U.S. patent application number 15/294196 was filed with the patent office on 2017-02-02 for techniques for search optimization.
The applicant listed for this patent is A9.com, Inc.. Invention is credited to Matthew W. Amacker.
Application Number | 20170032041 15/294196 |
Document ID | / |
Family ID | 57120873 |
Filed Date | 2017-02-02 |
United States Patent
Application |
20170032041 |
Kind Code |
A1 |
Amacker; Matthew W. |
February 2, 2017 |
TECHNIQUES FOR SEARCH OPTIMIZATION
Abstract
Techniques for optimizing a search are disclosed. In one
embodiment, a method for optimizing search on a Web site comprises:
receiving from a client computing device a first search request
including a first search term, retrieving and rendering to the
client computing device first search results including one or more
items corresponding to the first search term included in the first
search request, receiving from the client computing device a second
search request associated with an item of the one or more items
rendered with the first search results, retrieving one or more
second search terms behaviorally associated with the selected item,
and retrieving and rendering to the client computing device second
search results corresponding to the one or more second search
terms. The second search results may be rendered "in-line."
Inventors: |
Amacker; Matthew W.; (Santa
Clara, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
A9.com, Inc. |
Palo Alto |
CA |
US |
|
|
Family ID: |
57120873 |
Appl. No.: |
15/294196 |
Filed: |
October 14, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12985985 |
Jan 6, 2011 |
9471681 |
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15294196 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/248 20190101;
G06F 16/24534 20190101; G06F 16/951 20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A computer-implemented method for optimizing search on a Web
site, comprising: receiving a search request associated with a
first set of search results; identifying a set of queries
associated with user behavior data corresponding to at least one
item in the first set of search results; determining at least one
updated query from the set of queries, the at least one updated
query having a level of behavioral association with the at least
one item at least meeting a behavioral association threshold value;
and providing a second set of search results corresponding to the
at least one updated query, the second set of search results
including at least one different item from the first set of search
results.
2. The computer-implemented method of claim 1, further comprising:
receiving a selection of the at least one item from the first set
of search results, the selection requesting the set of queries
associated with the at least one item.
3. The computer-implemented method of claim 2, further comprising:
causing a content element associated with the at least one item to
be displayed, wherein, when selected, the content element causes
the set of queries associated with the at least one item to be
displayed.
4. The computer-implemented method of claim 1, further comprising:
determining a level of behavioral association between the at least
one item and each query in the set of queries, the level of
behavioral association represented as a weighted value based at
least in part on the user behavior data;
5. The computer-implemented method of claim 1, further comprising:
providing the first set of search results on a web page; and
wherein the second set of search results is provided on the web
page without reloading the web page.
6. The computer-implemented method of claim 5, wherein providing
the second set of search results further comprises: providing a
script associated with the web page.
7. The computer-implemented method of claim 1, wherein the level of
behavioral association is based at least in part on a frequency of
appearance of a query in connection with an associated item in the
recorded behavior of the plurality of users.
8. A computer system comprising: at least one processor; and at
least one memory having computer-executable instructions that, when
executed by the processor, cause the computer system to: receive a
search request associated with a first set of search results;
identify a set of search queries associated with user behavior data
corresponding to at least one item in the first set of search
results; determine at least one updated search query from the set
of search queries, the at least one updated search query having a
level of behavioral association with the at least one item at least
meeting a behavioral association threshold value; and provide a
second set of search results corresponding to the at least one
updated search query, the second set of search results including at
least one different item from the first set of search results.
9. The computer system of claim 8, wherein the computer executable
instructions, when executed by the processor, further cause the
computer system to: receive a selection of the at least one item
from the first set of search results, the selection requesting the
set of queries associated with the at least one item.
10. The computer system of claim 9, wherein the computer executable
instructions, when executed by the processor, further cause the
computer system to: cause a content element associated with the at
least one item to be displayed, wherein, when selected, the content
element causes the set of queries associated with the at least one
item to be displayed.
11. The computer system of claim 8, wherein the computer executable
instructions, when executed by the processor, further cause the
computer system to: determine a level of behavioral association
between the at least one item and each query in the set of queries,
the level of behavioral association represented as a weighted value
based at least in part on the user behavior data;
12. The computer system of claim 8, wherein the computer executable
instructions, when executed by the processor, further cause the
computer system to: provide the first set of search results on a
web page; and wherein the second set of search results is provided
on the web page without reloading the web page.
13. The computer system of claim 12, wherein the computer
executable instructions to provide the second set of search
results, when executed by the processor, further cause the computer
system to: provide a script associated with the web page.
14. The computer system of claim 8, wherein the level of behavioral
association is based at least in part on a frequency of appearance
of a query in connection with an associated item in the recorded
behavior of the plurality of users.
15. A non-transitory computer-readable storage medium having
computer-executable instructions stored thereon that, when executed
by a computer, cause the computer to: receive a search request
associated with a first set of search results; identify a set of
search queries associated with user behavior data corresponding to
at least one item in the first set of search results; determine at
least one updated search query from the set of search queries, the
at least one updated search query having a level of behavioral
association with the at least one item at least meeting a
behavioral association threshold value; and provide a second set of
search results corresponding to the at least one updated search
query, the second set of search results including at least one
different item from the first set of search results.
16. The non-transitory computer-readable storage medium of claim
15, wherein the computer executable instructions, when executed by
the computer, further cause the computer to: receive a selection of
the at least one item from the first set of search results, the
selection requesting the set of queries associated with the at
least one item.
17. The non-transitory computer-readable storage medium of claim
16, wherein the computer executable instructions, when executed by
the computer, further cause the computer to: cause a content
element associated with the at least one item to be displayed,
wherein, when selected, the content element causes the set of
queries associated with the at least one item to be displayed.
18. The non-transitory computer-readable storage medium of claim
15, wherein the computer executable instructions, when executed by
the computer, further cause the computer to: determine a level of
behavioral association between the at least one item and each query
in the set of queries, the level of behavioral association
represented as a weighted value based at least in part on the user
behavior data;
19. The non-transitory computer-readable storage medium of claim
15, wherein the computer executable instructions, when executed by
the computer, further cause the computer to: provide the first set
of search results on a web page; and wherein the second set of
search results is provided on the web page without reloading the
web page.
20. The non-transitory computer-readable storage medium of claim
15, wherein the level of behavioral association is based at least
in part on a frequency of appearance of a query in connection with
an associated item in the recorded behavior of the plurality of
users.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a continuation of allowed U.S.
application Ser. No. 12/985,985, entitled "TECHNIQUES FOR SEARCH
OPTIMIZATION," filed Jan. 6, 2011; of which the full disclosure of
this application is incorporated herein by reference for all
purposes.
BACKGROUND
[0002] Electronic entities having a presence in an electronic
environment, such as may be provided via a Web site or other such
source of content, take advantage of the full capabilities of
modern Web-based services to deliver a compelling user experience.
For example, items offered for sale on a Web site may be divided by
categories, prices, types, and other identifiers. A user may search
for item(s) of interest using a search service associated with the
Web site. Search results for an item may include a list of items
including, for each item, price, description, images, availability
and other types of data associated with the item. Once initial
search results are received, a user may make further search
refinements by searching for an item within a particular category
(e.g., searching for a TV set under "Electronics" category), using
particular search criteria (e.g., price range), further defining
search terms (e.g., defining an original search term "TV" as an
"LCD TV") or otherwise refining his or her search.
[0003] However, further refinement of the original search still may
not bring the desired results or get a user closer to his or her
search target. For example, additional search terms or phrases may
be attempted by a user which may not locate or surface an item the
user is seeking. Accordingly, additional time, effort, and
resources are spent for any additional search for the item
requested by a user.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 illustrates an environment in which various
embodiments can be implemented.
[0005] FIG. 2 illustrates an environment in which various
embodiments may be practiced.
[0006] FIG. 3 illustrates a diagrammatic representation of an
environment which may incorporate one or more components that are
described above in connection with FIGS. 1 and 2 and that may be
used in accordance with various embodiments.
[0007] FIG. 4 illustrates a process flow diagram for operation of
the search service in accordance with an embodiment.
[0008] FIG. 5 illustrates a process flow diagram for operation of
the search service in accordance with another embodiment.
[0009] FIG. 6 illustrates a process flow diagram for operation of
the search service in accordance with yet another embodiment.
[0010] FIG. 7 illustrates an exemplary Web page showing first
search results in accordance with an embodiment.
[0011] FIG. 8 illustrates an exemplary Web page showing second
search results in accordance with an embodiment.
DETAILED DESCRIPTION
[0012] Techniques are presented for conducting search processes,
such as search requests submitted to at least one data source, in
accordance with various embodiments. In a Web-based environment,
for example, a data source may include a Web resource, such as a
Web site that is suitable for the World Wide Web and can be
accessed through a Web browser and displayed on a display element
of a computing device, where at least some content for the Web site
is pulled from at least one data store against which the search
request can be executed. A user may conduct a search for any item
of interest (e.g., a product, video or audio content, image, and
the like) on the Web resource using Web-based search services, such
as, for example, search engines. One skilled in the art will
appreciate that a variety of Web search services is available to a
user nowadays. In one embodiment, Web resources (such as Web pages)
visited by a user may contain information for one or more items
(e.g., products or services) that the user located, such as through
browsing or search.
[0013] Ordinarily, users of a Web resource, e.g., an electronic
repository, whether in the context of an electronic commerce
system, a general purpose search engine, or other system, use
search terms or search phrases in order to locate or "surface" an
item in the electronic repository. As should be understood, a
combination of search terms may comprise a query and may contain
one or more keywords needed for search of an item. However, search
terms submitted by a user to a search engine, electronic commerce
application or other system may fail to locate items that the user
seeks in an electronic repository. As a non-limiting example, a
user may submit one or more search terms in an attempt to locate an
item in an electronic commerce system. However, the electronic
commerce system may be unable to locate the item desired by the
user via an initial search based upon the search terms (e.g., a
search that employs word matching of the search terms and text
located in data fields associated with the item). As a result, the
electronic commerce system may fail to display the desired item to
the user. Such a failure can manifest itself if the submitted
search terms do not appear in item data that is associated with the
item in the electronic repository.
[0014] In a similar scenario, because search engines often rank
search results that are returned to a user, search terms (queries)
submitted by a user may result in a ranked list of search results
that has not appropriately ranked items according to the
expectation of the user. As one non-limiting example, upon
submission of search terms intended to locate one or more items in
an electronic repository, the search results may display the item
sought by a user below or outside the initial search results, which
may cause a less than optimal user experience. Accordingly, the
user is forced to refine his or her search by one of the methods
described above, e.g., by defining a particular search category,
using particular search criteria, further defining search terms or
otherwise refining his or her search. However, even this (often
times repeated) refinement of search is not a guarantee of
successful "surfacing" of a desired item.
[0015] In one embodiment, techniques described herein utilize
behavioral associations of unique item identifiers of items and
search terms associated with searched items as described below. An
electronic entity associated with a search engine may track a user
search session in order to identify search terms or search phrases
that can be associated with one or more items in the electronic
repository. Therefore, it is possible to identify search terms
(e.g., a query including one or more search terms) submitted by a
user and determine whether the terms could be associated with an
item in the electronic commerce system by detecting whether a
search engine or search application "surfaces" a particular item
that the user eventually purchases within the same session. In
other words, queries comprised of search terms can be behaviorally
associated with items in the electronic repository if the behavior
of a user while interacting with a system (e.g., a search engine,
electronic commerce system, etc.) illustrates an association
between user-submitted search terms and an item.
[0016] In one embodiment, an electronic entity associated with a
Web page may supply a unique identifier (item identifier) that is
associated with each item. A unique identifier, in one example,
refers to a unique identification number or other alphanumeric
string assigned to a product for identification purposes. The
unique identifiers may be internal or specific to the electronic
entity. Alternatively, unique identifiers of products may be
supplied by vendors associated with the electronic entity, and may
be the same for different electronic entities. A main
characteristic of a unique identifier is that the identifier can be
recognized by an electronic entity. In one embodiment, item
identifiers of items may be utilized for associating with search
terms used to locate the items during a search.
[0017] In one example, embodiments of the disclosure can determine
whether search terms are behaviorally associated with an item in an
electronic repository by tracking user behavior within a user
session. In the above example, user-submitted search terms can be
behaviorally associated with an item that the user eventually
purchases within a session. Such a behavioral association can be
detected even if a search of the electronic repository using the
user-submitted search terms does not directly surface the item that
the user eventually purchases. Alternatively, user-submitted search
terms (queries) can be behaviorally associated with the item in
cases, for example, of a user viewing an item, adding an item to a
shopping cart, or other user behavior.
[0018] If queries containing one or more search terms submitted by
a user are behaviorally associated with an item in the electronic
repository, embodiments described herein can calculate a weight
value representing a degree of association between the search query
and an item in the electronic repository. Weight values according
to embodiments of the disclosure can be calculated on an aggregate
basis, taking into account user sessions of multiple users of a
system. An association between an item and a search term (or a
query including one or more search terms) can get stronger when it
is determined from tracking user search behavior that an item in
association with the query has appeared with a determined
frequency. Simply speaking, an association between the query "egg
toaster" and a particular product (e.g., an egg toaster) is
stronger than an association between the query "egg and muffin
maker" and the egg toaster if the association "egg-toaster
(query)-egg toaster (item)" is detected more times than the
association "egg and muffin maker (query)-egg toaster (item)," in
at least some embodiments. Accordingly, weight values calculated on
an aggregate basis can yield more accurate associations between
search queries and items in an electronic repository, as such
calculations take into account the behaviors of a larger number of
users.
[0019] If a calculated weight value representing a degree (level)
of association between at least one search query and an item in an
electronic repository exceeds a threshold, the user-submitted
search queries can be identified by embodiments of the disclosure
as potentially missing search terms (keywords) for the item for
which a behavioral association exists. In other words, if a
user-submitted search query fails to directly surface an item or
fails to highly rank an item consistent with the expectations of a
user, the search query can be identified as potentially missing
keywords for the item. A threshold can be chosen to tune the
sensitivity of the detection of potential missing keywords.
Alternatively, the threshold can be lowered to a lower calculated
weight value (e.g., representing a lesser degree of association) so
that more potential missing keywords are identified. Behavioral
association techniques are disclosed in U.S. patent application
Ser. No. 12/560,983 entitled "Identifying Missing Search Phrases,"
filed Sep. 16, 2009, which is incorporated herein by reference in
its entirety.
[0020] Behavioral associations between searchable items and search
queries may be accumulated and stored in a behavior-based keyword
data store (behavioral association data store) and utilized for the
purposes of search optimization techniques described herein. In one
embodiment, a user conducting an initial search for an item may
submit one or more search terms comprising a query to a search
engine. The search engine returns search results of items
associated with the submitted search term (or terms). The initial
search results may include item information associated with each
item, including, for example, images of items and/or item
descriptions. Also, each item returned with the search results may
have one or more search queries behaviorally associated with the
item (in one embodiment, with the item's unique identifier).
[0021] The search queries may be generated and stored in a
behavioral association data store as discussed above and retrieved
from the data store as needed. Thus, each search query may be
associated with an item of the search results via a behavioral
association described above. Some queries may have a stronger
association with an item than others, as discussed above. In one
embodiment, only a determined number of search queries with the
strongest association with the item (e.g., one search query) may be
retrieved from the behavioral association data store and returned
with the item. For example, in one embodiment, only the search
query that has the strongest association with the item may be
retrieved.
[0022] In one embodiment, the search queries behaviorally
associated with the item may be retrieved from the behavioral
association data store upon a user request. For example,
information for each item returned with the list of initial (first)
search results may include a content element of any type known in
the art that, when accessed, may initiate a request for a retrieval
of queries behaviorally associated with the item's unique
identifier. The content element may include, but may not be limited
to, a hyperlink, a button, an icon, an image, or any other type of
content element accessible (e.g., clickable) by a user. In one
embodiment, a content element may have a descriptor included in the
element to help a user identify the functionality the content
element represents. For example, the content element may be
implemented as a hyperlink, icon, image, or button with a
descriptor "more" or "more like this."
[0023] In one embodiment, the user may view (e.g., by scrolling
down) the first search results list and identify an item in the
search results that might be "closer" to the desired item, but
still not quite the one that the user had in mind when commencing
the search. In other words, the item may comply with, or represent,
at least one aspect of a user's intent in conducting the search.
This determination may be made based on the information associated
with the item returned in the search results. For example, a user
may view the item's description and decide to continue a search in
the direction identified by the description (e.g., a user was
looking for a TV set and the first search results returned one LCD
TV that the user liked and so the user wanted to search more of the
LCD TVs). In another example, a user may simply like the look of an
image of the item (e.g., a user was looking for sunglasses and
liked sunglasses of a particular shape). The user then may want to
access the content element "more like this" associated with the
item the user liked the best among the returned results. For the
purposes of simplicity, the item that user liked the best among the
returned results, i.e., the item in the search results that is most
representative of the user's intent, will be hereinafter called a
"similar item."
[0024] When the user clicks on the content element with the
descriptor "more like this" that is associated with the similar
item, a request to retrieve search queries behaviorally associated
with the similar item is initiated, the search queries are
retrieved from the behavioral association data store and sent to
the search engine to conduct a search associated with the retrieved
search terms. As described above, in one embodiment, only "most
popular" search queries, namely the queries with the strongest
association with the similar item, may be retrieved and used in a
new search. For example, a predetermined number (e.g. one, two, or
three) search queries that have the strongest association with the
similar item may be retrieved for a further search.
[0025] A list of new, second search results is then returned to the
user. As in the first search results list, the second list rendered
to the user may include "more of this" content elements associated
with each returned item. Thus, the user, if not yet satisfied with
the search, may repeat the above steps again and conduct another
search of items that are "more like" the item selected by the user
as a new similar item. In one embodiment, the second search results
may be rendered to a user "in-line," i.e., without a page reload,
via a trusted script embedded in the page, e.g., Ajax.RTM. script.
In another embodiment, the second search results may be included in
a page in a separate space (e.g., a "band") specially allocated for
the search results associated with the "more like this"
functionality.
[0026] In one embodiment, only the search query that has a highest
level of behavioral association with the similar item may be used
for further (second) search. One skilled in the art will appreciate
that there may be different selection algorithms aimed at selecting
the query with the highest level of behavioral association with the
item among all search terms behaviorally associated with that item.
For example, as discussed above, an association between an item and
a search query gets stronger when a substantial statistic is
gathered in which an item in association with the query has
appeared with a frequency exceeding a predetermined threshold.
[0027] In another example, as should be appreciated by one skilled
in the art, the most "specific" behaviorally associated search
queries have to be the most tightly (strongly) associated with the
item, and, consequently, have a higher level of behavioral
association with the item relative to that of the less "specific"
queries. The behavioral association of items to search queries may
be explained in the context of "specificity" of a search query. A
behaviorally associated search query will, in general, be more
"specific" than an original search query or the queries that
brought in the last content item (e.g., a Web page) viewed by a
user. A "more specific" query may mean that the query contains an
extra word or words further defining the original query. In another
embodiment, the "more specific" query may be a query that brought
in a number of results that is less than the number any other
related query would bring. For example, a search for Bob Dylan
sunglasses using a query "Bob Dylan apparel" may bring many more
results than a search for "Bob Dylan sunglasses." Accordingly, "Bob
Dylan sunglasses" is a more specific query than "Bob Dylan apparel"
and has a tighter (stronger) association with Bob Dylan sunglasses
than the query "Bob Dylan apparel."
[0028] In another embodiment, a more specific query may be selected
based on its entropy metrics, e.g., a growing popularity of the
query. For example, if a particular query has been increasingly
used in connection with a particular item in a determined period of
time, it becomes apparent that the query's "popularity" in
connection with the item is growing and the query may be more
specific than others in identifying the particular item.
[0029] In yet another embodiment, a degree (level) of behavioral
association of a query to an item may be determined based on a
number of different metrics; for example, how many items have come
up in search results based on the associated search query, how many
users "clicked" on the item found through the associated search
query, how many times the item associated with the search query
have been "added to cart," or purchased, and the like. One skilled
in the art will appreciate that there may be a number of different
algorithms for determining a level of behavioral association of an
item to a particular search query. The metrics defining the
"specificity" or a level of behavioral association of a search
query to an item may be pre-computed and stored along with the
search queries in the behavioral association data store.
[0030] In one embodiment, a degree (level) of behavioral
association of a query to an item may be determined based on a
direction of the search. For example, it may be determined from
recorded search history that a particular query, when used in a
search, may be different from one or more search queries used by
the user in the previous search rounds, such that the user would
have an entirely new direction of further search.
[0031] In another example, a "market" analysis of a search query
may be done that would determine whether a query contains a
"cluster" of keywords that, taken together, define a brand, a line
of product, or any type of a known entity. For example, a search
query containing the keywords "red," "socks," and "shirt" may be
considered as including an entity "red socks" ("red sox"). This
query may have a precedence over other search queries behaviorally
associated with a particular similar item because a user is likely
to be searching for an item or items associated with the brand "red
sox" rather than using a query including the words "red," "socks,"
and "shirt" with no meaningful association between these words.
[0032] The described techniques have a number of advantages over
conventional, repeatedly refined searches. First, a user clearly
identifies a direction of further search by selecting a similar
item in the initial search results. The user has at least some
concept of a desired object in his/her mind when he/she conducts
initial search. Thus, "sending" the search in a desired direction
by using search queries associated with the similar item, in
effect, substantially narrows down further search by effectively
"validating" further search.
[0033] In contrast, conventional search systems may utilize
product-query (or product-search term) associations, but in a
different, less productive way, for example, on a "product by
product" basis. As anyone who has done Web searches knows, a first,
initial search typically brings a number of random results that may
or may not present a user with a sufficient number of good options.
By way of example, a user may click on one item brought in search
results (e.g., LCD TV) and later click on another item (e.g., red
shirts). However, a conventional system would associate an LCD TV
with a red shirt for future reference, such as, for example, for a
recommendation "a user who viewed this item also viewed that item."
Understandably, the data assembled by conventional search systems
as described above is fairly "noisy" and the "noise" (incorrect
item associations) disappears only when a substantial volume of
data has been gathered and processed by the system.
[0034] Second, further search is based on behavior-based
associations between the new search queries and the similar item
that have already been tested numerous times by a plethora of users
and therefore are likely to bring the user much closer to the
desirable item than a regular "refined query" search, for example.
This is the case because, in effect, the second search involves
search queries that have historically been most used to find the
similar item and therefore these queries are most likely to bring
the desired item to the user.
[0035] Third, a user does not have to enter a new, refined search
query or even think about how to refine the original query. Indeed,
all the user has to do is to visually identify an item among
initial search results that, in the user's view, is the closest to
a desired item, and click on the "more like this" content element
associated with the identified item. In one embodiment, the second
search may render, along with the second search results, the most
popular search query associated with the similar item and used by
the system in the second search.
[0036] FIG. 1 illustrates an example of an environment 100 for
implementing aspects in accordance with various embodiments. As
will be appreciated, although a Web-based environment is used for
purposes of explanation, different environments may be used, as
appropriate, to implement various embodiments. The environment 100
includes an electronic client device 102, which can include any
appropriate device operable to send and receive requests, messages,
or information over an appropriate network 104 and convey
information back to a user of the device. Examples of such client
devices include personal computers, cell phones, handheld messaging
devices, laptop computers, set-top boxes, personal data assistants,
electronic book readers, and the like. The network can include any
appropriate network, including an intranet, the Internet, a
cellular network, a local area network, or any other such network
or combination thereof. Components used for such a system can
depend at least in part upon the type of network and/or environment
selected. Protocols and components for communicating via such a
network are well known and will not be discussed herein in detail.
Communication over the network can be enabled by wired or wireless
connections, and combinations thereof. In this example, the network
includes the Internet, and the environment includes a Web server
106 for receiving requests and serving content in response thereto,
although for other networks, an alternative device serving a
similar purpose could be used, as would be apparent to one of
ordinary skill in the art.
[0037] The illustrative environment includes at least one
application server 108 and a data store 110. It should be
understood that there can be several application servers, layers,
or other elements, processes, or components, which may be chained
or otherwise configured, which can interact to perform tasks such
as obtaining data from an appropriate data store. As used herein,
the term "data store" refers to any device or combination of
devices capable of storing, accessing, and retrieving data, which
may include any combination and number of data servers, databases,
data storage devices, and data storage media, in any standard,
distributed, or clustered environment. The application server can
include any appropriate hardware and software for integrating with
the data store as needed to execute aspects of one or more
applications for the client device, handling a majority of the data
access and business logic for an application. The application
server provides access control services in cooperation with the
data store, and is able to generate content such as text, graphics,
audio, and/or video to be transferred to a viewer, which may be
served to the viewer by the Web server in the form of HTML, XML, or
another appropriate structured language in this example. The
handling of all requests and responses, as well as the delivery of
content between the client device 102 and the application server
108, can be handled by the Web server. It should be understood that
the Web and application servers are not required and are merely
example components, as structured code discussed herein can be
executed on any appropriate device or host machine as discussed
elsewhere herein.
[0038] The data store 110 can include several separate data tables,
databases, or other data storage mechanisms and media for storing
data relating to a particular aspect. For example, the data store
illustrated includes mechanisms for storing production data 112 and
user information 116, which can be used to serve content for the
production side. The data store also is shown to include a
mechanism for storing log data 114, which can be used for
reporting, generating statistics, and other such purposes. It
should be understood that there can be many other aspects that may
need to be stored in the data store, such as for page image
information and access right information, which can be stored in
any of the above-listed mechanisms as appropriate or in additional
mechanisms in the data store 110. The data store 110 is operable,
through logic associated therewith, to receive instructions from
the application server 108 and obtain, update, or otherwise process
data in response thereto. In one example, a viewer might submit a
search request for a certain type of item. In this case, the data
store might access the user information to verify the identity of
the viewer, and can access the catalog detail information to obtain
information about items of that type. The information then can be
returned to the viewer, such as in a results listing on a Web page
that the viewer is able to view via a browser on the user device
102. Information for a particular item of interest can be viewed in
a dedicated page or window of the browser.
[0039] Each server typically will include an operating system that
provides executable program instructions for the general
administration and operation of that server, and typically will
include a computer-readable medium storing instructions that, when
executed by a processor of the server, allow the server to perform
its intended functions. Suitable implementations for the operating
system and general functionality of the servers are known or
commercially available, and are readily implemented by persons
having ordinary skill in the art, particularly in light of the
disclosure herein.
[0040] The environment in one embodiment is a distributed computing
environment utilizing several computer systems and components that
are interconnected via communication links, using one or more
computer networks or direct connections. However, it will be
appreciated by those of ordinary skill in the art that such a
system could operate equally well in a system having fewer or a
greater number of components than are illustrated in FIG. 1. Thus,
the depiction of the system 100 in FIG. 1 should be taken as being
illustrative in nature, and not limited to the scope of the
disclosure.
[0041] FIG. 2 shows an example environment 200 in which various
embodiments may be practiced. The environment 200 may be realized
utilizing one or more of the components of the environment
described above in connection with FIG. 1. The environment 200, in
an embodiment, includes a plurality of users operating client
computing devices 202 that utilize a network such as the Internet
204 to browse content of various content providers. While the
environment 200 shows the client computing devices 202 utilizing
the Internet, it should be understood that other networks may be
used as an alternative to or in addition to the Internet 204.
Examples of other networks include mobile networks, intranets, and
generally any suitable communications network. Likewise, it should
be understood that client computing devices 202 may include
personal computers, cell phones, handheld messaging devices, laptop
computers, set-top boxes, personal data assistants, electronic book
readers, and the like.
[0042] In an embodiment, users may interact through devices 202
with other entities in order to consume content, search for
information or items for consumption, purchase items for
consumption, and the like. As used herein, an item for consumption
includes any product or item which may be consumed by a consumer.
Examples include tangible items, such as consumer products.
Examples also include electronic items which may be downloaded
and/or streamed, including audio files, video files, and other
digital content. Examples of items for consumption also include
services which may be performed for and/or on behalf of consumers.
As shown in FIG. 2, the environment 200 includes an electronic
entity, or merchant 206. The merchant 206, for example, may operate
an electronic marketplace such that the users may search, via
devices 202 utilizing search services (e.g., a Web search engine
214), for items for consumption offered by the merchant 206. The
merchant 206 may offer items for consumption that come from various
sources, including the merchant's own sources. For example, the
merchant 206 may operate systems, such as those described above in
connection with FIG. 1, that enable others to utilize the systems
to offer items for consumption. In short, the merchant 206 may
facilitate user search and consumption of items or products offered
by the merchant 206 and/or vendors 208 and/or 210.
[0043] For example, in an embodiment, the environment 200 includes
one or more affiliate or third-party vendors 208. In an embodiment,
an affiliate vendor is a merchant who offers for consumption items
that are actually consumed by consumers from the merchant 206.
Affiliate vendors 208 may cooperate with the merchant 206 in
various ways. In one embodiment, the merchant 206 may operate an
electronic marketplace, such as a Web site 212 and advertise and/or
sell items for consumption that are offered by the affiliate vendor
208. Affiliate vendors 208 may utilize various systems provided by
the merchant 206, such as electronic search services that enable
users to search for items for consumption, and other systems.
[0044] The environment 200 may include one or more subsidiary
vendors 210. In an embodiment, a subsidiary vendor is a merchant
whose operations may be controlled, either completely or partially,
by the merchant 206. For example, a subsidiary vendor 210 may be
operated by the merchant 206, but may offer items for consumption
under a different brand than the merchant 206. The subsidiary
vendor 210 may offer the same or different products for consumption
than the merchant 206. Alternatively, the subsidiary vendor 210 may
be operated by the affiliate vendor 208, but may offer items for
consumption under a different brand than the affiliate vendor 208,
which may be advertised and/or offered for sale by the affiliate
vendor 208 on the merchant 206's Web site 212.
[0045] In an embodiment, the environment 200 includes components
and instructions for generating one or more Web sites 212. One or
more of the Web sites 212 may be operated by the merchant 206,
although they need not be. The Web sites 212 may offer various
types of content, such as news, video, shopping for products, and
the like. The Web sites 212 may offer other services, such as
content or product search services, audio, social networking
services, and the like. While the example environment 200 shows Web
sites for the purpose of illustration, it should be understood that
any type of content provider may be included in the environment 200
or variations thereof. For example, content may be provided in
various ways, such as through an application interface or through
other mechanisms that may not be properly classified as Web
sites.
[0046] As noted, in an embodiment, the users may interact with
content from the various entities, such as by searching products
offered by the entities, viewing content from the entities,
consuming items from the entities, and the like. In order to search
for products or other content provided by the entities, the users
may utilize a search service, e.g., the Web search engine 214. As
one skilled in the art will appreciate, a Web search engine may be
a software or a combination of software and hardware designed to
search for information on the Internet. The search engine may be
rendered to a client device 202 via a particular Web site or Web
sites, such as the Web sites 212. The Web search engine 214 may be
affiliated with the merchant 206 in a number of different ways,
such as, for example, it may be designed and/or owned by the
merchant 206, operated by the merchant 206, contracted by the
merchant 206, be independent or autonomous from the merchant 206,
and the like.
[0047] FIG. 3 shows an illustrative environment 300 that an
electronic entity such as the merchant 206 may utilize in order to
provide a user with an ability to repeatedly search for product
items or other content offered by the merchant 206 and/or vendors
208 and 210 in accordance with an embodiment. The environment 300
may be implemented to increase search efficiency for the users
utilizing one or more client devices 202 and thus increase
effectiveness for one or more of the entities 206, 208, and/or 210
shown in FIG. 2. In an embodiment, the environment 300 of FIG. 3 is
utilized by an electronic entity (such as the merchant 206 shown in
FIG. 2) to provide an improved search experience to a user using a
search service described herein. It is to be understood that the
search service may comprise a number of various implementations,
including, but not limited to, a software application component
executed on a computing device, such as one or more Web servers. In
one embodiment, the service may be offered as a part of remote
computing services that together make up a cloud computing
platform, offered over the Internet. The service may be configured
to run in a Web server environment, such as a pure Java HTTP Web
server environment.
[0048] In an embodiment, the environment 300 includes a client
computing device 302 utilized by a user for interaction with
electronic entities that provide content. Content may be accessed
by a user operating the device 302 through a browser 304. For
example, content may include products or items of various types
that are to be placed on a Web site 306. The Web site 306 may
utilize a search engine 308 in order to provide a user with the
ability to search for products offered by the merchant 206 or other
entities. The content may be stored in the remote content data
store(s) 314, 316, and 318 and may be utilized by merchants, Web
sites, or other entities in order to provide users an opportunity
to search for, and view, items for consumption and/or other
content. In an embodiment, the content data stores 314-318 store
content to be provided to users, such as when users request the
content by navigating to a Web site 306 operated by a merchant
(e.g., merchant 206 on FIG. 2) through a Web server 320. In one
embodiment, one or more of the data stores 314-318 may be allocated
as a behavioral association data store to store information related
to behavioral associations between items (or item unique
identifiers) and search queries. Alternatively, information related
to behavioral associations may be stored in a local data store 310.
The local data store 310 may be implemented in a number of
different ways. For example, in an embodiment, the data store 310
may comprise a dedicated database accessible by the trusted script
312. In some Web environments, such as, for example, one provided
by the HyperText Markup Language (HTML) 5, the local data store 310
may be allocated within a Web site or within a domain. For example,
the local data store 310 may be embedded in the pages of the Web
site 306 and may be shared with any page visited by the user on
that Web site.
[0049] It will be appreciated that the content may be stored in one
or more data stores and that three data stores 314, 316, and 318
are shown for purely illustrative purposes, such that the number of
data stores is not limited to three or any other number. The term
"remote" used in conjunction with the term "data store" simply
means that the data stores 314, 316, or 318 are not placed in an
application operating on the computing device 302 or in the
computing device itself. The remote data stores may be accessible
with the search engine 308 through the Internet or any other type
of computer network known in the art. The remote data stores may be
associated with the electronic entity, such as above-noted merchant
206. For example, the remote data stores may be associated with the
server 320 serving the Web site 306 operated by the merchant
206.
[0050] In an embodiment, a trusted script 312, implemented in one
embodiment as an Ajax.RTM. script and operating on the Web site 306
that is loaded by the browser 304, is configured to provide search
results for the second search "in-line," i.e., without reloading
the results page. In some embodiments, the trusted script may also
be implemented as an ActionScript.RTM., JavaScript.RTM.,
VBScript.RTM., Java Servelet.RTM., Flash.RTM., JScript.RTM. or
other scripting languages. The trusted script 312 may be loaded on
a page of the Web site 304 when a user accesses the Web site. In
another embodiment, the trusted script may be implemented as an
extension (i.e., a plug-in application) to the browser.
[0051] In operation, Web site 306 is displayed on computing device
302 via the browser 304. A user may conduct a search using search
engine 308 in order to search items in one or more data stores
314-318. When a user sends a first search request for an item via
the search engine 308, the data corresponding to the user's request
is assembled and returned to the user via the Web site 306 served
by the server 320 and rendered by browser 304 on computing device
302. The returned data may also be stored in the local data store
310. As described above, the returned data may include item
identifiers of returned items. When the user selects a similar item
and submits a second, "refined" search request "more like this" via
the search engine 308, the server 320 will retrieve corresponding
behavioral associations between the item identifier and search
queries from the behavioral association data store, determine the
search query with the strongest association with the item (in one
embodiment, this determination may have been made in advance and
relevant information stored with the search queries), and conduct a
second search using the search query with the strongest
association. As described above, in one embodiment, a predefined
number of search queries with the strongest association with the
item may be used. The returned data is subsequently rendered for
display to the user's computing device 302 via the Web site 306.
The returned results may be displayed "in-line," without a page
reload, via the trusted script 312.
[0052] FIG. 4 illustrates a process flow diagram for the operation
of the search service described herein in accordance with an
embodiment. The process 400 starts at block 402 where behavioral
associations of each searchable item and search queries are
generated. As described above, the search queries may be selected
based on recorded user behavior during search for items. The search
queries may be associated with an item identifier of each item. In
one embodiment, the search queries behaviorally associated with
each item are stored in a behavioral association data store
accessible by a Web server providing the search service.
[0053] At block 406 the first search request is received. The
request may contain a search query comprising one or more keywords.
At block 410, the first search results associated with the request
are returned and rendered to the user's computing device. As
described above, the returned results may contain items, item
description, item identifiers, and/or other such information. The
associated item information may include, but is not limited to,
price, availability, shipping requirements, and so on. In one
embodiment, the returned results may include a content element
rendered with the item information for each item, e.g., an icon, a
button, a hyperlink, or any other content element configured to be
accessible by the user and, when accessed, to cause a retrieval of
the search queries behaviorally associated with the item and stored
in the behavioral association data store. As noted above, the
content element may be marked with a descriptor "more," "more like
this," or the like.
[0054] At determination block 416, it is determined whether a
second search request is received. The second search request may be
initiated by the user accessing a content element associated with
the item that, in user's view, is a similar item, but not quite the
one. If the request has been received, the process moves to block
420 where the behavioral associations of the item identifier of the
similar item with the search queries are retrieved from the
behavioral association data store. At block 424, the query with the
strongest association with the item identifier is selected from the
retrieved item identifiers. As described above, the determination
regarding a level of association between an item and each search
query may be pre-computed. Alternatively, this determination may be
made each time the request for retrieval of behaviorally associated
search queries is received. Various selection algorithms described
above may be used for the level of association determination.
[0055] At block 428, a search for items associated with the search
query selected in the previous step is conducted. The item
information is retrieved from one or more data stores associated
with the Web site in accordance with the request. Finally, at block
432, the second search results are rendered for display to the user
and the process 400 ends. In one embodiment, the results retrieved
at block 414 may be first rendered for display "in-line" using a
trusted script embedded in a page of the Web site as described
above. In this instance, the page does not have to be reloaded. The
second search results may include the search query identified by
the search system and used in the second search.
[0056] It is to be understood that a user may conduct any number of
searches, such as more than two searches as described above. In
this case, a subsequent search may utilize the search techniques
described above: a user may again select a "more like this" content
element associated with another similar item returned with the
second search results as a result of the previous search and the
process of selecting a search query with the strongest behavioral
association with that item and subsequent search utilizing the
selected query may occur.
[0057] FIG. 5 illustrates the process flow diagram for the
operation of one aspect of the search service in accordance with an
embodiment. Specifically, FIG. 5 illustrates a method for
determination of search queries behaviorally associated with an
item searchable on the Web site. The method involves assembling and
recording information related to users' search for items on the Web
site using various search queries. As discussed above, a search
query may comprise one or more keywords to be input in a search
engine, e.g., "Panasonic TV," "LCD TV," "TV set," "television set"
and the like. At block 502, for all items, the search queries used
by users in relation to a particular item are identified. For
example, if the queries "Panasonic TV" and "LCD TV" were used to
identify (e.g., by clicking, viewing, or purchasing) a particular
television set (e.g., a Panasonic LCD TV of a particular model),
the identified television set will be associated with the search
queries "Panasonic TV" and "LCD TV."
[0058] At decision block 506, it is determined whether a user
behavior demonstrates a behavioral association of an item and a
search query. As noted above, a behavioral association can be
detected by analyzing user session data of user sessions.
Accordingly, if it is determined that one or more search queries
are not behaviorally associated with an item, these search queries
may be excluded from a behavioral association. For example, it may
be determined that the search query "LCD TV" has a weak behavioral
association with the Panasonic LCD TV of the particular model and
thus should not be included in the list of search queries
behaviorally associated with that TV set. This determination, i.e.
a determination of a degree (level) of association may be
implemented in a number of ways, as briefly described above.
[0059] For example, a weight value may be calculated based on user
session data that reveals the activity of a user in relation to a
search for the item. If a user purchases a product for which a
behavioral association exists, this activity can be weighted
heavily, as a purchase decision can be deemed to represent a high
degree of behavioral association. Likewise, if a user views an item
and adds the item to a shopping cart, but does not purchase the
item, this activity can be weighted less than a purchase decision.
Additionally, if a user simply views an item but fails to either
add the item to a shopping cart or purchase the item, this activity
can be weighted to an even lesser degree when calculating a weight
value. Accordingly, at block 506 a determination is made as to
whether a behavioral association between the query and item exists,
and a level (degree) of the behavioral association may also be
calculated. The level of association may be determined at different
times. For example, as described above, the level of association
may be calculated when a request for a search query with the
strongest behavioral association is received from a user accessing
the "more like this" content element associated with the similar
item returned by the initial search as described above in reference
to FIG. 4.
[0060] If it is determined that a search query belongs to a list of
search queries behaviorally associated with a particular item, at
block 510 the query is added to the list of behaviorally associated
search queries. At block 514, the list may be stored in one or more
behavioral association data stores. As noted above, the data store
may be implemented as a "remote" or "local" data store depending on
a particular implementation of the search service described herein.
Thus, a database of search queries behaviorally associated with the
items searchable on the Web site may be assembled and the process
514 ends.
[0061] FIG. 6 illustrates a process flow diagram for the operation
of the search service in accordance with an embodiment. At block
602, the first search request (as described in reference to FIG. 4)
is received, a corresponding search is executed, and first search
results are provided and rendered for display. At decision block
606, it is determined whether an item that in user's view is
similar to a desired item among the items returned with the first
search results has been identified. If the similar item has been
identified, at decision block 610 it is determined whether a search
query with the strongest behavioral association with the similar
item has been identified. If no such query has been identified, at
block 614 a selection algorithm implemented as described above is
applied in order to identify a search query with the strongest
behavioral association with the similar item.
[0062] It should be noted that, in some embodiments, more than one
search query with the strongest behavioral associations may be
identified. For example, a number of search queries with the
strongest behavioral association to be selected among the search
queries may be predetermined (e.g., two, three, or four queries).
In one embodiment, all search queries that have a behavioral
association with the item may be selected to be used in further
search. When the search query with the strongest behavioral
association with the similar item has been identified, at block
618, a new search utilizing the selected query is conducted and the
results are rendered to the user. In one embodiment, the selected
query may also be rendered to the user so that the user can
ascertain which query was selected to assist the user in his/her
search. Subsequently, the user may refine his or her search by
selecting another similar to a desired item rendered in the search
results and receiving the new search results associated with the
search query with the strongest behavioral association with the new
similar item.
[0063] FIG. 7 illustrates an exemplary Web page 700 showing search
results in accordance with an embodiment. The Web page includes a
number of item categories 702 as shown on the left side of the page
700, such as "any," "music," "books," "movies," and "downloads." It
will be appreciated that any number of item categories of any kind
may be included in the Web page. Alternatively, the Web page may
contain no categories at all; the item categories are shown in FIG.
7 for illustrative purposes only. The page 700 includes the search
window 704 and the search button. As shown in the window 704, a
user is conducting a search for "John Doe." The (first) search
results 706 are displayed in response to the search for "John Doe."
The search results, as shown in FIG. 7, may include CDs of John
Doe, John Doe's biography, John Doe's MP3 available for download,
John Doe's memoirs, and biography.
[0064] Additional information associated with each search result
(item) may be presented, such as an indication whether the item is
new or used, price, shipping information, availability information
and the category to which the item belongs. The item information
may contain the item description, such as, for example, item 1, a
CD, is described as "John Doe's Silver Collection 1," item 2 is
"John Doe's Biography," item 3 is "John Doe's Silver Collection 2,"
and so on. A content element 710 described above in reference to
FIG. 4 is also included in the item information for each item in
the search results. As illustrated, the content element includes a
descriptor "more like this." As noted above in reference to FIGS.
4-6, clicking on the "more like this" element associated with a
similar item a user may trigger further, more focused search. In
this particular example, a user clicked on the "more like this"
element next to item 2 "John Doe's Biography," as illustrated by a
cursor 712 applied to the content element. As described above in
reference to FIGS. 4-6, this action may trigger a retrieval of the
search queries behaviorally associated with item 2, a selection of
the search query with the strongest behavioral association with
item 2, and subsequent search using the selected query.
[0065] FIG. 8 illustrates an exemplary Web page showing the refined
search results in accordance with an embodiment. The refined
(second) search results are returned in response to a search using
the selected query with the strongest behavioral association with
item 2. FIG. 8 includes the Web page 800 with a Web browser which
renders page 800 for display to the user. As in FIG. 7, the Web
page contains search results 806 including the "more like this"
content elements 810 associated with each item returned in the
search results 806. The Web page further includes a number of item
categories 802 as shown on the left side of the page 800, such as
"any," "music," "books," "movies," and "downloads."
[0066] A search query with the strongest association with item 2
that has been identified and used in the current search may be
shown in the search window 804. As illustrated in window 804, the
selected search query that was used in the current search is "John
Doe books." As shown in the search results 806, all items returned
with the search results are indeed books or related to books
written by, or about, John Doe. As noted above, a user may further
narrow down his/her search by clicking on the "more like this"
content element associated with the item that, in user's view, is
the closest to the item desired by the user. The process will then
again retrieve and select a search query with the strongest
behavioral association with the newly identified similar item, the
new search using the selected query will be conducted, and new
search results will be returned and rendered to the user.
[0067] As discussed above, the various embodiments can be
implemented in a wide variety of operating environments, which in
some cases can include one or more client computers, computing
devices, or processing devices which can be used to operate any of
a number of applications. Client devices can include any of a
number of general purpose personal computers, such as desktop or
laptop computers running a standard operating system, as well as
cellular, wireless, and handheld devices running mobile software
and capable of supporting a number of networking and messaging
protocols. Such a system also can include a number of workstations
running any of a variety of commercially available operating
systems and other known applications for purposes such as
development and database management. These devices also can include
other electronic devices, such as dummy terminals, thin-clients,
gaming systems, and other devices capable of communicating via a
network.
[0068] Various aspects also can be implemented as part of at least
one service or Web service, such as may be part of a
service-oriented architecture. Services such as Web services can
communicate using any appropriate type of messaging, such as by
using messages in extensible markup language (XML) format and
exchanged using an appropriate protocol such as SOAP (derived from
the "Simple Object Access Protocol"). Processes provided or
executed by such services can be written in any appropriate
language, such as the Web Services Description Language (WSDL).
Using a language such as WSDL allows for functionality such as the
automated generation of client-side code in various SOAP
frameworks.
[0069] Most embodiments utilize at least one network that would be
familiar to those skilled in the art for supporting communications
using any of a variety of commercially available protocols, such as
TCP/IP, OSI, FTP, UPnP, NFS, CIFS, and AppleTalk. The network can
be, for example, a local area network, a wide-area network, a
virtual private network, the Internet, an intranet, an extranet, a
public switched telephone network, an infrared network, a wireless
network, and any combination thereof.
[0070] In embodiments utilizing a Web server, the Web server can
run any of a variety of server or mid-tier applications, including
HTTP servers, FTP servers, CGI servers, data servers, Java servers,
and business application servers. The server(s) also may be capable
of executing programs or scripts in response to requests from
client devices, such as by executing one or more Web applications
that may be implemented as one or more scripts or programs written
in any programming language, such as Java.RTM., C, C# or C++, or
any scripting language, such as Perl, Python, or TCL, as well as
combinations thereof. The server(s) may also include database
servers, including without limitation, those commercially available
from Oracle.RTM., Microsoft.RTM., Sybase.RTM., and IBM.RTM..
[0071] The environment can include a variety of data stores and
other memory and storage media as discussed above. These can reside
in a variety of locations, such as on a storage medium local to
(and/or resident in) one or more of the computers or remote from
any or all of the computers across the network. In a particular set
of embodiments, the information may reside in a storage-area
network ("SAN") familiar to those skilled in the art. Similarly,
any necessary files for performing the functions attributed to the
computers, servers, or other network devices may be stored locally
and/or remotely, as appropriate. Where a system includes
computerized devices, each such device can include hardware
elements that may be electrically coupled via a bus, the elements
including, for example, at least one central processing unit (CPU),
at least one input device (e.g., a mouse, keyboard, controller,
touch screen, or keypad), and at least one output device (e.g., a
display device, printer, or speaker). Such a system may also
include one or more storage devices, such as disk drives, optical
storage devices, and solid-state storage devices, such as random
access memory ("RAM") or read-only memory ("ROM"), as well as
removable media devices, memory cards, flash cards, and the
like.
[0072] Such devices also can include a computer-readable storage
media reader, a communications device (e.g., a modem, a network
card (wireless or wired), an infrared communication device), and
working memory as described above. The computer-readable storage
media reader can be connected with, or configured to receive, a
computer-readable storage medium, representing remote, local,
fixed, and/or removable storage devices, as well as storage media
for temporarily and/or more permanently containing, storing,
transmitting, and retrieving computer-readable information. The
system and various devices also typically will include a number of
software applications, modules, services, or other elements located
within at least one working memory device, including an operating
system and application programs, such as a client application or
Web browser. It should be appreciated that alternate embodiments
may have numerous variations from that described above. For
example, customized hardware might also be used and/or particular
elements might be implemented in hardware, software (including
portable software, such as applets), or both. Further, connection
to other computing devices such as network input/output devices may
be employed.
[0073] Storage media and computer-readable media for containing
code, or portions of code, can include any appropriate media known
or used in the art, including storage media and communication
media, such as, but not limited to, volatile and non-volatile,
removable and non-removable media implemented in any method or
technology for storage and/or transmission of information such as
computer-readable instructions, data structures, program modules,
or other data, including RAM, ROM, EEPROM, flash memory or other
memory technology, CD-ROM, digital versatile disk (DVD) or other
optical storage, magnetic cassettes, magnetic tape, magnetic disk
storage or other magnetic storage devices, or any other medium
which can be used to store the desired information and which can be
accessed by the system device. Based on the disclosure and
teachings provided herein, a person of ordinary skill in the art
will appreciate other ways and/or methods to implement the various
embodiments.
[0074] The specification and drawings are, accordingly, to be
regarded in an illustrative rather than a restrictive sense. It
will, however, be evident that various modifications and changes
may be made thereunto without departing from the broader spirit and
scope of the present disclosure as set forth in the claims.
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