U.S. patent number RE48,437 [Application Number 14/290,672] was granted by the patent office on 2021-02-16 for collecting and scoring online references.
This patent grant is currently assigned to BrightEdge Technologies, Inc.. The grantee listed for this patent is BrightEdge Technologies, Inc.. Invention is credited to Lemuel S. Park, Jimmy Yu.
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United States Patent |
RE48,437 |
Park , et al. |
February 16, 2021 |
Collecting and scoring online references
Abstract
One example embodiment includes a method for indexing online
references of an entity. The method includes identifying one or
more channels of the Internet to be searched for references to an
entity and identifying one or more signals to be evaluated within
each of the one or more channels. The method also includes crawling
the Internet for online references to the entity, wherein crawling
the Internet comprises searching the one or more channels of the
Internet for references to the entity and evaluating the one or
more signals. The method further includes constructing a reverse
index of the references, wherein the reverse index is based on each
channel in which a reference is found and the one or more signals
evaluated for the reference.
Inventors: |
Park; Lemuel S. (Foster City,
CA), Yu; Jimmy (Foster City, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
BrightEdge Technologies, Inc. |
Foster City |
CA |
US |
|
|
Assignee: |
BrightEdge Technologies, Inc.
(San Mateo, CA)
|
Family
ID: |
41401137 |
Appl.
No.: |
14/290,672 |
Filed: |
May 29, 2014 |
Related U.S. Patent Documents
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|
Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
|
61060033 |
Jun 9, 2008 |
|
|
|
Reissue of: |
12436704 |
May 6, 2009 |
8190594 |
May 29, 2012 |
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
16/951 (20190101); G06F 7/00 (20130101); G06F
16/951 (20190101); G06Q 30/02 (20130101) |
Current International
Class: |
G06F
16/951 (20190101); G06F 7/00 (20060101) |
Field of
Search: |
;707/711,736,741,E17.108,709,999.003 ;706/45 |
References Cited
[Referenced By]
U.S. Patent Documents
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2600910 |
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Mar 2008 |
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1 182 581 |
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Feb 2002 |
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EP |
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2199969 |
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Jun 2010 |
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EP |
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2008192157 |
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Aug 2008 |
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JP |
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WO 2004/003701 |
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Jan 2004 |
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WO |
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WO 2004/079551 |
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Sep 2004 |
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WO |
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WO 2005/052755 |
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Jun 2005 |
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WO |
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WO 2007/070199 |
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Jun 2007 |
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WO |
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WO 2007/103646 |
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Sep 2007 |
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WO |
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WO 2008/024997 |
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Feb 2008 |
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WO |
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|
Primary Examiner: Ke; Peng
Attorney, Agent or Firm: Baker Botts L.L.P.
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of and priority to U.S.
Provisional Patent Application Ser. No. 61/060,033, entitled
"COLLECTING AND SCORING ONLINE ADVERTISEMENTS, ONLINE MARKETING
CHANNELS, AND ORGANIC SEARCH," filed on Jun. 9, 2008, which
application is incorporated herein by reference in its entirety.
Claims
What is claimed is:
1. A method of indexing online references of an entity, the method
comprising: identifying an entity, wherein the entity is an
individual, corporation, brand, or product; crawling the Internet
for online references to the entity .[.after identifying the
entity.]., wherein crawling the Internet comprises: .Iadd.by a
computing device, determining a keyword to search; by the computing
device, .Iaddend.querying a .[.first.]. .Iadd.plurality of
.Iaddend.search .[.engine.]. .Iadd.engines across a plurality of
online channels .Iaddend.for .[.a first.]. search .Iadd.engine
.Iaddend.results .[.page.]. .Iadd.pages .Iaddend.using .[.a.].
.Iadd.the .Iaddend.keyword.Iadd., wherein the search engine results
pages comprise one or more search results identified by the
respective search engine as responsive to the keyword.Iaddend.;
.Iadd.by the computing device, .Iaddend.parsing the .[.first.].
search .[.result.]. .Iadd.engine results .Iaddend.pages .[.into.].
.Iadd.to determine .Iaddend.organic search results .Iadd.and paid
search results, wherein the organic search results appear in the
search engine results pages based on relevance of webpages
corresponding to the organic search results to the
keyword.Iaddend.; .Iadd.by the computing device,
.Iaddend.identifying .[.an.]. organic online .[.reference to the
entity based on.]. .Iadd.references to entities in .Iaddend.the
organic search results .Iadd.from the search engine results pages
and paid online references to entities in the paid search results
based on the paid search results from the search engine results
pages.Iaddend.; .Iadd.by the computing device, .Iaddend.parsing the
organic search results to identify one or more .[.organic.].
.Iadd.first .Iaddend.signals .Iadd.to be evaluated .Iaddend.that
include information about the organic online .[.reference to the
entity.]. .Iadd.references and parsing the paid search results to
identify one or more second signals to be evaluated that include
information about the paid online references.Iaddend.; searching
social networks for social network search results that refer to the
entity; identifying a social online reference to the entity based
on the social network search results; parsing the social network
search results to identify one or more .[.social.]. .Iadd.third
.Iaddend.signals .Iadd.to be evaluated .Iaddend.that include
information about the social online reference to the entity;
.Iadd.by the computing device, .Iaddend.evaluating the .[.social.].
.Iadd.third .Iaddend.signals .[.and organic.]..Iadd.,the first
.Iaddend.signals.Iadd., and the second signals.Iaddend.; .[.and.].
.Iadd.by the computing device, .Iaddend.constructing.[., using a
processor,.]. a reverse index of .[.the.]. online references based
on the evaluated .[.social.]. .Iadd.third .Iaddend.signals
.[.and.]..Iadd., .Iaddend.the evaluated .[.organic.]. .Iadd.first
.Iaddend.signals.Iadd., and the evaluated second signals; by the
computing device, in response to receiving a query related to the
entity, computing, using the reverse index, statistics regarding
organic online references to the identified entity, wherein
computing the statistics comprises attributing an organic online
reference to the identified entity identified via a first search
engine of the plurality of search engines with a weight greater
than an organic online reference to the entity identified via a
second search engine; and by the computing device, presenting the
computed statistics regarding organic online references to the
identified entity.Iaddend..
2. The method of claim 1, .[.further comprising.]. .Iadd.wherein
computing statistics regarding organic online references to the
identified entity further comprises .Iaddend.computing a search
engine optimization score for the identified .[.organic online
reference to the.]. entity based on .Iadd.the organic online
references in .Iaddend.the reverse index.
3. The method of claim 1, wherein the reverse index lists the
social online reference and the organic online reference.
4. The method of claim 1, wherein the reverse index lists the
organic online .[.reference with respect to the keyword.].
.Iadd.references by entity identifier.Iaddend..
5. The method of claim .[.1.]. .Iadd.3.Iaddend., wherein the
.[.social.]. .Iadd.third .Iaddend.signals comprise a rank, a
uniform resource locator (URL), a title, or a description of the
social online .[.reference.]. .Iadd.references.Iaddend..
6. The method of claim 1, wherein the .[.social.]. .Iadd.third
.Iaddend.signals are evaluated to identify .[.the.]. relevance of
the social online .[.reference to the entity.]. .Iadd.references to
entities in the social network search results.Iaddend..
7. The method of claim 1, wherein the .[.organic.]. .Iadd.first
.Iaddend.signals comprise a rank, a uniform resource locator (URL),
a title, or a description of the organic online .[.reference.].
.Iadd.references.Iaddend..
8. The method of claim 1, wherein the .[.organics.]. .Iadd.first
.Iaddend.signals are evaluated to identify .[.the.]. relevance of
the .[.organics.]. .Iadd.organic .Iaddend.online .[.reference to
the entity.]. .Iadd.references to entities in the organic search
results.Iaddend..
9. The method of claim .[.1.]. .Iadd.3.Iaddend., wherein the social
networks comprise a social structure of nodes that are tied by one
or more specific types of interdependency.
10. The method of claim 1, .[.wherein the organic search results
are search results in a search engine results page that appear
based on their relevance to a search term used to generate the
search results.]. .Iadd.further comprising identifying backlinks to
the organic online references based on the search engine results
pages.Iaddend..
.Iadd.11. The method of claim 1, further comprising: determining,
using the reverse index, paid online references to the identified
entity..Iaddend.
.Iadd.12. The method of claim 1, further comprising determining a
popularity of the organic online references based on the search
engine results pages..Iaddend.
.Iadd.13. The method of claim 1, wherein the first signals are
evaluated to assess relevance of the online channels to the
identified entity..Iaddend.
.Iadd.14. The method of claim 1, wherein parsing the search engine
results pages to determine organic search results and paid search
results further comprises parsing the search results pages to
identify which search results are organic results and which search
results are paid results..Iaddend.
.Iadd.15. A method comprising: receiving information identifying an
entity, wherein the entity is an individual, corporation, brand, or
product; determining a plurality of keywords based on the received
information; retrieving search engine results pages from a
plurality of search engines across a plurality of online channels
using the plurality of keywords, wherein the search engine results
pages comprise one or more search results identified by the
respective search engine as responsive to the plurality of
keywords; parsing the search engine results pages to determine
organic search results identified by the search engine, wherein the
organic search results appear in the search engine results pages
based on relevance of web pages corresponding to the organic search
results to the plurality of keywords; identifying organic online
references to entities in the organic search results from the
search engine results pages; parsing the organic search results to
identify one or more first signals to be evaluated that include
information about the organic online references; constructing,
using a processor, a reverse index of online references on the web
pages corresponding to the organic search results based on
evaluating the identified organic online references and the first
signals; computing, using the reverse index, statistics regarding
organic online references to the identified entity, wherein
computing the statistics comprises attributing an organic online
reference to the identified entity identified via a first search
engine of the plurality of search engines with a weight greater
than an organic online reference to the entity identified via a
second search engine; and presenting the computed statistics
regarding organic online references to the identified
entity..Iaddend.
.Iadd.16. The method of claim 15, wherein computing statistics
regarding organic online references to the identified entity
further comprises: computing a score for the identified entity
based on the organic online references in the reverse
index..Iaddend.
.Iadd.17. The method of claim 15, wherein the reverse index lists
the online references by entity identifier..Iaddend.
.Iadd.18. The method of claim 15, wherein the first signals
comprise a rank, a uniform resource locator (URL), a title, or a
description of the organic online references..Iaddend.
.Iadd.19. The method of claim 15, wherein the first signals are
evaluated to identify relevance of the organic online references to
entities in the organic search results..Iaddend.
.Iadd.20. The method of claim 15, further comprising identifying
backlinks to the organic online references based on the search
engine results pages..Iaddend.
.Iadd.21. The method of claim 15, further comprising determining a
popularity of the organic online references based on the search
engine results pages..Iaddend.
.Iadd.22. The method of claim 15, wherein the first signals are
evaluated to assess relevance of the online channels to the
identified entity..Iaddend.
.Iadd.23. The method of claim 15, wherein the parsing the search
engine results pages to determine organic search results further
comprises parsing the search results pages to identify which search
results are organic results..Iaddend.
.Iadd.24. The method of claim 15, further comprising: parsing the
search engine results pages to determine paid search results;
identifying paid online references to entities in the paid search
results; parsing the paid search results to identify one or more
second signals to be evaluated that include information about the
paid online references; evaluating the second signals; and
computing, using the reverse index, statistics regarding paid
online references to the identified entity, wherein the reverse
index of online references is further constructed based on the
identified paid online references and the evaluated second
signals..Iaddend.
.Iadd.25. The method of claim 24, wherein the parsing the search
engine results pages to determine paid search results further
comprises parsing the search engine results pages to identify which
search results are paid results..Iaddend.
.Iadd.26. The method of claim 24, wherein computing statistics
regarding paid online references to the identified entity further
comprises: computing a score for the identified entity based on the
paid online references in the reverse index..Iaddend.
.Iadd.27. The method of claim 15, further comprising: searching
social networks for social network search results; identifying
social online references to entities in the social network search
results based on the social network search results; parsing the
social network search results to identify one or more third signals
to be evaluated that include information about the social online
references to entities; evaluating the third signals; and
computing, using the reverse index, statistics regarding social
online references to the identified entity, wherein the reverse
index of online references is further constructed based on the
identified social online references and the evaluated third
signals..Iaddend.
.Iadd.28. The method of claim 27, wherein the third signals are
evaluated to identify relevance of the social online references to
entities in the social network search results..Iaddend.
.Iadd.29. The method of claim 27, wherein the third signals
comprise a rank, a uniform resource locator (URL), a title, or a
description of the social online references..Iaddend.
.Iadd.30. The method of claim 27, wherein the social networks
comprise a social structure of nodes that are tied by one or more
specific types of interdependency..Iaddend.
.Iadd.31. A system comprising: one or more processors; and a memory
coupled to the processors comprising instructions executable by the
processors, the processors being operable when executing the
instructions to: receive information identifying an entity, wherein
the entity is an individual, corporation, brand, or product;
determine a plurality of keywords based on the received
information; retrieve search engine results pages from a plurality
of search engines across a plurality of online channels using the
plurality of keywords, wherein the search engine results pages
comprise one or more search results identified by the respective
search engine as responsive to the plurality of keywords; parse the
search engine results pages to determine organic search results
identified by the search engine, wherein the organic search results
appear in the search engine results pages based on relevance of web
pages corresponding to the organic search results to the plurality
of keywords; identify organic online references to entities in the
organic search results from the search engine results pages; parse
the organic search results to identify one or more first signals to
be evaluated that include information about the organic online
references; construct, using a processor, a reverse index of
organic online references on the web pages corresponding to the
organic search results based on evaluating the identified organic
online references and the first signals; compute, using the reverse
index, statistics regarding organic online references to the
identified entity, wherein computing the statistics comprises
attributing an organic online reference to the identified entity
identified via a first search engine of the plurality of search
engines with a weight greater than an organic online reference to
the entity identified via a second search engine; and present the
computed statistics regarding organic online references to the
identified entity..Iaddend.
.Iadd.32. The system of claim 31, the processors being further
operable when executing the instructions to compute statistics
regarding organic online references to the identified entity to:
compute a score for the identified entity based on the organic
online references in the reverse index..Iaddend.
.Iadd.33. The system of claim 31, wherein the reverse index lists
the online references by entity identifier..Iaddend.
.Iadd.34. The system of claim 31, wherein the first signals
comprise a rank, a uniform resource locator (URL), a title, or a
description of the organic online references..Iaddend.
.Iadd.35. The system of claim 31, wherein the first signals are
evaluated to identify relevance of the organic online references to
entities in the organic search results..Iaddend.
.Iadd.36. The system of claim 31, wherein the processors are
further operable when executing the instructions to identify
backlinks to the organic online references based on the search
engine results pages..Iaddend.
.Iadd.37. The system of claim 31, wherein the processors are
further operable when executing the instructions to determine a
popularity of the organic online references based on the search
engine results pages..Iaddend.
.Iadd.38. The system of claim 31, wherein the first signals are
evaluated to assess relevance of the online channels to the
identified entity..Iaddend.
.Iadd.39. The system of claim 31, wherein the processors being
operable when executing the instructions to parse the search engine
results pages to determine organic search results further comprise
the processors being operable when executing the instructions to
parse the search results pages to identify which search results are
organic results..Iaddend.
.Iadd.40. The system of claim 31, the processors being further
operable when executing the instructions to: parse the search
engine results pages to determine paid search results; identify
paid online references to entities in the paid search results;
parse the paid search results to identify one or more second
signals to be evaluated that include information about the paid
online references; evaluate the second signals; and compute, using
the reverse index, statistics regarding paid online references to
the identified entity, wherein the reverse index of online
references is further constructed based on the identified paid
online references and the evaluated second signals..Iaddend.
.Iadd.41. The system of claim 40, wherein the processors being
operable when executing the instructions to parse the search engine
results pages to determine paid search results further comprise the
processors being operable when executing the instructions to parse
the search engine results pages to identify which search results
are paid results..Iaddend.
.Iadd.42. The system of claim 40, the processors being further
operable when executing the instructions to compute statistics
regarding paid online references to the identified entity to:
compute a score for the identified entity based on the paid online
references in the reverse index..Iaddend.
.Iadd.43. The system of claim 31, the processors being further
operable when executing the instructions to: search social networks
for social network search results; identify social online
references to entities in the social network search results based
on the social network search results; parse the social network
search results to identify one or more third signals to be
evaluated that include information about the social online
references to entities; evaluate the third signals; and compute,
using the reverse index, statistics regarding social online
references to the identified entity, wherein the reverse index of
online references is further constructed based on the identified
social online references and the evaluated third
signals..Iaddend.
.Iadd.44. The system of claim 43, wherein the third signals are
evaluated to identify relevance of the social online references to
entities in the social network search results..Iaddend.
.Iadd.45. The system of claim 43, wherein the third signals
comprise a rank, a uniform resource locator (URL), a title, or a
description of the social online references..Iaddend.
.Iadd.46. The system of claim 43, wherein the social networks
comprise a social structure of nodes that are tied by one or more
specific types of interdependency..Iaddend.
.Iadd.47. The method of claim 1, wherein the querying, parsing the
search engine results pages, identifying, parsing the organic
search results, evaluating, constructing, and computing steps are
each performed by a respective worker node of the computing device,
wherein each respective worker node is assigned one or more of the
querying, parsing the search engine results pages, identifying,
parsing the organic search results, evaluating, constructing, and
computing steps via a job queue maintained by a deep index engine
of the computing device..Iaddend.
.Iadd.48. The method of claim 15, wherein the receiving,
retrieving, parsing the search engine results pages, identifying,
parsing the organic search results, constructing, and computing
steps are each performed by a respective worker node of a deep
index engine, wherein each respective worker node is assigned one
or more of the receiving, retrieving, parsing the search engine
results pages, identifying, parsing the organic search results,
constructing, and computing steps via a job queue maintained by the
deep index engine..Iaddend.
.Iadd.49. The system of claim 31, wherein the receiving,
retrieving, parsing the search engine results pages, identifying,
parsing the organic search results, constructing, and computing
steps are each performed by a respective worker node of the system,
wherein each respective worker node is assigned one or more of the
receiving, retrieving, parsing the search engine results pages,
identifying, parsing the organic search results, constructing, and
computing steps via a job queue maintained by a deep index engine
of the system..Iaddend.
Description
BACKGROUND OF THE INVENTION
Search engine optimization, in general, is a process webmasters
apply to improve the volume and quality of traffic to a given Web
Page or other Internet site. Typical techniques include keywords in
title tags, keywords in meta tags, keywords in body text, anchor
text in inbound links, age of site, site structure, link popularity
in a site's internal link structure, amount of indexable text/page
content, number of links to a site, popularity/relevance of links
to site and topical relevance of inbound link tags. Additional
techniques are sometimes employed based on the search engine for
which the webmaster is attempting to optimize. Since search engine
algorithms and metrics are proprietary, search engine optimization
techniques are widely used to improve visibility of a Web Page or
other Internet site on search engine result pages.
Search engine marketing is a form of Internet marketing that
includes search engine optimization (SEO), paid inclusion, and paid
placement. Paid inclusion and paid placement are forms of paid
Internet advertising that place advertisements on the result page
of a particular keyword search. Paid inclusion and paid placement
vary in price based on factors such as keyword or search term.
Online advertising is a form of advertising that leverages the
Internet or World Wide Web to convey a message. Online
advertisements include text advertisements, banner advertisements,
skyscraper advertisements, floating advertisements, expanding
advertisements, polite advertisements, wallpaper advertisements,
trick banner advertisements, pop-up advertisements, pop-under
advertisements, video advertisements, map advertisements, mobile
advertisements and many other forms of online advertisement.
The subject matter claimed herein is not limited to embodiments
that solve any disadvantages or that operate only in environments
such as those described above. Rather, this background is only
provided to illustrate one exemplary technology area where some
embodiments described herein may be practiced.
BRIEF SUMMARY OF SOME EXAMPLE EMBODIMENTS
This Summary is provided to introduce a selection of concepts in a
simplified form that are further described below in the Detailed
Description. This Summary is not intended to identify key features
or essential characteristics of the claimed subject matter, nor is
it intended to be used as an aid in determining the scope of the
claimed subject matter.
In general, example embodiments of the invention relate to
collecting and scoring online references of an entity. One example
embodiment includes a method for indexing online references of an
entity. The method includes identifying one or more channels of the
Internet to be searched for references to an entity and identifying
one or more signals to be evaluated within each of the one or more
channels. The method also includes crawling the Internet for online
references to the entity, wherein crawling the Internet comprises
searching the one or more channels of the Internet for references
to the entity and evaluating the one or more signals. The method
further includes constructing a reverse index of the references,
wherein the reverse index is based on each channel in which a
reference is found and the one or more signals evaluated for the
reference.
Another example embodiment includes a system for indexing online
references of an entity. The system includes a deep index engine,
wherein the deep index engine is configured to assemble parameters
for crawling the Internet and to insert crawls to be performed into
a job queue. The system also includes one or more worker nodes,
wherein the worker nodes are configured to perform the Internet
crawls assembled by the deep index engine. The system further
includes one or more coordinators, wherein the coordinators are
configured to launch jobs for the one or more worker nodes from the
job queue.
These and other aspects of example embodiments of the invention
will become more fully apparent from the following description and
appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
To further clarify various aspects of some embodiments of the
present invention, a more particular description of the invention
will be rendered by reference to specific embodiments thereof that
are illustrated in the appended drawings. It is appreciated that
these drawings depict only typical embodiments of the invention and
are therefore not to be considered limiting of its scope. The
invention will be described and explained with additional
specificity and detail through the use of the accompanying drawings
in which:
FIG. 1 illustrates an embodiment of an example system for indexing
online references of an entity;
FIGS. 2A-2C illustrate various configurations of worker nodes that
can be employed in the system of FIG. 1;
FIG. 3 illustrates aspects of a method of using a page search to
find appropriate Web Pages to be searched for online
references;
FIG. 4 illustrates aspects of a method of parsing a search engine
result page;
FIG. 5 illustrates an example of a method for presenting results
within a reverse index;
FIGS. 6A-6C illustrate various examples of presenting results to a
user; and
FIG. 7 illustrates a flow chart of an example method for indexing
online references of an entity.
DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
Reference will now be made to the figures wherein like structures
will be provided with like reference designations. It is understood
that the figures are diagrammatic and schematic representations of
some embodiments of the invention, and are not limiting of the
present invention, nor are they necessarily drawn to scale.
Reference is first made to FIG. 1, which illustrates an embodiment
of a system 105 for indexing online references of an entity. The
entities whose online references are indexed can include
individuals, corporations, brands, products, models or any other
entities referenced anywhere on the Internet. References can
include organic references, online advertisements, news items or
any other reference to the entity. In particular, the system 105
can be used to identify online references of an entity where both
the type of online reference and the entity are identified based on
a customer's request. For example, in response to receiving a
request from a customer to index online advertisements of a
competitor, the system 105 can be used to perform the index.
The system 105 includes a deep index engine 110. The deep index
engine 110 is configured to assemble the parameters for crawling a
network 112 into a search job. The network 112 exemplarily includes
the Internet, comprising a global internetwork formed by logical
and physical connections between multiple wide area networks and/or
local area networks and can optionally include the World Wide Web
("Web"), comprising a system of interlinked hypertext documents
accessed via the Internet. Alternately or additionally, the network
112 includes one or more cellular RF networks and/or one or more
wired and/or wireless networks such as, but not limited to, 802.xx
networks, Bluetooth access points, wireless access points, IP-based
networks, or the like. The network 112 also includes servers that
enable one type of network to interface with another type of
network.
The parameters assembled by the deep index engine 110 can include
one or more channels. These channels are the particular media
within the Internet/network 112 that is to be searched. In some
embodiments, channels can include organic searches, page searches,
linked advertisement networks, banner advertisements, contextual
advertisements, e-mail, blogs, social networks, social news,
affiliate marketing, mobile advertisements, media advertisements,
video advertisements, discussion forums, news sites, rich media,
social bookmarks, paid searches and in-game advertisements.
Nevertheless, the channels are not limited to those mentioned but
can include any relevant areas of the Internet to be searched
whether now existing or created in the future.
Organic searches refer to those listings in search engine result
pages that appear by virtue of their relevance to the search terms,
as opposed to their being advertisements. Page searches refer to
the listings in search engine result pages regardless of the reason
for appearing. Linked advertisement networks are advertisements
that are automatically inserted into Web Pages if they contain
relevant subject matter. Banner advertisements are advertisements
that are placed on a particular Web Page in a particular location.
Contextual advertisements are advertisements that are placed when
certain key words or other identifiers are present, e.g., keyword
advertisements. E-mail--electronic mail or email--is any method of
creating, transmitting, or storing primarily text-based human
communications with digital communications systems.
Blogs--weblogs--are a type of Web Page, usually maintained by an
individual with regular entries of commentary, descriptions of
events, or other material such as graphics or video. Social
networks area social structure made of nodes (which are generally
individuals or organizations) that are tied by one or more specific
types of interdependency, such as values, visions, ideas, financial
exchange, friendship, kinship, dislike, conflict or trade. Social
news refers to Web Pages where users submit and vote on news
stories or other links, thus determining which links are presented.
Affiliate marketing includes using a Web Page to drive traffic to a
different Web Page maintained by an affiliate of the first Web
Page's owner. Mobile advertisements include personalized
advertisements presented on wireless devices. Media advertisements
include advertisements placed within a type of media, or means of
communication, whether online, in print, in video or any other
format. Video advertisements are advertisements presented in video
format. Discussion forums--or message boards--are online discussion
sites featuring user-generated content. News sites are Web Pages
with the primary purpose of reporting news, including both general
news and subject-specific news. Rich media--or interactive
media--is media that allows for active participation by the
recipient. Social bookmarks relate to a method for network users to
store, organize, search, and manage bookmarks of Web Pages on the
network and save the bookmarks privately, share the bookmarks with
the general public, share the bookmarks with specified people or
groups, share the bookmarks within certain networks or share the
network with any other combination of private and public access.
Paid searches are a type of contextual advertising where Web site
owners pay an advertising fee, usually based on click-throughs or
ad views, to have their Web Site search results shown in top
placement on search engine result pages. In-game advertisements are
advertisements placed within a video game either online or on a
game console.
Returning to FIG. 1, the parameters assembled by the deep index
engine 110 can also include one or more signals to be evaluated.
The signals include information about the references to the entity.
For example, advertisements placed at the top of a Web Page are
much more visible, and therefore, are generally more expensive and
are considered more effective than advertisements placed at the
bottom of a Web Page. Therefore, if the references to be indexed
include online advertisements, advertisement placement is a signal
that can be identified for the indexing. Alternately or
additionally, signals to be evaluated can include one or more of:
frequency of the reference on a given Web Page, location of the
reference on the Web Page, calendar date of the crawl, calendar
date of Web Page posting, time of day of the crawl, time of day of
Web Page posting, context-driven Web indexing, time to download the
Web Page, Web browser compatibility of the Web Page, Web plug-in
compatibility of the Web Page, or the like. Additionally or
alternatively, signals within an e-mail message to be evaluated can
include frequency of the e-mail message received, outbound links on
the e-mail message, calendar date of the e-mail message received,
time of day of the e-mail message received, or the like. The
context-driven Web indexing signal can further include links within
the Web Page and/or current events surrounding the posting and the
topic of the Web Page. Nevertheless, the signals to be evaluated
are not limited to those mentioned but can include any relevant
information about the references to the entity, whether now
existing or created in the future. It should also be noted that
"Web Page" as used herein refers to any online posting, including
domains, subdomains, Web posts, Uniform Resource Identifiers
("URIs"), Uniform Resource Locators ("URLs") and non-permanent
postings such as e-mail and chat unless otherwise specified.
With continued reference to FIG. 1, the deep index engine 110
creates, defines and/or identifies jobs and inserts the jobs,
including, for search jobs, the assembled parameters of each search
job, into a job queue 115. Insertion into the job queue 115 may be
through direct insertion or by sending the job through a
coordinator 120 or through any other method by which the job is
sent from the deep index engine 110 to the job queue 115. In some
embodiments, the job queue 115 maintains the jobs that are to be
performed and provides the jobs to worker nodes 125 for
execution.
Jobs in the job queue 115 include, but are not limited to, search
jobs, e.g., crawling the Internet. In some embodiments, once the
Internet has been crawled data is obtained. In general, data refers
to any information that the deep index engine has specified as
relevant. In some embodiments, data can include information
regarding the channels searched and the signals evaluated. In other
embodiments, data can include downloading the Web Page for further
processing, as discussed below. In further embodiments, data can
include search results to be parsed, as discussed below.
In some embodiments, once data has been obtained, it must be
processed. The deep index engine 110 can insert such processing
jobs into the job queue 115. In some embodiments, processing the
data can include evaluating the signals. In other embodiments,
processing the data can include parsing search results, as
discussed below. In further embodiments, processing can include
evaluating the reference for positive or negative connotations. For
example, a blog entry about a product can be processed to determine
whether the entry is generally positive or negative in regards to
the product.
In other embodiments, once the data has been obtained, the data may
need to be compressed, which is another job that can be inserted by
the deep index engine 110 into the job queue 115. In some
embodiments, compressing the data can include saving the data for
later processing. In other embodiments, compressing the data can
include parsing the Web Page for the relevant signals and saving
only the portion of the Web Page which relates to the relevant
signals. It will be appreciated, with the benefit of the present
disclosure, that the deep index engine 110 may insert any job that
needs to be performed, including collecting and/or processing data,
into the job queue 115.
In some embodiments, the system 105 includes worker nodes 125.
Worker nodes 125 comprise nodes that perform the jobs that have
been inserted by the deep index engine 110 into the job queue 115.
In some embodiments, jobs performed by the worker nodes 125
include: crawling the Web and performing the relevant search,
compressing the data, processing the data, constructing a reverse
index, calculating a search engine optimization score or any other
job that has been inserted into the job queue 115. In some
embodiments, every worker node 125 can be a general worker node
that is configured to perform any job inserted into the job queue
115. In other embodiments, worker nodes 125 can be specialized
worker nodes that are each configured to perform a single job. In
further embodiments, worker nodes 125 can be any combination of
general worker nodes and specialized worker nodes.
In some embodiments, the worker nodes 125 are further configured to
simulate the activities of a human user of the Internet. In some
embodiments, simulating the activities of a human user of the
internet includes mimicking and/or providing one or more attributes
typically associated with a human user, including one or more of: a
geographic location, a particular time of browsing, an age, an
income level, an e-mail address, or other demographics of human
users. For example, the worker nodes 125 can be configured to
connect to the Internet through multiple Internet service providers
to simulate human users of the Internet in different geographic
locations. Alternately or additionally, the worker nodes 125 can be
configured to connect to the Internet at a particular time of day.
Alternately or additionally, the worker nodes 125 can be configured
to input, in certain websites, an age, income level, or the like,
corresponding to a particular demographic of human users.
Alternately or additionally, the worker nodes 125 can be configured
to input, in certain websites, an e-mail address. In some
embodiments of the invention, simulating the activities of a human
user of the Internet allows for more relevant search results as the
search references concern how such references would be presented to
a user of the Internet.
FIG. 2A illustrates one example configuration of a worker node 205
crawling the Internet in the performance of a search job, for
instance. The worker node 205 connects directly to a Web Page 210.
The worker node 205 may then perform a search for online references
to an entity within the Web Page 210 and/or within additional Web
Pages by looking through the appropriate channels. In some
embodiments, the worker node 205 may also locate and/or evaluate
the appropriate signals to obtain signal information, as directed
within the search job by the deep index engine 110 of FIG. 1,
outlined above, for later evaluation. In this and other
embodiments, the worker node 205 may evaluate the appropriate
signals while connected to the Web Page 210.
FIG. 2B illustrates another example of a worker node 220 crawling
the Internet in the performance of a search job. In this example,
the worker node 220 uses a proxy 225 to connect to a Web Page 230.
In computer networks, a proxy is a server (e.g., a computer system
or an application program), that acts as a go-between for requests
from clients seeking resources from other servers. A client
connects to the proxy, requesting some service, such as a file,
connection, Web Page, or other resource, available from a different
server. In some embodiments, a worker node 220 connecting to a Web
Page 230 through a proxy 225 may allow the worker node 220 to
appear to be from a different geographic origin than it is actually
from.
FIG. 2C illustrates yet another example of a worker node 240
crawling the Internet in the performance of a search job. In this
example, the worker node 240 connects to the Internet through a
proxy 245 and locates a Web Page 250 through a backlink 255.
Backlinks 255 are incoming links to a Web site or Web Page 250. The
backlinks 255 of a Web Page 250 may be of significant personal,
cultural or semantic interest because they can indicate who is
paying attention to that Web Page 250. A backlink 255 can include
any link to a Web Page 250 from another Web Page. Backlinks 255 are
also known as incoming links, inbound links, inlinks, and inward
links. Backlinks 255 are sometimes used as a measure of the
popularity of Web Page 250 and several search engines provide
backlink 255 information of Web Pages 250. In some embodiments, the
backlinks 255 are used to help find online references of an entity
or otherwise evaluate the references.
Returning to FIG. 1, in some embodiments, the system 105 also
includes one or more coordinators 120. The coordinators 120 can
receive jobs from the deep index engine 110 and insert them into
the job queue 115 as described above. The coordinators 120 can also
remove jobs from the job queue 115 and provide them to the worker
nodes 125 as needed for completion of the jobs. The coordinators
120 can also track which jobs are being performed by which worker
nodes 125 to optimize the performance of the worker nodes 125
(e.g., to make sure that specialized nodes have the work available
after finishing their current task).
The modules, or individual components, of the system 105, including
the deep index engine 110, the job queue 115, the worker nodes 125
and the coordinators 120, can be implemented in hardware, software
or any combination thereof. If implemented in software, the modules
of the system 105 are stored in a computer-readable medium, to be
accessed as needed to perform their functions. Additionally, if
implemented in software, the tasks assigned to each module can be
carried out by a processor, field-programmable gate array (FPGA) or
any other logic device capable of carrying out software
instructions or other logic functions.
FIG. 3 illustrates aspects of a method of using a page search to
find appropriate Web Pages to be searched for online references.
The method of FIG. 3 can be performed by a worker node 305 in
conjunction with a keyword database 310. The keyword database 310
contains one or more keywords to be used in the page search. In
some embodiments, the deep index engine 110 of FIG. 1 can compile
the keyword database 310. In other embodiments, the worker nodes
125 of FIG. 1 can, prior to the page search, compile the keyword
database 310.
Returning to FIG. 3, the worker node 305 obtains 315 a keyword from
the keyword database 310. The worker node 305 then queries 320 a
search engine for a page search. The search engine can be any
public or private search engine used to perform searches, whether
now existing or created in the future. Multiple search engines can
be used for each keyword to compile results or individual search
engines can be used as preferred for each entity.
Once the search is performed, the worker node 305 collects 325 the
search engine result page. The result page can be collected 325 as
text to be processed by the worker node 305 or to be inserted into
the job queue for processing by other worker nodes. The search
engine result page can also be collected 325 in the original format
or only the links themselves preserved with the links inserted into
the job queue for additional Web crawls by the worker nodes.
Nevertheless, any method that collects the search engine result
page, either now existing or created in the future, is contemplated
for collecting the search engine result page 325.
After the search engine result page is collected, the search engine
result page is parsed 330 for relevant information. The result page
can be parsed 330 by worker node 305 or can be inserted into a job
queue for parsing by other worker nodes. The information that is
considered relevant can be determined by the parameters assembled
previously by the deep index engine 110 of FIG. 1. For example, if
so desired, only organic search results can be considered.
Alternatively, only paid results may be considered if the online
references are limited to paid advertisements within search
engines. In some embodiments, the rank of the search result may be
relevant to the indexing, whereas in other embodiments the rank may
be of little or no relevance to the indexing.
FIG. 4 illustrates aspects of a method of parsing a search engine
result page for references to an entity, such as a search engine
result page obtained from a keyword search as illustrated in FIG.
3. In some embodiments, the method of FIG. 4 is performed by one or
more worker nodes.
The method of FIG. 4 begins by parsing 405 the search engine result
page into one or more channel-related groupings, such as organic
results and paid advertisements. For the purposes of this example,
organic results and paid advertisements are treated differently,
although in other embodiments they may be treated the same or
differently. The worker node parses 410 the organic results to
identify 415 one or more signals in the organic results of the
search engine result page that refer to the entity, the one or more
signals including, for example, rank, URL, title and/or description
of a corresponding search engine result that refers to the entity.
These signals can help identify the relevance of a search engine
result as well as help identify one or more Web Pages 420 to be
searched in the future. The Web Pages 420 can then be parsed for
references to the entity.
The worker node can also parse 425 the paid advertisement results
to identify 430 one or more signals in the paid advertisement
results of the search engine result page that refer to the entity,
the one or more signals including, for example, placement and/or
URL of a corresponding paid advertisement in the search engine
result page that refers to the entity. Prominent placement is often
considered more effective and, therefore, will normally cost more,
than less prominent placement of a paid advertisement. Therefore,
placement of the paid advertisement within a search engine result
page and/or other Web Page gives an indication of how much was paid
for an advertisement and the relevance that is placed on the
correlation between the keyword searched and the marketer placing
the advertisement. As with the organic search results, a Web Page
435 pointed to by a paid advertisement can be identified and itself
parsed for additional references to the entity.
With combined reference to FIGS. 3 and 4, it is noted that, in this
example, the same worker node that performs the search need not
visit the Web Page that is ultimately searched for online
references. For example, one worker node can obtain 315 a keyword
from the keyword database 310, query 320 the search engine and
collect 325 the results. A second worker node can parse 330, 405
the result page, identifying 410, 425 which results are organic and
which results are paid. A third worker node can identify 415 the
appropriate signals of the organic results while a forth worker
node can identify 430 the appropriate signals of the paid results.
The Web Pages 420, 435 can then be inserted into the job queue for
crawling by additional worker nodes. In other embodiments, fewer or
more than four worker nodes can be employed to perform the steps of
FIGS. 3 and 4.
Returning to FIG. 1, once the system 105 has completed the search
for the online references, the system can present the results to a
user. In some embodiments, a reverse index 130 can be created that
lists the online references to the entity and ranks them according
to a set of criteria, such as cost-effectiveness, visibility, or
any other criteria. When a search engine tabulates all documents
that contain a given word, that is called a reverse index. This is
in contrast to a regular index, which contains the locations of all
words within a document.
In other embodiments, the results can be presented as raw data. For
example, the results could be presented as the number of hits on a
particular Web Page, i.e., the traffic history of the Web Page, or
as the organic search result rank for a particular keyword or set
of keywords. In further embodiments, the results can be presented
as mentions in a particular media. For example, the results can be
presented as the number of mentions within blogs. Alternately or
additionally, the results can be further broken down. For example,
blog mentions can be broken down into positive mentions and
negative mentions.
FIG. 5 illustrates one example of a method for presenting the
results within a reverse index. In this example, the results have a
search engine optimization (SEO) score generated 505 for a Web Page
that has been crawled. In other words, the results are presented
based on some predefined criteria, such as placement within search
engine results. The search engine results can include organic
searches, paid searches or both. Additionally, the SEO score can be
factored to weigh more heavily search results from a particular
search engine. For example, high placement in the results of a more
popular search engine can influence the SEO score more than the
results of a less popular search engine.
Alternatively or additionally, a reverse index generated according
to the method of FIG. 5 can include an advertisement score. In some
embodiments, the advertisement score can be used to indicate the
cost-effectiveness of an advertisement. For example, if a first
advertisement generates 50% of the traffic of a second
advertisement, but the first advertisement costs only 10% to run
compared to the second advertisement, it may be given a higher
advertisement score to indicate that it is more cost-effective in
generating traffic.
The method of FIG. 5 further includes identifying 510 keywords
where the Web Page ranks--i.e., keywords that return the Web Page
when searched. In some embodiments, identifying 510 keywords where
the Web Page ranks can be done by obtaining possible keywords from
a keyword database 515 and performing a search on the keywords. In
other embodiments, the keywords can be keywords of interest and the
results of the keyword search can have SEO scores generated. In
further embodiments, the keyword database can be compiled in any
other way that is desired to optimize the indexing.
Once the keyword(s) has been searched, the organic rank for the Web
Page is identified 520. A weighted multiplier is then applied 525
to the organic rank, where the weighted multiplier can be based on
the organic rank. That is, the weighted multiplier is different for
each ranking (i.e., not a constant). In some embodiments, the
weighted multiplier considers 530 the distribution of click
analysis of the organic rank. That is, the multiplier takes into
account the number of users that follow the link to the URL. For
example, a search may turn up a result that is irrelevant to the
majority of users for whatever reason. Even if the ranking of the
result is high, the multiplier can be adjusted to reflect the low
number of users who follow the link. From the weighted multiplier
and the organic rank, an SEO score can be generated 535. The SEO
score allows an analysis of the relevance of the references based
on the predefined criteria.
FIG. 6A illustrates an example of presenting results to a user. In
this example, the results are presented in the form of a chart 615.
The chart 615 can be produced, for example, at the request of a
client who wishes to see how organic rank has changed over time
both of his own Web Page, and the Web Pages of his competitors. The
chart 615 has multiple lines representing the organic rank of
different Web Pages. The first line 610 represents organic rank of
the client's Web Page and the changes in organic rank over one week
intervals. The second line 615 represents organic rank of a first
competitor's Web Page and the changes in organic rank over one week
intervals. The third line 620 represents organic rank of a second
competitor's Web Page and the changes in organic rank over one week
intervals.
In some embodiments, the chart 615 can be limited to the organic
rank history of the client. In other embodiments, the chart 615 can
be limited to the organic rank history of competitors and can
exclude the organic rank history of the client. The chart 615 can
include the organic rank history of more or less than two
competitors, as specified by the client. Additionally, competitors
can be identified in any manner. For example, only the largest
competitors could be shown or certain competitors could be
identified which are of particular interest.
FIG. 6B illustrates an additional example of presenting results to
a user. The chart 640 of FIG. 6B presents mentions of a Web Page in
social media channels. Mentions can include various references to a
Web Page, product or any other entity. The x-axis 645 of the chart
640 includes the channels searched. The y-axis 650 of the chart 640
indicates the number of mentions discovered by the system. In some
embodiments, number of mentions can include the absolute number of
mentions. In other embodiments, number of mentions can be a
relative number of mentions. For example, the number of mentions
for a Web Page can be compared to the number of mentions of a
competitor's Web Page.
FIG. 6C illustrates another example of presenting results to a
user. The chart 670 of FIG. 6C is a pie chart presenting to the
user the quality of the backlinks to their Web Page. In some
embodiments, quality can be determined using a pagerank value (0 to
10). Pagerank is a link analysis algorithm that assigns a numerical
weighting to each element of a hyperlinked set of documents, such
as the Web, with the purpose of measuring each element's relative
importance within the set. In some embodiments, it is more
beneficial to have an inbound link (backlink) from a single (or
few) domain(s) with a high pagerank value of 6 or higher than it is
to have hundreds of backlinks from domains with lower pagerank
values of 0 and 1.
The first region 675 of the chart 670 indicates premium backlinks,
or backlinks from Web Pages with a pagerank value of 7 to 10. The
second region 680 of the chart 670 indicates quality backlinks, or
backlinks from Web Pages with a pagerank value of 3 to 6. The third
region 685 of the chart 670 indicates regular backlinks, or
backlinks from Web Pages with a pagerank value of 0 to 2. Quality
of backlinks can be evaluated using other methods and is not
limited to pagerank.
It is appreciated that the charts of FIGS. 6A-6C are merely
examples of charts that can be generated to present results to a
user according to some embodiments of the invention. Indeed,
results can be presented to users by generating other types of
charts, or without generating any charts at all.
With additional reference to FIG. 7, an example method 700 is
disclosed for indexing online references of an entity. Entities
whose online references are indexed can include individuals,
corporations, brands, products, models or any other entities
referenced anywhere on the Internet. References can include organic
references, online advertisements, news items or any other
reference to the entity. In particular, the method 700 can be used
to identify online references of an entity where both the type of
online reference and the entity are identified based on a
customer's request. For example, a customer can request indexing of
online advertisements of a competitor and the method 700 can be
used to perform the index. Alternately or additionally, the method
700 can be used to identify online references of an entity where
one or both of the type of online reference or the entity is
identified other than by customer request.
The method 700 includes identifying 705 the channel or channels to
be searched. As explained above, channels are the particular medium
within the Internet that is to be searched. In some embodiments,
channels can include organic searches, page searches, linked
advertisement networks, banner advertisements, contextual
advertisements, e-mail, blogs, social networks, social news,
affiliate marketing, mobile advertisements, media advertisements,
video advertisements discussion forums, news sites, rich media,
social bookmarks, paid searches and in-game advertisements.
Nevertheless, the channels are not limited to those mentioned but
can include any relevant areas of the Internet to be searched,
whether now existing or created in the future.
The method 700 further includes identifying 710 signals to be
evaluated. The signals include relevant information about the
references to the entity. For example, advertisements placed at the
top of a Web Page are much more visible, and therefore, are
generally more expensive and are considered more effective.
Therefore, if the references to be indexed include online
advertisements, advertisement placement is a signal that can be
identified for the indexing. Signals to be evaluated can
alternately or additionally include frequency of the reference on a
given Web Page, location of the reference on the Web Page, calendar
date of the crawl, calendar date of Web Page posting, time of day
of the crawl, time of day of Web Page posting, context-driven Web
indexing, time to download the Web Page, Web browser compatibility
of the Web Page, Web plug-in compatibility of the Web Page, or the
like. Additionally or alternatively, signals within an e-mail
message to be evaluated can include frequency of the e-mail message
received, outbound links on the e-mail message, calendar date of
the e-mail message received, time of day of the e-mail message
received, or the like. Context-driven Web indexing can further
include links within the Web Page, current events surrounding the
posting and the topic of the Web Page. Nevertheless, the signals to
be evaluated are not limited to those mentioned but can include any
relevant information about the references to the entity, whether
now existing or created in the future.
The method 700 also includes crawling 715 the Web, the Internet, or
other network, such as the network 112 of FIG. 1, for online
references to the entity. Crawling 715 the Web/Internet/network 112
may be via direct connection to a Web Page, may include the use of
proxy servers, may use backlinks to identify an appropriate Web
Page or can include any other method of locating and evaluating Web
Pages. Crawling 715 the Web/Internet/network 112 may also include
simulating the activities of a human user of the Internet. For
example, crawling 715 the Web can be done through multiple Internet
service providers to simulate human users of the Internet in
different geographic locations. Simulating the activities of a
human user of the Internet may allow for more relevant search
results as they concern how such references would be presented to a
user of the Internet.
The method 700 further includes constructing 720 a reverse index of
the results. For example, a reverse index can be constructed 720
that references the online references to the entity and ranks them
according to a set of predetermined criteria. Constructing 720 a
reverse index can optionally include performing a trend analysis. A
trend analysis shows how the online references have changed over
time. For instance, the chart 605 of FIG. 6A shows how the organic
ranks of a client Web page, a first competitor's Web page, and a
second competitor's Web page have changed over time. As such, the
data included in the chart 605 of FIG. 6A may be representative of
a particular type of trend analysis.
Constructing 720 a reverse index can alternately or additionally
include generating an SEO score. The SEO score allows a user, such
as the customer that requested the index, to see the relevance of
the references based on one or more predetermined criteria, e.g.,
cost-effectiveness.
In some embodiments, the method 700 may be performed using a
system, such as the system described in FIG. 1. The modules, or
individual components, of a system used to perform the method can
be implemented in hardware, software or any combination thereof. If
implemented in software, the modules of the system are stored in a
computer-readable medium, to be accessed as needed to perform their
functions. Additionally, if implemented in software, the tasks
assigned to each module can be carried out by a processor,
field-programmable gate array (FPGA) or any other logic device
capable of carrying out software instructions or other logic
functions.
The embodiments described herein may include the use of a special
purpose or general purpose computer including various computer
hardware and/or software modules, as discussed in greater detail
below.
Embodiments within the scope of the present invention may also
include physical computer-readable media and/or intangible
computer-readable media for carrying or having computer-executable
instructions or data structures stored thereon. Such physical
computer-readable media and/or intangible computer-readable media
can be any available media that can be accessed by a general
purpose or special purpose computer. By way of example, and not
limitation, such physical computer-readable media can comprise RAM,
ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk
storage or other magnetic storage devices, or any other physical
medium which can be used to carry or store desired program code
means in the form of computer-executable instructions or data
structures and which can be accessed by a general purpose or
special purpose computer. Within a general purpose or special
purpose computer, intangible computer-readable media can include
electromagnetic means for conveying a data signal from one part of
the computer to another, such as through circuitry residing in the
computer.
When information is transferred or provided over a network or
another communications connection (either hardwired, wireless, or a
combination of hardwired or wireless) to a computer, hardwired
devices for sending and receiving computer-executable instructions,
data structures, and/or data signals (e.g., wires, cables, optical
fibers, electronic circuitry, chemical, and the like) should
properly be viewed as physical computer-readable mediums while
wireless carriers or wireless mediums for sending and/or receiving
computer-executable instructions, data structures, and/or data
signals (e.g., radio communications, satellite communications,
infra-red communications, and the like) should properly be viewed
as intangible computer-readable mediums. Combinations of the above
should also be included within the scope of computer-readable
media.
Computer-executable instructions comprise, for example,
instructions, data, and/or data signals which cause a general
purpose computer, special purpose computer, or special purpose
processing device to perform a certain function or group of
functions. Although not required, aspects of the invention have
been described herein in the general context of computer-executable
instructions, such as program modules, being executed by computers,
in network environments and/or non-network environments. Generally,
program modules include routines, programs, objects, components,
and content structures that perform particular tasks or implement
particular abstract content types. Computer-executable
instructions, associated content structures, and program modules
represent examples of program code for executing aspects of the
methods disclosed herein.
Embodiments may also include computer program products for use in
the systems of the present invention, the computer program product
having a physical computer-readable medium having computer-readable
program code stored thereon, the computer-readable program code
comprising computer-executable instructions that, when executed by
a processor, cause the system to perform the methods of the present
invention.
The present invention may be embodied in other specific forms
without departing from its spirit or essential characteristics. The
described embodiments are to be considered in all respects only as
illustrative and not restrictive. The scope of the invention is,
therefore, indicated by the appended claims rather than by the
foregoing description. All changes that come within the meaning and
range of equivalency of the claims are to be embraced within their
scope.
* * * * *
References