U.S. patent application number 14/589375 was filed with the patent office on 2015-06-25 for method and system for generating a fraud risk score using telephony channel based audio and non-audio data.
The applicant listed for this patent is VERINT AMERICAS INC.. Invention is credited to Lisa Marie Guerra, Richard Gutierrez, David Hartig, Anthony Rajakumar.
Application Number | 20150178736 14/589375 |
Document ID | / |
Family ID | 43220220 |
Filed Date | 2015-06-25 |
United States Patent
Application |
20150178736 |
Kind Code |
A1 |
Hartig; David ; et
al. |
June 25, 2015 |
METHOD AND SYSTEM FOR GENERATING A FRAUD RISK SCORE USING TELEPHONY
CHANNEL BASED AUDIO AND NON-AUDIO DATA
Abstract
Disclosed is a method for generating a fraud risk score
representing a fraud risk associated with an individual, the method
comprising: a) determining a telephony channel risk score from at
least one of audio channel data and non-audio channel data of the
individual; and b) generating the fraud risk score based on at
least one of the telephony channel risk score, the audio channel
data, and the non-audio channel data.
Inventors: |
Hartig; David; (Oakland,
CA) ; Rajakumar; Anthony; (Fremont, CA) ;
Guerra; Lisa Marie; (Los Altos, CA) ; Gutierrez;
Richard; (Mountain View, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
VERINT AMERICAS INC. |
Alpharetta |
GA |
US |
|
|
Family ID: |
43220220 |
Appl. No.: |
14/589375 |
Filed: |
January 5, 2015 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
12856118 |
Aug 13, 2010 |
8930261 |
|
|
14589375 |
|
|
|
|
11404342 |
Apr 14, 2006 |
|
|
|
12856118 |
|
|
|
|
60673472 |
Apr 21, 2005 |
|
|
|
61335677 |
Jan 11, 2010 |
|
|
|
Current U.S.
Class: |
705/44 |
Current CPC
Class: |
G06Q 20/305 20130101;
H04M 15/00 20130101; G06Q 20/40 20130101; G06Q 20/24 20130101; G06Q
20/40145 20130101; G06Q 20/4016 20130101; G06Q 20/4014 20130101;
H04M 15/47 20130101; H04M 2215/0148 20130101 |
International
Class: |
G06Q 20/40 20060101
G06Q020/40; G06Q 20/30 20060101 G06Q020/30 |
Claims
1. A method for generating fraud risk scores representing fraud
risks associated with an individual, the method comprising:
obtaining a voice sample from audio channel data associated with an
individual; matching the voice sample with at least one of a
plurality of stored voice samples stored in a database to perform a
first identification of the individual; calculating an audio
channel fraud risk score from the audio channel data of the
individual, the audio channel fraud risk score being calculated by
a computer processor coupled to a memory that includes a telephony
risk score calculator; obtaining identity data from non-audio
channel data associated with the individual, the identity data
being used to perform a second identification of the individual;
calculating a non-audio channel fraud risk score from the non-audio
channel data of the individual the non-audio channel fraud risk
score being calculated by the telephony risk score calculator; and
generating an aggregate fraud risk score based on the audio channel
fraud risk score and the non-audio channel fraud risk score, the
aggregate fraud risk score being generated by an aggregate risk
score generator included in the computer processor coupled to the
memory.
2. The method of claim 1, wherein the audio channel data comprises
at least one of an emotion in the obtained voice sample of the
individual when the individual responded to specific questions
designed to trigger stress in fraudsters but not in legitimate
callers, the obtained voice sample of the individual, keywords in
the obtained voice sample, a telephony system used by the
individual, and a geographic location of the individual.
3. The method of claim 2, wherein the telephony system comprises
one of a Public Switched Telephone Network, a mobile phone, and
Voice Over Internet Protocol network.
4. The method of claim 1, wherein the non-audio channel data
comprises at least one of a phone number called from, a phone
number called to, a time duration for which the individual has had
the phone number, a call frequency to/from the phone number called
from, the area code of the phone number called from, a number of
routing hops needed to complete the call, geography associated with
an area code of the phone number called from, geographical location
of the individual, whether the individual called from an expected
phone number or a non-published n umber, whether the phone number
is being call-forwarded and when that call-forward was initiated,
the identity data, transaction data, and Short Message Service
channel data.
5. The method of claim 4, wherein the identity data comprises at
least one of a name of the individual, a Social Security Number of
the individual, an address of the individual, a phone number from
which the individual called, and in formation related to background
of the individual.
6. The method of claim 5, wherein the information related to the
background of the individual includes at least one of previous
addresses lived at, persons that the individual shared a residence
with, mother's maiden name and a color of a first car.
7. A risk score calculator for generating fraud risk scores
representing fraud risks associated with an individual, the
calculator comprising: a hardware processor; and a memory coupled
to the processor, the memory storing instructions which when
executed by the processor cause the system to: obtain a voice
sample from audio channel data associated with an individual; match
the voice sample with at least one of a plurality of stored voice
samples stored in a database to perform a first identification of
the individual calculate an audio channel fraud risk score from the
audio channel data of the individual; obtain identity data from
non-audio channel data associated with the individual, the identity
data being used to perform a second identification of the
individual calculate a non-audio channel risk score from the
non-audio channel data of the individual; and generate an aggregate
fraud risk score based on the audio channel fraud risk score and
the non-audio channel fraud risk score.
8. The risk score calculator of claim 7, wherein the audio channel
data comprises at least one of an emotion in the obtained voice
sample of the individual when the individual responded to specific
questions designed to trigger stress in fraudsters but not in
legitimate callers, the obtained voice sample of the individual,
keywords in the obtained voice sample, a telephony system used by
the individual, and a geographic location of the individual.
9. The risk score calculator of claim 8, wherein the telephony
system comprises one of a Public Switched Telephone Network, a
mobile phone, and Voice Over Internet Protocol network.
10. The risk score calculator of claim 7, wherein the non-audio
channel data comprises at least one of a phone number called from,
a phone number called to, a time duration for which the individual
has had the phone number, a call frequency to/from the phone number
called from, the area code or the phone number called from, a
number of routing hops needed to complete the call, geography
associated with an area code of the phone number called from,
geographical location of the individual, whether the individual
called from an expected phone number or a non-published number,
whether the phone number is being call-forwarded and when that
call-forward was initiated, the identity data, transaction data,
and Short Message Service channel data.
11. The risk score calculator or claim 10, wherein the identity
data comprises at least one or a name of the individual, a Social
Security Number of the individual, an address or the individual, a
phone number from which the individual called, and information
related to background of the individual.
12. The risk score calculator of claim 12, wherein the information
related to the background of the individual includes at least one
of previous addresses lived at, persons that the individual shared
a residence with, mother's maiden name, and a color of a first
car.
13. A non-transitory computer readable medium containing a computer
program product for generating fraud risk scores representing fraud
risks associated with an individual, the computer program product
comprising: program code for obtaining a voice sample from audio
channel data associated with an individual; program code for
matching the voice sample with at least one of a plurality of
stored voice samples stored in a database to perform a first
identification of the individual; program code for calculating
audio channel fraud risk score from the audio channel data of the
individual; program code for obtaining identity data from non-audio
channel data associated with, the individual the identity data
being used to perform a second identification of the individual;
program code for calculating a non-audio channel fraud risk score
from the non-audio channel data of the individual, the non-audio
channel fraud risk score being calculated by the telephony risk
score calculator; program code for generating an aggregate fraud
risk score based the audio channel fraud risk score and the
non-audio channel fraud risk score.
14. The computer program product or claim 13, wherein the audio
channel data comprises at least one of an emotion in the obtained
voice sample of the individual when the individual responded to
specific questions designed to trigger stress in fraudsters but not
in legitimate callers, the obtained voice sample of the individual,
keywords in the obtained voice sample, a telephony system used by
the individual, and a geographic location of the individual.
15. The computer program product of claim 14, wherein the telephony
system comprises one of a Public Switched Telephone Network, a
mobile phone, and Voice Over Internet Protocol network.
16. The computer program product of claim 13, wherein the non-audio
channel data comprises at least one of a phone number called from,
a phone number called to, a time duration for which the individual
has had the phone number, a call frequency to/from the phone number
called from, the area code of the phone number called from, a
number of routing hops needed to complete the call, geography
associated with an area code of the phone number called from,
geographical location of the individual, whether the individual
called from an expected phone number or a non-published number,
whether the phone number is being call-forwarded and when that
call-forward was initiated, the identity data, transaction data,
and Short Message Service channel data.
17. The computer program product of claim 16, wherein the identity
data comprises at least one of a name of the individual, a Social
Security Number of the individual, an address of the individual, a
phone number from which the individual called, and information
related to background of the individual.
18. The computer program product of claim 17, wherein the
information related to the background of the individual includes at
least one of previous addresses lived at, persons that the
individual shared a residence with, mother's maiden name, and a
color of a first car.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 12/465,118, filed Aug. 13, 2010, which is
continuation-in-part of the U.S. patent application Ser. No.
11/404,342 filed Apr. 14, 2006. This application also claims the
benefit of priority to the U.S. Provisional Patent Application No.
61/335,677 filed Jan. 11,2010.
TECHNICAL FIELD OF THE DISCLOSURE
[0002] Embodiments of the disclosure relate to a method and system
to generate a risk score for a caller.
BACKGROUND OF THE DISCLOSURE
[0003] Modern enterprises such as merchants, banks, insurance
companies, telecommunications companies, and payments companies are
susceptible to many forms of fraud, but one form that is
particularly pernicious is credit card fraud. With credit card
fraud, a fraudster fraudulently uses a credit card or credit card
credentials (name, expiration, etc.) of another to enter into a
transaction for goods or services with a merchant.
[0004] Another form of fraud that is very difficult for merchants,
particularly large merchants, to detect, ii at all, occurs in the
job application process where an applicant has been designated as
undesirable in the past--perhaps as a result of having been fired
from the employ of the merchant at one location or for failing a
criminal background check fraudulently assumes a different identity
and then applies for a job with the same merchant at a different
location. In such cases, failure to detect the fraud could result
in the rehiring of the fraudster to the detriment of the merchant.
If the fraudster has assumed a new identity, background checks
based on identity factors such as names or social security numbers
become essentially useless. For example consider that case of a
large chain store, such as, for example, Walmart. In this case, an
employee can be terminated for say then at one location, but then
rehired under a different identity at another location. The
employee represents a grave security risk to the company
particularly since the employee, being familiar with the company's
systems and internal procedures will be able to engage in further
conduct injurious to the company.
SUMMARY OF THE DISCLOSURE
[0005] In one aspect, the present disclosure provides a method for
generating a fraud risk score representing a fraud risk associated
with an individual, the method comprising: a) determining a
telephony channel risk score from at least one of audio channel
data and non-audio channel data of the individual; and b)
generating the fraud risk score based on at least one of the
telephony channel risk score, the audio channel data, and the
non-audio channel data.
[0006] In another aspect, the present disclosure provides a risk
score calculator for generating a fraud risk score representing a
fraud risk associated with an individual, the system comprising: a)
a telephony risk score calculator capable of determining a
telephony channel risk score from at least one of audio channel
data and non-audio channel data of the individual; and b) an
aggregate risk score generator capable of generating the fraud risk
score based on at least one of the telephony channel risk score,
the audio channel data, and the non-audio channel data.
[0007] In yet another aspect of the present disclosure, the present
disclosure provides computer-implemented methods, computer systems
and a computer readable medium containing a computer program
product for generating a fraud risk score representing a fraud risk
associated with an individual, the computer program product
comprising: a) program code for determining a telephony channel
risk score from at least one of audio channel data and non-audio
channel data of the individual; and b) program code for generating
the fraud risk score based on at least one of the telephony channel
risk score, the audio channel data, and the non-audio channel
data.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The accompanying drawings, where like reference numerals
refer 10 identical or functionally similar elements throughout the
separate views, together with the detailed description below, are
incorporated in and form part of the specification, and serve to
further illustrate embodiments of concepts that include the claimed
disclosure, and explain various principles and advantages of those
embodiments.
[0009] FIG. 1 shows a pictorial representation of a system used for
calculating an Aggregate Fraud Risk Score, in accordance with an
embodiment of the present disclosure;
[0010] FIG. 2 shows a high level block diagram of an Risk Score
Calculator, in accordance with one embodiment of the present
disclosure;
[0011] FIG. 3 shows a high level flowchart of a method for
generating a fraud risk score representing a fraud risk associated
with an individual, in accordance with an embodiment of the present
disclosure;
[0012] FIG. 4 shows hardware to implement the method disclosed
herein, in accordance with an embodiment of the present
disclosure.
[0013] The method and system have been represented where
appropriate by conventional symbols in the drawings, showing only
those specific details that are pertinent to understanding the
embodiments of the present disclosure so as not to obscure the
disclosure with details that will be readily apparent to those of
ordinary skill in the art having the benefit of the description
herein.
DETAILED DESCRIPTION
[0014] In the following description, for purposes of explanation,
numerous specific details are set forth in order to provide a
thorough understanding of the disclosure. It will be apparent,
however, to one skilled in the art, that the disclosure may be
practiced without these specific details. In other instances,
structures and devices are shown at block diagram form only in
order to avoid obscuring the disclosure.
[0015] Reference in this specification to "one embodiment" or "an
embodiment" means that a particular feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment of the disclosure. The
appearances of the phrase "in one embodiment" in various places in
the specification are not necessarily all referring to the same
embodiment, nor are separate or alternative embodiments mutually
exclusive of other embodiments. Moreover, various features are
described which may be exhibited by some embodiments and not by
others. Similarly, various requirements are described which may be
requirements for some embodiments but not other embodiments.
[0016] Broadly, embodiments of the present disclosure calculate an
Aggregate Fraud Risk Score (AFRS) representing a fraud risk
associated with an individual who called a modern enterprise such
as merchants, banks, insurance companies, telecommunications
companies, and payments companies. The AFRS is calculated based on
an audio channel risk score and a non-audio channel risk score. The
audio channel risk score is calculated based on an analysis of a
first set of parameters i.e. audio channel data associated with an
audio of the individual. The non-audio channel risk score is
calculated based on an analysis of a second set of parameters i.e.
non-audio channel data associated with non-audio information
related to the individual. The AFRS may be used in an automated
system or in a system with an agent review. The AFRS along with
other pieces of information may help in making a final decision on
the individual that whether the individual should be accepted,
rejected, or investigated further. Further, the final decision
along with the other information may be displayed on a display
screen.
[0017] Referring to FIG. 1, a pictorial representation of a system
used for calculating an Aggregate Fraud Risk Score (AFRS) is shown,
in accordance with an embodiment of the present disclosure. In one
embodiment, a caller 2 may call a modem enterprise 4 using a
suitable telephone network such as PSTN/Mobile/VOIP 6 for placing
an order for goods or services. In one embodiment, a Private Branch
Exchange (PBX) 8 may be used to receive the call. The PBX 8 may
send the call audio to an audio recording device 10 which may
record the call audio. In one embodiment, a call-center `X` may
receive and record the call on behalf of the modern enterprise 4,
however, in another embodiment, the modern enterprise 4 may employ
an agent (in house or outsourced) or any other third party to
receive and record the call.
[0018] The audio recording device 10 may be configured to transmit
the recoded call to a database 12. The database 12 includes phone
details of all possible calls received at the modern enterprise 4.
In one embodiment, the phone details may include at least one of an
audio conversation between the modern enterprise 4 and the callers,
an amount of the transaction, type of goods or services ordered for
(in case of a credit card transaction), a lime of call, quantity of
goods, reason for the call like placing an order or checking
status, credit card credentials, a recipient of the goods, a place
of shipment, billing address, caller identity such as a name and/or
a social security number of the caller or agent ID (in case of an
agent) or an employee ID number, a phone number from which the call
is made, a phone number to which the call is made, and other
transaction information. In one embodiment, the database 12 may
include an audio database 14 and an order database 16. The audio
database 14 is capable of storing call audios and the order
database 16 is capable of storing order details.
[0019] The modern enterprise 4 may also include a fraudster
database 18. The fraudster database 18 includes voice prints of
known fraudsters. Essentially, a voice print includes a set of
voice characteristics that uniquely identify a person's voice. In
one embodiment, each voice print in the fraudster database 18 is
assigned a unique identifier (ID), which in accordance with one
embodiment may include one or more incident details such as a
social security number used, a name used, credit card credentials
used, date and time of fraud, an amount of the fraud, a type of
fraud, enterprise impacted, and other details associated with the
fraud incident.
[0020] In one embodiment, the phone details of all callers may be
transmitted to a Risk Score Calculator (RSC) 20 via a file transfer
server 22 using internet/LAN 24. The RSC 20 helps in generating an
Aggregate Fraud Risk Score (AFRS) representing a fraud risk
associated with the caller 2 who called the modem enterprise 4. In
one embodiment, the RSC 20 may be a distributed system that
includes components that are not all located, at a single location,
but instead are distributed over multiple locations. The RSC 20 may
include software to facilitate communications with the modern
enterprise 4 or the call-center `X` to access the database 12. In
one embodiment, the software may include a browser which is an
application that facilitates communications via the Internet with
the modem enterprise 4 or the call center `X` using networking
protocols such as for example the Hypertext Transfer Protocol
(HTTP)/the Internet Protocol (IP), the Simple Object Access
Protocol (SOAP), etc, In another embodiment, the EFD may be
integrated in the modern enterprise 4, thereby alleviating the need
of transferring the phone details of callers.
[0021] Referring now to FIG. 2, an internal block diagram of the
RSC 20 is shown, in accordance with an embodiment of the present
disclosure. The RSC 20 includes a telephony risk score calculator
(TRSC) 26 and an aggregate risk score generator 28. Each of the
components 26 and 28 may be implemented in hardware or in software
or as a combination of both hardware and software. Further, it is
to be understood that while the components 26 and 28 are shown as
separate components based on function, some or all the components
may be integrated.
[0022] In one embodiment, the TRSC 26 may generate a telephony
channel risk score from at least one of audio channel data and
non-audio channel data of the caller 2. Subsequently, the aggregate
risk score generator 28 may generate a fraud risk score based on at
least one of the telephony channel risk score, the audio channel
data and the non-audio channel data. For each piece of data
(whether audio channel data or non-audio channel data), either a
score (e.g. likelihood that their voice matches a known fraudster),
the data itself (e.g. geographic location of the handset), or both
are retrieved. The collection of scores/data gels fed into the
aggregate risk score generator 28 for the generation of the fraud
risk score.
[0023] In one embodiment, the audio channel data may include at
least one of an emotion such as stress in the voice sample of the
caller 2 when the caller 2 responded to specific questions designed
to trigger stress in fraudsters but not in legitimate callers, a
voice audio sample of the caller 2 to determine whether the voice
sample of the caller 2 matches with a known fraudster by using
speaker identification techniques. The speaker identification
techniques are generally helpful because fraudsters tend to commit
the same crime multiple times once a specific scheme is known to be
success, resulting in multiple telephone calls by the same
individual when committing fraud on the phone. Further, the first
set of parameters may include keywords in the voice sample of the
caller 2 and how the voice sample relates to keywords commonly used
by known fraudsters, a telephony system used by the caller 2 i.e.
whether Public Switched Telephone Network, a mobile phone, and
Voice Over Internet Protocol network is being used, a geographic
location of the caller 2.
[0024] In the present embodiment, the non-audio channel data may
include at least one of a phone number called from, a phone number
called to, a time duration for which the caller 2 has had the phone
number called from, a call frequency to/from the phone number
called from, the area code of the phone number called from, number
of routing hops needed to complete the call (indication of how far
away the call is coming from), geography associated with an area
code of the phone number called from, geographical location of the
caller 2, whether the caller 2 called from an expected phone number
or a non-published number, whether the phone number is being
call-forwarded and when that call-forward was initiated, an
identity data, transaction data, and Short Message Service channel
data.
[0025] The identity data may include at least one of a name, social
security number, address, phone number, answers to questions about
their background (like previous addresses lived at, persons that
they shared a residence with, mother's maiden name, color of first
car, etc. The transaction data may include at least one of a
shipping/recipient address, recipient name, goods ordered, amount
of transaction, type of payment (e.g. credit card, Pay Pal, wire,
etc.), type and frequency of recent actions (e.g. status checks,
change of address, etc.). Further, the SMS channel data may include
at least one of a phone number of phone used to send SMS.
[0026] Subsequent to the generation of the aggregate fraud risk
score, it may be used by an automated system or in a system with
agent review. The RSC 20 may also integrate the AFRS against a list
of known fraudsters. Further, the RSC 20 may additionally
incorporate the fraud data associated with individual fraudster's
past fraud activity. In other words, the RSC 20 compares the
application or transaction data of the individual with that of
individual fraudster's fraud data.
[0027] In one embodiment, when the AFRS is used in a system with
manual agent review, the aggregate fraud risk score as well as many
other pieces of information can be used to help them in making a
final determination on an individual (e.g. accept, reject, or
investigate further). Thereafter, data about the person being
screened and data about each potential match in the DB are
displayed on a display screen as shown in FIG. 1. Data about the
person being screened may include transaction amount, geographical
info (maybe fraud is more prevalent in city x), response delay -
amount of time the screening lakes before answering a question. If
they take longer than average, it may indicate that they are
looking up (stolen) information. Further, data about each potential
match in the DB may include a voice match score (score that tells
how closely the individual matches a voiceprint of a fraudster in
the fraudster database 18), data about the match's previous fraud
incidents (there may be many fraud incidents associated with the
individual), damage amounts (exact amount or approximate--e.g.
$0-99; $100-500; etc), geographical info, fraud type (i.e. credit
card not-present fraud, credit card issuance fraud, etc.),
classification: definite fraudster, suspicious activity, etc.
[0028] Referring to FIG. 3, a high level flowchart of a method for
generating a fraud risk score representing a fraud risk associated
with an individual is shown, in accordance with an embodiment of
the present disclosure. At 300, a telephony channel risk score is
generated from at least one of audio channel data and non-audio
channel data of the caller 2. Subsequently, at 302 the aggregate
risk score generator 28 may generate a fraud risk score based on at
least one of the telephony channel risk score, the audio channel
data and the non-audio channel data.
[0029] Referring now FIG. 4, hardware 40 to implement the method
disclosed herein is shown, in accordance with an embodiment of the
present disclosure. The RSC 20, thus far, has been described in
terms of their respective functions. By way of example, each of the
RSC 20 may be implemented using the hardware 40 of FIG. 4. The
hardware 40 typically includes at least one processor 42 coupled to
a memory 44. The processor 42 may represent one or more processors
(e.g., microprocessors), and the memory 44 may represent random
access memory (RAM) devices comprising a main storage of the system
40, as well as any supplemental levels of memory e.g., cache
memories, non-volatile or back-up memories (e.g. programmable or
flash memories), read-only memories, etc. In addition, the memory
44 may be considered to include memory storage physically located
elsewhere in the system 40, e.g. any cache memory in the processor
42, as well as any storage capacity used as a virtual memory, e.g.,
as stored on a mass storage device 50.
[0030] The system 40 also typically receives a number of inputs and
outputs for communicating information externally. For interface
with a user or operator, the system 40 may include one or more user
input devices 46 (e.g.; a keyboard, a mouse, etc.) and a display 48
(e.g., a Liquid Crystal Display (LCOD) panel).
[0031] For additional storage, the system 40 may also include one
or more mass storage devices 50, e.g., a floppy or other removable
disk drive, a hard disk drive, a Direct Access Storage Device
(DASD), an optical drive (e.g. a Compact Disk (CD) drive, a Digital
Versatile Disk (DVD) drive, etc.) and/or a tape drive, among
others. Furthermore, the system 40 may include an interface with
one or more networks 52 (e.g., a local area network (LAN), a wide
area network (WAN), a wireless network, and/or the Internet among
others) to permit the communication of information with other
computers coupled to the networks. It should be appreciated that
the system 40 typically includes suitable analog and/or digital
interfaces between the processor 42 and each of the components 44,
46, 48 and 52 as is well known in the art.
[0032] The system 40 operates under the control of an operating
system 54, and executes various computer software applications,
components, programs, objects, modules, etc. to perform the
respective functions of the RSC 20 and server system of the present
disclosure. Moreover, various applications, components, programs,
objects, etc. may also execute on one or more processors in another
computer coupled to the system 40 via a network 52, e.g. in a
distributed computing environment, whereby the processing required
to implement the functions of a computer program may be allocated
to multiple computers over a network.
[0033] In general, the routines executed to implement the
embodiments of the present disclosure, may be implemented as part
of an operating system or a specific applications component,
program, object, module or sequence of instructions referred to as
"computer programs." The computer programs typically comprise one
or more instructions set at various times in various memory and
storage devices in a computer, and that, when read and executed by
one or more processors in a computer, cause the computer to perform
operations necessary to execute elements involving the various
aspects of the present disclosure. Moreover, while the disclosure
has been described in the context of fully functioning computers
and computer systems, those skilled in the art will appreciate that
the various embodiments of the present disclosure are capable of
being distributed as a program product in a variety of forms, and
that the present disclosure applies equally regardless of the
particular type of machine or computer-readable media used to
actually effect the distribution. Examples of computer-readable
media include but are not limited to recordable type media such as
volatile and non-volatile memory devices, floppy and other
removable disks, hard disk drives, optical disks (e.g., Compact
Disk Read-Only Memory (CD ROMS), Digital Versatile Disks, (DVDs),
etc.), among others, and transmission type media such as digital
and analog communication links.
* * * * *