U.S. patent application number 13/753550 was filed with the patent office on 2013-08-01 for system and method for analyzing a resume and displaying a summary of the resume.
This patent application is currently assigned to FORMCEPT TECHNOLOGIES AND SOLUTIONS PVT LTD. The applicant listed for this patent is Anuj Kumar, FORMCEPT TECHNOLOGIES AND SOLUTIONS PVT LTD., Suresh Srinivasan. Invention is credited to Anuj Kumar, FORMCEPT TECHNOLOGIES AND SOLUTIONS PVT LTD., Suresh Srinivasan.
Application Number | 20130198599 13/753550 |
Document ID | / |
Family ID | 48871162 |
Filed Date | 2013-08-01 |
United States Patent
Application |
20130198599 |
Kind Code |
A1 |
Kumar; Anuj ; et
al. |
August 1, 2013 |
SYSTEM AND METHOD FOR ANALYZING A RESUME AND DISPLAYING A SUMMARY
OF THE RESUME
Abstract
A computer implemented method for generating a summary of one or
more resume from one or more of resumes to analyze insights of the
one or more resume is provided. The computer implemented method
includes (i) processing a first input includes a first indication
to select a first resume from one or more of resumes, (ii)
extracting, from the first resume, a first information, (iii)
obtaining, from the first resume, a second information, (iv)
generating a first table based on the first information and the
second information, and (v) generating a first summary based on the
first table, the first summary indicates a first correlation
between (i) the one or more events associated with the first
section and (ii) the one or more events associated with the second
section over years.
Inventors: |
Kumar; Anuj; (Bangalore,
IN) ; Srinivasan; Suresh; (Bangalore, IN) ;
AND SOLUTIONS PVT LTD.; FORMCEPT TECHNOLOGIES; (Bangalore,
IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Kumar; Anuj
Srinivasan; Suresh
AND SOLUTIONS PVT LTD.; FORMCEPT TECHNOLOGIES |
Bangalore
Bangalore
Bangalore |
|
IN
IN
IN |
|
|
Assignee: |
FORMCEPT TECHNOLOGIES AND SOLUTIONS
PVT LTD
Bangalore
IN
|
Family ID: |
48871162 |
Appl. No.: |
13/753550 |
Filed: |
January 30, 2013 |
Current U.S.
Class: |
715/227 |
Current CPC
Class: |
G06F 40/177 20200101;
G06N 5/00 20130101; G06N 5/02 20130101; G06F 16/245 20190101; G06F
40/279 20200101 |
Class at
Publication: |
715/227 |
International
Class: |
G06F 17/24 20060101
G06F017/24 |
Foreign Application Data
Date |
Code |
Application Number |
Jan 30, 2012 |
IN |
350/CHE/2012 |
Claims
1. A computer implemented method for generating a summary of at
least one resume from a plurality of resumes to analyze insights of
said at feast one resume, said method comprising: processing a
first input comprising a first indication to select a first resume
from said plurality of resumes; extracting, from said first resume,
a first information, wherein said first information comprises (a) a
first section, (b) a second section, (e) at least one event
associated with said first section, and (d) at least one event
associated with said second section; obtaining, from said first
resume, a second information; generating a first table based on
said first information and said second information; and generating
a first summary based on said first table, wherein said first
summary indicates a first correlation between (i) said at least one
event associated with said first section and (ii) said at least one
event associated with said second section over years.
2. The computer implemented method of claim 1, wherein said first
information further comprises (i) at least one first date range of
said first section, (ii) at least one second date range of said
second section, (iii) at least one third date range of said at
least one event associated with said first section, and (iv) at
least one fourth date range of said at least one event associated
with said second section.
3. The computer implemented method of claim 2, wherein said at
least one first date range, said at least one second date range,
said at least one third date range and said at least one fourth
date mage each comprise a start date and an end date, wherein said
start date and said end date comprise a year, a date or a
month.
4. The computer implemented method of claim 2, wherein said second
information comprises (i) a first period associated with said first
section, (ii) a second period associated with said second section,
(iii) a third period associated with said at least one event
associated with said first section, and (iv) a fourth period
associated with said at least one event associated with said second
section.
5. The computer implemented method of claim 2, wherein (i) said at
least one third date range of said at least one event associated
with said first section and (ii) said at least one fourth date
range of said at least one event associated with said second
section overlaps with each other.
6. The computer implemented method of claim 1, wherein said first
summary comprises a first graphical representation that illustrates
said first correlation.
7. The computer implemented method of claim 6, wherein said first
graphical representation illustrates (i) said at least one event
associated with said first section using first color and (ii) said
at least one event associated with said second section using second
color.
8. The computer implemented method of claim 7, wherein said first
graphical representation displays at least one of said first
information when a cursor moves over said first graphical
representation.
9. The computer implemented method of claim 1, further comprises:
processing said first input comprising a second indication to
select a second resume from said plurality of resumes; extracting,
said second resume, a third information, wherein said third
information comprises (a) a third section, (b) a fourth section,
(c) at least one event associated with said third section, and (d)
at least one event associated with said fourth section; obtaining,
from said second resume, a fourth information; generating a second
table based on said third information and said fourth information;
and generating a second summary based on said second table, wherein
said second summary indicates a second correlation between (i) said
at least one event associated with said third section and (ii) said
at least one event associated with said fourth section over years,
wherein said first summary and said second summary indicates said
insights of said first resume and said second resume.
10. The computer implemented method of claim 9, further comprises:
processing a third input comprising an indication to select at
least one of (i) said first resume and (ii) said second resume
based on a comparison between (a) said first summary of said first
resume and (b) said second summary of said second resume; and
identifying a duration within at least one of (i) said first
section and (i) said second section of at least one of (a) said
first resume and (b) said second resume, wherein said duration does
not comprise an event.
11. A non-transitory program storage device readable by computer,
and comprising a program of instructions executable by said
computer to generate a summary of at least one resume from a
plurality of resumes, said method comprising: processing a first
input comprising a first indication to select a first resume from
said plurality of resumes; extracting, from said first resume, a
first information, wherein said first information comprises (a) a
first section, (b) a second section, (c) at least one event
associated with said first section, and (d) at least one event
associated with said second section; obtaining, from said first
resume, a second information; generating said first table based on
said first information and said second information; and generating
a first summary, wherein said first summary comprises a first
graphical representation that is generated based on said table,
wherein said first graphical representation illustrates a first
correlation between (i) said at least one event associated with
said first section and (ii) said at least one event associated with
said second section over years.
12. The non-transitory program storage device of claim 11, wherein
said first summary further comprises a second graphical
representation that is generated based on said table, wherein said
second graphical representation illustrates an overall summary of
said first section and said second section.
13. The non-transitory program storage device of claim 11, wherein
said method further comprising: processing a second input
comprising a second indication to select at least one section from
said first section and said second section; and generating a second
summary for said at least one section selected by said second
input, wherein said second summary comprises a third graphical
representation that is generated based on said table, wherein said
third representation illustrates a third correlation between said
at least one event associated with said at least one section
selected by said second input over years.
14. A system for summarizing at least one resume from a plurality
of resumes, said system comprising: a memory unit that stores a
database and a set of modules, wherein said database stores said
plurality of resumes; a display unit; and a processor that executes
said set of modules, wherein said set of modules comprising: a
content extracting module that extracts, from said at least one
resume, a first information and a second information, wherein said
first information comprises: (a) a first section, (b) a second
section, (c) at least one event associated with said first section,
(d) at least one event associated with said second section and (e)
at least one first date range of said first section, (f) at least
one second date range of said second section, (g) at least one
third date range of said at least one event associated with said
first section and (h) at least one fourth date range of said at
least one event associated with said second section; and a report
generation module that generates a summary based on said first
information and said second information, wherein said summary is
displayed in said display unit.
15. The system of claim 14, further comprising: a table generating
module that generates a table based on said first information and
said second information, wherein said summary is generated based on
said table, wherein said second information comprises (a) a first
period associated with said first section, (b) a second period
associated with said second section, (c) a third period associated
with said at least one event associated with said first section,
and (d) a fourth period associated with said at least one event
associated with said second section.
16. The system of claim 14, wherein said content extracting module
further comprises: a duration determining module that extracts a
plurality of date ranges from said at least one resume; and a
boundary annotation module determines, from said plurality of date
ranges, (i) said at least one first date range of said first
section, (ii) said at least one second date range of said second
section, (iii) said at least one third date range of said at least
one event associated with said first section, and (iv) said at
least one fourth date range of said at least one event associated
with said second section.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to Indian patent
application no. 350/CHE/2012 filed on Jan. 30, 2012, the complete
disclosure of which, in its entirety, is herein incorporated by
reference.
BACKGROUND
[0002] 1. Technical Field
[0003] The embodiments herein generally relate to a resume
summarizer tool, and more particularly, to a system and method for
summarizing one or more resumes using the resume summarizer tool
using natural language processing and weighted formal concept
analysts (wFCA).
[0004] 2. Description of the Related Art
[0005] Recruitment is the process of attracting, screening and
selecting a qualified person for a job. Irrespective of the
organization size, the entire organization needs the right
candidate who suits for their needs. The process of recruitment is
not at all an easy task. It has always been a challenge for any
organization due to the high number of candidate resumes coming in
for a specific job description.
[0006] At present, recruiter has to manually check these candidates
resume for their relevancy with respect to the job description.
Thus, for preliminary screening of the candidate, one has to
manually check the resume. Usually, this process is time consuming
and also increases labor costs.
[0007] Further, there are many existing job portals that provide
recruiters a way for searching candidates in their database. The
recruiters can search for the resumes using the keywords associated
with the job. The job portal retrieves the number of resumes which
are matches the keywords. The recruiters need to download and
analyze each resume to identify a best resume. The recruiters may
also interest to know the key insights of each resume. Accordingly
there remains a need for analyzing one or mote resumes to identify
key insights in the resume.
SUMMARY
[0008] In view of a foregoing, the embodiment herein provides a
computer implemented method for generating a summary of one or more
resume from one or more of resumes to analyse insights of the one
or more resume. The comparer implemented method includes (i)
processing a first input includes a first indication to select a
first resume from the one or more of resumes, (ii) extracting, from
the first resume, a first information, the first information
includes (a) a first section, (b) a second section, (c) one or more
events associated with the first section, and (d) one or more
events associated with the second section, (iii) obtaining, from
the first resume, a second information, (iv) generating a first
table based on the first information and the second information,
and (v) generating a first summary based on the first table, the
first summary indicates a first correlation between (i) the one or
more events associated with the first section and (ii) the one or
more events associated with the second section over years. In one
embodiment, the first information further includes (a) one or more
first date range of the first section, (b) one or more second date
range of the second section, (c) one or more third date range of
the one or more events associated with the first section, and (d)
one or more fourth date range of the one or more events associated
with the second section. In another embodiment, the one or more
first date range, the one or more second date range, the one or
more third date range and the one or more fourth date range each
includes a start date and an end date, the start date and the end
date include a year, a date or a month. The second information may
includes (a) a first period associated with the first section, (b)
a second period associated with the second section, (c) a third
period associated with the one or more events associated with the
first section, and (d) a fourth period associated with the one or
more events associated with the second section.
[0009] In yet another embodiment, (a) the one or more third date
range of the one or more events associated with the first section
and (b) the one or more fourth date range of the one or more events
associated with the second section overlaps with each other. The
first summary may includes a first graphical representation that
illustrates the first correlation. The first graphical
representation may illustrates (a) the one or more events
associated with the first section using first color and (b) the one
or more events associated with the second section using second
color. The first graphical representation may display at least one
of the first information when a cursor moves over the first
graphical representation. The computer implemented method may
further includes (i) processing the first input includes a second
indication to select a second resume from the one or more of
resumes, (ii) extracting, the second resume, a third information,
the third information includes (a) a third section, (b) a fourth
section, (c) one or more events associated with the third section,
and (d) one or more events associated with the fourth section,
(iii) obtaining, from the second resume, a fourth information, (iv)
generating a second table based on the third information and the
fourth information, and (v) generating a second summary based on
the second table, the second summary indicates a second correlation
between (i) the one or more events associated with the third
section and (ii) the one or more events associated with the fourth
section over years, the first summary and second summary indicates
the insights of the first resume and the second resume.
[0010] In one aspect, a non-transitory program storage device
readable by computer, and includes a program of instructions
executable by the computer to generate a summary of one or more
resume from one or more of resumes is provided. The method includes
(i) processing a first input includes a first indication to select
a first resume from the one or more of resumes, (ii) extracting,
from the first resume, a first information, the first information
includes (a) a first section, (b) a second section, (c) one or more
events associated with the first section, and (d) one or more
events associated with the second section, (iii) obtaining, from
the first resume, a second information, (iv) generating the first,
table based on the first information and the second information,
and (v) generating a first summary, the first summary includes a
first graphical representation that is generated based on the
table. The first graphical representation illustrates a first
correlation between (i) the one or more events associated with the
first section and (ii) the one or more events associated with the
second section over years. The first summary may further include a
second graphical representation that is generated based on the
table. The second graphical representation illustrates an overall
summary of the first section and the second section.
[0011] In one embodiment, the computer implemented method further
includes (i) processing a second input includes a second indication
to select one or more sections from the first section and the
second section, and (ii) generating a second, summary for the one
or more sections selected by the second input, the second summary
includes a third graphical representation that is generated based
on the table. The third representation illustrates a third
correlation between the one or more events associated with the one
or more sections selected by the second input over years. The
method may further include (i) processing a third input include an
indication to select at least one of (a) the first resume and (b)
the second resume based on a comparison between the first summary
of the first resume and the second summary of the second resume,
and (ii) identifying a duration within at least one of (i) the
first section and (i) the second section of at least one of (a) the
first resume and (b) the second resume, wherein the duration does
not comprise an event.
[0012] In another aspect, a system for summarizing one or more
resume from one or more of resumes is provided. The system includes
(a) a memory unit that stores a database and a set of modules, the
database stores the one or more of resumes, (b) a display unit, and
(e) a processor that executes the set of modules. The set of
modules includes (i) a content extracting module that extracts,
from the one or more resume, a first information and a second
information, and (ii) a report generation module that generates a
summary based on the first information and the second information,
the summary is displayed in the display unit. The first information
includes (a) a first section, (b) a second section, (c) one or more
events associated with the first section, (d) one or more events
associated with the second section and (e) one or more first date
range of the first section, (f) one or more second date range of
the second section, (g) one or mote third date range of the one or
more events associated with the first section and (h) one or more
fourth date range of the one or more events associated with the
second section. The second information may includes (a) a first
period associated with the first section, (b) a second period,
associated with the second section, (e) a third period associated
with the one or more events associated with the first section, and
(d) a fourth period associated with the one or more events
associated with the second section.
[0013] In one embodiment, the set of modules farther include (i) a
table generating module that generates a table based on the first
information and the second information, the summary is generated
based on the table, (ii) a duration determining module that
extracts one or more of date ranges from the one or more resume;
and (ii) a boundary annotation module determines, from the one or
more of date ranges, (i) the one or more first date range of the
first section, (ii) the one or more second date range of the second
section, (iii) the one or more third date range of the one or more
events associated with the first section, and (iv) the one or more
fourth date range of the one or more events associated with the
second section.
[0014] These and other aspects of the embodiments herein will be
better appreciated and understood when considered in conjunction
with the following description and the accompanying drawings. It
should be understood, however, that the following descriptions,
while indicating preferred embodiments and numerous specific
details thereof, are given by way of illustration and not of
limitation. Many changes and modifications may be made within the
scope of the embodiments herein without departing from the spirit
thereof, and the embodiments herein include ail such
modifications.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The embodiments herein will be better understood font the
following detailed description with reference to the drawings, in
which:
[0016] FIG. 1 illustrates a system view of users communicating with
a user system for summarizing one or more resumes using a resume
summarizer tool according to an embodiment herein;
[0017] FIG. 2 illustrates an exploded view of the user system with
a memory storage unit for storing the resume summarizer tool of
FIG. 1 and an external database according to an embodiment
herein;
[0018] FIG. 3 is an exploded view of the resume summarizer tool of
FIG. 1 illustrating a process of analysing the one or more resumes
according to an embodiment herein;
[0019] FIG. 4 illustrates a user interface view of the content
collection module of FIG. 3 of the resume summarizer tool of FIG. 1
according to an embodiment herein;
[0020] FIG. 5 illustrates a user interface view of a resume
provided to the resume summarizer tool of FIG. 1 according to an
embodiment herein;
[0021] FIG. 6 illustrates an exploded view of the content
annotation module of FIG. 3 of the resume summarizer tool of FIG. 1
according to an embodiment herein;
[0022] FIG. 7 illustrates a table that is generated using the table
generating module of the resume summarizer tool of FIG. 3 according
to an embodiment herein;
[0023] FIG. 8 illustrates a graphical representation generated
using the report generation module of FIG. 3 according to an
embodiment herein;
[0024] FIG. 9 illustrates a user interface view of intent selection
by the users of FIG. 1 according to an embodiment herein;
[0025] FIG. 10 illustrates a graphical representation of the event
line of FIG. 8 that indicates one or more overlapping events
according to an embodiment herein;
[0026] FIG. 11A is a user interface view illustrating a comparison
two or more resumes using the resume comparison module of FIG. 3
according to an embodiment herein;
[0027] FIG. 11B is the user interface view illustrating a first
graphical representation associated with the resume R1 and a second
graphical representation associated with the resume R5 according to
an embodiment herein; and
[0028] FIG. 12 is a flow diagram illustrating a method for
generating a summary of at least one resume from a set of resumes
to analyze insights of the at least one resume using the resume
summarizer tool of FIG. 1 according to an embodiment herein;
and
[0029] FIG. 13 illustrates a schematic diagram of a computer
architecture used in accordance with the embodiments herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0030] The embodiments herein and the various features and
advantageous details thereof are explained more fully with
reference to the non-limiting embodiments that are illustrated in
the accompanying drawings and detailed in the following
description. Descriptions of well-known components and processing
techniques are omitted so as to not unnecessarily obscure the
embodiments herein. The examples used herein are intended merely to
facilitate an understanding of ways in which the embodiments herein
may be practiced and to further enable those of skill in the art to
practice the embodiments herein. Accordingly, the examples should
not be construed as limiting the scope of the embodiments
herein.
[0031] As mentioned, there remains a need for a resume summarizer
tool that analyzes resume(s) and generates a summary of the resumes
in graphical representation to illustrate the key insights of the
resumes. The resume summarizer tool performs overall resume
summarization, compares one or more resumes irrespective of their
formats, extracts the events and provides key insights. Referring
now to the drawings, and more particularly to FIGS. 1 through 13,
where similar reference characters denote corresponding features
consistently throughout the figures, there are shown preferred
embodiments.
[0032] FIG. 1 illustrates a system view of users 102A-B
communicating with a user system 104A-N for summarizing one or more
resumes using a resume summarizer tool 106 according to an
embodiment herein. The user system 104A-N may be a personal
computer (PC) 104A, a tablet 104B and/or a smart phone 104N. A user
102A is one or more recruiters and a user 102B is one or more job
seekers. The user system 104A-N includes the resume summarizer tool
106 that summarizes the one or more resumes. The resume summarizer
tool 106 receives one or more resumes from the user 102B, in one
example embodiment. The resume summarizer tool 106 may (i) obtain
the one or more resumes from one or more job portals, and/or (ii)
fetch the one or more resumes from emails, etc., in another example
embodiment.
[0033] FIG. 2 illustrates an exploded view of the user system
104A-N with a memory storage unit 202 for storing the resume
summarizer tool 106 of FIG. 1 and an external database 216
according to an embodiment herein. The user system 104A-N includes
a memory storage unit 202, a bus 204, a communication device 206, a
processor 208, a cursor control 210, a keyboard 212 and a display
214. The memory storage unit 202 stores the resume summarizer tool
106. The resume summarizer tool 106 includes one or more software
modules to perform various functions on an input content and
assists one or more recruiters 102A in choosing the right candidate
for a given job description. The external database 216 includes a
knowledge base 218 that is populated with a set of categories based
on one or more concepts of linked data. The set of categories
correspond to various keywords.
[0034] FIG. 3 is an exploded view of the resume summarizer tool 106
of FIG. 1 illustrating a process of analyzing the one or more
resumes according to an embodiment herein. The resume summarizer
tool 106 includes a database 302, a content collection module 304,
a content parsing/extraction module 306, a content cleaning module
308, a content annotation module 310, an extracting module 312
(e.g. a content extracting module), a lattice construction module
314, a table generating module 316, a report generation module 318,
and a report comparison module 320.
[0035] The database 302 stems the one or more resumes that are
uploaded in the resume summarizer tool 106. The content collection
module 304 collects content or text associated with the one or more
resumes. The one or more resumes may be obtained from the user 102B
(one or more candidates who are interested in seeking a job), in
one example embodiment. The one or more resumes may be in a.doc
format, a pdf, an .rtf and/or obtained from a Uniform (or
universal) resource locator (URL), etc. The content
parsing/extraction module 306 extracts the content and/or text from
the one or more resumes. Further, the content parsing/extraction
module 306 parses HTML content when the one or more resumes is
obtained from the URL. The content cleaning module 308 cleans the
content before sending it to the content annotation module 310.
Cleaning may include removal of junk characters, new lines that are
not useful, application specific symbols (e.g., MS Word bullets),
and/or non-unicode characters etc. In one embodiment, specific
parts of the document (e.g., a header and/or a footer) may be
excluded.
[0036] The content annotation module 310 annotates the content of
the one or more resumes for useful information. The useful
information may include sentences, keywords, tokens, new lines, one
or more sections (e.g., objectives, a work experience, education,
circular activities, and/or personal information, etc.), durations
(e.g., a first date range such as 2010-2012 that is associated with
the one or more sections of the resume, and a second date range
2012-2013 that is associated with the one or more events of the one
or more sections), durations within the sections, sentences
associated with sections, and sentences associated with duration of
the resume associated with a candidate. The one or more sections
may include one or more events (e.g., the candidate has 2 years of
experience in Java, C and C++).
[0037] Once the annotations are done in the content annotation
module 310, the extracting module 312 extracts one or more
artifacts (e.g., sentences, keywords, tokens, new lines, sections
such as objectives, a work experience, education, circular
activities, and/or personal information, etc., durations such as
one or more date ranges, durations within the sections, sentences
associated with sections, and sentences associated with duration of
the one or more resumes associated with the one or more
candidates). The extracting module 312 extracts a name, an email
address, a phone number, and any other contact details that are
mentioned in the resume, etc. In addition, the extracting module
312 may also identity and extract a second set of information
(e.g., second information, fourth information). The second set of
information includes (a) a first period that corresponds to at
least one section in the one or more resumes and (b) a third period
that corresponds to at least one event associated with the at least
one section in the one or more resumes.
[0038] The lattice construction module 314 disambiguates one or
more keywords in the resume to compute the context in which the
keywords from the one or more resumes are used. The lattice
construction module 314 constructs a lattice based on a weighted
Formal Concept Analysis (wFCA) using the one or more keywords from
the one or more resumes as objects, and their corresponding
categories as attributes.
[0039] The table generating module 316 generates a table (e.g., a
first table and a second table) for each of the one or more resumes
using the one or more artifacts extracted in the extracting module
312. The report generation module 318 generates a summary (e.g., a
first summary and a second summary) based on the table generated in
the table generating module 316 for each of the one or more
resumes. In one embodiment, the report generation module 318
generates one or more graphical representations based on the table.
The report comparison module 320 compares the one or more resumes
and assigns a weight for each of the one or more resumes based on
the one or more keywords from the one or more resumes as objects,
and the one or more resume as attributes.
[0040] FIG. 4 illustrates a user interface view of the content
collection module 304 of FIG. 3 of the resume summarizer tool 106
of FIG. 1 according to an embodiment herein. The user interface
view of the content collection module 302 includes a header 402, a
test field 404, an upload button 406, an URL text field 408, a
fetch button 410, a drag and drop field 412, an upload a file
button 414, a task status table 416, a task progress field 418, and
a proceed button 420. The header 402 displays a logo, a welcome
message, and the status of an application. The user 102B (e.g., a
job seeker) may upload the resume in one or more format. Through,
the text field 404, the user 102B can provide details of the resume
in the form of plain text and clicks on the upload button 406 to
upload the plain text provided in the text field 404 to a remote
server.
[0041] The plain text may also be provided as an URL m the URL text
field 408 and the resume associated, with the URL is crawled using
the fetch button 410. The drag and drop field 412 helps the user
102B to drag and drop his/her resume(s) to be uploaded. Through,
the upload a file button 414, the user 502 can browse his/her
resume(s) to be uploaded. The task status table 416 displays the
uploaded resume as plain text, the URL, and/or the resume. The task
progress field 418 notifies the user 102B about the progress of
analyzing the resume. The user 102B is redirected to a next page
when he/she clicks on the proceed button 420.
[0042] FIG. 5 illustrates a user interface view 500 of a resume 502
provided to the resume summarizer tool 106 of FIG. 1 according to
an embodiment herein. The resume 502 may be either submitted by a
recruiter or by a candidate, in one example embodiment. The resume
502 may be obtained in the form of a document, a URL, and/or a
plain-text. In one embodiment, the content in the resume 502 is
patted/extracted (e.g., using the content parsing/extraction module
306 of FIG. 3). In another embodiment, the resume 502 may he fed as
an URL (e.g., www.abc.com/xyz-resume.html).
[0043] The content cleaning module 308 cleans the content obtained
from the content parsing/extraction module 306 before sending for
annotation. Cleaning the content is required, to remove junk
characters, new lines that are not useful, application specific
symbols (word processing bullets, etc.), and/or non-Unicode
characters, etc. In one embodiment, the content from the resume 502
itself is a cleaned text (e.g., a ready text),
[0044] FIG. 6 illustrates an exploded view of the content
annotation module 310 of FIG. 3 of the resume summarizer tool 106
of FIG. 1 according to an embodiment herein. The content annotation
module 310 includes a token annotations module 602, a sentence
annotations module 604, a stem annotations module 606, a forced new
lines, paragraphs and indentations computing module 608, a parts of
speech tag (POS) token annotations module 610, a POS line
annotation module 612, a duration determining module 614, a section
annotations module 616, and a boundary annotation module 618. The
dotted lines of FIG. 6 represent internal dependencies among the
various modules. The solid lines represent the flow of annotation
process. The content annotation module 310 annotates a cleaned
content obtained from the content cleaning module 308 for useful
information. The useful information may include sentences,
keywords, tokens, new lines and a first set of information (e.g., a
first information, a third information). The first set of
information includes one or more sections (a first section, a
second section, a third section, a fourth section), durations
(e.g., a first date range, such as 2010-2012 that is associated
with the one or mote sections of the resume, and a second date
range 2012-2013 that is associated with the one or more events of
the one or more sections), durations within the sections, sentences
associated with sections, and sentences associated with duration of
the resume associated with a candidate. The one or more sections
include objectives, a work experience, education, circular
activities, and/or personal information, etc. The one or more
sections may include one or more events (e.g., the candidate has 2
years of experience in Java, C and C++).
[0045] The cleaned content obtained from the content cleaning
module 308 is annotated by performing various levels of annotations
using the modules of content annotation module 310 of FIG. 3. The
sentence annotations module 604 extracts each and every sentence
from the cleaned content. For example, the first sentence of the
cleaned content obtained from the resume 502 is extracted by the
sentence annotations module 604 includes [0046] "PhD at MIT Media
Lab, Massachusetts Institute of Technology."
[0047] Similarly, the sentence annotations module 604 extracts all
the sentences from the resume 502.
[0048] The token annotations module 602 determines each and every
token in the extracted sentences. For example, "PhD", "at", "MIT",
"Media", "Lab", "," "Massachusetts", "Institute", "of",
"Technology" are all tokens in the first line of the cleaned
content of the resume 502. The stem annotations module 606 compotes
the root word for each and every token identified by the token
annotations module 602.
[0049] The POS token annotations module 610 generates one or more
parts of speeches (PPS) tag such as noun, and/or verb, etc. for
each token in the sentences such that each token annotation has an
associated POS tag. The forced new lines, paragraphs and
indentations computing module 608 determines white spaces like new
lines that are forced (e.g., pressed enter, list of items that are
not proper sentence), paragraphs, and/or indentations, etc.
Further, the POS line annotations module 612 tags each token in the
extracted new lines as a noun, and/or a verb, etc. In addition, new
lines are also useful for section extraction because section names
may not be proper sentences. For example, in the resume content
502, "education" and "working experience" are not proper sentences
but a word, and a fragment of two words respectively. These are
captured as a new line (e.g., using the section annotations module
616) because they occur in a separate line.
[0050] The duration determining module 614 extracts one or more
duration(s) wherever it occurs in the content of the resume 502.
For example, it extracts duration(s), like "2008 to current", "2006
to current", etc. The section annotations module 616 determines a
group of sentences that form a section that has a heading. To
determine a start point and an end point of the section, various
heuristics such as lookup for well known sections, sentence
construction based on parts of speech, relevance with respect to
surrounded text, exclusion terms, term co-occurrence, etc.
[0051] The boundary annotations module 618 associates related text
with the duration identified by the duration determining module
614. Most often, there may be information that is associated with
the duration but is not mentioned in the same line where duration
occurs. The boundary annotations module 618 assigns a right
boundary and a left boundary to identify exact information
associated with the duration. For example, [0052] "PhD at MIT Media
Lab, Massachusetts Institute of Technology 2008 to current;
Massachusetts Institute of Technology; CPA 5.0/5.0 [0053] Master of
Science at MIT Media Lab, Massachusetts Institute of Technology
2006 to current; Media Arts and Sciences; Massachusetts institute
of Technology; CPA 4.9/5.0 [0054] Master of Design at IDC, IIT
Bombay 2003 to 2005; Industrial Design Centre, Indian Institute of
Technology, Bombay; CPA 4.9/5.0 [0055] Bachelor of Computer
Engineering at Gujarat University 1999 to 2003; Nirma Institute of
Technology; Gujarat University; CPA 4.7/5.0 [0056] Working
Experience"
[0057] In the example, the text shown is selected from the
education section and a new section ("working experience") of the
cleaned content of the resume 502. The duration determining module
614 determines the periods such as "2008 to current", "2006 to
current", "2003 to 2005" and "1993 to 2003". The section
annotations module 616 determines "working experience" as a new
section. The boundary annotations module 618 assigns the left
boundary and the right boundary for each of the identified
duration. The left boundary for the duration "2008 to current" is
"PhD at MIT Media Lab, Massachusetts Institute of Technology". The
right boundary is Master of Science at MIT Media Lab Massachusetts
Institute of Technology. Both these lines, left and right to the
duration annotations are considered as possible associations with
the duration "2008 to current". Similarly, left and right
boundaries are assigned for each of the duration. The right
boundary for the fast duration "1999 to 2003" is a new section
("working experience"). Therefore, the boundary annotations module
618 computes that right boundary for the last duration is not
associated with the context of that duration. Further, the resume
analyzer tool 106 understands the section and the context in which
the year like numbers are occurring and include/exclude based on
the context. For example, a candidate's resume states that the
"person stands 1st out of 2000 people who have all attended the
interview" then the resume analyser tool 106 correctly identifies
that 2000 is not part of the duration.
[0058] Further, the boundary annotations module 618 uses a simple
heuristics to determine the best possible association for entire
section. The heuristic counts the number of left and right
associations for the entire section. In the above example, the
numbers of left associations are more compared to the number of
right associations since the last duration annotation does not have
any line covered by the right boundary. Since, the left
associations are more compared to the right associations, the
boundary annotations module 618 will consider left association as
the best possible association. Thus the duration "2008 to current"
is associated with the "PhD at MIT Media Lab, Massachusetts
Institute of Technology".
[0059] Once the annotations are done, Ore extracting module 312 one
or more artifacts (e.g., sentences, keywords, tokens, new lines,
sections such as objectives, a work experience, education, circular
activities, and/or personal information, etc., durations such as
one or more date ranges, durations within the sections, a gap
duration, (e.g. a duration within the sections does not includes an
event), sentences associated with sections, and sentences
associated with duration of the one or more resumes associated with
the one or more candidates.
[0060] The keywords in the one or more artifacts are extracted
based on the parts of speech tag generated by the POS modules using
the token annotations module 602 and the forced new lines,
paragraphs and indentations computing module 608. For example, a
noun is very likely to be a keyword in the sentence. Similarly
co-occurring nouns and its derivatives are also a keyword. A
keyword chamber is used to obtain these keyword and keyword phrases
depending on the noun and related tags. The extracting module 312
extracts keywords (e.g., 3 keywords) using POS tag generated by the
POS token annotations module 610 and the POS line annotations
module 612. The extracted keywords are: [0061] PhD--POS Tag says
that it is a noun [0062] MIT media lab--POS Tag says that it is a
noun [0063] Massachusetts Institute--POS Tag says that it is a
noun.
[0064] Once these keywords are identified and extracted, they are
disambiguated to find the right meaning. To disambiguate, the
resume summarizer tool 106 determines the different disambiguated
terms for the extracted keywords and their related categories using
the lattice construction module 314. Further, the resume summarizer
tool 106 uses the knowledge base 218 stored in the external
database 216 for obtaining the categories for the extracted
keywords. Each keyword is queried separately against the knowledge
base 218 and corresponding categories are obtained. For example,
for the above keywords. For example, the categories obtained are:
[0065] PhD--{Education, Qualifications, Academic Degrees, Doctoral
Degrees, Doctor of Philosophy} [0066] MIT Media Lab--{Education,
Educational Organizations, Educational Institutions, Academic
Institutions, Universities and Colleges, Universities and Colleges
by Country, Universities and Colleges in the United States,
Universities and Colleges in Massachusetts, Massachusetts Institute
of Technology} [0067] Massachusetts Institute--{Education,
Educational Organizations, Educational Institutions, Academic
Institutions, Universities and Colleges, Universities and Colleges
by Country, Universities and Colleges in the United States,
Universities and Colleges in Massachusetts}
[0068] These keywords are either nouns or noun phrases. The resume
summarizer tool 106 allows certain prepositions as well to
determine the keywords, for example, "in". For example, if
preposition "in" is considered, the keywords extracted will
include--"PhD at MIT Media Lab" and "Massachusetts Institute of
Technology". These keywords are then queried against the knowledge
base 216. If a match is found then they are included in the set of
keywords. Here, there are no disambiguations found. All the
extracted keywords are unique in the context of right meaning.
[0069] FIG. 7 illustrates a table 700 that is generated using the
table generating module 316 of the resume summarizer tool 106 of
FIG. 3 according to an embodiment herein. The table generating
module 316 generates the table 700 for the resume 502. The table
700 is generated based on the information extracted in the
extracting module 312. The information includes the one or more
artifacts (e.g., sentences, keywords, tokens, new lines, one or
more sections such as an education, a work experience, etc.,
durations such as one or more date ranges, durations within the
sections, sentences associated with sections, and sentences
associated with duration of the resume 502). The table 700 includes
one or more columns 702A-E. The first data series column 702A
includes the one or mom sections (e.g., education, experience) of
the resume 502. The second data series column 702B includes the one
or more events (e.g., Bachelor of computer engineering, Project
Manager at company A) associated with each of the one or more
sections of the resume 502. The third data series column 702C
includes at least one duration corresponding to each event. For
example, a candidate has completed Bachelor of computer engineering
(which is an event) in the duration 1999 to 2003. The table 700 is
generated for a current year of 2012, in one example embodiment.
The fourth data series column 702D includes at least, one period
associated with each event. For example, the period is 4 years in
which the candidate has completed Bachelor of computer engineering.
Here, the period is calculated based on the current year for the
most recent event. For example in FIG. 7, 2012 is considered as
current year. The fifth data series column 702E includes a period
associated with each section. For example, the candidate has
completed his/her education (e.g., Bachelor of computer
engineering, Master of Design, Master of Science and PhD.) in 13
years (e.g., 1999 to 2012)).
[0070] FIG. 8 illustrates a graphical representation 800 generated
using the report generation module 318 of FIG. 3 according to an
embodiment herein. The graphical representation 800 is generated
for the resume 502 based on the table 700. The graphical
representation 800 includes an event line 802 (e.g., a first
graphical representation), an events distribution 804, and a
sections span 806 (e.g., a second graphical representation)
generated based on the table 700 using the report generation module
318. The event line 802 indicates a correlation between the one or
more events extracted from the resume 502. The users 102A-B can
view the one or more events and key insights tor the entire resume
502 in the graphical representation 800. The event line 802 also
indicates the one or more events that are overlapping with each
other that are useful to obtain key insights (e.g., what the
candidate was doing from skills perspective while studying
(Education) or working (Experience)). The event line 802 also
indicates the gaps in a profile of the one or more resumes. One or
more colour codes may be used to indicate an overlap between the
one or more events, in one example embodiment. Other techniques may
be used to indicate the overlap between the one or more events, in
another example embodiment.
[0071] Through, the event distribution 804, the users 102A-B may
view the number of events within each section for a specific year.
This distribution gives an insight on the area under which the user
102B has been very active for a particular time period. For
example, the event distribution 804 indicates that the user 102B
has been very active from the year 2007 till 2010. The sections
span 806 indicates an overall distribution of the one or more
events with respect to the one or more sections (e.g., education,
experience, and awards) in the resume 502. Each of the event line
802, the events distribution 804, and the sections span 806 may
indicate a gap/duration in the resume 502 (the gap is indicated
based on the gap duration that is extracted in the extracting
module 312). For example, a candidate may have pursued his/her
education (e.g., an engineering degree in computer science) during
the years August 2002 till June 2006, and has a work experience
from a year 2008 January till December 2009. The resume summarizer
tool 106 identifies the gap between the education and the work
experience. The gap indicates a duration (e.g., July 2006 till
December 2007) which represents that the candidate has (i) not
pursued an education, (ii) no work experience, and/or (iii) not
performed any events/activities (e.g., pursuing internship in an
institute organization) with respect to his/her career.
[0072] FIG. 9 illustrates a user interface view 900 of intent
selection by the users 102A-B of FIG. 1 according to art embodiment
herein. The user interlace view of an intent selection includes the
header 402, one or more resumes 902A-N stored in the database 302,
a create folder(s) or organize content button 904, an intent
analytics field 906, and an intent selection field 908. The one or
more resumes 902A-N stored in the database 302 may be listed and/or
displayed as a scrollable list (e.g., a left to right scrollable
list and/or a right to left scrollable list).
[0073] The create folder(s) or organize content button 904 is used
for organizing these resumes and can create new (folders). The
users 102A-B can drag-drop one or more resumes from the scrollable
list to a specific folder to organize them. The intent analytics
field 906 displays the analysis details of the one or more resumes
selected from the scrollable list using the report generation
module 318 of FIG. 3. Such analysis details include graphical
representation of the one or more resumes 902A-N. The graphical
representation includes an event line 802 with years on the left
side of the section and corresponding details adjacent to it. The
intent selection field 908 provides one or more options to specify
various intents (e.g., analysis details) around which one or more
reports are to be generated. For example, the users 102A-B can
select at least one section from the one or more sections of the
one or more resumes 902A-N and may summarize the content around a
selected section. In one embodiment, the one or more resumes 902A-N
includes the resume 502. The users 102A-B may select one section
either education or experience for the resume 502 to summarize the
content around the selected section.
[0074] FIG. 10 illustrates a graphical representation 1000 of the
event line 802 of FIG. 8 that indicates one or more overlapping
events according to an embodiment herein. The graphical
representation 1000 of the event line 802 indicates (i) the key
insights, and (ii) the one or more overlapping events of the resume
502 upon receiving an input (e.g., a mouse-hover, and/or a cursor
when moved on a particular event line). For example, the users
102A-B may view the key insights during the year 2009 such as an
education event 1002, and an experience event 1004.
[0075] The education event 1002 indicates information that is
associated with an education for a particular event line (e.g., the
event line 802 such as Media Arts and sciences; Massachusetts
institute of technology; CPA 4.9/5.0) when the users 102A-B
experiences a mouse-hover on that particular event line 802.
Similarly, the experience event 1004 indicates an experience
information when an input is received, (e.g., when a mouse-hover is
experienced on the event line 802). Since this event overlaps with
education, this indicates a clear insight about the experience
earned as an internship while the user 102B was pursuing his/her
one or more degrees.
[0076] FIG. 11A is a user interface view 1100 illustrating a
comparison two or more resumes using the resume comparison module
320 of FIG. 3 according to an embodiment herein. The user interface
view of resume comparison includes the header 402, a resume display
filed 1102 which displays the one or more resumes (R1, R2 . . . ,
RN), a resume analytic field 1104, a compare button 1106, and a
cancel button 1108. The resume display field may include one or
more check boxes 1110 which allows the users 102A-B to select the
one or more resumes displayed in the resume display field 1102. The
one or more resumes may also be selected by using (i) a control
option and (ii) a shift option from a keyboard interface, or any
other method that includes selection of two or mom resumes (e.g.,
drag and drop method, a touch interface, etc). For example, a first
resume (R1) may be selected using a control option, and a second
resume (R5) using the shift option, etc. The resume analytic field
1104 displays analysis details (e.g., one or more events from R1
are compared with one or more events from R5 using the event line
802, the events distribution 804, and the sections span 806) when
an input is received on the compare button 1106. The user interface
view 1100 further includes a text field 1112 which displays
information (e.g., a name of a first candidate, a name of a second
candidate, an email address of the first candidate, an email
address of the second candidate, etc.) of the one or more resumes
(R1, and R5) that are compared.
[0077] With reference to FIG. 11A, FIG. 11B is the user interlace
view illustrating a first graphical representation associated with
the resume R1 and a graphical representation associated with the
resume R5 according to an embodiment herein. The resume summarizer
tool 106 extracts one or more keywords from the resumes R1 and R5.
The report comparison module 320 compares the one or more keywords
in the resumes R1 and R5 using the weighted Formal Concept Analysts
(wFCA). The report comparison module 320 computes a weight for the
resumes R1 and R5 based on the weighted Formal Concept Analysis
(wFCA) using the keywords extracted from the resumes R1 and R5 as
objects, and the resumes R1 and R5 as attributes.
[0078] For example, the resumes R1 and R5 may have the following
set of keywords: [0079] R1: {Languages, Functional Scala} [0080]
R5: {Languages, Functional Scala, Erlang}
[0081] In this case the objects are resumes and the attributes are
keywords. The hierarchical FCA gives the result as [0082] First
Level Concepts [0083] Concept 1: [R1, R5]: [Languages] Both the
resume has the keyword language [0084] Second Level Concepts [0085]
Concept 2: [R1, R5]: [Functional] Both, the resumes has the keyword
functional programming language (e.g., similarity) [0086] Third
Level Concepts [0087] Concept 3: [R1, R5]: [Scala] [0088] [R5]:
[Scala, Erlang]
[0089] Both the resumes R1 and R5 have the keyword `Scala` but
resume R5 has the keyword Erlang, in addition to Scala. The report
comparison module 320 assigns a weight (e.g., 8.5) to the resume R5
that is higher to a weight assigned to the resume R1 (e.g., 5.5).
Further, the other categories "Languages", "Functional", "Scala"
are common for both the resumes. The resume analytic field 1104
displays the analysis details such as the event line 802 for the
resumes R1 and R5 along with the weights for the resumes R1 and
R5.
[0090] When one or more events in R1 do not match with one or more
events in R5, the resume summarizer tool 106 indicates/displays a
message (e.g., R1 and R5 are not comparable). For example, the
users 102A-B select a resume R2 and the resume R5 from the one or
more resumes displayed in the resume display field 1102. The resume
summarizer tool 106 extracts at least one keyword from the resumes
R2 and R5. For example, the keywords are as below: [0091] R2:
{Biotech, Genes, Plantation} [0092] R5: {Languages, Functional,
Scala, Erlang}
[0093] In this case, the resumes R2 and R5 are considered as
objects, and the keywords are considered as attributes. Thus, based
on the keywords, the resume summarizer tool 106 displays a message
that indicates R2 and R5 not comparable.
[0094] FIG. 12 is a flow diagram illustrating a method tor
generating a summary of at least one resume from a set of resumes
to analyze insights of the at least one resume using the resume
summarizer tool of FIG. 1 according to an embodiment herein. In
step 1202, a first input is processed. The first input includes a
first indication to select a first resume from one or more of
resumes. In step 1204, a first information is extracted from the
first resume. The first information includes (a) a first section,
(b) a second section, (c) one or more events associated with the
first section, and (d) one or more events associated with the
second section. In step 1206, a second information is obtained from
the first resume. In step 1208, a first table is generated based on
the first information and the second information. In step 1210, a
first summary is generated based on the first table. The first
summary indicates a first correlation between (i) the one or more
events associated with the first section and (ii) the one or more
events associated with the second section over years.
[0095] In one embodiment, the first information further includes
(a) one or more first date range of the first section, (b) one or
more second date range of the second section, (c) one or more third
date range of the one or more events associated with the first
section, and (d) one or more fourth date range of the one or mote
events associated with the second section.
[0096] In another embodiment, the one or more first date range, the
one or more second date range, the one or more third date range and
the one or more fourth date range each includes a start date and an
end date, the start date and the end date include a year, a date or
a month. The second information may includes (a) a first period
associated with the first section, (b) a second period associated
with the second section, (c) a third period associated with the one
or more events associated with the first section, and (d) a fourth
period associated with the one or more events associated with the
second section. In yet another embodiment, (a) the one or more
third date range of the one or more events associated with the
first section and (b) the one or more fourth date range of the one
or more events associated with the second section overlaps with
each other. The first summary may includes a first graphical
representation that illustrates the first correlation. The first
graphical representation may illustrates (a) the one or more events
associated with the first section using first color and (b) the one
or more events associated with the second section using second
color. The first graphical representation may display at least one
of the first information when a cursor moves over the first
graphical representation.
[0097] The method may further includes (i) processing the first
input includes a second indication to select a second resume from
the one or more of resumes, (ii) extracting, the second resume, a
third information, the third information includes (a) a third
section, (b) a fourth section, (c) one or more events associated
with the third section, and (d) one or more events associated with
the fourth section, (iii) obtaining, from the second resume, a
fourth information, (iv) generating a second table based on the
third information and the fourth information, and (v) generating a
second summary based on the second table, the second summary
indicates a second correlation between (a) the one or more events
associated with, the third section and (b) the one or more events
associated with the fourth section over years, the first summary
and the second summary indicates the insights of the first resume
and the second resume. A third input may be processed. The third
input includes an indication to select at least one of (a) the
first resume and (b) the second resume based on a comparison
between the first summary of the first resume and the second
summary of the second resume. A duration within at least one of (i)
the first section and (i) the second section of at least one of (a)
the first resume and (b) the second resume may be identified. The
duration does not include an event (e.g., one or more activities
such as an internship).
[0098] The embodiments herein can take the form of an entirely
hardware embodiment, an entirely software embodiment or an
embodiment including both hardware and software elements. The
embodiments that are implemented in software include but are not
limited to, firmware, resident software, microcode, etc.
[0099] Furthermore, the embodiments herein can take the form of a
computer program product accessible from a computer-usable or
computer-readable medium providing program code for use by or in
connection with a computer or any instruction execution system. For
the purposes of this description, a computer-usable or computer
readable medium can be any apparatus that can comprise, store,
communicate, propagate, or transport the program for use by or in
connection with the instruction execution system, apparatus, or
device.
[0100] The medium can be an electronic, magnetic, optical,
electromagnetic, infrared, or semiconductor system (or apparatus or
device) or a propagation medium. Examples of a computer-readable
medium include a semiconductor or solid state memory, magnetic
tape, a removable computer diskette, a random access memory (RAM),
a read-only memory (ROM), a rigid magnetic disk and an optical
disk. Current examples of optical disks include compact disk-read
only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD.
[0101] A data processing system suitable for storing and/or
executing program code will include at least one processor coupled
directly or indirectly to memory elements through a system bus. The
memory elements can include local memory employed during actual
execution of the program code, bulk storage, and cache memories
which provide temporary storage of at least some program code in
order to reduce the number of times code must be retrieved from
bulk storage during execution.
[0102] Input/output (I/O) devices (including but not limited to
keyboards, displays, pointing devices, etc.) can be coupled to the
system either directly or through intervening I/O controllers.
Network adapters may also be coupled to the system to enable the
data processing system to become coupled to other data processing
systems or remote printers or storage devices through intervening
private or public networks. Modems, cable modem and Ethernet cards
are just a few of the currently available types of network
adapters.
[0103] FIG. 13 illustrates a schematic diagram of a computer
architecture used in accordance with the embodiments herein. The
computer architecture includes one or more processor or central
processing unit (CPU) 10. The CPUs 10 are interconnected via system
bus 12 to various devices such as a random access memory (RAM) 14,
read-only memory (ROM) 16, and an input/output (I/O) adapter 18.
The I/O adapter 18 can connect to peripheral devices, such as disk
units 11 and tape drives 13, or other program storage devices that
are readable by the system. The system can read the inventive
instructions on the program storage devices and follow these
instructions to execute the methodology of the embodiments
herein.
[0104] The computer architecture further includes a user interface
adapter 19 that connects a keyboard 15, mouse 17, speaker 24,
microphone 22, and/or other user interface devices such as a touch
screen device (not shown) to the bus 12 to gather user input.
Additionally, a communication adapter 20 connects the bus 12 to a
data processing network 25, and a display adapter 21 connects the
bus 12 to a display device 23 which may be embodied as an output
device such as a monitor, printer, or transmitter, for example.
[0105] The foregoing description of the specific embodiments will
so fully reveal the general nature of the embodiments herein chat
others can, by applying current knowledge, readily modify and/or
adapt for various applications such specific embodiments without
departing from the generic concept, and, therefore, such
adaptations and modifications should and are intended to be
comprehended within the meaning and range of equivalents of the
disclosed embodiments. It is to be understood that the phraseology
or terminology employed herein is for the purpose of description
and not of limitation. Therefore, while the embodiments herein have
been described in terms of preferred embodiments, those skilled in
the art will recognize that the embodiments herein can be practiced
with modification within the spirit and scope of the appended
claims.
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References