U.S. patent application number 14/628135 was filed with the patent office on 2015-06-11 for system and method for social data mining that learns from a dynamic taxonomy.
The applicant listed for this patent is eBay Inc.. Invention is credited to Sudha Jamthe, Zih-Hao Lin.
Application Number | 20150161677 14/628135 |
Document ID | / |
Family ID | 50975925 |
Filed Date | 2015-06-11 |
United States Patent
Application |
20150161677 |
Kind Code |
A1 |
Jamthe; Sudha ; et
al. |
June 11, 2015 |
SYSTEM AND METHOD FOR SOCIAL DATA MINING THAT LEARNS FROM A DYNAMIC
TAXONOMY
Abstract
A system, method and article of manufacture that selects at
least keyword from a taxonomy of a publication system; used the at
least one keyword for performing data mining of a social network to
detect conversations relating to products; compared elements of the
conversations with functions of the publication system; and
responsive to comparing, uses the elements of the conversation to
change the taxonomy or the offering of products on the publication
system. The conversations comprise posts relating to a product, the
functions of the publication system comprise searches relating to
the product, and changing the taxonomy comprises adding a category
to the taxonomy.
Inventors: |
Jamthe; Sudha; (San Jose,
CA) ; Lin; Zih-Hao; (Zurich, CH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
eBay Inc. |
San Jose |
CA |
US |
|
|
Family ID: |
50975925 |
Appl. No.: |
14/628135 |
Filed: |
February 20, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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13725391 |
Dec 21, 2012 |
9002889 |
|
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14628135 |
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Current U.S.
Class: |
705/14.66 |
Current CPC
Class: |
G06F 16/951 20190101;
G06F 16/26 20190101; G06F 16/2465 20190101; G06Q 30/0269 20130101;
G06Q 50/01 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 50/00 20060101 G06Q050/00; G06F 17/30 20060101
G06F017/30 |
Claims
1. A publication system comprising: at least one hardware processor
configured to include: a selection module to select one or more
keywords; a data mining module to use the one or more keywords for
performing data mining of user communications on an additional
system to detect user conversations relating to a plurality of
products; a collection module to compare a number of user
conversations on the additional system that relate to a specific
product of the plurality of products with a number of searches of
the publication system for the specific product; a detection module
to detect that the number of user conversations that relate to the
specific product is substantially higher than the number of
searches of the publication system for the specific product; and a
database module responsive to the detecting, to change the taxonomy
of the publication system and to offer the specific product on the
publication system.
2. The system of claim 1 wherein changing the taxonomy comprises
adding a new category to the taxonomy.
3. The system of claim 2, wherein the selection module selects the
one or more keywords from the taxonomy after the adding of a new
category or a new product to the taxonomy.
4. The system of claim 1 wherein the additional system is one of a
social network system or another publication system.
5. The system of claim 4 wherein detecting number of user
conversations that relate to the specific product comprises
detecting conversations of users of the social network that
illustrate an interest to obtain the specific product or detecting
conversations of users of the social network that illustrate an
interest to sell the specific product, the database module further
to use the conversations of users that illustrate an interest to
obtain the specific product or the conversations of users that
illustrate an interest to sell the specific product to revise the
way products are offered on the publication system.
6. The system of claim 5 wherein using the conversations of users
that illustrate an interest to obtain the specific product
comprises featuring the specific product on the publication
system.
7. The system of claim 1 wherein the one or more keywords comprise
a plurality of keywords that defines buyers, sellers and products
dynamically.
8. A computer implemented method comprising: selecting one or more
keywords; using the one or more keywords for performing data mining
of user communications on a first system to detect user
conversations relating to a plurality of products; comparing, by at
least one computer processor, a number of data mined user
conversations on the first system that relate to a specific product
of the plurality of products with a number of searches of a
publication system for the specific product; and responsive to the
comparing, detecting that the number of user conversations that
relate to the specific product is substantially higher than the
number of searches of the publication system for the specific
product, and changing the taxonomy of the publication system and
offering the specific product on the publication system.
9. The method of claim 8 wherein changing the taxonomy comprises
adding a new category to the taxonomy.
10. The method of claim 9, wherein the selection module selects the
one or more keywords from the taxonomy after the adding of a new
category or a new product to the taxonomy.
11. The method of claim 9 wherein adding the new category to the
taxonomy comprises: defining a category of the taxonomy of the
publication system as set A; defining a subset of set A as A.sub.1
A.sub.2 . . . An; defining set B as a category based on a social
conversation being mined from a social network; defining subset
B.sub.1 B.sub.2 . . . Bn as keywords people use to define the
category on the social conversation; defining A=B if and only if
(A.sub.1 U A.sub.2 UA.sub.3 . . . UA.sub.n)=(B.sub.1 UB.sub.2
UB.sub.3 . . . UB.sub.n); detecting B.sub.x as a subset of B, where
B.sub.x, does not belong to A; and modifying the taxonomy of the
publication system dynamically with the data mining by re-defining
A as A* where A*=AUB.sub.x=(A.sub.1 UA.sub.2 UA.sub.3 . . .
UA.sub.n UB.sub.x) and A.sub.1 UA.sub.2 UA.sub.3 . . . UA.sub.n is
the publication system taxonomy, and B.sub.x is added to the
publication system taxonomy.
12. The method of claim 8 wherein the first system is one of a
social network system or another publication system.
13. The method of claim 11 wherein detecting the number of user
conversations relating to a plurality of products comprises
detecting conversations of users of the social network that
illustrate an interest to obtain a product or detecting
conversations of users of the social network that illustrate an
interest to sell a product, the method further comprising using the
conversations of users that illustrate an interest to obtain a
product or the conversations of users that illustrate an interest
to sell a product to revise the way products are offered on the
publication system.
14. The method of claim 13 wherein the publication system is an
ecommerce system and revising the way products are offered
comprises featuring a product for sale.
15. A machine-readable storage device having embedded therein a set
of instructions which, when executed by a machine, causes execution
of operations comprising: selecting one or more keywords; using the
one or more keywords for performing data mining of user
communications on a first system to detect user conversations
relating to a plurality of products; comparing, by at least one
computer processor, a number of data mined user conversations on
the first system that relate to a specific product of the plurality
of products with a number of searches of a publication system for
the specific product; and responsive to the comparing, detecting
that the number of user conversations that relate to the specific
product is substantially higher than the number of searches of the
publication system for the specific product, and changing the
taxonomy of the publication system and offering the specific
product on the publication system.
16. The computer-readable storage device of claim 15 wherein
changing the taxonomy comprises adding a new category or a new
product to the taxonomy.
17. The computer-readable storage device of claim 16, wherein the
one or more keywords is selected from the taxonomy after the adding
of a new category or a new product to the taxonomy.
18. The computer-readable storage device of claim 16 wherein adding
the new category to the taxonomy comprises: defining a category of
the taxonomy of the publication system as set A; defining a subset
of set A as A.sub.1 A.sub.2 . . . An; defining set B as a category
based on a social conversation being mined from a social network;
defining subset B.sub.1 B.sub.2 . . . Bn as keywords people use to
define the category on the social conversation; defining A=B if and
only if (A.sub.1 U A.sub.2 UA.sub.3 . . . UA.sub.n)=(B.sub.1
UB.sub.2 UB.sub.3 . . . UB.sub.n); detecting B.sub.x as a subset of
B, where B.sub.x, does not belong to A; and modifying the taxonomy
of the publication system dynamically with the data mining by
re-defining A as A* where A*=AUB.sub.x=(A.sub.1 UA.sub.2 UA.sub.3 .
. . UA.sub.n UB.sub.x) and A.sub.1 UA.sub.2 UA.sub.3 . . . UA.sub.n
is the publication system taxonomy, and B.sub.x is added to the
publication system taxonomy.
19. The computer-readable storage device of claim 15, wherein
detecting user conversations relating to a plurality of products
comprises detecting conversations of users of the social network
that illustrate an interest to obtain a product or detecting
conversations of users of the social network that illustrate an
interest to sell a product, the method further comprising using the
conversations of users that illustrate an interest to obtain a
product or the conversations of users that illustrate an interest
to sell a product to revise the way products are offered on the
publication system.
20. The computer-readable storage device of claim 19 wherein
revising the way products are offered comprises featuring a product
for sale.
Description
RELATED APPLICATION
[0001] This application is a continuation of U.S. patent
application Ser. No. 13/725,391, filed on Dec. 21, 2012,
TECHNICAL FIELD
[0002] Example embodiments of the present disclosure relate
generally to the field of computer technology and, more
specifically, to providing and using a learning system to mine data
from public and private market data feeds tied to a marketplace
taxonomy to feed market intelligence back to the marketplace to
adapt the taxonomy dynamically. Mined data may also be beneficially
used for promoting products.
BACKGROUND
[0003] Websites provide a number of publishing, listing, and
price-setting mechanisms whereby a publisher (e.g., a seller) may
list or publish information concerning items for sale on its site,
and where a visitor may view items on the site.
BRIEF DESCRIPTION OF DRAWINGS
[0004] Various ones of the appended drawings merely illustrate
example embodiments of the present disclosure and are not to be
considered to be limiting its scope.
[0005] FIG. 1 is a block diagram illustrating an example embodiment
of a network architecture of a system used to identify items
depicted in images.
[0006] FIG. 2 is a block diagram illustrating a network
environment, according to some embodiments.
[0007] FIG. 3 is a block diagram illustrating a publication system,
according to some embodiments.
[0008] FIG. 4 is a block diagram illustrating a data mining module,
according to some embodiments.
[0009] FIG. 5 is a diagram of an example category tree used in
accordance with example embodiments.
[0010] FIG. 6 is an example of trending products determined using a
taxonomy of a publication system.
[0011] FIG. 7A is a graphical illustration the number of searches
for a product on a publication system website.
[0012] FIG. 7B is a graphical illustration the number of searches
for a product on a social network.
[0013] FIG. 8 is a graphical illustration of Gross Merchandise
Bought (GMB) as a function of conversation searches and product
searches on a social network.
[0014] FIG. 9A is a flowchart illustrating the process of finding
social product conversations on a social network.
[0015] FIG. 9B is a flowchart illustrating the process of finding
buyer and seller conversations on a social network.
[0016] FIG. 10 is a simplified block diagram of a machine in an
example form of a computing system within which a set of
instructions for causing the machine to perform any one or more of
the methodologies discussed herein may be executed.
DETAILED DESCRIPTION
[0017] The description that follows includes systems, methods,
techniques, instruction sequences, and computing machine program
products that embody illustrative embodiments of the present
disclosure. In the following description, for purposes of
explanation, numerous specific details are set forth in order to
provide an understanding of various embodiments of the inventive
subject matter. It will be evident, however, to those skilled in
the art that embodiments of the disclosed subject matter may be
practiced without these specific details. In general, well-known
instruction instances, protocols, structures, and techniques have
not been shown in detail.
[0018] As used herein, the term "or" may be construed in either an
inclusive or exclusive sense. Additionally, although various
example embodiments discussed below focus on a network-based
publication system environment, the embodiments are given merely
for clarity in disclosure. As used herein, "publication system"
includes an ecommerce system. Thus, any type of electronic
publication, electronic commerce, or electronic business system and
method, including various system architectures, may employ various
embodiments of the listing creation system and method described
herein and may be considered as being within a scope of the example
embodiments. Each of a variety of example embodiments is discussed
in detail below.
[0019] Example embodiments described herein provide systems and
methods to provide improved user experience when visiting a
publication system site. This may be done by determining from data
sets of the publication system's data logs of visitors, using the
appropriate analytics, the "gross merchandise bought" on the site,
referred to herein "GMB." GMB may be viewed as an indicator of
total gross revenue for the site. GMB may be estimated using the
GMB dataset of an ecommerce system.
[0020] As used herein, "taxonomy" refers to a classification of
items that may be in a hierarchy, as discussed more fully below. A
learning system that may mine data from public and private market
data feeds that are tied to publication system taxonomy, and that
may feed market intelligence back to adapt the publication system
taxonomy, may be used to improve GMB. In one embodiment the
taxonomy may be used to mine social data for product information,
in order to see trending products in various categories of the
taxonomy. At any given time there are many people tweeting or
otherwise s haring updates on social networks. Mining social
networks may allow importation of trending information and feeding
it back into the publication system taxonomy to improve that
system's original taxonomy dynamically in order to catch, or
include, social trending. The system may also allow the publication
system taxonomy to change dynamically with social trending
products. In an embodiment, taxonomy may be created or revised
based on social data mining and searching for information in the
social feed using the dynamic taxonomy. Further, the publication
system's taxonomy may be used to mine social networks to find
buyers' conversations and sellers' conversations with respective
propensities to buy and sell. A conversation among buyers and/or
sellers may be about a particular product that may be listed in the
publication system taxonomy. Buyer conversations about a particular
product may indicate they are looking to buy that product. Other
conversations may indicate that a seller is willing to sell a
particular product. Those analyzing these data could provide the
data to business units like an electronics team, a social marketing
team, and a "daily deals" team (the forgoing examples being for a
publication system that is an ecommerce system). Such teams might
then take action. For example, if data mining found that sellers
who have a lot of social conversation and fans (for example, a lot
of followers in Twitter, a very popular blogger, and the like) in
different product "verticals" (fashion, electronics, etc.), the
publication system's social marketing team may connect with those
sellers and ask them to promote the publication system on their
blogs/twitter posts. A system may mathematically look at the items
in the taxonomy and search for those in the feed of social data or
market data. An example of a feed of social data may be a
Twitter.TM. feed, and an example of a feed of market data may be a
feed from a marketplace competitor which may, in some examples, be
an ecommerce marketplace such as Amazon.TM..
[0021] FIG. 1 is a network diagram depicting a network system 100,
according to one embodiment, having a client-server architecture
configured for exchanging data over a network. For example, the
network system 100 may include a publication/publisher system 102
where clients may communicate and exchange data within the network
system 100. The data may pertain to various functions (e.g., online
item purchases) and aspects (e.g., managing content) associated
with the network system 100 and its users. Although illustrated
herein as a client-server architecture as an example, other
embodiments may include other network architectures, such as a
peer-to-peer or distributed network environment.
[0022] A data exchange platform, in an example form of a
network-based publisher 102, may provide server-side functionality,
via a network 104 (e.g., the Internet, wireless network, cellular
network, or a Wide Area Network (WAN)) to one or more clients. The
one or more clients may include users that utilize the network
system 100 and more specifically, the network-based publisher 102,
to exchange data over the network 104. These transactions may
include transmitting, receiving (communicating) and processing data
to, from, and regarding content and users of the network system
100. The data may include, but are not limited to, content and user
data such as feedback data; user profiles; user attributes; product
attributes; product and service reviews; product, service,
manufacture, and vendor recommendations and identifiers; social
network commentary, product and service listings associated with
buyers and sellers; auction bids; and transaction data, among other
things.
[0023] In various embodiments, the data exchanges within the
network system 100 may be dependent upon user-selected functions
available through one or more client or user interfaces (UIs). The
UIs may be associated with a client device, such as a client device
110 using a web client 106. The web client 106 may be in
communication with the network-based publisher 102 via a web server
116. The UIs may also be associated with a client device 112 using
a programmatic client 108, such as a client application. It can be
appreciated in various embodiments the client devices 110, 112 may
be associated with a buyer, a seller, a third party electronic
commerce platform, a payment service provider, or a shipping
service provider, each in communication with the network-based
publisher 102 and optionally each other. The buyers and sellers may
be any one of individuals, merchants, or service providers, among
other things. The client devices 110 and 112 may comprise a mobile
phone, desktop computer, laptop, or any other communication device
that a user may use to access the networked system 102.
[0024] Turning specifically to the network-based publisher 102, an
application program interface (API) server 114 and a web server 116
are coupled to, and provide programmatic and web interfaces
respectively to, one or more application servers 118. The
application servers 118 host one or more publication application(s)
120 and one or more payment systems 122. The application servers
118 are, in turn, shown to be coupled to one or more database
server(s) 124 that facilitate access to one or more database(s)
126.
[0025] In one embodiment, the web server 116 and the API server 114
communicate and receive data pertaining to products, listings,
transactions, social network commentary and feedback, among other
things, via various user input tools. For example, the web server
116 may send and receive data to and from a toolbar or webpage on a
browser application (e.g., web client 106) operating on a client
device (e.g., client device 110). The API server 114 may send and
receive data to and from an application (e.g., client application
108) running on another client device (e.g., client device
112).
[0026] The publication system 120 publishes content on a network
(e.g., the Internet). As such, the publication system 120 provides
a number of publication and marketplace functions and services to
users that access the networked system 102. For example, the
publication application(s) 120 may provide a number of services and
functions to users for listing goods and/or services for sale,
facilitating transactions, and reviewing and providing feedback
about transactions and associated users. Additionally, the
publication application(s) 120 may track and store data and
metadata relating to products, listings, transactions, and user
interaction with the network-based publisher 102. The publication
application(s) 120 may aggregate the tracked data and metadata to
perform data mining to identify trends or patterns in the data. The
publication system 120 is discussed in more detail in connection
with FIG. 3. While the publication system 120 is discussed in terms
of a marketplace environment, it is noted that the publication
system 120 may be associated with a non-marketplace
environment.
[0027] The payment system 122 provides a number of payment services
and functions to users. The payment system 122 allows users to
accumulate value (e.g., in a commercial currency, such as the U.S.
dollar, or a proprietary currency, such as "points") in accounts,
and then later to redeem the accumulated value for products (e.g.,
goods or services) that are made available via the publication
system 120. The payment system 122 also facilitates payments from a
payment mechanism (e.g., a bank account, PayPal account, or credit
card) for purchases of items via the network-based marketplace.
While the publication system 120 and the payment system 122 are
shown in FIG. 1 to both form part of the networked system 102, it
will be appreciated that, in alternative embodiments, the payment
system 122 may form part of a payment service that is separate and
distinct from the networked system 102.
[0028] FIG. 2 is a block diagram illustrating a network
environment, according to some embodiments. Referring to FIG. 2, a
client device 110 executing a web client 106 and a client device
112 executing a programmatic client 108 may communicate with a
network-based publisher 102, as described with respect to FIG. 1,
or a third-party platform 204 via the network 104. In some
embodiments, the third-party platform 204 may be a social
networking platform, a gaming platform, or another network-based
publisher platform. In some embodiments, the network-based
publisher 102 may publish content or applications (e.g., games,
social networking applications) on the third-party platform 204
either directly or via the network 104. As client devices 110, 112
interact with third-party platform 204, the network-based publisher
102 may receive data pertaining to the interactions. The data may
be received through the use of API calls to open a connection or
transmit data between the network-based publisher 102 and the
third-party platform 204.
[0029] Referring now to FIG. 3, an example block diagram
illustrating multiple components that, in one example embodiment,
are provided within the publication system 120 of the networked
system 102 (see FIG. 1), is shown. The publication system 120 may
be hosted on dedicated or shared server machines (not shown) that
are communicatively coupled to enable communications between the
server machines. The multiple components themselves are
communicatively coupled (e.g., via appropriate interfaces), either
directly or indirectly, to each other and to various data sources,
to allow information to be passed between the components or to
allow the components to share and access common data. Furthermore,
the components may access the one or more database(s) 126 via the
one or more database servers 124, both shown in FIG. 1.
[0030] In one embodiment, the publication system 120 provides a
number of publishing, listing, and price-setting mechanisms whereby
a seller may list (or publish information concerning) goods or
services for sale, a buyer can express interest in or indicate a
desire to purchase such goods or services, and a price can be set
for a transaction pertaining to the goods or services. To this end,
the publication system 120 may comprise at least one publication
engine 302 and one or more auction engines 304 that support
auction-format listing and price setting mechanisms (e.g., English,
Dutch, Chinese, Double, reverse auctions, etc.). The various
auction engines 304 also provide a number of features in support of
these auction-format listings, such as a reserve price feature
whereby a seller may specify a reserve price in connection with a
listing and a proxy-bidding feature whereby a bidder may invoke
automated proxy bidding.
[0031] A pricing engine 306 supports various price listing formats.
One such format is a fixed-price listing format (e.g., the
traditional classified advertisement-type listing or a catalog
listing). Another format comprises a buyout-type listing.
Buyout-type listings (e.g., the Buy-It-Now (BIN) technology
developed by eBay Inc., of San Jose, Calif.) may be offered in
conjunction with auction-format listings and may allow a buyer to
purchase goods or services, which are also being offered for sale
via an auction, for a fixed price that is typically higher than a
starting price of an auction for an item.
[0032] A store engine 308 allows a seller to group listings within
a "virtual" store, which may be branded and otherwise personalized
by and for the seller. Such a virtual store may also offer
promotions, incentives, and features that are specific and
personalized to the seller. In one example, the seller may offer a
plurality of items as Buy-It-Now items in the virtual store, offer
a plurality of items for auction, or a combination of both.
[0033] A reputation engine 310 allows users that transact,
utilizing the networked system 102, to establish, build, and
maintain reputations. These reputations may be made available and
published to potential trading partners. Because the publication
system 120 supports person-to-person trading between unknown
entities, users may otherwise have no history or other reference
information whereby the trustworthiness and credibility of
potential trading partners may be assessed. The reputation engine
310 allows a user, for example through feedback provided by one or
more other transaction partners, to establish a reputation within
the network-based publication system over time. Other potential
trading partners may then reference the reputation for purposes of
assessing credibility and trustworthiness.
[0034] Navigation of the network-based publication system may be
facilitated by a navigation engine 312. For example, a search
engine (not shown) of the navigation engine 312 enables keyword
searches of listings published via the publication system 120. In a
further example, a browse engine (not shown) of the navigation
engine 312 allows users to browse various category, catalog, or
inventory data structures according to which listings may be
classified within the publication system 120. The search engine and
the browse engine may provide retrieved search results or browsed
listings to a client device. Various other navigation applications
within the navigation engine 312 may be provided to supplement the
searching and browsing applications.
[0035] In order to make listings available via the networked system
102 as visually informing and attractive as possible, the
publication system 120 may include an data mining engine 314 that
enables users to upload images for inclusion within listings and to
incorporate images within viewed listings. The data mining engine
314 also receives social data from a user and utilizes the social
data to identify an item depicted or described by the social
data.
[0036] An API engine 316 stores API information for various
third-party platforms and interfaces. For example, the API engine
316 may store API calls used to interface with a third-party
platform. In the event a publication application(s) 120 is to
contact a third-party application or platform, the API engine 316
may provide the appropriate API call to use to initiate contact. In
some embodiments, the API engine 316 may receive parameters to be
used for a call to a third-party application or platform and may
generate the proper API call to initiate the contact.
[0037] A listing management creation and management engine 318
(which could be a separate creation engine and a separate
management engine) allows sellers to create and manage listings.
Specifically, where a particular seller has authored or published a
large number of listings, the management of such listings may
present a challenge. The listing creation and management engine 318
provides a number of features (e.g., auto-relisting, inventory
level monitors, etc.) to assist the seller in managing such
listings.
[0038] A post-listing management engine 320 also assists sellers
with a number of activities that typically occur post-listing. For
example, upon completion of an auction facilitated by the one or
more auction engines 304, a seller may wish to leave feedback
regarding a particular buyer. To this end, the post-listing
management engine 320 provides an interface to the reputation
engine 310 allowing the seller to conveniently provide feedback
regarding multiple buyers to the reputation engine 310.
[0039] A messaging engine 322 is responsible for the generation and
delivery of messages to users of the networked system 102. Such
messages include, for example, advising users regarding the status
of listings and best offers (e.g., providing an acceptance notice
to a buyer who made a best offer to a seller). The messaging engine
322 may utilize any one of a number of message delivery networks
and platforms to deliver messages to users. For example, the
messaging engine 322 may deliver electronic mail (e-mail), an
instant message (IM), a Short Message Service (SMS), text,
facsimile, or voice (e.g., Voice over IP (VoIP)) messages via wired
networks (e.g., the Internet), a Plain Old Telephone Service (POTS)
network, or wireless networks (e.g., mobile, cellular, WiFi,
WiMAX).
[0040] A data mining engine 324 analyzes the data gathered by the
publication system 102 from interactions between the client
machines 110, 112 and the publication system 102. In some
embodiments, the data mining engine 324 also analyzes the data
gathered by the publication system 102 from interactions between
components of the publication system 102 and/or client machines
110, 112 and third-party platforms such as social networks like
Twitter, and also publications such as eBay and Amazon. The data
mining engine 324 uses the data to identify certain trends or
patterns in the data. For example, the data mining engine 324 may
identify patterns which may help to improve search query
processing, user profiling, and identification of relevant search
results, among other things.
[0041] A taxonomy engine 326 uses the patterns and trends
identified by the data mining module 324 to obtain a variety of
data, including products, item listings, search queries, keywords,
search results, and individual attributes of items, users, or
products, among other things, and revise the publication system
taxonomy as discussed below. In some embodiments, the taxonomy
engine 326 may assign a score to each piece of data based on the
frequency of occurrence of the piece of data in the mined set of
data. In some embodiments, the taxonomy engine 326 may assign or
adjust a score of a piece of data pertaining to an item (e.g., one
or more keywords with logic, a product listing, an individual
attribute of the item) based on input data received from users. The
score may represent a relevance of the piece of data to the item or
an aspect of the item. In some embodiments, the taxonomy engine 326
may compare data received from the third party platform to
previously received and stored data from the third party platform.
Alternatively, the taxonomy engine may compare data received from
the third party platform with data in the publication system's own
taxonomy.
[0042] Although the various components of the publication system
120 have been defined in terms of a variety of individual modules,
a skilled artisan will recognize that many of the items can be
combined or organized in other ways. Furthermore, not all
components of the publication system 120 have been included in FIG.
3. In general, components, protocols, structures, and techniques
not directly related to functions of example embodiments (e.g.,
dispute resolution engine, loyalty promotion engine,
personalization engines, etc.) have not been shown or discussed in
detail. The description given herein simply provides a variety of
example embodiments to aid the reader in an understanding of the
systems and methods used herein.
[0043] FIG. 4 is a block diagram illustrating the data mining
module 326, according to some embodiments. Implementations for data
mining are well known and need not be described in detail here. For
example, mining social networks such as Twitter is described at
length in the texts Mining the Social Web: Analyzing Data from
Facebook, Twitter, LinkedIn, and Other Social Media Sites, by
Matthew A. Russell, Copyright.COPYRGT. 2011, published by O'Reilly
Media, Inc., and 21 Recipes for Mining Twitter, by Matthew A.
Russell, Copyright.COPYRGT. 2011, published by O'Reilly Media,
Inc.
[0044] Referring to FIG. 4, an interface module 402 may store
components used to interface with a third party platform such as
204 of FIG. 2 from which data is mined. The third party platform
could be from eBay and/or Amazon, or from a social network such as
Twitter. Interfacing with third party platforms may entail
providing data related to items about which searches or opinions
from users of the third party platform are solicited. The user
input may include search keywords, descriptions, opinions, or other
text, along with non-textual input, such as clicks, highlighting,
and other interactions with the provided item text and visual
data.
[0045] A collection module 404 collects the data mined from the
third party platform. For mining Twitter, tweets and retweets of a
particular search may be included. In some embodiments the
publication system may also store Twitter IDs, their bio, location,
how many followers, their following, and similar information that
may be publically available from the social network. In some
embodiments, the collection module 404 interfaces with the third
party platform 204 directly and collects data entered by the user.
In some embodiments, the collection module 404 collects the data
from the interface module 402.
[0046] A database module 406 interfaces with one or more databases
such as 126 of FIG. 1 to store the data collected by the collection
module 404. The database module 406 also interfaces with the one or
more databases to retrieve data related to the items presented in
the third party platform. For example, the database module 406 may
retrieve searches related to a certain product, and provide by the
third party platform for purposes of comparing a user's search to
previously stored searches. Based on the comparison, the interface
module 402 or the taxonomy engine 326 may revise the publication
systems taxonomy. For example, the data mining may proceed as
follows:
DEFINITION
[0047] Set A: A category of the publication system's taxonomy (ex:
Fashion)
[0048] Subset A.sub.1 A.sub.2 . . . An: Index to build the category
(ex: shoes . . . )
[0049] Set B: A category based on social conversation being mined
from a third party platform which is a social network such as
Twitter.
[0050] Subset B.sub.1 B.sub.2 . . . Bn: Keywords people use to
define that category on the social conversation
[0051] A=B if and only if (A.sub.1 U A.sub.2 UA.sub.3 . . .
UA.sub.n)=(B.sub.1 UB.sub.2 UB.sub.3 . . . UB.sub.n)
[0052] If we could find a subset of B, for example B.sub.x, which
does not belong to A, then B.sub.xA
[0053] Then we should re-define A as A* in order to update the
publication system taxonomy dynamically A*=AUB.sub.x=(A.sub.1
UA.sub.2 UA.sub.3 . . . UA.sub.n UB.sub.x)
[0054] where A.sub.1 UA.sub.2 UA.sub.3 . . . UA.sub.n is the old
the publication system taxonomy, and B.sub.x is added by mining the
social conversation as above
[0055] FIG. 5 illustrates an example of a category tree structure
500. In the present example, the category tree structure 500 is
applied to a marketplace environment in which items in various
categories may be offered for sale. It is noted that this is merely
an example, and other embodiments may contemplate the use of the
category tree structure 500 and example embodiments described
herein in non-marketplace environments such as an informational
environment. Additionally, the categories within the category tree
structure are merely provided as an example and may comprise any
type of category.
[0056] As shown, the category tree structure 500 starts with a
virtual node 502 and branches into various categories (also
referred to as leaf categories). In the example, two 2nd level
categories are shown as an electronics category 504 and clothing,
shoes, and accessories (CSA) category 506. In a next lower level
(i.e., 3rd level), each of the 2nd level categories is further
divided into more narrowing categories. For example, the CSA
category 506 is split into at least a men's clothing category 508
and a women's clothing category 510. Further still, the women's
clothing category 510 may include at least a women's dress category
512 and a women's jeans category 514 in a next lower level. It is
noted that further categories may exist at each category level and
further lower category levels may exist, but have not been shown in
FIG. 5.
[0057] When a user (e.g., seller on a networked marketplace) lists
an item using the category tree structure 500, the user may be
restricted to listing the item within a lower level category. For
example, a seller may be listing a pair of women's jeans for sale.
The seller may be restricted to listing the jeans within a women's
jeans category 514. In an even more restrictive embodiment, the
seller may be restricted to listing the jeans within a boot-cut
category 516, a skinny category 518, or a low rise category 520. In
some embodiments, the seller may not be allowed to list the jeans
in a higher category such as the women's clothing category 510. By
restricting the user to listing their item in a more specific
category, more specific category information may be tracked by
embodiments of the present invention.
[0058] Additionally, the most relevant category aspects (e.g.,
aspect name or aspect values) for each category may be determined
from logged user behavior data. The category aspects comprise
attributes or characteristics of an item which in some embodiments
may be in the form of metadata. For example, the women's jeans
category may comprise relevant category aspects of brands, sizes,
and styles.
[0059] Each category of the category tree structure 500 may have a
different set of relevant category aspects. For example, the CSA
(clothes, shoes, and accessories) category 506 may have relevant
aspects directed to conditions, prices, sellers, and buying
formats. The women's clothing category 510 may have relevant
aspects directed to women's brands, women's clothing sizes, colors,
and aspects inherited from the CSA category 506. Moving further
down in levels, the women's jeans category 514 may comprise
relevant aspects of women's popular jeans brands, jeans bottom
sizes, jeans styles, jeans inseams, and aspects inherited from the
CSA category 506 and the women's clothing category 510. Within the
jean style category, aspects of boot-cut, skinny, low-rise, and
others may be identified. Within the skinny (jeans) category,
relevant aspects may comprise women's popular skinny jeans brands,
women's skinny jeans materials, and aspects inherited from higher
level categories. As such, each category at each level may comprise
different relevant category aspects (e.g., aspect names or aspect
values).
[0060] Additionally, categories within the same level may comprise
different relevant aspects and corresponding values. For example,
within the women's jeans category 514, the relevant aspects (e.g.,
aspect name) in descending order of relevancy based on past user
behavior may be size, brand, style, and inseam. Furthermore, the
relevant aspect values for the brand in descending order of
relevancy may be 7 For All Mankind, True Religion, American Eagle,
Abercrombie & Fitch, and Levis, while relevant values for style
in descending order of relevancy may be boot cut, slim/skinny,
low-rise/hipster, and stretch. In contrast, a men's jeans category
522 may comprise relevant aspect name in descending order of
relevancy of waist size, brand, inseam, and style. The relevant
aspect values for the brand in the men's jeans category 522 in
descending order of relevancy may be Levis, Diesel, 7 For All
Mankind, Ralph Lauren, and Calvin Klein, while relevant aspect
values for style in descending order of relevancy may be boot cut,
straight leg, relaxed, and classic.
[0061] In example embodiments, user behavior data is collected. The
user behavior data comprises tracked user actions associated with
past queries involving a query term. The user behavior data is
compiled and listing data is accessed. The listing data includes
aspect data for each listing. The user behavior data is joined with
the listing data and a determined category of each listing to
create joined data. Demand scores are determined based on the
joined data. The determined demand scores are then sorted to
determine at least one relevant aspect name for a category.
Additionally, the determined demand scores may be sorted to
determine at least one relevant aspect value for the at least one
relevant aspect name.
[0062] By using embodiments of the present invention, a publisher
(e.g., seller) may be notified of one or more relevant aspect names
to include when creating their publication (e.g., listing for an
item to sell). As a result, a user performing a search can be
provided results that are more specific to their search.
Accordingly, one or more of the methodologies discussed herein may
obviate a need for additional searching or navigation by the user,
which may have the technical effect of reducing computing resources
used by one or more devices within the system. Examples of such
computing resources include, without limitation, processor cycles,
network traffic, memory usage, storage space, and power
consumption.
[0063] FIG. 6 illustrates finding trending products in digital
cameras. Social mining is performed, perhaps on third party
competitor sites, based on the taxonomy the publication system has
for digital cameras. Similarly, social mining is performed on, for
example, Twitter, and the number of posts on Twitter for digital
camera is obtained. Both mining functions may use data mining
module 326, with the publication system interfacing with the third
party platforms (here, for example, eBay and Twitter) using
interface module 402, feed them mining data back to the publication
system. The publication system may compare them in collection
module 404 with internal publication system website searches. If
the data mining provides information that indicates the publication
system's taxonomy should be changed, then a change can be made as
discussed below.
[0064] FIG. 7A illustrates the result of the searches on the
publication system and FIG. 7B illustrates posts on Twitter, both
for digital cameras. In these examples, three types of digital
cameras are considered, Nikon.TM. Coolpix.TM. camera, GoPro.TM.
camera and Canon.TM. PowerShot.TM. camera. FIG. 7A illustrates that
for searches on the publication system website, Nikon (50%) and
Canon (30%) are much higher than GoPro. However, FIG. 7B
illustrates the same products in Twitter, with the result that #
Posts with GoPro (42%) is much higher than Nikon and Canon. These
examples illustrate a trending product on Twitter, namely GoPro,
but people don't search GoPro as much on the publication system as
on Twitter. The reason people do not search "GoPro" on the
publication system is that "GoPro" belongs to the "sport camera"
taxonomy. However; there is no "sport camera" category built into
the publication system taxonomy. By applying this social trend
finding technique, a publication system such as eBay could
dynamically add a new category, here "sport camera" into the eBay
taxonomy in order to catch the trending products on one or more
social networks. Thus a system is created that can dynamically
learn new items added to the node and adapt the publication system
taxonomy. Further, after adding the new category, the publication
system may continue performing data mining on Twitter or on other
third party sites, using "sport camera" as one of the categories
(among others) to see what additional trending products there may
be. Usually, when data mining, more than one keyword is used. For
example, if data mining for tweens for a Canon 400.TM. camera, one
might use a combination of keywords with AND and OR functions to
build a logic to find a particular product. Also, because people
sometimes make spelling mistakes in tweets or other social network
posts, one could include multiple keywords even for one
product.
[0065] The system can also perform data mining for the information
looking for buyers, sellers and product conversations. After
obtaining the social information, it can be fed back to eBay
marketplace to improve marketing of eBay products. For example, if
data mining shows people on social networks having conversations
about a particular product and using words like "looking to buy"
"want this item" and the like, it may indicate a person with a
propensity to buy. In that instance it may be beneficial for eBay
to feature or highlight that product.
[0066] FIG. 8 illustrates feeding the data mining information from
Twitter for a product conversation back to eBay. In this case,
comparison with eBay's taxonomy may illustrate that a particular
product may be a trending product, or that people with a propensity
to buy have been located. eBay has a function called Daily Deal.
The example of FIG. 8 illustrates that the eBay daily deal spiked
based on featuring a product because data mining yielded the
conversation of a product (in this case is Garmin Nuvi 1300). FIG.
8 illustrates that mining social conversation brings more GMB to a
publication system, in this case eBay, indicating a strong
correlation coefficient between GMB and # of tweets.
[0067] FIG. 10A is a flowchart illustrating the process of finding
social product conversations on a social network. Beginning with
the publication system's (here an electronic marketplace) taxonomy
at 1010, the system selects items relative to which data mining is
to be undertaken and determines keywords to use from the taxonomy
at 1020. At 1030 the keywords are fed into the appropriate social
mining tools to find, in this case, a Twitter social products
conversation. The returned data will, at 1040 be used to identify
new trending products that do not have a category built into the
marketplace taxonomy. This may be done as at 1050 where that mined
data is fed back to the marketplace and is compared with internal
marketplace taxonomy search results. If the mined data shows that
the trending product has significantly higher posts on Twitter, for
the current example, than searches on the marketplace, and that the
marketplace does not have a taxonomy category for that product, the
marketplace may wish to update its taxonomy by adding the category.
This may be done in real-time as the data is mined, such that the
update is accomplished dynamically as the data mining is
accomplished and the comparison is and category checking is
performed.
[0068] FIG. 10B is a flowchart illustrating the process of finding
buyer and seller conversations on a social network and enhancing
product sales as a result. Beginning with the publication system's
(here an electronic marketplace) taxonomy at 1011, the system
selects items relative to which data mining is to be undertaken and
determines keywords to use from the taxonomy at 1021. At 1031 the
keywords are fed into the appropriate social mining tools to find,
in this case, a Twitter social product conversation. At 1041 the
data mining will identify social network members who are buyers and
who are sellers with respect to a product, based the keywords used,
and conversations of the members such as phrases like "looking to
buy" "want this item" and the like indicating buyers, and phrases
like "want to sell," and the like" indicating prospective sellers.
This mined data can be fed back to the marketplace as at 1051 and
compared to internal marketplace taxonomy search results and
product offerings. For example, if the number of buyers found by
mining indicate a product is favored, that product may be promoted
by the marketplace as at 1061.
[0069] The above data mining process may be extended by applying it
to a bundle of keywords that defines buyers, sellers, and product
dynamically as discussed above. This is done as of the filing of
this patent by text mining of a bundle of keywords as a batch job
instead of dynamically. The dynamic set of keywords may be extended
to define what shows a user with propensity to buy and what shows a
user with propensity to sell. As one example, the process could be
applied to the fashion taxonomy of a marketplace as in FIG. 5,
using it as keywords to mine social buyer/seller conversations and
compare with the marketplace's competitors. Keywords used may
include clothing, clothes, purse, purses, shoes, handbag, handbags,
jeans, dress, dresses, wallet, wallets, jewelry, fashion, bag,
bags, top, tops, shirt, shirts, skirt, skirts, pants, denim, boots,
coat, coats, jacket, jackets, accessories, apparel, bikini,
bikinis, watches, necklace, necklaces, earrings, bracelet,
bracelets, style, stylish, designer, sample sale, flash sale. These
words may be used as a union (as the term union is used in set
theory, for example) to run a social search query: for example:
find all tweets which contain: clothing OR clothes OR purse OR
shoes . . . ). So the conversation found could be define people
that talk about "Fashion" on social networks. As examples of data
mining scenarios, and keywords used to capture them, a user may
tweet saying "I want to buy a Kodak.TM.." Another says "1 want to
buy a canon 400." Another would say, "friends what camera should I
ask Santa for Christmas." Another user would say any of these but
make a typo and say, "friends what camera should I ask Santa for
Christmas," (note she typed "camera" instead of "camera"). So
keywords that would capture these scenarios should be used in data
mining. As examples, a set of keywords could include camera OR
camera OR camera. As another example, for finding smartphone buying
conversations (but not selling), data mining should take into
account "smartphone" and "smartphones" and "smart phone." For
example, using terms such as term=smartphone OR smartphones OR
(smart AND phone) AND buy NOT Sell. As discussed above, provision
can be made for detecting typos and spelling mistakes; and Internet
language could be used; in an effort to catch all related
conversations. Moreover, a mathematical model may be used to teach
the system what is a camera, or any product. Then the system would
be asked to search for this product. This may go beyond keywords
and include a set of logic or rules. Ex: And, Or, include, exclude,
XO, time, location, bio, etc. Other conditions to consider is that
if a person tweets and is looking for a shower curtain as a
product, the keyword "shower" without "curtain" could refer to a
rain shower so care should be taking in creating search logic.
Further, exclusion conditions or inclusion conditions can be built
into the logic based on some keywords being next to each other or
to avoid the same keyword in the same tweet. The logic discussed
will allow the marketplace to use social conversation to understand
its market better. For example, finding conversations around
handbags with, for example, eBay or Amazon and see buyers/sellers
conversations. This will allow a marketplace to identify which
category is stronger/weaker in social comparisons to competitors
and to sort buyer/seller conversations to identify propensities to
buy/sell.
Modules, Components, and Logic
[0070] Additionally, certain embodiments described herein may be
implemented as logic or a number of modules, engines, components,
or mechanisms. A module, engine, logic, component, or mechanism
(collectively referred to as a "module") may be a tangible unit
capable of performing certain operations and configured or arranged
in a certain manner. In certain example embodiments, one or more
computer systems (e.g., a standalone, client, or server computer
system) or one or more components of a computer system (e.g., a
processor or a group of processors) may be configured by software
(e.g., an application or application portion) or firmware (note
that software and firmware can generally be used interchangeably
herein as is known by a skilled artisan) as a module that operates
to perform certain operations described herein.
[0071] In various embodiments, a module may be implemented
mechanically or electronically. For example, a module may comprise
dedicated circuitry or logic that is permanently configured (e.g.,
within a special-purpose processor, application specific integrated
circuit (ASIC), or array) to perform certain operations. A module
may also comprise programmable logic or circuitry (e.g., as
encompassed within a general-purpose processor or other
programmable processor) that is temporarily configured by software
or firmware to perform certain operations. It will be appreciated
that a decision to implement a module mechanically, in dedicated
and permanently configured circuitry, or in temporarily configured
circuitry (e.g., configured by software) may be driven by, for
example, cost, time, energy-usage, and package size
considerations.
[0072] Accordingly, the term "module" should be understood to
encompass a tangible entity, be that an entity that is physically
constructed, permanently configured (e.g., hardwired), or
temporarily configured (e.g., programmed) to operate in a certain
manner or to perform certain operations described herein.
Considering embodiments in which modules or components are
temporarily configured (e.g., programmed), each of the modules or
components need not be configured or instantiated at any one
instance in time. For example, where the modules or components
comprise a general-purpose processor configured using software, the
general-purpose processor may be configured as respective different
modules at different times. Software may accordingly configure the
processor to constitute a particular module at one instance of time
and to constitute a different module at a different instance of
time.
[0073] Modules can provide information to, and receive information
from, other modules. Accordingly, the described modules may be
regarded as being communicatively coupled. Where multiples of such
modules exist contemporaneously, communications may be achieved
through signal transmission (e.g., over appropriate circuits and
buses) that connect the modules. In embodiments in which multiple
modules are configured or instantiated at different times,
communications between such modules may be achieved, for example,
through the storage and retrieval of information in memory
structures to which the multiple modules have access. For example,
one module may perform an operation and store the output of that
operation in a memory device to which it is communicatively
coupled. A further module may then, at a later time, access the
memory device to retrieve and process the stored output. Modules
may also initiate communications with input or output devices and
can operate on a resource (e.g., a collection of information).
Example Machine Architecture and Machine-Readable Storage
Medium
[0074] With reference to FIG. 11 an example embodiment extends to a
machine in the example form of a computer system 1100 within which
instructions for causing the machine to perform any one or more of
the methodologies discussed herein may be executed. In alternative
example embodiments, the machine operates as a standalone device or
may be connected (e.g., networked) to other machines. In a
networked deployment, the machine may operate in the capacity of a
server or a client machine in server-client network environment, or
as a peer machine in a peer-to-peer (or distributed) network
environment. The machine may be a personal computer (PC), a tablet
PC, a set-top box (STB), a Personal Digital Assistant (PDA), a
cellular telephone, a web appliance, a network router, a switch or
bridge, or any machine capable of executing instructions
(sequential or otherwise) that specify actions to be taken by that
machine. Further, while only a single machine is illustrated, the
term "machine" shall also be taken to include any collection of
machines that individually or jointly execute a set (or multiple
sets) of instructions to perform any one or more of the
methodologies discussed herein.
[0075] The example computer system 1100 may include a processor
1102 (e.g., a central processing unit (CPU), a graphics processing
unit (GPU) or both), a main memory 1104 and a static memory 1106,
which communicate with each other via a bus 1107. The computer
system 1100 may further include a video display unit 1110 (e.g., a
liquid crystal display (LCD) or a cathode ray tube (CRT)). In
example embodiments, the computer system 1100 also includes one or
more of an alpha-numeric input device 1112 (e.g., a keyboard), a
user interface (UI) navigation device or cursor control device 1114
(e.g., a mouse), a disk drive unit 1116, a signal generation device
1118 (e.g., a speaker), and a network interface device 1120.
Machine-Readable Medium
[0076] The disk drive unit 1116 includes a machine-readable storage
medium 1122 on which is stored one or more sets of instructions
1124 and data structures (e.g., software instructions) embodying or
used by any one or more of the methodologies or functions described
herein. The instructions 1124 may also reside, completely or at
least partially, within the main memory 1104 or within the
processor 1102 during execution thereof by the computer system
1100, with the main memory 1104 and the processor 1102 also
constituting machine-readable media.
[0077] While the machine-readable storage medium 1122 is shown in
an example embodiment to be a single medium, the term
"machine-readable storage medium" may include a single storage
medium or multiple storage media (e.g., a centralized or
distributed database, or associated caches and servers) that store
the one or more instructions. The term "machine-readable storage
medium" shall also be taken to include any tangible medium that is
capable of storing, encoding, or carrying instructions for
execution by the machine and that cause the machine to perform any
one or more of the methodologies of embodiments of the present
application, or that is capable of storing, encoding, or carrying
data structures used by or associated with such instructions. The
term "machine-readable storage medium" shall accordingly be taken
to include, but not be limited to, solid-state memories and optical
and magnetic media. Specific examples of machine-readable storage
media include non-volatile memory, including by way of example
semiconductor memory devices (e.g., Erasable Programmable Read-Only
Memory (EPROM), Electrically Erasable Programmable Read-Only Memory
(EEPROM), and flash memory devices); magnetic disks such as
internal hard disks and removable disks; magneto-optical disks; and
CD-ROM and DVD-ROM disks.
Transmission Medium
[0078] The instructions 1124 may further be transmitted or received
over a communications network 1126 using a transmission medium via
the network interface device 1120 and utilizing any one of a number
of well-known transfer protocols (e.g., Hypertext Transfer Protocol
(HTTP)). Examples of communication networks include a local area
network (LAN), a wide area network (WAN), the Internet, mobile
telephone networks, Plain Old Telephone Service (POTS) networks,
and wireless data networks (e.g., WiFi and WiMax networks). The
term "transmission medium" shall be taken to include any intangible
medium that is capable of storing, encoding, or carrying
instructions for execution by the machine, and includes digital or
analog communications signals or other intangible medium to
facilitate communication of such software.
[0079] Although an overview of the inventive subject matter has
been described with reference to specific example embodiments,
various modifications and changes may be made to these embodiments
without departing from the broader spirit and scope of embodiments
of the present application. Such embodiments of the inventive
subject matter may be referred to herein, individually or
collectively, by the term "invention" merely for convenience and
without intending to voluntarily limit the scope of this
application to any single invention or inventive concept if more
than one is, in fact, disclosed.
[0080] The embodiments illustrated herein are described in
sufficient detail to enable those skilled in the art to practice
the teachings disclosed. Other embodiments may be used and derived
there from, such that structural and logical substitutions and
changes may be made without departing from the scope of this
disclosure. The Detailed Description, therefore, is not to be taken
in a limiting sense, and the scope of various embodiments is
defined only by the appended claims, along with the full range of
equivalents to which such claims are entitled.
[0081] Moreover, plural instances may be provided for resources,
operations, or structures described herein as a single instance.
Additionally, boundaries between various resources, operations,
modules, engines, and data stores are somewhat arbitrary, and
particular operations are illustrated in a context of specific
illustrative configurations. Other allocations of functionality are
envisioned and may fall within a scope of various embodiments of
the present application. In general, structures and functionality
presented as separate resources in the example configurations may
be implemented as a combined structure or resource. Similarly,
structures and functionality presented as a single resource may be
implemented as separate resources. These and other variations,
modifications, additions, and improvements fall within a scope of
embodiments of the present application as represented by the
appended claims. The specification and drawings are, accordingly,
to be regarded in an illustrative rather than a restrictive
sense.
* * * * *