U.S. patent application number 13/743738 was filed with the patent office on 2013-05-30 for social network-based recommendation.
This patent application is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Aaron K. Baughman, Barry M. Graham, Rick A. Hamilton, II, Brian M. O'Connell.
Application Number | 20130138531 13/743738 |
Document ID | / |
Family ID | 47721303 |
Filed Date | 2013-05-30 |
United States Patent
Application |
20130138531 |
Kind Code |
A1 |
Baughman; Aaron K. ; et
al. |
May 30, 2013 |
SOCIAL NETWORK-BASED RECOMMENDATION
Abstract
Embodiments of the invention provide methods and program
products for making a recommendation to a purchaser and/or member
of a social network. A first aspect of the invention provides a
method of making a recommendation to a purchaser, the method
comprising: determining a plurality of features of a first product
selected by a purchaser; prioritizing the plurality of features of
the first product; and making at least one recommendation to the
purchaser, the at least one recommendation being selected from a
group consisting of: a second product sharing at least one feature
of the first product and a social network connection determined to
have purchased another product sharing at least one feature of the
first product.
Inventors: |
Baughman; Aaron K.; (Silver
Spring, MD) ; Graham; Barry M.; (Silver Spring,
MD) ; Hamilton, II; Rick A.; (Charlottesville,
VA) ; O'Connell; Brian M.; (Cary, NC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION; |
Armonk |
NY |
US |
|
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION
Armonk
NY
|
Family ID: |
47721303 |
Appl. No.: |
13/743738 |
Filed: |
January 17, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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13295434 |
Nov 14, 2011 |
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13743738 |
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Current U.S.
Class: |
705/26.7 |
Current CPC
Class: |
G06Q 50/01 20130101;
G06Q 30/0631 20130101; G06Q 30/0282 20130101 |
Class at
Publication: |
705/26.7 |
International
Class: |
G06Q 30/06 20120101
G06Q030/06; G06Q 50/00 20060101 G06Q050/00; G06Q 30/02 20120101
G06Q030/02 |
Claims
1. A method of making a recommendation to a purchaser, the method
comprising: determining a plurality of features of a first product
selected by a purchaser; prioritizing the plurality of features of
the first product; and making at least one recommendation to the
purchaser, the at least one recommendation being selected from a
group consisting of: a second product sharing at least one feature
of the first product and a social network connection determined to
have purchased another product sharing at least one feature of the
first product.
2. The method of claim 1, further comprising: broadcasting the
plurality of features of the first product to a social network.
3. The method of claim 1, wherein prioritizing the plurality of
features includes comparing at least one of the plurality of
features to at least one feature of at least one
previously-purchased product.
4. The method of claim 3, wherein the previously-purchased product
was purchased by a member of a social network other than the
purchaser.
5. The method of claim 3, wherein comparing the at least one of the
plurality of features to at least one feature of at least one
previously-purchased product includes calculating a correlation
value for the compared features.
6. The method of claim 5, wherein prioritizing the plurality of
features includes ranking the correlation values.
7. The method of claim 6, wherein a relatively low correlation
value is deemed indicative of an anomalous feature having
purchasing significance and is given a higher priority than a
relatively high correlation value.
8. The method of claim 7, further comprising: constructing a
dendrogram including a plurality of products for recommendation,
wherein each of the plurality of products for recommendation shares
at least one feature of the first product.
9. The method of claim 8, wherein constructing the dendrogram
includes: including in a first level of the dendrogram a product
that shares with the first product a feature having a highest
ranked correlation value; and including in a second level of the
dendrogram a product that shares with the first product a feature
having a second-highest ranked correlation value.
10. The method of claim 5, further comprising: determining whether
the at least one previously-purchased product was purchased within
a predetermined period; and in the case that the at least one
previously-purchased product was purchased within the predetermined
period, including the at least one previously-purchased product
when calculating the correlation value.
11. A program product stored on a computer-readable storage medium,
which when executed, is operable to make a recommendation to a
purchaser by performing a method comprising: determining a
plurality of features of a first product selected by a purchaser;
prioritizing the plurality of features of the first product; and
making at least one recommendation to the purchaser, the at least
one recommendation being selected from a group consisting of: a
second product sharing at least one feature of the first product
and a social network connection determined to have purchased
another product sharing at least one feature of the first
product.
12. The program product of claim 11, wherein prioritizing the
plurality of features includes: calculating at least one
correlation value between at least one of the plurality of features
and at least one feature of at least one additional product
selected by another member of the social network; ranking the
calculated correlation values, wherein a relatively low correlation
value is deemed indicative of an anomalous feature having
significance and is given a higher priority than a relatively high
correlation value; and constructing a dendrogram including the at
least one of the plurality of features and the at least one feature
of the at least one product selected by another member of the
social network, wherein constructing the dendrogram includes:
including in a first level of the dendrogram at least one
additional product that shares with the product selected by the
member of the social network a feature having a highest ranked
correlation value; and including in a second level of the
dendrogram any additional product included in the first level that
shares with the product selected by the member of the social
network a feature having a second-highest ranked correlation
value.
13. The program product of claim 11, wherein the method further
comprises: determining whether the additional product was selected
within a predetermined period; and in the case that the additional
product was selected within the predetermined period, including the
additional product when prioritizing the plurality of features.
14. A system comprising: at least one computing device adapted to
make a recommendation to a purchaser by carrying out a method
comprising: determining a plurality of features of a first product
selected by a purchaser; prioritizing the plurality of features of
the first product; and making at least one recommendation to the
purchaser, the at least one recommendation being selected from a
group consisting of: a second product sharing at least one feature
of the first product and a social network connection determined to
have purchased another product sharing at least one feature of the
first product.
15. A system comprising: at least one computing device adapted to
make a recommendation to a member of a social network by carrying
out a method comprising: determining a plurality of features of a
product selected by a member of a social network; prioritizing the
plurality of features, including: calculating at least one
correlation value between at least one of the plurality of features
and at least one feature of at least one additional product
selected by another member of the social network; and ranking the
calculated correlation values, wherein a relatively low correlation
value is deemed indicative of an anomalous feature having
significance and is given a higher priority than a relatively high
correlation value; and making at least one recommendation to the
member of the social network, the at least one recommendation being
selected from a group consisting of: a product sharing at least one
feature of the product selected by the member of the social network
and a member of the social network that has selected a product
sharing at least one feature of the product selected by the member
of the social network.
16. The system of claim 15, wherein prioritizing the plurality of
features includes constructing a dendrogram including the at least
one of the plurality of features and the at least one feature of
the at least one product selected by another member of the social
network.
17. The system of claim 16, wherein constructing the dendrogram
includes: including in a first level of the dendrogram at least one
additional product that shares with the product selected by the
member of the social network a feature having a highest ranked
correlation value; and including in a second level of the
dendrogram any additional product included in the first level that
shares with the product selected by the member of the social
network a feature having a second-highest ranked correlation
value.
18. The system of claim 16, wherein the method further comprises:
determining whether the additional product was selected within a
predetermined period; and in the case that the additional product
was selected within the predetermined period, including the
additional product when prioritizing the plurality of features.
19. The system of claim 15, wherein the method further comprises:
broadcasting to the social network the plurality of features.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of co-pending U.S. patent
application Ser. No. 13/295,534 filed on Nov. 14, 2011, which is
hereby incorporated herein in its entirety for all that it
contains.
BACKGROUND
[0002] Embodiments of the invention provide methods and program
products for making a recommendation to a purchaser and/or member
of a social network.
[0003] Social networks provide a forum for individuals, typically
connected by some sort of interdependency, to interact. Such
interdependencies may include, for example, friendship, kinship,
common interest(s), pursuit(s), or belief(s), financial exchange or
relationship, etc. Some social networks include recommendation
systems designed to compare characteristics of a member of the
social network to reference characteristics and then predict a
value for a recommendation to be made, i.e., a likelihood that the
member would be interested in what is recommended. The
recommendation may be almost anything in which the member may be
interested, such as a product for purchase, an event the member
might attend, an individual with whom the member might wish to
connect or otherwise interact, etc.
[0004] Many such recommendation systems rely on something selected
by the member (e.g., an item purchased) and then recommend to the
member other selections made by other members of the social network
who have also made the same selection as the member. These and
other such recommendation systems consider a plurality of features
or characteristics of the selected and unselected items together in
calculating a value and determining which items will be
recommended. Often, items most similar to those already selected by
the member are then recommended to the member. As such, anomalous
features or characteristic that may have great significance to a
member may be overwhelmed by non-anomalous features, resulting in
an item that would be of interest to the member not being included
among those items recommended to the member.
SUMMARY
[0005] A first aspect of the invention provides a method of making
a recommendation to a purchaser, the method comprising: determining
a plurality of features of a first product selected by a purchaser;
prioritizing the plurality of features of the first product; and
making at least one recommendation to the purchaser, the at least
one recommendation being selected from a group consisting of: a
second product sharing at least one feature of the first product
and a social network connection determined to have purchased
another product sharing at least one feature of the first
product.
[0006] A second aspect of the invention provides a method of making
a recommendation to a member of a social network, the method
comprising: determining a plurality of features of a product
selected by a member of a social network; prioritizing the
plurality of features, including: calculating at least one
correlation value between at least one of the plurality of features
and at least one feature of at least one additional product
selected by another member of the social network; and ranking the
calculated correlation values, wherein a relatively low correlation
value is deemed indicative of an anomalous feature having
significance and is given a higher priority than a relatively high
correlation value; and making at least one recommendation to the
member of the social network, the at least one recommendation being
selected from a group consisting of: a product sharing at least one
feature of the product selected by the member of the social network
and a member of the social network that has selected a product
sharing at least one feature of the product selected by the member
of the social network.
[0007] A third aspect of the invention provides a program product
stored on a computer-readable storage medium, which when executed,
is operable to make a recommendation to a member of a social
network by performing a method comprising: determining a plurality
of features of a product selected by a member of a social network;
prioritizing the plurality of features; and making at least one
recommendation to the member of the social network, the at least
one recommendation being selected from a group consisting of: a
product sharing at least one feature of the product selected by the
member of the social network and a member of the social network
that has selected a product sharing at least one feature of the
product selected by the member of the social network.
[0008] A fourth aspect of the invention provides a system
comprising: at least one computing device configured for making a
recommendation to a purchaser by performing a method comprising:
determining a plurality of features of a first product selected by
a purchaser; prioritizing the plurality of features of the first
product; and making at least one recommendation to the purchaser,
the at least one recommendation being selected from a group
consisting of: a second product sharing at least one feature of the
first product and a social network connection determined to have
purchased another product sharing at least one feature of the first
product.
[0009] The illustrative aspects of the present invention are
designed to solve the problems herein described and other problems
not discussed, which are discoverable by a skilled artisan.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0010] These and other features of this invention will be more
readily understood from the following detailed description of the
various aspects of the invention taken in conjunction with the
accompanying drawings that depict various embodiments of the
invention, in which:
[0011] FIG. 1 shows a flow diagram of a method according to an
embodiment of the invention.
[0012] FIG. 2 shows a detailed flow diagram of a portion of FIG.
1.
[0013] FIG. 3 shows a dendrogram constructed according to an
embodiment of the invention.
[0014] FIG. 4 shows a schematic of a system according to an
embodiment of the invention.
[0015] It is noted that the drawings of the invention are not to
scale. The drawings are intended to depict only typical aspects of
the invention, and therefore should not be considered as limiting
the scope of the invention. In the drawings, like numbering
represents like elements among the drawings.
DETAILED DESCRIPTION OF THE INVENTION
[0016] Turning now to the drawings, FIG. 1 shows a flow diagram of
a method according to an embodiment of the invention. At 51, once a
purchaser and/or a member of a social network has selected an item,
a plurality of features of that item are determined. The features
determined will vary, of course, depending on the type of item
selected, but may include, in addition to type-specific features,
the brand or price of the item. With respect to type-specific
features, using, merely for illustrative purposes, the case where
the selected item is a book, the plurality of features may include,
for example, the author, the publisher, the retailer, the genre,
and/or the format (hardcover, softcover, e-book).
[0017] At S2, the plurality of features determined at 51 are
prioritized. In some embodiments of the invention, prioritizing the
features of an item may include detecting one or more anomalies in
the features of a selected item. For example, in the context of
making a valuable recommendation to a purchaser, it may be of
greater significance that a feature of a selected item is
dissimilar to the features of items previously selected by the
purchaser. Thus, in some embodiments of the invention, prioritizing
the features of an item may include comparing one or more features
of the selected item to one or more features of a product or
products previously purchased by the purchaser. Additional details
of the prioritization and comparison at S2 are described below with
reference to FIG. 2.
[0018] In some embodiments of the invention, the
previously-purchased products may be limited to those purchased
within a predetermined period. This may be useful, for example,
where it is desirable to account for longer-term changes in a
purchaser's buying habits. For example, as a purchaser's habits or
tastes change, it may be necessary or desirable to restrict from
the comparison items purchased in the more distant past, which may
necessarily possess features dissimilar from those of more recently
purchased items. That is, by restricting the comparison to
purchases made within a predetermined period, the likelihood of
spuriously detected anomalies is reduced.
[0019] In other embodiments of the invention, the
previously-purchased products may include products purchased by
members of the purchaser's social network with whom the purchaser
is somehow connected. For example, it may be desirable or useful to
compare features of a selected item to the features of items
purchased by others known to have similar interests, beliefs, etc.
Again, it may be useful to limit such comparisons to those products
purchased within a predetermined period, as the habits and tastes
of groups of purchasers may change just as those of an individual
purchaser may change.
[0020] In making a comparison between one or more feature of a
selected item and features of a previously-purchased item or items,
some embodiments of the invention include the calculation of a
correlation score or value. One skilled in the art will recognize
that there are any number of correlation scores or values that may
be calculated. One such correlation value is a Pearson's
correlation value, typically expressed as r. Pearson's correlation
values range from +1.0 to -1.0, representing, respectively, a
perfect positive correlation and a perfect negative correlation. In
the context of feature prioritization, and more particularly
anomaly detection, a feature having a negative correlation may be
of greater significance than a feature having a positive
correlation. Similarly, weaker positive correlations are more
indicative of anomalous features than are stronger positive
correlations.
[0021] For example, continuing with the example above of a
purchaser selecting a book, in comparing previous purchases of the
same purchaser, if it is found that the format of the selected book
is an electronic book (e-book) and previously-purchased books were
in hardcover or softcover formats, the correlation value for this
feature will likely be negative, indicating an anomaly. In terms of
feature prioritization, this may be of great significance, as it
may indicate that the purchaser has recently acquired an electronic
book reader (e-reader) and therefore may be more likely to respond
favorably to items recommended in this format.
[0022] At S3, the features of the selected item determined at S1
may optionally be broadcasted to a social network to which a
purchaser belongs. In some embodiments of the invention, the
plurality of features may be broadcasted after being prioritized at
S2.
[0023] At S4, a recommendation is made to a purchaser and/or member
of a social network. In some embodiments of the invention, the
recommendation may include a product that shares at least one
feature of the item selected by the purchaser/social network
member. In other embodiments of the invention, the recommendation
may include another member of the social network with whom the
purchaser/social network member is not yet connected, but who has
selected or purchased an item sharing at least one feature of the
item selected by the purchaser/social network member. As described
above, it may be desirable to recommend to the purchaser/social
network member an item (or member of the social network) sharing a
highly-ranked feature, such as a feature indicative of an anomalous
selection by the purchaser/social network member.
[0024] FIG. 2 shows a flow diagram of the details of S2, according
to some embodiments of the invention. As can be seen in FIG. 2,
prioritizing the plurality of features may include a number of
optional sub-steps. For example, at S2A, a comparison is made to
previously-purchased items. As noted above, these may be items
previously purchased by the purchaser/social network member, or
they may be items previously purchased by other social network
members with whom the purchaser/social network member is already
affiliated.
[0025] At S2B, it may be determined whether the
previously-purchased item was purchased within a predetermined
period. If so (i.e., Yes at S2B), the previously-purchased item may
be included in a correlation value calculated at S2D. If not (i.e.,
No at S2B), the previously-purchased item may be excluded from the
correlation values (S2C), with flow iteratively looped to S2A for
comparison to other previously-purchased items, if desired. In
other embodiments of the invention, determining whether the
previously-purchased item was purchased within a predetermined
period may be performed before comparing its feature(s) to
feature(s) of the selected item.
[0026] At S2E, the correlation values calculated at S2D may be
ranked. As described above, it may be the case that a low positive
or a negative correlation value may be of greater significance, in
the context of making a recommendation to a purchaser/social
network member, than would a high positive correlation value. That
is, an anomalously selected item may indicated a change in a
purchaser's buying habits or a social network member's interests or
affiliations, the consequence of which may be a greater interest in
items or people sharing features with that anomalously selected
item.
[0027] At S2F, a dendrogram may be constructed to aid in making a
recommendation to a purchaser/social network member at S3 (FIG. 1).
Dendrograms are well known in the industry as a method of
classifying or grouping items according to shared or similar
characteristics. As such, the basics of their construction will not
be described here. In the context of embodiments of the invention,
however, it should be noted that dendrograms constructed at S2F may
consider a single feature of a selected item at each level and
include within that level only other items sharing that single
feature.
[0028] For example, FIG. 3 shows an illustrative dendrogram 100 as
may be constructed according to one embodiment of the invention.
For the sake of brevity and ease of explanation, dendrogram 100 is
shown in a very simple form. Actual dendrograms constructed
according to embodiments of the invention may, in fact, be
considerably more complex, as would be apparent to one skilled in
the art.
[0029] Dendrogram 100 is shown having three levels: 10, 20, 30,
each of which includes an item or items sharing a particular
feature of a selected item. For example, level 10 includes a single
item 12. Continuing with the example above of a purchaser selecting
an e-book, it may be that the electronic format of the selected
item is highly anomalous in the purchaser's buying history and is,
therefore, the feature given the highest ranking at S2E (FIG. 2).
Item 12 may, therefore, be another book by the same author in an
electronic format.
[0030] Level 20 includes two items, 22 and 24. Again, continuing
with the example above, it may have been determined that the
authorship of the selected item is the second-highest ranked
feature at S2E. That is, it may be that the author of the selected
item represents an anomaly in the purchaser's buying history,
although one not as anomalous as the electronic format of the
selected item. As such, item 22 may represent a hardcover book by
the same author and item 24 may represent a softcover book by the
same author.
[0031] Level 30 includes four items, 32, 34, 36, and 38. Here,
continuing with the example above, it may have been determined that
the genre of the selected item is the third-highest ranked feature
at S2E. That is, it may be that the genre of the selected item is a
feature less anomalous than the electronic format and the author.
As such, items 32 and 34 may represent a hardcover books by
different authors within the same genre as the selected item and
items 36 and 38 may represent softcover books by different authors
within the same genre as the selected item.
[0032] FIG. 4 shows an illustrative environment 416 for making a
recommendation to a purchaser/social network member. To this
extent, environment 416 includes a computer system 420 that can
perform a process described herein in order to make a
recommendation to a purchaser/social network member. In particular,
computer system 420 is shown including a recommendation program
430, which makes computer system 420 operable to make a
recommendation to a purchaser/social network member by performing a
process described herein.
[0033] Computer system 420 is shown including a processing
component 422 (e.g., one or more processors), a storage component
424 (e.g., a storage hierarchy), an input/output (I/O) component
426 (e.g., one or more I/O interfaces and/or devices), and a
communications pathway 428. In general, processing component 422
executes program code, such as recommendation program 430, which is
at least partially fixed in storage component 424. While executing
program code, processing component 422 can process data, which can
result in reading and/or writing transformed data from/to storage
component 424 and/or I/O component 426 for further processing.
Pathway 428 provides a communications link between each of the
components in computer system 420. I/O component 426 can comprise
one or more human I/O devices, which enable a human user, such as
user 418, to interact with computer system 420 and/or one or more
communications devices to enable a system user (e.g., another
computer system used to interact with user 418) to communicate with
computer system 420 using any type of communications link. To this
extent, recommendation program 430 can manage a set of interfaces
(e.g., graphical user interface(s), application program interface,
and/or the like) that enable human and/or system users to interact
with recommendation program 430. Further, recommendation program
430 can manage (e.g., store, retrieve, create, manipulate,
organize, present, etc.) the data, such as item feature(s) data
440, purchasing history data 442, and social network data 444 using
any solution.
[0034] In any event, computer system 420 can comprise one or more
general purpose computing articles of manufacture (e.g., computing
devices) capable of executing program code, such as recommendation
program 430, installed thereon. As used herein, it is understood
that "program code" means any collection of instructions, in any
language, code or notation, that cause a computing device having an
information processing capability to perform a particular action
either directly or after any combination of the following: (a)
conversion to another language, code or notation; (b) reproduction
in a different material form; and/or (c) decompression. To this
extent, recommendation program 430 can be embodied as any
combination of system software and/or application software.
[0035] Further, recommendation program 430 can be implemented using
a set of modules 432. In this case, a module 432 can enable
computer system 420 to perform a set of tasks used by
recommendation program 430, and can be separately developed and/or
implemented apart from other portions of recommendation program
430. As used herein, the term "component" means any configuration
of hardware, with or without software, which implements the
functionality described in conjunction therewith using any
solution, while the term "module" means program code that enables a
computer system 420 to implement the actions described in
conjunction therewith using any solution. When fixed in a storage
component 424 of a computer system 420 that includes a processing
component 422, a module is a substantial portion of a component
that implements the actions. Regardless, it is understood that two
or more components, modules, and/or systems may share some/all of
their respective hardware and/or software. Further, it is
understood that some of the functionality discussed herein may not
be implemented or additional functionality may be included as part
of computer system 420.
[0036] When computer system 420 comprises multiple computing
devices, each computing device can have only a portion of
recommendation program 430 fixed thereon (e.g., one or more modules
432). However, it is understood that computer system 420 and
recommendation program 430 are only representative of various
possible equivalent computer systems that may perform a process
described herein. To this extent, in other embodiments, the
functionality provided by computer system 420 and recommendation
program 430 can be at least partially implemented by one or more
computing devices that include any combination of general and/or
specific purpose hardware with or without program code. In each
embodiment, the hardware and program code, if included, can be
created using standard engineering and programming techniques,
respectively.
[0037] Regardless, when computer system 420 includes multiple
computing devices, the computing devices can communicate over any
type of communications link. Further, while performing a process
described herein, computer system 420 can communicate with one or
more other computer systems using any type of communications link.
In either case, the communications link can comprise any
combination of various types of wired and/or wireless links;
comprise any combination of one or more types of networks; and/or
utilize any combination of various types of transmission techniques
and protocols.
[0038] As discussed herein, recommendation program 430 enables
computer system 420 to make a recommendation to a purchaser/social
network member. To this extent, computer system 420 can acquire
and/or utilize information before, during, and after making a
recommendation to a purchaser/social network member.
[0039] For example, computer system 420 can acquire and/or utilize
item feature(s) data 440 corresponding to a selected item. The item
feature(s) data 440 can comprise various information regarding a
selected item.
[0040] Computer system 420 also can acquire and/or utilize
purchasing history data 442, which can include various information
regarding previously-purchased items. Similarly, computer system
420 also can acquire and/or utilize social network data 444, which
can include information regarding individuals or groups within a
social network, including those with whom a purchaser may be
affiliated.
[0041] As used herein, it is understood that the terms "program
code" and "computer program code" are synonymous and mean any
expression, in any language, code or notation, of a set of
instructions intended to cause a computer system having an
information processing capability to perform a particular function
either directly or after either or both of the following: (a)
conversion to another language, code or notation; and (b)
reproduction in a different material form. To this extent, program
code can be embodied as one or more types of program products, such
as an application/software program, component software/a library of
functions, an operating system, a basic I/O system/driver for a
particular computing and/or I/O device, and the like.
[0042] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the disclosure. As used herein, the singular forms "a," "an," and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof.
[0043] The foregoing description of various aspects of the
invention has been presented for purposes of illustration and
description. It is not intended to be exhaustive or to limit the
invention to the precise form disclosed, and obviously, many
modifications and variations are possible. Such modifications and
variations that may be apparent to a person skilled in the art are
intended to be included within the scope of the invention as
defined by the accompanying claims.
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