U.S. patent application number 15/961120 was filed with the patent office on 2018-08-23 for automatically obtaining real-time, geographically-relevant product information from heterogeneus sources.
The applicant listed for this patent is eBay Inc.. Invention is credited to Jack Phillip Abraham, Aaron Adelson, Matthew Barto, Theodore James Dziuba, John Evans, Neville Newey, Justin Van Winkle.
Application Number | 20180239781 15/961120 |
Document ID | / |
Family ID | 46601382 |
Filed Date | 2018-08-23 |
United States Patent
Application |
20180239781 |
Kind Code |
A1 |
Abraham; Jack Phillip ; et
al. |
August 23, 2018 |
AUTOMATICALLY OBTAINING REAL-TIME, GEOGRAPHICALLY-RELEVANT PRODUCT
INFORMATION FROM HETEROGENEUS SOURCES
Abstract
Techniques for obtaining geographically-relevant product
inventory information, in real-time, from heterogeneous data
sources are described. Product inventory information, including the
volume of available products in specific geographical locations, is
obtained from at least three different sources. First, one or more
data feeds may be received. Second, a data obtaining module uses
one or more APIs to obtain product inventory information from one
or more third-party inventory management systems. Finally, a
structured data mining module uses a web crawler, at the direction
of a crawler configuration, to systematically obtain product
inventory information from various third-party websites.
Accordingly, a user's search query is processed to provide
geographically relevant product inventory information in near real
time.
Inventors: |
Abraham; Jack Phillip; (Palo
Alto, CA) ; Adelson; Aaron; (San Jose, CA) ;
Barto; Matthew; (San Francisco, CA) ; Dziuba;
Theodore James; (San Mateo, CA) ; Evans; John;
(Palo Alto, CA) ; Newey; Neville; (Pleasanton,
CA) ; Van Winkle; Justin; (San Jose, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
eBay Inc. |
San Jose |
CA |
US |
|
|
Family ID: |
46601382 |
Appl. No.: |
15/961120 |
Filed: |
April 24, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13366962 |
Feb 6, 2012 |
9977790 |
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15961120 |
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61439724 |
Feb 4, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/951 20190101;
G06F 16/29 20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A method comprising: providing a web-based crawler configuration
application to client device; receiving, via the web-based crawler
configuration application, a selection of an element of a web page
displayed at the client device; identifying, in a source document
of the web page, an element of product inventory information
corresponding to the selected element of the web page; and
generating a crawler configuration file based on the identified
element of product inventory information, the crawler configuration
file configuring an operation of a crawler application.
2. The method of claim 1, further comprising: executing a first set
of instructions representing an instance of the crawler
application, the instance of the crawler application configured to
perform a set of operations specified in the crawler configuration
file, the set of operations resulting in a retrieval of product
inventory information for one or more products hosted at one or
more web servers; executing a second set of instructions
representing an instance of a real-time product availability lookup
(RTPAL) application, the RTPAL application to use one or more
application programming interfaces (APIs) to request and receive
product inventory information from one or more third-party
network-connected inventory management systems; enhancing the
product inventory information from the crawler application with the
product inventory information from the RTPAL application; and
storing the enhanced product inventory information in the
database.
3. The method of claim 2, further comprising: subsequent to
receiving product inventory information as a result of executing
either of the crawler application and the RTPAL application,
determining that received product inventory information for a
particular product does not specify a unique product identifier;
performing a matching operation by comparing various elements of
information concerning the particular product with corresponding
information from one or more known products to determine a product
with which the received product inventory information for the
particular product not specifying the unique product identifier
best matches; and storing the received product inventory
information for the particular product not specifying the unique
product identifier in the database.
4. The method of claim 2, further comprising: storing product
inventory information for a particular product received via the
crawler application or the RTPAL application in a data cache with
each cache entry having a cache key based on a zipcode for a
location at which the particular product received via the crawler
application or the RTPAL application is available, a product
identifier, and a product variation identifier.
5. The method of claim 2, further comprising: assigning to each
product identified in the received product inventory information as
a result of executing either of the crawler application and the
RTPAL application one or more category identifiers for a particular
category in a hierarchical category.
6. The method of claim 1, further comprising: receiving a search
query from a user, the search query including information
identifying a desired geographical area; and processing the search
query to provide product inventory information for a particular
product satisfying the search query, the product inventory
information for the particular product satisfying the search query
specifying one or more merchant stores in a geographical location
satisfying the desired geographical area identified in the query,
and indicating a quantity of the particular product satisfying the
search query available at each of the one or more merchant
stores.
7. The method of claim 1, wherein the web-based crawler
configuration application enables the user to specify one of a
suite of web crawlers for use with a particular crawler
configuration file generated by the web crawler configuration
application.
8. The method of claim 1, wherein the crawler configuration
identifies one crawler from a plurality of crawlers to be used with
the crawler configuration.
9. The method of claim 1, wherein the web-based crawler
configuration application enables the user to invoke a code editing
application to specify customized code for obtaining a particular
element of product inventory information, the customized code for
inclusion in a crawler configuration file for use with a particular
crawler.
10. The method of claim 1, wherein the web-based crawler
configuration application defines a set of selectors, each selector
describing how to extract a single item of information for
insertion into the database.
11. A server comprising: one or more processors for executing one
or more sets of instructions stored in a memory, the one or more
set of instructions comprising: providing a web-based crawler
configuration application to client device; receiving, via the
web-based crawler configuration application, a selection of an
element of a web page displayed at the client device; identifying,
in a source document of the web page, an element of product
inventory information corresponding to the selected element of the
web page; and generating a crawler configuration file based on the
identified element of product inventory information, the crawler
configuration file configuring an operation of a crawler
application.
12. The server of claim 11, wherein the one or more sets of
instructions further comprise: executing a first set of
instructions representing an instance of the crawler application,
the instance of the crawler application configured to perform a set
of operations specified in the crawler configuration file, the set
of operations resulting in a retrieval of product inventory
information for one or more products hosted at one or more web
servers; executing a second set of instructions representing an
instance of a real-time product availability lookup (RTPAL)
application, the RTPAL application to use one or more application
programming interfaces (APIs) to request and receive product
inventory information from one or more third-party
network-connected inventory management systems; enhancing the
product inventory information from the crawler application with the
product inventory information from the RTPAL application; and
storing the enhanced product inventory information in the
database.
13. The server of claim 12, wherein the one or more sets of
instructions further comprise: subsequent to receiving product
inventory information as a result of executing either of the
crawler application and the RTPAL application, determining that
received product inventory information for a particular product
does not specify a unique product identifier; performing a matching
operation by comparing various elements of information concerning
the particular product with corresponding information from one or
more known products to determine a product with which the received
product inventory information for the particular product not
specifying the unique product identifier best matches; and storing
the received product inventory information for the particular
product not specifying the unique product identifier in the
database.
14. The server of claim 12, wherein the one or more sets of
instructions further comprise: storing product inventory
information for a particular product received via the crawler
application or the RTPAL application in a data cache with each
cache entry having a cache key based on a zipcode for a location at
which the particular product received via the crawler application
or the RTPAL application is available, a product identifier, and a
product variation identifier.
15. The server of claim 11, wherein the one or more sets of
instructions further comprise: receiving a search query from a
user, the search query including information identifying a desired
geographical area; and processing the search query to provide
product inventory information for a particular product satisfying
the search query, the product inventory information for the
particular product satisfying the search query specifying one or
more merchant stores in a geographical location satisfying the
desired geographical area identified in the query, and indicating a
quantity of the particular product satisfying the search query
available at each of the one or more merchant stores.
16. The server of claim 11, wherein the web-based crawler
configuration application enables the user to specify one of a
suite of web crawlers for use with a particular crawler
configuration file generated by the web crawler configuration
application.
17. The server of claim 11, wherein the crawler configuration
identifies one crawler from a plurality of crawlers to be used with
the crawler configuration.
18. The server of claim 11, wherein the web-based crawler
configuration application enables the user to invoke a code editing
application to specify customized code for obtaining a particular
element of product inventory information, the customized code for
inclusion in a crawler configuration file for use with a particular
crawler.
19. The server of claim 11, wherein the web-based crawler
configuration application defines a set of selectors, each selector
describing how to extract a single item of information for
insertion into the database.
20. A machine-readable storage medium storing instructions thereon,
which, when executed by a processor of a server, will cause the
server to perform a set of operations comprising: providing a
web-based crawler configuration application to client device;
receiving, via the web-based crawler configuration application, a
selection of an element of a web page displayed at the client
device; identifying, in a source document of the web page, an
element of product inventory information corresponding to the
selected element of the web page; and generating a crawler
configuration file based on the identified element of product
inventory information, the crawler configuration file configuring
an operation of a crawler application.
Description
RELATED APPLICATIONS
[0001] The present application is a continuation of U.S. patent
application Ser. No. 13/366,962, filed Feb. 6, 2012, which claims
the benefit of priority, under 35 U.S.C. .sctn. 119(e), to U.S.
Provisional Patent Application Ser. No. 61/439,724, entitled,
"Methods and Systems for Automatically Obtaining Real-Time,
Geographically-Relevant Product Information From Heterogeneous
Sources, and Enhancing and Presenting the Product Information",
filed on Feb. 4, 2011, which is by way of reference incorporated
herein in its entirety.
TECHNICAL FIELD
[0002] The present disclosure generally relates to data processing
techniques for obtaining from disparate and heterogeneous sources,
real-time, geographically-relevant information concerning products
and their availability.
BACKGROUND
[0003] The Internet and the World Wide Web have given rise to a
wide variety of on-line retailers that operate virtual stores from
which consumers can purchase products (i.e., merchandise, or goods)
as well as services. Although the popularity of these on-line
retail sites is clearly evidenced by their increasing sales, for a
variety of reasons, some consumers may still prefer to purchase
products and services in a more conventional manner--i.e., via a
brick-and-mortar store.
DESCRIPTION OF THE DRAWINGS
[0004] Some embodiments are illustrated by way of example and not
limitation in the figures of the accompanying drawings:
[0005] FIG. 1 is an example of a web page from which various
elements of information are retrieved by a crawler operating in
conjunction with a crawler configuration generated with a crawler
configuration application, according to some embodiments of the
invention;
[0006] FIG. 2 is a block diagram illustrating an example of a cache
key in the form of a three-tuple with a zip code, offer or product
code, and offer variant, consistent with some embodiments of the
invention;
[0007] FIG. 3 is a block diagram illustrating the data source and
data flows that occur for populating a database with product
inventory information, according to some embodiments of the
invention; and
[0008] FIG. 4 is a block diagram of a machine in the form of a
computing device 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
[0009] The present disclosure describes data processing techniques
for obtaining from disparate and heterogeneous sources, real-time,
geographically-relevant information concerning products and their
availability. In the following description, for purposes of
explanation, numerous specific details are set forth in order to
provide a thorough understanding of the various aspects of
different embodiments of the present invention. It will be evident,
however, to one skilled in the art, that the present invention may
be practiced without all of the specific details.
[0010] Embodiments of the present invention involve a set of
sophisticated and computer-implemented automated tools and
processes for obtaining current data about products and their
availability from a wide variety of data sources, such as web
sites, network-connected databases, inventory systems, and so
forth. In particular, the systems and methods described herein
facilitate obtaining and presenting in near real-time,
geographically-relevant data concerning products and their
availability, such that a potential consumer can perform a
web-based search to locate a product, with its current inventory
information, at a retail store in a particular geographical area.
For example, an automated process (e.g., a crawler) can be
configured to obtain product information from a variety of web
sites. Alternatively, an external database may be accessed via an
application programming interface (API). In any case, once the data
is obtained, this data is enhanced and stored in a local database.
The data can then be presented to potential consumers in response
to a consumer browsing or searching for relevant products and
specifying a particular location. As there are many stages involved
in the overall process of obtaining, enhancing and presenting this
product data, the following description of the inventive subject
matter is presented in sections, which loosely correlate with the
various stages.
Data Acquisition--Structured Data Mining
[0011] Consistent with some embodiments of the inventive subject
matter, data from a wide variety of sources is obtained via a
system and method of structured data mining. The system and related
automated processes that facilitate the structured data mining
consist primarily of two components. The first is a web-based
application (referred to herein as the crawler construction kit, or
CCK) used to configure one or more proprietary crawlers. A crawler
(sometimes referred to as a web crawler, or bot) is an automated
computer program process that operates to browse the Internet or
World Wide Web in a methodical manner, gathering or obtaining data
in an orderly fashion. The CCK is a web-based application that
allows its user to browse a retailer's web site and quickly
establish a crawler configuration--e.g., a set of automated
steps--that is required to obtain some item of information (e.g.,
the color, price, quantity available, etc.) about a particular
product being offered via a particular retailer. Accordingly, using
the CCK, a user can create a crawler configuration (e.g., a set of
interpretable, or executable, instructions), which is then used to
direct a crawler to perform a particular set of operations to
obtain a particular set or item of data, and thereby populate a
database with product inventory information obtained automatically
from various websites. This type of technique is generally referred
to as web scraping.
[0012] With some embodiments, the CCK provides a user with a
web-based set of tools for selecting and tagging various elements
of a web page that correspond with elements of product inventory
information that can be automatically extracted by an automated
crawler. For instance, with some embodiments, the CCK application
enables a user to manipulate a cursor with a pointing device to
interact with elements on a web page, for example, by clicking,
selecting, dragging, etc. When a particular item or element of
information displayed on the web pages has been selected, the
source document underlying the web page is analyzed to identify
information that might be used by a crawler to extract or obtain
the element of information. This information is then automatically
populated in a crawler configuration (e.g., a configuration file)
for a particular crawler that will later be used to periodically
obtain the set or item of information. In some cases, the CCK
application may prompt the user to select various options or
settings for use in obtaining a particular element of information.
Additionally, as discussed briefly below, the user may opt to open
a separate window, pane or similar user interface element in which
the user can directly edit a snippet of code for inclusion with the
crawler configuration for the specified crawler. For instance, in
certain scenarios, a user may be required to customize a crawler
configuration to direct a crawler to perform some specialized
operation(s) that are required to obtain a particular element of
information.
[0013] Once extracted, the data may be manipulated or enhanced and
then inserted into a database and used in the processing of users'
queries, and presentation in search results, etc. With some
embodiments, normalizing the information so that common
characteristics can be compared with a common nomenclature may
enhance the information. Additionally, with some embodiments,
specific products may be categorized and classified into a
proprietary hierarchy. Similarly, with some embodiments, products
may be assigned to proprietary product identifiers, where common,
publicly available SKU's (or other identifiers) are not used.
[0014] As illustrated in FIG. 1, an example user interface for a
merchant website is displayed. Using the CCK tool, a user can
select various elements of information presented in the web page,
and specify configuration information for use by a crawler in
obtaining the elements of information. For instance, any of the
following elements of information may be selected with the CCK tool
for purposes of customizing or configuring the crawler: the
descriptive name of the product 10, the users' ratings and reviews
12, the text within the details tab 14, the information presented
within the fit tab 16, the shipping information 18, the color
information 20, the size information 22, the picture 24, the
pricing information 26, and the item number 28. In addition, with
some embodiments, the crawler may identify a volume or amount of a
particular product that is available within a particular
geographical location.
[0015] The second component that is part of the structured data
mining system is a suite of crawlers that are configured to use a
crawler configuration created by the web-based CCK application. In
contrast to conventional web crawlers, crawlers consistent with
embodiments of the invention are configured to be driven by the
crawler configurations that are created by the CCK, which can be
quite complex. As a result, the crawlers can be configured to crawl
web sites and obtain data that many conventional automated crawlers
would have no way of accessing. As there may be many different
crawlers in the suite of crawlers, the crawler configuration may
specify the particular crawler for which the configuration is to be
used.
[0016] Consistent with some embodiments of the invention, the
web-based CCK application enables an approach to describing how to
select desired information from various sources (HTML, XML, JSON,
javascript, etc.). Fundamentally, the web-based CCK application
defines sets of what are referred to herein as selectors, where
each selector describes how to extract a single item of information
(e.g. product title, retail price, product image URL, description,
etc). Each selector is in essence a set of steps, or a pipeline,
that describes a series of operations that are to be performed in
order to request and then extract the desired information from a
web server, for insertion into a database.
[0017] To establish a pipeline, the following steps or stages are
followed. [0018] 1. Select elements from data source [0019] 2.
Apply filters to the selected elements [0020] 3. Apply filters to
the values of the selected elements [0021] 4. Apply custom
treatments to values.
[0022] Each of the first three stages have several built-in
mechanisms, but in most cases the user can, if necessary, fall back
to writing code (e.g., python code) directly in the user interface
of the web-based CCK application in order to define custom
behaviors. For instance, the web-based CCK application includes a
code editing module that enables a user to define a script or
section of executable code, which can be executed to perform a
customized operation that is not easily definable by the automated
tools of the web-based CCK application. This code can be
arbitrarily complex, so for example it can open new network
resources, download additional web pages or assets, use third party
libraries, and so on. Accordingly, the web-based CCK application
enables a user to very quickly automate a crawler to retrieve an
item of information from a web site, by generating a crawler
configuration, and if necessary, customizing the behavior of the
crawler to perform more complex operations. The custom treatments
in stage four (4) are all built-in optimizations for common cases
that are frequently encountered in this problem domain (e.g.
handling of currency in prices).
[0023] In addition to configuration, the web-based CCK application
also supports live testing of configurations as well as automated
validation of crawler configurations. Accordingly, a user
attempting to generate a crawler configuration to obtain a
particular item of information about a product can select to test
the crawler configuration in real-time, and observe how the
crawler, controlled by the crawler configuration, performs the
operations. This allows the user to tweak or modify the
configuration to obtain the required data item.
Real-Time Product Availability Lookup (RTPAL)
[0024] In addition to using a crawler to obtain information, with
some embodiments, a more formal or dedicated process might also be
used. Whereas a crawler can obtain information from websites when
the operator of the website does not provide a publically available
API, the RTPAL generally relies on the existence of formal,
publically accessible inventory systems to obtain product inventory
information. For instance, with some embodiments, a Real-Time
Product Availability Lookup (RTPAL) system is used to query
external inventory systems. The RTPAL system consists primarily of
three components. The first component is a framework for retrieval
and caching of information from individual merchant inventory
systems. The second component is a suite of components to make
building clients to individual inventory systems easy. The third
component is a set of individual clients (which are built using
these components to run inside the framework) for accessing
specific merchant inventory systems (i.e. individual big box
retailers like Target, Best Buy, etc., as well as aggregate sources
like Volusion or MerchantOS, and small merchant sources, such as
Quickbooks or Microsoft Dynamic).
[0025] The RTPAL system has a cache system that uses ZVOTs
(Zip-Code, Variation, Offer tuples) as cache keys. Specifically,
the cache key used in querying the cache includes three components,
a zip code relevant to the query, an offer identifier corresponding
with the specific offer or product, and a variation identifier
specifying or indicating the particular variant of the product or
offering. The offer identifier is essentially synonymous with a
product identifier, and uniquely identifies at a top level a
particular product or item that is being offered for sale. A
variation is a set of product specific characteristics. For
example, for clothing, the variation may specify such
characteristics as size and color, etc. With other products, other
variations are possible. For instance, with a tablet computer, a
variation may specify the amount of member (16 GB, 32 GB, 64 GB,
etc.) included with the computer. The zip code is used to specify
the zip code of relevance to the search. For instance, if a user is
looking for a product in a particular zip code, the specified zip
code can be used to query the cache and ensure only relevance cache
information is returned. In other embodiments, the system might
implement fuzzy geographic-based caching in order to drastically
increase the cache hit rate and to support significantly higher
traffic volumes.
[0026] As illustrated in FIG. 2, an example of a three-tuple (e.g.,
ZVOT) cache key for querying a cache is shown. For example, the
cache key with reference 30 scores a cache hit with the cache entry
having reference number 32 when used to query the cache entries on
the right with reference number 34. By including the zip code in
the cache key, those cache entries that are geographically relevant
to a particular user's product query can be returned and presented
with minimal processing delay.
[0027] In addition to using the RTPAL and the structured data
mining techniques described above, with some embodiments, product
inventory information is received from third party sources via a
simple data feed. Accordingly, FIG. 3 illustrates a block diagram
of the various data sources from which product inventory
information can be obtained. For instance, the data sources 40
include data feeds 42, application programming interfaces (APIs) 44
for accessing merchant-specific product inventory systems, and
structured data mining of third-party websites 46. As illustrated
in FIG. 3, the data obtaining module 48 facilitates the receiving
of the product inventory information from the data feeds 42 and the
RTPAL-based APIs 44, while the structured data mining module 50
facilitates the real-time receipt of information from third-party
websites. As described in greater detail below, once obtained, the
date is stored in a product inventory database 52 and enhanced, for
example, by the product offering matching module 54.
Automated Product Matching
[0028] With some embodiments, automated product matching is
performed by a product offering matching module 54 (FIG. 3). The
goal of product matching is to aggregate offers for the same
product to enhance the user experience. When data is collected, all
attempts are made to capture unique product identifiers such as:
UPC, EAN, ASIN, ISBN, SKU and Model Number. When one or more of the
above identifiers are available, a rule-based algorithm is evoked
to determine if the offer matches an existing product. If a match
is achieved, the product or offer is assigned to the matching
product. If none of the above identifiers are available, other
attributes of the offer, for example title, description, brand and
specifications are used to determine a similarity score with
respect to one or more existing products. If a match is found, the
matching product is assigned, and if not, a new product is
created.
Automated Product Categorization
[0029] With some embodiments of the invention, a product type
taxonomy is used. For instance, with some embodiments, the taxonomy
may consist of approximately three-thousand (3000) unique
categories and sub-categories, arranged as nodes of a tree-like
hierarchical structure. Approximately twenty-six hundred (2600) of
these unique categories may be leaf nodes. An example of a leaf
node would be: Vehicle GPS Units. The aim of categorization is to
ensure that every product offer has at least one category node
assigned to it.
[0030] With some embodiments, labelled offers are collected. These
labelled offers are used as training data in a machine learning
algorithm, which then classifies the remaining unlabeled offers.
The classification algorithm is a hybrid of variations on several
different classic algorithms: Naive Bayes, Rocchio, and kNN. With
some embodiments, precisions vary by category and are typically
upwards of 0.9. Overall precision may be upwards of 0.96. With some
embodiments, approximately 80% of active offers can be classified
with the automated categorization system.
[0031] The various operations of example methods described herein
may be performed, at least partially, by one or more processors
that are temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Whether temporarily
or permanently configured, such processors may constitute
processor-implemented modules or objects that operate to perform
one or more operations or functions. The modules and objects
referred to herein may, in some example embodiments, comprise
processor-implemented modules and/or objects.
[0032] Similarly, the methods described herein may be at least
partially processor-implemented. For example, at least some of the
operations of a method may be performed by one or more processors
or processor-implemented modules. The performance of certain
operations may be distributed among the one or more processors, not
only residing within a single machine or computer, but deployed
across a number of machines or computers. In some example
embodiments, the processor or processors may be located in a single
location (e.g., within a home environment, an office environment or
at a server farm), while in other embodiments the processors may be
distributed across a number of locations.
[0033] The one or more processors may also operate to support
performance of the relevant operations in a "cloud computing"
environment or within the context of "software as a service"
(SaaS). For example, at least some of the operations may be
performed by a group of computers (as examples of machines
including processors), these operations being accessible via a
network (e.g., the Internet) and via one or more appropriate
interfaces (e.g., Application Program Interfaces (APIs)).
[0034] FIG. 4 is a block diagram of a machine in the form of a
computer 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. In alternative 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 a client-server network environment, or as a peer machine in
peer-to-peer (or distributed) network environment. In a preferred
embodiment, the machine will be a server computer, however, in
alternative embodiments, the machine may be a personal computer
(PC), a tablet PC, a set-top box (STB), a Personal Digital
Assistant (PDA), a mobile telephone, a web appliance, a network
router, 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.
[0035] The example computer system 1500 includes a processor 1502
(e.g., a central processing unit (CPU), a graphics processing unit
(GPU) or both), a main memory 1501 and a static memory 1506, which
communicate with each other via a bus 1508. The computer system
1500 may further include a display unit 1510, an alphanumeric input
device 1517 (e.g., a keyboard), and a user interface (UI)
navigation device 1511 (e.g., a mouse). In one embodiment, the
display, input device and cursor control device are a touch screen
display. The computer system 1500 may additionally include a
storage device 1516 (e.g., drive unit), a signal generation device
1518 (e.g., a speaker), a network interface device 1520, and one or
more sensors 1521, such as a global positioning system sensor,
compass, accelerometer, or other sensor.
[0036] The drive unit 1516 includes a machine-readable medium 1522
on which is stored one or more sets of instructions and data
structures (e.g., software 1523) embodying or utilized by any one
or more of the methodologies or functions described herein. The
software 1523 may also reside, completely or at least partially,
within the main memory 1501 and/or within the processor 1502 during
execution thereof by the computer system 1500, the main memory 1501
and the processor 1502 also constituting machine-readable
media.
[0037] While the machine-readable medium 1522 is illustrated in an
example embodiment to be a single medium, the term
"machine-readable medium" may include a single medium or multiple
media (e.g., a centralized or distributed database, and/or
associated caches and servers) that store the one or more
instructions. The term "machine-readable 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 the present invention, or that is capable of
storing, encoding or carrying data structures utilized by or
associated with such instructions. The term "machine-readable
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 media include non-volatile memory,
including by way of example semiconductor memory devices, e.g.,
EPROM, 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.
[0038] The software 1523 may further be transmitted or received
over a communications network 1526 using a transmission medium via
the network interface device 1520 utilizing any one of a number of
well-known transfer protocols (e.g., HTTP). Examples of
communication networks include a local area network ("LAN"), a wide
area network ("WAN"), the Internet, mobile telephone networks,
Plain Old Telephone (POTS) networks, and wireless data networks
(e.g., Wi-Fi.RTM. and WiMax.RTM. 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.
[0039] Although an embodiment has been described with reference to
specific example embodiments, it will be evident that various
modifications and changes may be made to these embodiments without
departing from the broader spirit and scope of the invention.
Accordingly, the specification and drawings are to be regarded in
an illustrative rather than a restrictive sense. The accompanying
drawings that form a part hereof, show by way of illustration, and
not of limitation, specific embodiments in which the subject matter
may be practiced. The embodiments illustrated are described in
sufficient detail to enable those skilled in the art to practice
the teachings disclosed herein. Other embodiments may be utilized
and derived therefrom, such that structural and logical
substitutions and changes may be made without departing from the
scope of this disclosure. This 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.
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