describing the candidate's position at Oracle.
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[00038] The Al services module 256 comprises artificial intelligence
algorithms that are
configured to extract contextual information about or associated with the
candidate, for
example, skills, entities, themes, patterns. For example, this allows the
system to be configured
to derive and understand a work experience as a database experience, even if
the candidate has
not explicitly described the experience with the term database, based on other
database-related
technology information being extracted from the candidate's resume.
[00039] Reference is next made to Fig. 3, which shows a training process
or method
according to an embodiment of the present invention and indicated generally by
reference 300.
The training process 300 is executed by the system 200 to generate or build an
ideal candidate
template. The ideal candidate template comprises attributes or parameters
representing what a
good candidate would look like for a given company and/or a given position at
the company. As
described above, the system 200 utilizes data from the ATS 240 (Fig. 2)
comprising historical
data and historical actions taken (for example, interviewing, hiring, employee
performance
and/or dismissal(s)). The historical actions are characterized as decisions.
According to another
aspect, the system 200 is configured to associate each decision with
additional application data,
comprising: candidate resume; screening question(s) (e.g. Have you ever been
convicted of a
crime?); additional application questions (e.g. Are you available to work
weekends?); location
information; assessment data (e.g. psychometrics); and any additional
information provided by
the applicant (e.g. information or statements contained in a cover letter).
[00040] As shown in Fig. 3, the training process 300 commences execution
301 by
connecting to the Applicant Tracking System (ATS) 240 (Fig. 2) 310 and
importing job(s) and
candidate(s) data as indicated by reference 312. The candidate data is stored
in a candidate
database 314 and the jobs data is stored in a job database 315. The jobs can
be grouped into
one or more buckets based on similarities as indicated by 316, and stored in
the job database
315. Data from the candidate database 314 and the job database 315 is imported
by the
artificial intelligence services module 256 (Fig. 2) and machine learning
algorithms are applied
and executed to generate an ideal candidate template or profile as indicated
by reference 318.
[00041] According to an exemplary embodiment, the machine learning
algorithms 318
executed by the artificial intelligence services module 256 generate the
candidate profile based
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on historical decision data (imported from the candidate database 314 and/or
the job database
315) and candidate data for each candidate from the ATS as indicated by
reference 320.
According to an exemplary implementation, the candidate data 320 comprises:
answers to
screening questions 321; candidate profile data from the ATS 322; information
extracted by the
resume parser (indicated by reference 250 In Fig. 2) 324; extracted entity
information 326;
personal candidate information extracted or imported from public and/or
private data sources
328; company information gathered or imported from public and/or private
databases 330; data
extracted or imported from social networking sites or services 332; and/or
keywords extracted
from the candidates resumes and/or job applications, as indicated by reference
334. The
historical decision data, i.e. decisions, processed by the machine learning
algorithms 318
comprises data on candidate interviews, dismissals, performance reviews, and
the like, which is
retrieved from the ATS, as indicated by reference 350. According to another
aspect, the
historical decision data for dismissals further includes information on the
dismissal reasons and
information on the candidate's comments, as indicated by reference 352. The
candidate profile
generated by the machine learning algorithms 318 comprises training data which
is exported as
indicated by reference 340 to a training database indicated by reference 342.
A new candidate
applying for a job is compared to the candidate profile associated with the
job and subsequently
scored as described in more detail below.
[00042] According to an exemplary embodiment, the Al services module 256
(Fig. 2) is
implemented with machine learning algorithms comprising a Bayes Classifier. As
described
above, the system 200 imports applicant data from the applicant tracking
system (ATS) 240. This
information provides a list of applicants that have applied to the company or
have been
considered for a particular job at the company. The applicant data comprises:
the applicant
name, contact information, resume, any questions asked during the application
process,
answers given by the applicant during the application process, and/or any
information collected
or stored in the ATS 240.
[00043] Completion or execution of the training process 300 results in
the generation of an
ideal candidate template or data model. The ideal candidate template or data
model is available
for use by the system 200 as will be described in more detail below. As new
candidates apply for
jobs, candidate data and information are retrieved from the ATS 240 (Fig. 2)
and a template or
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model is generated for the candidate, and the candidate model or template is
compared to the
ideal candidate model or template, and a score or other hiring recommendation
is generated for
the candidate by the system 200 as will be described in more detail below.
[00044] With reference to Fig. 6A, the system 200 is configured to
receive a resume 600
from the ATS 240 and process the resume 600 with the resume parser 250.
According to an
exemplary embodiment, the resume parser 250 is configured to convert the
resume 600 from
its document format (e.g. Microsoft Word, or Adobe PDF) into a machine-
readable form
indicated generally by reference 610 and comprising extracted or
contextualized data
components or fields. According to another aspect, the imported resume is
processed by a text
processor to convert the resume to plain text form and remove stop words, such
as "in", "and",
and the resulting plain text is parsed and tokenized, i.e. converted into
tokens 640, as described
in more detail below.
[00045] According to an exemplary implementation, the extracted or
contextualized data
comprises: contact information 612, work experience 1 data 614, work
experience 2 data 616,
and skills data 618. As shown, the first work experience data 614 comprises
"company name"
data, which is normalized using a company database 620 resulting in a
normalized company
name indicated generally by reference 622. For example, Microsoft and
Microsoft Corp are the
same entity. The normalized name eliminates redundancy or ambiguity and
provides a token
642 that is then utilized by the Bayes Classifier. The system 200 also
utilizes the company
database 620 to import or extract other company information, such as, company
size, founding
date, industry, keywords, and other company-specific information. Company data
or
information having variable values, for example, number of employees or
founding date, are
normalized into brackets, for instance, small, medium and large. The Bayes
Classifier utilizes
these brackets together with the absolute values.
100046] As shown, the tokens 642 for the first company comprise:
employer industry -
"emp:industry:software"; employer size - "emp:size:snnall"; employer domain or
URL ¨
"emp:domain:idealcandidate.conn"; and employer keywords ¨
"emp:keyword:software" and
"emp:keyword:saas".
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[00047] A similar process is applied to tokenize the data associated with
the second work
experience 616 listed in the candidate's resume to generate a normalized
company name 624
and a token set 644 comprising: "emp:industry:marketing"; "emp:size:large";
"emp:domain:abccornm.com"; "emp:keyword:marketing" and "ennp:keyword:web", as
shown
in Fig. 6A. According to another aspect, the system 200 is configured to
generate a token(s) 646
from the candidate's previous employment positions, i.e. "title:nnarketing
director". As shown,
the skills data 618 is tokenized to generate tokens 648, which comprise
individual tokens:
"skills:seo"; "skill:marketing"; "skill:email marketing"; and
"skill:management". The system 200
is configured to extract and tokenize other information provided by the ATS
240, such as, a
candidate's answers to screening questions during the application process.
[00048] In addition to company database or data services 622, the system
200 is configured
to utilize other external databases or services. The external services
comprise: people & social
services; education data services; and/or artificial intelligence or Al
services. The system 200
utilizes the people & social services to look up information about the
individual candidate, for
example, based on email address, phone number or other personally identifiable
information)
from social media applications and other public services that maintain
information about
individuals. The system 200 utilizes the company data services or database to
look up each
company listed on the candidate's resume in order to extract more information
about the listed
company, such as, the industry associated with the company, company size,
company
location(s), etc. The system 200 utilizes the education data services or
database to look up
educational institutions listed on the candidate's resume and extract
information to determine
the ranking of the school, the quality of the degree programs, location, etc.
The system 200
utilizes the Al services to extract more information from the application
data, such as, skills that
are not explicitly listed in the resume of the candidate. The system 200 may
also utilize Al
services or functions to group candidates based on their skills and
experiences.
[00049] According to another aspect, the system 200 is configured to
extract or import
personal information unique to the candidate, for example, email address and
phone number.
The system 200 utilizes the unique personal information to look up the
candidate in a people
information database and/or social media services. The system 200 uses these
services to gather
additional information about the candidate, for example, the candidate's
social networking
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identifier, interests that the person has expressed online. The system 200 is
further configured
to extract and tokenize this information for further processing by the Bayes
Classifier. The
system 200 is
[00050] Following this process, the system 200 generates a token list or
set for the
candidate, for example, a token list as indicated by reference 680 in Fig. 6C.
The token set 680
for the candidate is compared to a token list generated and associated with an
ideal candidate
template as described above, for example, a token list as indicated by
reference 670 in Fig. 6C.
The system 200 is configured to compare the tokens 670 for the ideal candidate
to the tokens
680 for the candidate and calculate or generate "a score" as indicated by
reference 690, for
example, "0.93". The system 200 can be further configured to map the score to
a "grade" as
indicated by reference 692, for example, "A". The score 690 and/or grade 692
is then utilized by
the ATS 240 to trigger a manual or automated workflows comprising contacting
high-grade
candidates, i.e. scores > 0.80 or grades > A-, to schedule interviews. There
will be instances
where not all candidates will have all data points available and therefore the
list of tokens 680
for a candidate may not include all tokens 670 associated with the candidate
template or model,
for example, as shown in Fig. 6C. The system 200 is configured to score the
candidate based on
the information available.
[00051] According to an exemplary implementation, the Bayes Classifier
comprises a
Bayesian Engine that is configured to predict outcomes based on a-priori
knowledge of previous
outcomes. The engine is configured to utilize heuristically developed tweaks
to a pure naive
Bayes engine. The tweaks include eliminating weak indicators, and implementing
a custom
combining algorithm to ensure that overly strong indicators do not overpower
the system.
These particular implementation details will be within the understanding of
those skilled in the
art.
[00052] It will be appreciated that a resume can result or generate
several features based on
the resume data that is contextualized and tokenized. According to an
embodiment, the system
200 is configured with a "5-word sliding window" as depicted in Fig. 6B and
indicated generally
by 650.
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[00053] Reference is next made to Fig. 4, which shows a production
process or method for
selecting a candidate according to an embodiment of the present invention and
indicated
generally by reference 400. The production process 400 is executed by the
system 200 to
compare a candidate to the ideal candidate template and generate a score or
other
recommendation for the candidate.
[00054] As shown in Fig. 4, when a candidate applies for a job or a new
position 410,
applicant data is sent and stored in the ATS 240 (Fig. 2) as indicated by
reference 412. The
system 200 is configured to retrieve or import the candidate's application
data, and send the
application data to the system 200, as indicated by reference 414. According
to another
embodiment, the system 200 is configured to periodically poll or contact the
ATS 240 to
determine if any new applicant(s) have been added. As will be described in
more detail, the
addition of any new applicants or candidates can be used to generate
additional applicant data
for comparison and/or creating an ideal candidate model or template. The
candidate's
application data is stored in the candidate database 314 as indicated by
reference 420. As
indicated by reference 416, the job data for the given role or position is
also loaded. The job
data is retrieved from the job database 314 (Fig. 3), as indicated by
reference 417. As indicated
by reference 418, training data for the given role is also loaded, for
instance, from the training
database 342 (Fig. 3), as shown in Fig. 4.
100055] As depicted in Fig. 4, the system 200 is configured to execute
the machine learning
algorithms 318 (Fig. 3) to generate an ideal candidate profile or template
based on historical
decision data (imported from the candidate database 314 (Fig. 3) and/or the
job database 315),
and also comprising candidate data for each candidate from the candidate
database 314 as
indicated by reference 420. According to another aspect, the system 200 is
configured to
execute the machine learning algorithms 318 to process training data to
further refine or revise
the ideal candidate profile or template, as described in more detail below.
[00056] The machine learning algorithm module 318 is also configured to
generate a
candidate profile or template for the applicant as indicated by reference 422
in Fig. 4. As also
shown, the machine learning algorithm module 318 is also configured to process
candidate data
that has been imported from the ATS and/or processed the system 200. According
to an
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exemplary implementation as described above, the candidate data processed by
the system 200
comprises: answers to screening questions 430; candidate profile data from the
ATS (412)
indicated by reference 432; information extracted by the resume parser
(indicated by reference
250 In Fig. 2) 434; extracted entity information 436; personal candidate
information extracted or
imported from public and/or private data sources 438; company information
gathered or
imported from public and/or private databases 440; data extracted or imported
from social
networking sites or services 442; and/or keywords extracted from the
candidates resumes
and/or job applications, as indicated by reference 444.
[00057] As shown in Fig. 3, the historical decision data, i.e.
decisions, processed by the
machine learning algorithms 318 comprises data on candidate interviews,
dismissals,
performance reviews, and the like, which is retrieved from the ATS, as
indicated by reference
350 in Fig. 3. According to another aspect, the historical decision data for
dismissals further
includes information on the dismissal reasons and information on the
candidate's comments, as
indicated by reference 352. The candidate profile generated by the machine
learning algorithms
318 comprises training data which is exported as indicated by reference 340 to
a training
database indicated by reference 342. The candidate profile generated for the
new candidate
applying for a job is compared to the ideal candidate profile or template
associated with the job
and subsequently scored as described in more detail below.
[00058] According to another aspect, the system 200 is configured to
classify the applicant
according to the role or position being applied for by the applicant. The
system 200 is configured
with a number of buckets, each bucket corresponding to or being associated
with a role or
position. The role or position is further characterized by an ideal candidate
profile or template,
which is generated as described above. The candidate's applicant is assigned
to the relevant
ideal candidate profile or template corresponding to the associated bucket.
[00059] The candidate profile is compared to the ideal candidate profile
or template
associated with the job bucket, and a numeric score is generated, for
instance, as described
above with reference to Fig. 6C, and indicated by reference 450 in Fig. 4. The
system 200 is
configured to assign, i.e. through a look-up table, a grade for the score
value, comprising a letter
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grade, A/B/C/D/F, as indicated by reference 460. The system 200 may be
configured to send the
score to the ATS, as indicated by reference 462 in Fig. 4.
[00060] According to another embodiment, the system 200 may further
include a grade-
based automation module as indicated by reference 470. The grade-based
automation module
is configured to provide additional functions based on the grade generated for
the candidate.
According to an exemplary embodiment, the grade-based module 470 is
configured: to
automatically move "A" candidates to an interview stage; to send an email to
the candidate
(which may be dependent on the grade); and/or trigger or initiate a video
interview request
with the candidate. The video interview can be linked through an external
video interview
system. As indicated by reference 472, the system 200 may also be configured
to send a status
update based on the grade-based operation to the Applicant Tracking System 240
(Fig. 2)
associated with the client or organization. According to another aspect, the
system 200 is
configured to process candidates utilizing other communication mechanisms or
protocols. For
instance, the system 200 may be configured with a SMS communication interface
to process
SMS candidates.
[00061] According to another embodiment, the system 200 is configured to
execute a
training process indicated generally by reference 500 in Fig. 5A. The training
process 500
comprises feedback components and is configured to update and revise the ideal
candidate
profile or model based on the performance of new candidates, e.g. new
candidates hired for a
position or job associated with the ideal candidate profile for that job or
position bucket.
[00062] As shown in Fig. 5A, the process 500 starts execution 501 with
the ATS 240 (Fig. 2)
being updated with decision data, e.g. selected for interview, hired,
performance review,
probation, dismissal, for a new candidate, as indicated by reference 510. The
system 200 is
configured to import or input the updated data from the ATS, as indicated by
reference 512.
According to an embodiment, the system 200 includes a registration module 514
configured to
register a change in candidate data prior to the updated candidate data is
stored or saved in the
candidate database 314 (Fig 3) as indicated by reference 516 in Fig. 5A.
[00063] As shown in Fig. 5A, the training process 200 comprises two
feedback or training
loops. The first feedback or training loop indicated by reference 520 is
configured to process
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decision data associated with a candidate dismissal. The second feedback or
training loop
indicated by reference 540 is configured to process decision data associated
with a candidate
hire. As will be described in more detail below, the feedback loops 520, 540
are configured to
further train the machine learning algorithms implemented or embodied in the
machine
learning module 210 (Fig. 2).
[00064] The first feedback training loop 520 is configured to process
decision data for
candidate dismissal(s). As shown, the feedback training loop 520 comprises a
decision block 522
configured to determine if the decision data corresponds to a previous
dismissal for the
candidate. If yes, then the candidate data is retrieved from the candidate
database 314, as
indicated by reference 524, and the machine learning algorithms are retrained
with the data
characterized or tagged a "do not interview", as indicated by reference 526,
and based on the
premise that an organization does not necessarily want to grant an interview
to a candidate hire
that was previously dismissed. The processed candidate data is stored in the
training database
342 as shown in Fig. 5A. According to another aspect, the ideal candidate
profile or model for
the role (or a job bucket) is regenerated based on the additional decision
data. If the
determination in decision block 522 is no or false, then the candidate data is
retrieved from the
candidate database 314, as indicated by reference 524, and the machine
learning algorithms are
retrained with the data characterized or tagged a "do not hire", as indicated
by reference 530.
The processed candidate data is stored in the training database 342, for
example, as described
in more detail below with reference to Figure 5B.
[00065] The second feedback training loop 540 is configured to process
decision data for
candidate hire(s). As shown, the feedback training loop 540 comprises a
decision block
configured to determine if the decision data corresponds to a candidate that
was hired, for
example, by the organization. If yes, then the candidate data is retrieved
from the candidate
database 314, as indicated by reference 542, and the machine learning
algorithms are retrained
with the data characterized or tagged as a "hire", as indicated by reference
544. The processed
candidate data corresponding to the hire decision data is stored in the
training database 342.
According to another aspect, the ideal candidate profile or model for the role
(or a job bucket) is
regenerated based on the additional decision data. If the candidate is not a
hire, the feedback
training loop 540 includes another decision block to determine if the decision
data is for a
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candidate who was interviewed, as indicated by reference 550. If yes, then the
candidate data is
retrieved from the candidate database 314, as indicated by reference 552, and
the machine
learning algorithms are retrained with the data characterized or tagged an
"interview", as
indicated by reference 554 in Fig. 5A. The processed candidate data is stored
in the training
database 342.
[00066] Reference is next made to Fig. 5B, which shows in flowchart form
a training process
according to another aspect. The training process is indicated generally by
reference 560 and
configured to further train, i.e. update and revise, the ideal candidate
profile or model based on
a candidate that is a "good candidate", or a "bad candidate", i.e. a candidate
that has been
dismissed, fired or otherwise terminated.
[00067] As shown in Fig. 5B, the training process 560 starts execution
561 and the first
operation comprises fetching candidates that are new/modified since a last
fetch (i.e. from the
candidate database 314 in Fig. 3). In decision block 563, the training process
560 determines if
the candidate is new. If the fetched candidate is a new candidate, then
execution terminates as
indicated by reference 564. If the candidate is not new, then the process 500
is configured to
determine if the candidate status has changed in decision logic block 565. If
the candidate status
has not changed, for instance, the candidate is still an employee and has not
been dismissed,
then the training process 560 finishes execution as indicated by reference
566. If it is
determined that the candidate status has changed (decision block 565), the
training process 560
is configured with a decision logic block 568 to determine if the change in
candidate status is a
dismissal. For a dismissed candidate, the training process 560 may include a
decision logic block
to determine if the candidate has been contacted, for example, for additional
information
concerning the employment and/or dismissal, and if yes, then training process
560 terminates
or completes execution in step 566. The next processing step executed in the
training process
560 comprises a decision logic block 580 configured to determine if the
candidate was dismissed
because they were not "a good fit". If no or false, then the training process
560 terminates as
indicated by block 581. If, on the other hand, the candidate was not a good
fit (as determined in
decision block 580), the training process 560 is configured to execute a
process as indicated by
reference 582 for further training the ideal candidate model based on
information and data for
the dismissed candidate, e.g. a candidate that was not "a good fit". Following
the execution of
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the training operations, the training process 560 terminates or completes
execution as indicated
by reference 575.
[00068] Referring still to Fig. 5B, if the candidate status has changed
(decision block 565),
but the candidate has not been dismissed (decision block 568), then the
training process 560 is
configured with a processing stream or loop configured to further train the
ideal candidate
model with what constitutes a "good fit" candidate. According to an exemplary
implementation,
the training process 560 includes a decision block configured to determine if
the candidate has
been contacted, for example, the candidate has been hired, as indicated by
reference 572. If
yes, then the training process 560 is configured to further train the ideal
candidate model based
on candidate information and data associated with a candidate that is a "good
fit". The training
process 560 then terminates or ends execution as indicated by reference 575.
If, on the other
hand, the candidate is not being contacted (decision block 572), but rather
the candidate is
being hired or being given an offer of employment as determined in decision
block 573, then the
training process 560 is configured to further train the ideal candidate model
based on
information and data on the candidate which constitutes a "good candidate" or
a "good fit", as
indicated by reference 574 in Fig. 5B. Once the training operations area
completed, the training
process 560 terminates execution as indicated in block 575.
[000691 It will be appreciated that the feedback loop(s) comprising the
training process 500
function to improve and revise the ideal candidate profile or template over
time, based on the
needs of the organization or business, changes to the role or position itself,
and/or as more
decision data concerning candidate(s) for the role is collected.
[00070] Reference is next made to Fig. 8, which shows in flowchart form
an auto-bucketing
process according to an embodiment of the present invention and indicated
generally by
reference 800. The auto-bucketing process 800 is configured to execute on a
computer system
with at least one or more hardware processors and execution is initiated as
indicated by
reference 801.
[00071] According to an embodiment, the auto-bucketing process 800
commences
execution by retrieving jobs or roles that have been saved or posted to the
Applicant Tracking
System (ATS) 802 as indicated by reference 810 in Fig. 8. According to one
embodiment, the
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system is configured to retrieve new jobs or roles that have not been
previously processed, i.e.
"bucketed". Each job is retrieved until there are no new jobs or roles left to
retrieve from the
ATS 802. If new jobs or roles are added to the ATS 802, then the system is
further configured to
retrieve the new job(s) or role(s) and execute the auto-bucketing process as
described in more
detail below. According to an exemplary implementation, the system is
configured to execute
the auto-bucketing process 800 periodically or continuously until disabled.
[00072] As shown in Fig. 8 and indicated by reference 830, the auto-
bucketing process 800 is
configured to create an auto-bucket for a job or a role. According to an
embodiment, the auto-
bucketing process 800 creates an auto-bucket title based on the description
associated with the
job or role. The auto-bucketing process 800 is configured to use the generated
auto-bucket titles
against existing job or role buckets as indicated in decision block 840.
According to an exemplary
implementation, the auto-bucketing process 800 comprises a string matching
mechanism which
is configured to compare the generated auto-bucket title against job title
buckets. If the
generated title matches an existing job title bucket, then the job or title is
associated with or
added to the existing job bucket as indicated in decision step 840. If on the
other hand, the
generated auto-bucket title does not match or fall into an existing job
bucket, the auto-
bucketing process 800 creates a new job title bucket which is configured in
the ATS (e.g.
implemented in a database or computer memory), and corresponds to the
generated auto-
bucket title, as indicated by reference 850 in Fig. 8. In subsequent
executions of the auto-
bucketing process 800, the generated auto-bucket title will also be compared
to the new job
title bucket. According to another aspect, the auto-bucketing process 800
assigns a low priority
to the new job title bucket as also indicated in block 850. According to this
embodiment, the
newly generated job bucket(s) are assigned a low priority so that existing job
title buckets have a
higher priority over the autogenerated job title bucket(s).
[000731 According to an exemplary implementation, the auto-bucketing
process 800
comprises a pre-trained job role classifier as indicated by reference 832 in
Fig. 8. The classifier
may be implemented, for example, in a naive Bayes classifier. The classifier
is trained utilizing a
pre-determined or internal dataset comprising a plurality of data sources. The
classifier is
configured to generate to generate a title for the job or role. The particular
implementation
details will be readily within the understanding of one skilled in the art.
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[00074] According to another embodiment, the auto-bucketing process 800
includes a job
rollout determination or mechanism indicated generally as a decision step 820.
The job rollout
determination 820 is configured to determine if the job retrieved from the ATS
802 in processing
step 810 is subject to any rollout restrictions or settings. According to an
embodiment, the auto-
bucketing process 800 is configured to restrict the creation of job or role
buckets according to
predetermined criteria, as indicated by reference 822, and including for
instance, job location,
business unit, and other criteria. If the job is subject to any rollout
restrictions and the rollout
restrictions are satisfied as determined in decision logic step 820, then a
corresponding auto-
bucket job title is generated in step 830 and the processing continues, as
described above. If, on
the other hand, the retrieved job does not satisfy the rollout restrictions,
then the auto-
bucketing process 800 loops back and the retrieved job is matched to any
existing job bucket(s)
using a standard job to bucket association process instead of the auto-
bucketing process as
described above. It will be appreciated that this functionality provides the
system with the
capability to roll-out business segment or departmental hiring or expansion in
an organization,
particularly, large organizations, in a controlled manner.
[00075] The present invention may be embodied in other specific forms
without departing
from the spirit or essential characteristics thereof. Certain adaptations and
modifications of the
invention will be obvious to those skilled in the art. Therefore, the
presently discussed
embodiments are considered to be illustrative and not restrictive, the scope
of the invention
being indicated by the appended claims rather than the foregoing description,
and all changes
which come within the meaning and range of equivalency of the claims are
therefore intended
to be embraced therein.
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