U.S. patent application number 17/092509 was filed with the patent office on 2021-02-25 for snippet generation and item description summarizer.
The applicant listed for this patent is eBay Inc.. Invention is credited to Justin Nicholas House, Chandra Prakash Khatri, Selcuk Kopru, Nish Parikh, Sameep Navin Solanki.
Application Number | 20210056265 17/092509 |
Document ID | / |
Family ID | 1000005199256 |
Filed Date | 2021-02-25 |
United States Patent
Application |
20210056265 |
Kind Code |
A1 |
Khatri; Chandra Prakash ; et
al. |
February 25, 2021 |
SNIPPET GENERATION AND ITEM DESCRIPTION SUMMARIZER
Abstract
In various example embodiments, a system and method for a Target
Language Engine are presented. The Target Language Engine augments
a synonym list in a base dictionary of a target language with one
or more historical search queries previously submitted to search
one or more listings in listing data. The Target Language Engine
identifies a compound word and a plurality of words present in the
listing data that have a common meaning in the target language.
Each word from the plurality of words is present in the compound
word. The Target Language Engine causes a database to create an
associative link between the portion of text and a word selected
from at least one of the synonym list or the plurality of
words.
Inventors: |
Khatri; Chandra Prakash;
(San Jose, CA) ; Kopru; Selcuk; (Santa Clara,
CA) ; Parikh; Nish; (Fremont, CA) ; House;
Justin Nicholas; (San Jose, CA) ; Solanki; Sameep
Navin; (San Jose, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
eBay Inc. |
San Jose |
CA |
US |
|
|
Family ID: |
1000005199256 |
Appl. No.: |
17/092509 |
Filed: |
November 9, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16546718 |
Aug 21, 2019 |
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17092509 |
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15237091 |
Aug 15, 2016 |
10521509 |
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16546718 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 40/242 20200101;
G06F 16/345 20190101; G06F 40/169 20200101; G06F 16/3349 20190101;
G06F 40/284 20200101; G06F 16/374 20190101 |
International
Class: |
G06F 40/284 20060101
G06F040/284; G06F 16/36 20060101 G06F016/36; G06F 16/33 20060101
G06F016/33; G06F 16/34 20060101 G06F016/34; G06F 40/169 20060101
G06F040/169; G06F 40/242 20060101 G06F040/242 |
Claims
1. A system comprising: a processor; and a memory coupled to the
processor and storing instructions that, when executed by the
processor, cause the system to perform operations comprising:
receiving a search query from a mobile device that is mapped to a
listing webpage for an item; selecting one or more text portions
from a plurality of text portions within the listing webpage based
at least in part on a relevancy determination for the one or more
text portions; generating a listing snippet based at least in part
on the one or more text portions; and transmitting the listing
snippet to the mobile device based at least in part on the search
query.
2. The system of claim 1, wherein the instructions to select the
one or more text portions, when executed by the processor, further
cause the system to perform operations comprising: selecting a
first text portion of the one or more text portions based at least
in part on a relevancy score of the first text portion satisfying a
threshold relevancy score.
3. The system of claim 1, the operations further comprising:
comparing the plurality of text portions to a list of keywords;
identifying a subset of the plurality of text portions that
includes one or more words from the list of keywords; and selecting
the one or more text portions from the subset of the plurality of
text portions.
4. The system of claim 1, the operations further comprising:
comparing the plurality of text portions to a list of avoid words;
identifying a subset of the plurality of text portions that
includes one or more words from the list of avoid words; and
refraining from selecting the one or more text portions from the
subset of the plurality of text portions.
5. The system of claim 1, the operations further comprising:
comparing one or more text portions of the plurality of text
portions to a list of words; identifying a subset of words in the
one or more text portions that include one or more words from the
list of words; removing, from the one or more text portions, the
subset of words that include the one or more words from the list of
words; and generating the listing snippet based at least in part on
removing the subset of words from the one or more text
portions.
6. The system of claim 1, the operations further comprising:
scoring the relevancy determination of the one or more text
portions based at least in part on a number of instances of one or
more keywords present in each of the one or more text portions.
7. The system of claim 6, wherein the instructions to generate the
listing snippet, when executed by the processor, further cause the
system to perform operations comprising: generating the listing
snippet that includes a first text portion of the one or more text
portions based at least in part on the first text portion
corresponding to a threshold relevancy determination.
8. A computer implemented method comprising: receiving, by at least
one processor, a search query from a mobile device that is mapped
to a listing webpage for an item; selecting one or more text
portions from a plurality of text portions within the listing
webpage based at least in part on a relevancy determination for the
one or more text portions; generating a listing snippet based at
least in part on the one or more text portions; and transmitting
the listing snippet to the mobile device based at least in part on
the search query.
9. The computer implemented method of claim 8, wherein selecting
the one or more text portions comprises: selecting a first text
portion of the one or more text portions based at least in part on
a relevancy score of the first text portion satisfying a threshold
relevancy score.
10. The computer implemented method of claim 8, further comprising:
comparing the plurality of text portions to a list of keywords;
identifying a subset of the plurality of text portions that
includes one or more words from the list of keywords; and selecting
the one or more text portions from the subset of the plurality of
text portions.
11. The computer implemented method of claim 8, further comprising:
comparing the plurality of text portions to a list of avoid words;
identifying a subset of the plurality of text portions that
includes one or more words from the list of avoid words; and
refraining from selecting the one or more text portions from the
subset of the plurality of text portions.
12. The computer implemented method of claim 8, further comprising:
comparing one or more text portions of the plurality of text
portions to a list of words; identifying a subset of words in the
one or more text portions that include one or more words from the
list of words; removing, from the one or more text portions, the
subset of words that include the one or more words from the list of
words; and generating the listing snippet based at least in part on
removing the subset of words from the one or more text
portions.
13. The computer implemented method of claim 8, further comprising:
scoring the relevancy determination of the one or more text
portions based at least in part on a number of instances of one or
more keywords present in each of the one or more text portions.
14. The computer implemented method of claim 13, wherein generating
the listing snippet comprises: generating the listing snippet that
includes a first text portion of the one or more text portions
based at least in part on the first text portion corresponding to a
threshold relevancy determination.
15. A non-transitory computer-readable medium storing instructions
which, when executed by a processor, cause the processor to perform
operations comprising: receiving a search query from a mobile
device that is mapped to a listing webpage for an item; selecting
one or more text portions from a plurality of text portions within
the listing webpage based at least in part on a relevancy
determination for the one or more text portions; generating a
listing snippet based at least in part on the one or more text
portions; and transmitting the listing snippet to the mobile device
based at least in part on the search query.
16. The non-transitory computer-readable medium of claim 15,
wherein the instructions, to select the one or more text portions,
when executed, further cause the processor to perform operations
comprising: selecting a first text portion of the one or more text
portions based at least in part on a relevancy score of the first
text portion satisfying a threshold relevancy score.
17. The non-transitory computer-readable medium of claim 15,
wherein the instructions, when executed, further cause the
processor to perform operations comprising: comparing the plurality
of text portions to a list of keywords; identifying a subset of the
plurality of text portions that include one or more words from the
list of keywords; and selecting the one or more text portions from
the subset of the plurality of text portions.
18. The non-transitory computer-readable medium of claim 15,
wherein the instructions, when executed, further cause the
processor to perform operations comprising: comparing the plurality
of text portions to a list of avoid words; identifying a subset of
the plurality of text portions that include one or more words from
the list of avoid words; and refraining from selecting the one or
more text portions from the subset of the plurality of text
portions.
19. The non-transitory computer-readable medium of claim 15,
wherein the instructions, when executed, further cause the
processor to perform operations comprising: comparing one or more
text portions of the plurality of text portions to a list of words;
identifying a subset of words in the one or more text portions that
include one or more words from the list of words; removing, from
the one or more text portions, the subset of words that include the
one or more words from the list of words; and generating the
listing snippet based at least in part on removing the subset of
words from the one or more text portions.
20. The non-transitory computer-readable medium of claim 15,
wherein the instructions, when executed, further cause the
processor to perform operations comprising: scoring the relevancy
determination of the one or more text portions based at least in
part on a number of instances of one or more keywords present in
each of the one or more text portions.
Description
CLAIM OF PRIORITY
[0001] This Application is a continuation of U.S. patent
application Ser. No. 16/546,718, entitled "SNIPPET GENERATION AND
ITEM DESCRIPTION SUMMARIZER," filed Aug. 21, 2019; which is a
continuation of U.S. patent application Ser. No. 15/237,091,
entitled "SNIPPET GENERATION AND ITEM DESCRIPTION SUMMARIZER,"
filed Aug. 15, 2016, now U.S. Pat. No. 10,521,509, issued Dec. 31,
2019; each of which is hereby incorporated by reference in its
entirety.
TECHNICAL FIELD
[0002] The subject matter disclosed herein generally relates to the
technical field of special-purpose machines that facilitate
augmenting a base dictionary and identifying a plurality of words
that share a meaning with a compound word, including
software-configured computerized variants of such special-purpose
machines and improvements to such variants, and to the technologies
by which such special-purpose machines become improved compared to
other special-purpose machines that facilitate the augmenting a
base dictionary and identifying a plurality of words that share a
meaning with a compound word.
BACKGROUND
[0003] Many conventional websites that provide web content are
accessible via mobile devices. The web content can be reformatted
for display on a mobile device. Such reformatting allows a user of
a mobile device to use their finger on the display screen to scroll
through and view the web content. In some conventional systems, the
web content is presented according to a different content layout
that is suitable of mobile device display. For example, the web
content includes multiple images that are concurrently displayed
when accessed from a personal computer or laptop. However, when
accessed by a mobile device, a single image may be initially
displayed and each additional image can be subsequently displayed
in response to the user pressing their finger on a currently
displayed image. Different content layouts for mobile devices also
include defining new display positions for various portions of the
web content in order to optimize display of the web content on a
mobile device.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] Various ones of the appended drawings merely illustrate
example embodiments of the present disclosure and cannot be
considered as limiting its scope.
[0005] FIG. 1 is a block diagram illustrating a networked system,
according to some example embodiments.
[0006] FIG. 2 is a block diagram showing example components of a
Listing Engine according to an example embodiment.
[0007] FIG. 3 is a block diagram illustrating a snippet for a given
listing according to an example embodiment.
[0008] FIG. 4 is a flow diagram illustrating a method of augmenting
a base dictionary, in accordance with an example embodiment.
[0009] FIG. 5 is a flow diagram illustrating a method of building
and training compound splitter module, in accordance with an
example embodiment.
[0010] FIG. 6 is a block diagram illustrating an example of a
software architecture that may be installed on a machine, according
to some example embodiments.
[0011] FIG. 7 illustrates a diagrammatic representation of a
machine in the form of a computer system within which a set of
instructions may be executed for causing the machine to perform any
one or more of the methodologies discussed herein, according to an
example embodiment.
[0012] The headings provided herein are merely for convenience and
do not necessarily affect the scope or meaning of the terms
used.
DETAILED DESCRIPTION
[0013] The description that follows describes systems, methods,
techniques, instruction sequences, and computing machine program
products that illustrate example embodiments of the disclosure. In
the following description, for the purposes of explanation,
numerous specific details are set forth in order to provide an
understanding of various embodiments of the inventive subject
matter.
[0014] In various example embodiments, a Target Language Engine
augments a synonym list in a base dictionary of a target language
with one or more historical search queries previously submitted to
search one or more listings in listing data. The Target Language
Engine identifies a compound word and a plurality of words present
in the listing data that have a common meaning in the target
language. Each word from the plurality of words is present in the
compound word.
[0015] The Target Language Engine causes a database to create an
associative link between the portion of text and a word selected
from at least one of the synonym list or the plurality of words.
For example, an associative link in the database can be a
relationship between a synonym of a word present in the portion of
text and the portion of text such that the synonym is accounted for
when scoring the portion of text according to a text scoring
algorithm. The associative link in the database can be a
relationship between a plurality of words and the portion of text
such that the plurality of words are accounted for when scoring the
portion of text.
[0016] The Target Language Engine analyzes one or more portions of
text (such as sentences) extracted from a given listing in order to
calculate a relevance score of the extracted text. The relevance
score of extracted text is based on one or more synonyms in the
synonym list for words that appear in the extracted text. The
relevance score is further based on identifying a plurality of
individual words that share a meaning in the target language with
an instance of a compound word that appears in the extracted text.
One or more portions of extracted text that have a respective score
that satisfies a score threshold are selected for inclusion in a
snippet for the given listing.
[0017] The Target Language Engine generates the listing snippet
that includes the most relevant extracted text. A listing snippet
is a partial view of the given listing suitable for display on a
mobile device. By displaying a listing snippet on a display of a
mobile device, the most relevant content of a listing is presented
to a user of the mobile device and the end-user experience is
greatly improved thereby increasing user engagement. While
conventional systems may reformat the layout of web content for
mobile devices, conventional systems fail to identify the most
relevant content for display on a mobile device.
[0018] It is understood that a portion of text extracted from a
given listing can be one or more sentences, a plurality of strings,
and a plurality of tokens. A sentence includes one or more words
(or strings).
[0019] With reference to FIG. 1, an example embodiment of a
high-level client-server-based network architecture 100 is shown. A
networked system 102, in the example forms of a network-based
marketplace or payment system, provides server-side functionality
via a network 104 (e.g., the Internet or wide area network (WAN))
to one or more client devices 110. FIG. 1 illustrates, for example,
a web client 112 (e.g., a browser, such as the Internet
Explorer.RTM. browser developed by Microsoft.RTM. Corporation of
Redmond, Wash.), an application 114, and a programmatic client 116
executing on client device 110.
[0020] The client device 110 may comprise, but are not limited to,
a mobile phone, desktop computer, laptop, portable digital
assistants (PDAs), smart phones, tablets, ultra books, netbooks,
laptops, multi-processor systems, microprocessor-based or
programmable consumer electronics, game consoles, set-top boxes, or
any other communication device that a user may utilize to access
the networked system 102. In some embodiments, the client device
110 may comprise a display module (not shown) to display
information (e.g., in the form of user interfaces). In further
embodiments, the client device 110 may comprise one or more of a
touch screens, accelerometers, gyroscopes, cameras, microphones,
global positioning system (GPS) devices, and so forth. The client
device 110 may be a device of a user that is used to perform a
transaction involving digital items within the networked system
102. In one embodiment, the networked system 102 is a network-based
marketplace that responds to requests for product listings,
publishes publications comprising item listings of products
available on the network-based marketplace, and manages payments
for these marketplace transactions. One or more users 106 may be a
person, a machine, or other means of interacting with client device
110. In embodiments, the user 106 is not part of the network
architecture 100, but may interact with the network architecture
100 via client device 110 or another means. For example, one or
more portions of network 104 may be an ad hoc network, an intranet,
an extranet, a virtual private network (VPN), a local area network
(LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless
WAN (WWAN), a metropolitan area network (MAN), a portion of the
Internet, a portion of the Public Switched Telephone Network
(PSTN), a cellular telephone network, a wireless network, a WiFi
network, a WiMax network, another type of network, or a combination
of two or more such networks.
[0021] Each of the client device 110 may include one or more
applications (also referred to as "apps") such as, but not limited
to, a web browser, messaging application, electronic mail (email)
application, an e-commerce site application (also referred to as a
marketplace application), and the like. In some embodiments, if the
e-commerce site application is included in a given one of the
client device 110, then this application is configured to locally
provide the user interface and at least some of the functionalities
with the application configured to communicate with the networked
system 102, on an as needed basis, for data and/or processing
capabilities not locally available (e.g., access to a database of
items available for sale, to authenticate a user, to verify a
method of payment, etc.). Conversely if the e-commerce site
application is not included in the client device 110, the client
device 110 may use its web browser to access the e-commerce site
(or a variant thereof) hosted on the networked system 102.
[0022] One or more users 106 may be a person, a machine, or other
means of interacting with the client device 110. In example
embodiments, the user 106 is not part of the network architecture
100, but may interact with the network architecture 100 via the
client device 110 or other means. For instance, the user provides
input (e.g., touch screen input or alphanumeric input) to the
client device 110 and the input is communicated to the networked
system 102 via the network 104. In this instance, the networked
system 102, in response to receiving the input from the user,
communicates information to the client device 110 via the network
104 to be presented to the user. In this way, the user can interact
with the networked system 102 using the client device 110.
[0023] An application program interface (API) server 120 and a web
server 122 are coupled to, and provide programmatic and web
interfaces respectively to, one or more application servers 140.
The application servers 140 may host one or more publication
systems 142 and payment systems 144, each of which may comprise one
or more modules or applications and each of which may be embodied
as hardware, software, firmware, or any combination thereof. The
application servers 140 are, in turn, shown to be coupled to one or
more database servers 124 that facilitate access to one or more
information storage repositories or database(s) 126. In an example
embodiment, the databases 126 are storage devices that store
information to be posted (e.g., publications or listings) to the
publication system 120. The databases 126 may also store digital
item information in accordance with example embodiments.
[0024] Additionally, a third party application 132, executing on
third party server(s) 130, is shown as having programmatic access
to the networked system 102 via the programmatic interface provided
by the API server 120. For example, the third party application
132, utilizing information retrieved from the networked system 102,
supports one or more features or functions on a website hosted by
the third party. The third party website, for example, provides one
or more promotional, marketplace, or payment functions that are
supported by the relevant applications of the networked system
102.
[0025] The publication systems 142 may provide a number of
publication functions and services to users 106 that access the
networked system 102. The payment systems 144 may likewise provide
a number of functions to perform or facilitate payments and
transactions. While the publication system 142 and payment system
144 are shown in FIG. 1 to both form part of the networked system
102, it will be appreciated that, in alternative embodiments, each
system 142 and 144 may form part of a payment service that is
separate and distinct from the networked system 102. In some
embodiments, the payment systems 144 may form part of the
publication system 142.
[0026] The Target Language Engine 150 provides functionality
operable to determine synonyms and one or more pluralities of
individual words to be accounted for during scoring one or more
portion of text. For example, the Target Language Engine 150
accesses the databases 126, the third party servers 130, the
publication system 120, and other sources. In some example
embodiments, the Target Language Engine 150 may communicate with
the publication systems 120 (e.g., accessing item listings) and
payment system 122. In an alternative embodiment, the Target
Language Engine 150 may be a part of the publication system
120.
[0027] Further, while the client-server-based network architecture
100 shown in FIG. 1 employs a client-server architecture, the
present inventive subject matter is of course not limited to such
an architecture, and could equally well find application in a
distributed, or peer-to-peer, architecture system, for example. The
various publication system 142, payment system 144, and Target
Language Engine 150 could also be implemented as standalone
software programs, which do not necessarily have networking
capabilities.
[0028] The web client 112 may access the various publication and
payment systems 142 and 144 via the web interface supported by the
web server 122. Similarly, the programmatic client 116 accesses the
various services and functions provided by the publication and
payment systems 142 and 144 via the programmatic interface provided
by the API server 120. The programmatic client 116 may, for
example, be a seller application (e.g., the Turbo Lister
application developed by eBay.RTM. Inc., of San Jose, Calif.) to
enable sellers to author and manage listings on the networked
system 102 in an off-line manner, and to perform batch-mode
communications between the programmatic client 116 and the
networked system 102.
[0029] Additionally, a third party application(s) 128, executing on
a third party server(s) 130, is shown as having programmatic access
to the networked system 102 via the programmatic interface provided
by the API server 114. For example, the third party application
128, utilizing information retrieved from the networked system 102,
may support one or more features or functions on a website hosted
by the third party. The third party website may, for example,
provide one or more promotional, marketplace, or payment functions
that are supported by the relevant applications of the networked
system 102.
[0030] FIG. 2 is a block diagram showing example components of a
Listing Engine 150 according to an example embodiment. While the
modules 210, 220, 230, 240, 250 and 260 are described as performing
specific operations, one of ordinary skill in the art will
appreciate that such modules may be configured to perform
alternative and/or additional operations.
[0031] In one example embodiment, the input module 210 is a
hardware-implemented module that controls, manages and stores
information related to any inputs from one or more components of
system 102 as illustrated in FIG. 1. In various example
embodiments, input can be one or more listings, listing data,
historical data (such as historical search queries), a base
dictionary of a target language and one or more portions of text
extracted from a given listing.
[0032] In one example embodiment, the augmenting module 220 is a
hardware-implemented module which manages, controls, stores, and
accesses information related to adding words and synonyms to a base
dictionary of a target language. In various example embodiments,
the Target Language Engine 150 adds words, new words, synonyms, and
new synonym lists to the based dictionary according historical
search queries and portions of historical search queries.
[0033] In one example embodiment, the compound splitter module 230
is a hardware-implemented module which manages, controls, stores,
and accesses information related to identifying a compound word and
a plurality of individual words have a shared meaning in a target
language. In example embodiments, the compound splitter module 230
receives as input a compound word present in a portion of text
extracted from a given listing. The compound splitter module 230
identifies a plurality of words that share a meaning in a target
language with the compound word.
[0034] In one example embodiment, the scoring module 240 is a
hardware-implemented module which manages, controls, stores, and
accesses information related to scoring a portion of extracted
text. In example embodiments, scoring of a portion of text
extracted from a given listing will account for one or more
synonyms of a word present in the extracted text. In addition,
scoring of a portion of text extracted from a given listing will
account for a plurality of individuals that share the same meaning
with a compound word present in the extracted text. It is
understand that various text scoring algorithms can be implemented
to score portions of texts extracted from a given listing.
[0035] In one example embodiment, the snippet module 250 is a
hardware-implemented module which manages, controls, stores, and
accesses information related to generating a snippet for display on
a mobile device. In various example embodiments, the snippet module
250 identifies one or more portions of text extracted from a given
listing that have a respective relevance score that meets a score
requirement, such as a score threshold. Those portions of text
which meet the score requirement are selected by the snippet module
250 for inclusion in a snippet. The snippet module 250 renders or
triggers rendering of the snippet for display on a mobile
device.
[0036] In one example embodiment, the output module 260 is a
hardware-implemented module that controls, manages and stores
information related to which sends any outputs to one or more
components of system 100 of FIG. 1 (e.g., one or more client
devices 110, 112, third party server 130, etc.) or to one more
components of system 102 of FIG. 1. In some example embodiments,
the output is is a snippet for a given listing. The output can also
be a rendered snippet for a given listing.
[0037] FIG. 3 is a block diagram illustrating a snippet for a given
listing according to an example embodiment.
[0038] In various example embodiments, the Target Language Engine
150 accesses a listing 300 that includes web content describing an
item for sale. The structure of the listing 300 is based on a
pre-defined listing structure with one or more types of listing
sections. Various listing sections are, for example, a Title, a
Product Description, item features, item functions, accessory
description and structural information (brand, model number, Stock
Keeping Unit (SKU) number, and technical specifications). The text
of each section of a given listing are analyzed by the Target
Language Engine 150 to generate a listing snippet 310.
[0039] A listing snippet 310 is a partial view of the web content
of the given listing 300 suitable for display on a mobile device.
The Target Language Engine 150 scores text from one or more
sections of the given listing 300 to determine which portions of
text include the most relevant text 305. The Target Language Engine
150 generates a listing snippet 310 for the given listing 300 that
includes the most relevant text portion 305. The most relevant text
portion 305 is included in a description section 315 of the listing
snippet 310
[0040] According to example embodiments, to score the one or more
listing sections of the given listing 300, the Target Language
Engine 150 extracts one or more sentences from the listing sections
and discards those sentences that exceed a maximum length or fail
to meet a minimum length. In addition, the Target Language Engine
150 compares the text in the remaining (non-discarded) sentences
against a list of unnecessary words (hereinafter "blacklist").
Based on detecting that a word from the blacklist appears in a
sentence, the Target Language Engine 150 modifies the sentence by
deleting the blacklist word from the sentence. The Target Language
Engine 150 also compares the text in the remaining (non-discarded)
sentences to a list of topic keywords. The list of topic keywords
are pre-determined as words that are typically highly relevant.
Topic keywords can have an inverse document frequency ("IDF") score
throughout a corpus of the listing data that meets a minimum IDF
score. The more topic keywords that appear in a given extracted
sentence, the higher the relevance score for the given sentence.
Such scoring calculation can also account for synonyms of words
present in an extracted sentence and for any compound word in an
extracted sentence.
[0041] In some example embodiments, the Target Language Engine 150
augments one or more synonym lists in a base dictionary of the
target language to build a robust amount of synonyms that are
relevant to listings in the listing data. The Target Language
Engine 150 also inserts new synonyms and new synonym lists in the
base dictionary. The Target Language Engine 150 augments the base
dictionary to include historical search queries (or portions of
historical search queries) previously submitted to search the
listing data. In addition, the Target Language Engine 150 builds
and trains the compound splitter module 230 in order to identify
when a compound word and a plurality of individual words share the
same meaning in the target language. The compound splitter module
230 receives as input a portion of text (such as a sentence) that
includes an instance of a compound word and the compound splitter
module 230 identifies a plurality of words that can be used for
scoring the portion of text.
[0042] Therefore, a sentence extracted from a given listing can be
scored as highly relevant based on a synonym of one or more words
in the sentence. In addition, scoring calculation can also be based
on a plurality of individual words in a compound word when a
sentence includes an instance of the compound word.
[0043] FIG. 4 is a flow diagram illustrating a method 400 of
augmenting a base dictionary, in accordance with an example
embodiment. It is understood that the method 400 illustrated by
FIG. 4 can be implemented by the augmenting module 220 of the
Target Language Engine 150.
[0044] At operation 405, the Target Language Engine 150 accesses a
first historical search query paired with a first related
historical search query. The first historical search query and the
first related historical search query corresponding to a first set
of historical user behaviors after search query submission.
[0045] At operation 410, the Target Language Engine 150 identifies
a first word in a base dictionary of a target language matches a
first historical search query. The base dictionary includes a
plurality of different words commonly used in the target
language.
[0046] In example embodiments, the Target Language Engine 150
accesses a plurality of historical search query mappings in a
target language in order to increase the amount of synonyms listed
in the base dictionary. An historical search query mapping
represents that two or more historical search queries (or query
portions) are synonyms. Such a determination that synonyms exists
between historical search queries is based on user behaviors made
in response to search results for each historical search query in
the historical search query mapping. Determination of the existence
of synonyms between historical search queries is thereby based on
the assumption that while two words may be different, they most
likely have the same meaning if the users who submitted the search
queries behave in a similar manner after submitting the search
queries. Such behaviors can include user selections (link clicks,
functionality selection), types of content viewed and purchase
activity (such as placing an item into a virtual shopping cart,
initiating a purchase of an item).
[0047] For example, a historical search query mapping is generated
by the Target Language Engine 150 based on a first user's
interaction with a first list of search results returned for a
first search query. The first user selected a search result that
was a reference to a webpage describing a particular item. A second
user submitted a second search query that was different than the
first search query. The second user received a second list of
search results for the second search query. The second user
selected a search result that was also a reference to a webpage
describing the same particular item. Such similarity in search
result selection by both users is a factor in determining whether
the first search query (or a portion of it) can be identified as a
synonym of the second search query (or a portion of it). When such
behaviors of both the first and second users meet a threshold
degree of similarity after submission of their respective first and
second search queries, a historical search query mapping is
generated by the Target Language Engine 150 to include the first
and second search queries. In an example embodiment, an historical
search query mapping can include one or more words from the first
search query and one or more words from the second search query. An
historical search query mapping can include any number of distinct
historical search queries.
[0048] At operation 415, the Target Language Engine 150 identifies
a synonym list for the first word in the base dictionary. For
example, the Target Language Engine 150 augments the base
dictionary with at least a portion of the plurality of historical
search query mappings The base dictionary includes base synonym
lists, each base synonym list includes words that are commonly
known in the target language to be synonyms of each other. The
Target Language Engine 150 accesses a first historical search query
mapping, which includes the first search query and a first related
historical search query. The Target Language Engine 150 determines
the first search query matches a word in the base dictionary.
[0049] At operation 420, the Target Language Engine 150 inserts the
first related historical search query in the synonym list for the
first word. For example, if the first related historical search
query is not listed in the matching word's base synonym list, the
Target Language Engine 150 inserts the first related historical
search query into the matching word's base synonym list.
[0050] At operation 425, based on detecting a lack of a matching
word in the base dictionary with the first related historical
search query, the Target Language Engine 150 inserts the first
related historical search query as a first new word in the base
dictionary. For example, while the first related historical search
query has been inserted into the base synonym list of the word that
matches the first search query, the Target Language Engine 150
determines whether the first related historical search query is
itself listed as a word in the base dictionary. If no match is
found, Target Language Engine 150 insert the first related
historical search query as a first new word in the base
dictionary.
[0051] At operation 430, the Target Language Engine 150 generates a
synonym list for the first new word in the base dictionary. For
example, the Target Language Engine 150 instantiates a synonym list
for the first new word in the base dictionary. The synonym list for
the first new word includes the original synonym list for the
matching word, the matching word itself and the first search
query.
[0052] At operation 435, the Target Language Engine 150 inserts the
synonym list for the first new word in the base dictionary.
[0053] In example embodiments, the Target Language Engine 150
accesses second historical search query mappings, which includes a
second historical search query paired with a second related
historical search query. The second historical search query and the
second related historical search query are classified by the Target
Language Engine 150 as synonyms based on a second set of historical
user behaviors that occurred after both queries were submitted. The
Target Language Engine 150 detects a lack of a matching word in the
base dictionary with the second historical search query and inserts
the second historical search query as a second new word in the base
dictionary. Target Language Engine 150 generates a synonym list for
the second new word in the base dictionary. The synonym list for
the second new word includes the second related historical search
query present in the second historical search query mapping. The
Target Language Engine 150 inserts the synonym list for the second
new word in the base dictionary. The Target Language Engine 150
inserts the second related historical search query as a third new
word in the base dictionary. The Target Language Engine 150
generates a synonym list for the third new word in the base
dictionary. The synonym list for the third new word includes the
second historical search query. The Target Language Engine 150
inserts the synonym list for the third new word in the base
dictionary. The Target Language Engine 150 thereby increases the
number of synonyms in the base dictionary of the target language to
also include search queries deemed to have similar meaning on the
basis of similarity of previous behaviors of users who submitted
those search queries.
[0054] According to example embodiments performing operations
405-435, the Target Language Engine 150 accesses an historical
search query of "transistorfassung", which has a plurality of
related historical search queries of: "transistor", "fassung",
"fassungen", "transistoren". The Target Language Engine 150
determines that there is no match in a base dictionary of the
target language for "transistorfassung". In response to determining
there is no match, the Target Language Engine 150 inserts
"transistorfassung" into the base dictionary as a first new word.
The Target Language Engine 150 instantiates a first synonym list
for the first new word. The instantiated first synonym list
includes "transistor", "fassung", "fassungen", "transistoren".
[0055] The Target Language Engine 150 performs the same operations
with respect to each related historical search query as well. For
example, the Target Language Engine 150 determines that there is no
match in the base dictionary for "fassungen". In response to
determining there is no match, the Target Language Engine 150
inserts "fassungen" into the base dictionary as a second new word.
The Target Language Engine 150 instantiates a second synonym list
for the second new word. The instantiated second synonym list
includes "transistor", "fassung", "transistorfassung",
"transistoren".
[0056] FIG. 5 is a flow diagram illustrating a method 500 of
building and training compound splitter module 230, in accordance
with an example embodiment. It is understood that the method 500
illustrated by FIG. 5 can be implemented by the compound splitter
module 230 of the Target Language Engine 150.
[0057] At operation 505, the Target Language Engine 150 determines
a frequency of the compound word in target language listing data
and a frequency of the plurality of words in the listing data each
meet a threshold frequency. For example, in addition to augmenting
the base dictionary of the target language with additional synonyms
and additional synonym lists, the Target Language Engine 150 builds
and trains a compound splitter module 230 that receives as input a
compound word (for example: "granitpflastersteine") of the target
language and returns as output a plurality of individual words (for
example: "granit" "pflaster" "steine") that combine to form the
input compound word. The plurality of individual words and the
compound word both have the same meaning according to the target
language.
[0058] However, if the compound word is partially split (for
example: "granit pflastersteine"), the target language assigns the
partially split compound word a different meaning--even though the
compound word and the plurality of individual words have the same
meaning. The Target Language Engine trains the compound splitter
module 230 to output individual words that have the same meaning as
the input compound word by differentiating when partially split
compound words have a different meaning. Therefore, the compound
splitter module 230 avoids providing as output a partially split
compound word that has a different meaning than an input compound
word.
[0059] The compound splitter module 230 is trained on historical
listing data that includes a plurality of listings of items for
sale. The Target Language Engine 150 determines that a compound
word (for example: "granitpflastersteine") and a plurality of
individual words (for example: "granit" "pflaster" "steine") have
the same meaning based on their respective number of occurrences in
the plurality of listings of items meeting a threshold frequency.
In other words, if the compound word's frequency of occurrence and
the frequency of occurrence for the plurality of individual words
both meet the threshold frequency, it can be assumed that compound
word and the plurality of individual words are each regularly used
in the target language according to some underlying meaning.
[0060] At operation 510, the Target Language Engine 150 determines
at least one similarity factor meets a corresponding threshold of
occurrence in respective listings that include the compound word
and respective listings that include the plurality of words, each
respective listing accessible in the target language listing
data.
[0061] For example, to add entries in the compound splitter module
230, Target Language Engine 150 analyzes listings in which the
compound word occurs and listings in which the plurality of
individual words occur and identifies factors that indicate the
compound word and the plurality of words most likely have the same
meaning in the target language. For example, a similarity factor
can be whether a Stock Keeping Identifier (SKU) number is common to
listings that include the compound word and listings that include
the plurality of individual words. Another similarity factor can be
how often the compound word and the plurality of individual words
occur in titles of the respective listings. Another similarity
factor can be whether a product category descriptor is common to
listings that include the compound word and listings that include
the plurality of individual words. Another similarity factor can be
whether a brand (or manufacturer) descriptor is common to listings.
Each similarity factor must meet a corresponding threshold of
occurrence to be considered relevant in determining whether the
compound word and the plurality of words have the same meaning.
[0062] At operation 515, based on the at least one similarity
factor meeting the corresponding threshold of occurrence, the
Target Language Engine 150 pairs the compound word with the
plurality of words in the compound splitter module 230 to represent
that the compound word and plurality of words have a shared meaning
in the target language listing data.
[0063] For example, if a threshold number of similarity factors
each meet their corresponding threshold of occurrence, the Target
Language Engine 150 determines that the compound word and the
plurality of words have the same meaning. The Target Language
Engine 150 inserts the compound word paired with the plurality of
words into the compound splitter module 230, where the pairing
represents that compound word and the plurality of words have the
same meaning.
Modules, Components, and Logic
[0064] Certain embodiments are described herein as including logic
or a number of components, modules, or mechanisms. Modules may
constitute either software modules (e.g., code embodied on a
machine-readable medium) or hardware modules. A "hardware module"
is a tangible unit capable of performing certain operations and may
be configured or arranged in a certain physical manner. In various
example embodiments, one or more computer systems (e.g., a
standalone computer system, a client computer system, or a server
computer system) or one or more hardware modules of a computer
system (e.g., a processor or a group of processors) may be
configured by software (e.g., an application or application
portion) as a hardware module that operates to perform certain
operations as described herein.
[0065] In some embodiments, a hardware module may be implemented
mechanically, electronically, or any suitable combination thereof.
For example, a hardware module may include dedicated circuitry or
logic that is permanently configured to perform certain operations.
For example, a hardware module may be a special-purpose processor,
such as a Field-Programmable Gate Array (FPGA) or an Application
Specific Integrated Circuit (ASIC). A hardware module may also
include programmable logic or circuitry that is temporarily
configured by software to perform certain operations. For example,
a hardware module may include software executed by a
general-purpose processor or other programmable processor. Once
configured by such software, hardware modules become specific
machines (or specific components of a machine) uniquely tailored to
perform the configured functions and are no longer general-purpose
processors. It will be appreciated that the decision to implement a
hardware module mechanically, in dedicated and permanently
configured circuitry, or in temporarily configured circuitry (e.g.,
configured by software) may be driven by cost and time
considerations.
[0066] Accordingly, the phrase "hardware module" should be
understood to encompass a tangible entity, be that an entity that
is physically constructed, permanently configured (e.g.,
hardwired), or temporarily configured (e.g., programmed) to operate
in a certain manner or to perform certain operations described
herein. As used herein, "hardware-implemented module" refers to a
hardware module. Considering embodiments in which hardware modules
are temporarily configured (e.g., programmed), each of the hardware
modules need not be configured or instantiated at any one instance
in time. For example, where a hardware module comprises a
general-purpose processor configured by software to become a
special-purpose processor, the general-purpose processor may be
configured as respectively different special-purpose processors
(e.g., comprising different hardware modules) at different times.
Software accordingly configures a particular processor or
processors, for example, to constitute a particular hardware module
at one instance of time and to constitute a different hardware
module at a different instance of time.
[0067] Hardware modules can provide information to, and receive
information from, other hardware modules. Accordingly, the
described hardware modules may be regarded as being communicatively
coupled. Where multiple hardware modules exist contemporaneously,
communications may be achieved through signal transmission (e.g.,
over appropriate circuits and buses) between or among two or more
of the hardware modules. In embodiments in which multiple hardware
modules are configured or instantiated at different times,
communications between such hardware modules may be achieved, for
example, through the storage and retrieval of information in memory
structures to which the multiple hardware modules have access. For
example, one hardware module may perform an operation and store the
output of that operation in a memory device to which it is
communicatively coupled. A further hardware module may then, at a
later time, access the memory device to retrieve and process the
stored output. Hardware modules may also initiate communications
with input or output devices, and can operate on a resource (e.g.,
a collection of information).
[0068] The various operations of example methods described herein
may be performed, at least partially, by one or more processors
that are temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Whether temporarily
or permanently configured, such processors may constitute
processor-implemented modules that operate to perform one or more
operations or functions described herein. As used herein,
"processor-implemented module" refers to a hardware module
implemented using one or more processors.
[0069] Similarly, the methods described herein may be at least
partially processor-implemented, with a particular processor or
processors being an example of hardware. For example, at least some
of the operations of a method may be performed by one or more
processors or processor-implemented modules. Moreover, the one or
more processors may also operate to support performance of the
relevant operations in a "cloud computing" environment or as a
"software as a service" (SaaS). For example, at least some of the
operations may be performed by a group of computers (as examples of
machines including processors), with these operations being
accessible via a network (e.g., the Internet) and via one or more
appropriate interfaces (e.g., an Application Program Interface
(API)).
[0070] The performance of certain of the operations may be
distributed among the processors, not only residing within a single
machine, but deployed across a number of machines. In some example
embodiments, the processors or processor-implemented modules may be
located in a single geographic location (e.g., within a home
environment, an office environment, or a server farm). In other
example embodiments, the processors or processor-implemented
modules may be distributed across a number of geographic
locations.
Machine and Software Architecture
[0071] The modules, methods, applications and so forth described in
conjunction with FIGS. 1-7 are implemented in some embodiments in
the context of a machine and an associated software architecture.
The sections below describe representative software architecture(s)
and machine (e.g., hardware) architecture that are suitable for use
with the disclosed embodiments.
[0072] Software architectures are used in conjunction with hardware
architectures to create devices and machines tailored to particular
purposes. For example, a particular hardware architecture coupled
with a particular software architecture will create a mobile
device, such as a mobile phone, tablet device, or so forth. A
slightly different hardware and software architecture may yield a
smart device for use in the "internet of things." While yet another
combination produces a server computer for use within a cloud
computing architecture. Not all combinations of such software and
hardware architectures are presented here as those of skill in the
art can readily understand how to implement the invention in
different contexts from the disclosure contained herein.
Software Architecture
[0073] FIG. 6 is a block diagram 600 illustrating a representative
software architecture 602, which may be used in conjunction with
various hardware architectures herein described. FIG. 6 is merely a
non-limiting example of a software architecture and it will be
appreciated that many other architectures may be implemented to
facilitate the functionality described herein. The software
architecture 602 may be executing on hardware such as machine 700
of FIG. 7 that includes, among other things, processors 710, memory
730, and I/O components 750. A representative hardware layer 604 is
illustrated and can represent, for example, the machine 700 of FIG.
7. The representative hardware layer 604 comprises one or more
processing units 606 having associated executable instructions 608.
Executable instructions 608 represent the executable instructions
of the software architecture 602, including implementation of the
methods, modules and so forth of FIGS. 2-5. Hardware layer 604 also
includes memory and/or storage modules 610, which also have
executable instructions 608. Hardware layer 604 may also comprise
other hardware as indicated by 612 which represents any other
hardware of the hardware layer 604, such as the other hardware
illustrated as part of machine 700.
[0074] In the example architecture of FIG. 6, the software 602 may
be conceptualized as a stack of layers where each layer provides
particular functionality. For example, the software 602 may include
layers such as an operating system 614, libraries 616,
frameworks/middleware 618, applications 620 and presentation layer
622. Operationally, the applications 620 and/or other components
within the layers may invoke application programming interface
(API) calls 624 through the software stack and receive a response,
returned values, and so forth illustrated as messages 626 in
response to the API calls 624. The layers illustrated are
representative in nature and not all software architectures have
all layers. For example, some mobile or special purpose operating
systems may not provide a frameworks/middleware layer 618, while
others may provide such a layer. Other software architectures may
include additional or different layers.
[0075] The operating system 614 may manage hardware resources and
provide common services. The operating system 614 may include, for
example, a kernel 628, services 630, and drivers 632. The kernel
628 may act as an abstraction layer between the hardware and the
other software layers. For example, the kernel 628 may be
responsible for memory management, processor management (e.g.,
scheduling), component management, networking, security settings,
and so on. The services 630 may provide other common services for
the other software layers. The drivers 632 may be responsible for
controlling or interfacing with the underlying hardware. For
instance, the drivers 632 may include display drivers, camera
drivers, Bluetooth.RTM. drivers, flash memory drivers, serial
communication drivers (e.g., Universal Serial Bus (USB) drivers),
Wi-Fi.RTM. drivers, audio drivers, power management drivers, and so
forth depending on the hardware configuration.
[0076] The libraries 616 may provide a common infrastructure that
may be utilized by the applications 620 and/or other components
and/or layers. The libraries 616 typically provide functionality
that allows other software modules to perform tasks in an easier
fashion than to interface directly with the underlying operating
system 614 functionality (e.g., kernel 628, services 630 and/or
drivers 632). The libraries 616 may include system 634 libraries
(e.g., C standard library) that may provide functions such as
memory allocation functions, string manipulation functions,
mathematic functions, and the like. In addition, the libraries 616
may include API libraries 636 such as media libraries (e.g.,
libraries to support presentation and manipulation of various media
format such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics
libraries (e.g., an OpenGL framework that may be used to render 2D
and 3D in a graphic content on a display), database libraries
(e.g., SQLite that may provide various relational database
functions), web libraries (e.g., WebKit that may provide web
browsing functionality), and the like. The libraries 616 may also
include a wide variety of other libraries 638 to provide many other
APIs to the applications 620 and other software
components/modules.
[0077] The frameworks 618 (also sometimes referred to as
middleware) may provide a higher-level common infrastructure that
may be utilized by the applications 620 and/or other software
components/modules. For example, the frameworks 618 may provide
various graphic user interface (GUI) functions, high-level resource
management, high-level location services, and so forth. The
frameworks 618 may provide a broad spectrum of other APIs that may
be utilized by the applications 620 and/or other software
components/modules, some of which may be specific to a particular
operating system or platform.
[0078] The applications 620 includes built-in applications 640
and/or third party applications 642. Examples of representative
built-in applications 640 may include, but are not limited to, a
contacts application, a browser application, a book reader
application, a location application, a media application, a
messaging application, and/or a game application. Third party
applications 642 may include any of the built in applications as
well as a broad assortment of other applications. In a specific
example, the third party application 642 (e.g., an application
developed using the Android.TM. or iOS.TM. software development kit
(SDK) by an entity other than the vendor of the particular
platform) may be mobile software running on a mobile operating
system such as iOS.TM., Android.TM., Windows.RTM. Phone, or other
mobile operating systems. In this example, the third party
application 642 may invoke the API calls 624 provided by the mobile
operating system such as operating system 614 to facilitate
functionality described herein.
[0079] The applications 620 may utilize built in operating system
functions (e.g., kernel 628, services 630 and/or drivers 632),
libraries (e.g., system 634, APIs 636, and other libraries 638),
frameworks/middleware 618 to create user interfaces to interact
with users of the system. Alternatively, or additionally, in some
systems interactions with a user may occur through a presentation
layer, such as presentation layer 644. In these systems, the
application/module "logic" can be separated from the aspects of the
application/module that interact with a user.
[0080] Some software architectures utilize virtual machines. In the
example of FIG. 6, this is illustrated by virtual machine 648. A
virtual machine creates a software environment where
applications/modules can execute as if they were executing on a
hardware machine (such as the machine of FIG. 7, for example). A
virtual machine is hosted by a host operating system (operating
system 614 in FIG. 6) and typically, although not always, has a
virtual machine monitor 646, which manages the operation of the
virtual machine as well as the interface with the host operating
system (i.e., operating system 614). A software architecture
executes within the virtual machine such as an operating system
650, libraries 652, frameworks/middleware 654, applications 656
and/or presentation layer 658. These layers of software
architecture executing within the virtual machine 648 can be the
same as corresponding layers previously described or may be
different.
Example Machine Architecture and Machine-Readable Medium
[0081] FIG. 7 is a block diagram illustrating components of a
machine 700, according to some example embodiments, able to read
instructions from a machine-readable medium (e.g., a
machine-readable storage medium) and perform any one or more of the
methodologies discussed herein. Specifically, FIG. 7 shows a
diagrammatic representation of the machine 700 in the example form
of a computer system, within which instructions 716 (e.g.,
software, a program, an application, an applet, an app, or other
executable code) for causing the machine 700 to perform any one or
more of the methodologies discussed herein may be executed. For
example the instructions may cause the machine to execute the flow
diagrams of FIGS. 4 and 5. Additionally, or alternatively, the
instructions may implement the actions and modules of FIGS. 2 and
3, and so forth. The instructions transform the general,
non-programmed machine into a particular machine programmed to
carry out the described and illustrated functions in the manner
described. In alternative example embodiments, the machine 700
operates as a standalone device or may be coupled (e.g., networked)
to other machines. In a networked deployment, the machine 700 may
operate in the capacity of a server machine or a client machine in
a server-client network environment, or as a peer machine in a
peer-to-peer (or distributed) network environment. The machine 700
may comprise, but not be limited to, a server computer, a client
computer, a personal computer (PC), a tablet computer, a laptop
computer, a netbook, a set-top box (STB), a personal digital
assistant (PDA), an entertainment media system, a cellular
telephone, a smart phone, a mobile device, a wearable device (e.g.,
a smart watch), a smart home device (e.g., a smart appliance),
other smart devices, a web appliance, a network router, a network
switch, a network bridge, or any machine capable of executing the
instructions 716, sequentially or otherwise, that specify actions
to be taken by machine 700. Further, while only a single machine
700 is illustrated, the term "machine" shall also be taken to
include a collection of machines 700 that individually or jointly
execute the instructions 716 to perform any one or more of the
methodologies discussed herein.
[0082] The machine 700 may include processors 710, memory 730, and
I/O components 750, which may be configured to communicate with
each other such as via a bus 702. In an example embodiment, the
processors 710 (e.g., a Central Processing Unit (CPU), a Reduced
Instruction Set Computing (RISC) processor, a Complex Instruction
Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a
Digital Signal Processor (DSP), an Application Specific Integrated
Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC),
another processor, or any suitable combination thereof) may
include, for example, processor 712 and processor 714 that may
execute instructions 716. The term "processor" is intended to
include multi-core processor that may comprise two or more
independent processors (sometimes referred to as "cores") that may
execute instructions contemporaneously. Although FIG. 7 shows
multiple processors, the machine 700 may include a single processor
with a single core, a single processor with multiple cores (e.g., a
multi-core process), multiple processors with a single core,
multiple processors with multiples cores, or any combination
thereof.
[0083] The memory/storage 730 may include a memory 732, such as a
main memory, or other memory storage, and a storage unit 736, both
accessible to the processors 710 such as via the bus 702. The
storage unit 736 and memory 732 store the instructions 716
embodying any one or more of the methodologies or functions
described herein. The instructions 716 may also reside, completely
or partially, within the memory 732, within the storage unit 736,
within at least one of the processors 710 (e.g., within the
processor's cache memory), or any suitable combination thereof,
during execution thereof by the machine 700. Accordingly, the
memory 732, the storage unit 736, and the memory of processors 710
are examples of machine-readable media.
[0084] As used herein, "machine-readable medium" means a device
able to store instructions and data temporarily or permanently and
may include, but is not be limited to, random-access memory (RAM),
read-only memory (ROM), buffer memory, flash memory, optical media,
magnetic media, cache memory, other types of storage (e.g.,
Erasable Programmable Read-Only Memory (EEPROM)) and/or any
suitable combination thereof. The term "machine-readable medium"
should be taken to include a single medium or multiple media (e.g.,
a centralized or distributed database, or associated caches and
servers) able to store instructions 716. The term "machine-readable
medium" shall also be taken to include any medium, or combination
of multiple media, that is capable of storing instructions (e.g.,
instructions 716) for execution by a machine (e.g., machine 700),
such that the instructions, when executed by one or more processors
of the machine 700 (e.g., processors 710), cause the machine 700 to
perform any one or more of the methodologies described herein.
Accordingly, a "machine-readable medium" refers to a single storage
apparatus or device, as well as "cloud-based" storage systems or
storage networks that include multiple storage apparatus or
devices. The term "machine-readable medium" excludes signals per
se.
[0085] The I/O components 750 may include a wide variety of
components to receive input, provide output, produce output,
transmit information, exchange information, capture measurements,
and so on. The specific I/O components 750 that are included in a
particular machine will depend on the type of machine. For example,
portable machines such as mobile phones will likely include a touch
input device or other such input mechanisms, while a headless
server machine will likely not include such a touch input device.
It will be appreciated that the I/O components 750 may include many
other components that are not shown in FIG. 7. The I/O components
750 are grouped according to functionality merely for simplifying
the following discussion and the grouping is in no way limiting. In
various example embodiments, the I/O components 750 may include
output components 752 and input components 754. The output
components 752 may include visual components (e.g., a display such
as a plasma display panel (PDP), a light emitting diode (LED)
display, a liquid crystal display (LCD), a projector, or a cathode
ray tube (CRT)), acoustic components (e.g., speakers), haptic
components (e.g., a vibratory motor, resistance mechanisms), other
signal generators, and so forth. The input components 754 may
include alphanumeric input components (e.g., a keyboard, a touch
screen configured to receive alphanumeric input, a photo-optical
keyboard, or other alphanumeric input components), point based
input components (e.g., a mouse, a touchpad, a trackball, a
joystick, a motion sensor, or other pointing instrument), tactile
input components (e.g., a physical button, a touch screen that
provides location and/or force of touches or touch gestures, or
other tactile input components), audio input components (e.g., a
microphone), and the like.
[0086] In further example embodiments, the I/O components 750 may
include biometric components 756, motion components 758,
environmental components 760, or position components 762 among a
wide array of other components. For example, the biometric
components 756 may include components to detect expressions (e.g.,
hand expressions, facial expressions, vocal expressions, body
gestures, or eye tracking), measure biosignals (e.g., blood
pressure, heart rate, body temperature, perspiration, or brain
waves), identify a person (e.g., voice identification, retinal
identification, facial identification, fingerprint identification,
or electroencephalogram based identification), and the like. The
motion components 758 may include acceleration sensor components
(e.g., accelerometer), gravitation sensor components, rotation
sensor components (e.g., gyroscope), and so forth. The
environmental components 760 may include, for example, illumination
sensor components (e.g., photometer), temperature sensor components
(e.g., one or more thermometer that detect ambient temperature),
humidity sensor components, pressure sensor components (e.g.,
barometer), acoustic sensor components (e.g., one or more
microphones that detect background noise), proximity sensor
components (e.g., infrared sensors that detect nearby objects), gas
sensors (e.g., gas detection sensors to detection concentrations of
hazardous gases for safety or to measure pollutants in the
atmosphere), or other components that may provide indications,
measurements, or signals corresponding to a surrounding physical
environment. The position components 762 may include location
sensor components (e.g., a Global Position System (GPS) receiver
component), altitude sensor components (e.g., altimeters or
barometers that detect air pressure from which altitude may be
derived), orientation sensor components (e.g., magnetometers), and
the like.
[0087] Communication may be implemented using a wide variety of
technologies. The I/O components 750 may include communication
components 764 operable to couple the machine 700 to a network 780
or devices 770 via coupling 782 and coupling 772 respectively. For
example, the communication components 764 may include a network
interface component or other suitable device to interface with the
network 780. In further examples, communication components 764 may
include wired communication components, wireless communication
components, cellular communication components, Near Field
Communication (NFC) components, Bluetooth.RTM. components (e.g.,
Bluetooth.RTM. Low Energy), Wi-Fi.RTM. components, and other
communication components to provide communication via other
modalities. The devices 770 may be another machine or any of a wide
variety of peripheral devices (e.g., a peripheral device coupled
via a Universal Serial Bus (USB)).
[0088] Moreover, the communication components 764 may detect
identifiers or include components operable to detect identifiers.
For example, the communication components 764 may include Radio
Frequency Identification (RFID) tag reader components, NFC smart
tag detection components, optical reader components (e.g., an
optical sensor to detect one-dimensional bar codes such as
Universal Product Code (UPC) bar code, multi-dimensional bar codes
such as Quick Response (QR) code, Aztec code, Data Matrix,
Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and
other optical codes), or acoustic detection components (e.g.,
microphones to identify tagged audio signals). In addition, a
variety of information may be derived via the communication
components 764, such as, location via Internet Protocol (IP)
geo-location, location via Wi-Fi.RTM. signal triangulation,
location via detecting a NFC beacon signal that may indicate a
particular location, and so forth.
Transmission Medium
[0089] In various example embodiments, one or more portions of the
network 780 may be an ad hoc network, an intranet, an extranet, a
virtual private network (VPN), a local area network (LAN), a
wireless LAN (WLAN), a wide area network (WAN), a wireless WAN
(WWAN), a metropolitan area network (MAN), the Internet, a portion
of the Internet, a portion of the Public Switched Telephone Network
(PSTN), a plain old telephone service (POTS) network, a cellular
telephone network, a wireless network, a Wi-Fi.RTM. network,
another type of network, or a combination of two or more such
networks. For example, the network 780 or a portion of the network
780 may include a wireless or cellular network and the coupling 782
may be a Code Division Multiple Access (CDMA) connection, a Global
System for Mobile communications (GSM) connection, or other type of
cellular or wireless coupling. In this example, the coupling 782
may implement any of a variety of types of data transfer
technology, such as Single Carrier Radio Transmission Technology
(1xRTT), Evolution-Data Optimized (EVDO) technology, General Packet
Radio Service (GPRS) technology, Enhanced Data rates for GSM
Evolution (EDGE) technology, third Generation Partnership Project
(3GPP) including 3G, fourth generation wireless (4G) networks,
Universal Mobile Telecommunications System (UMTS), High Speed
Packet Access (HSPA), Worldwide Interoperability for Microwave
Access (WiMAX), Long Term Evolution (LTE) standard, others defined
by various standard setting organizations, other long range
protocols, or other data transfer technology.
[0090] The instructions 716 may be transmitted or received over the
network 780 using a transmission medium via a network interface
device (e.g., a network interface component included in the
communication components 764) and utilizing any one of a number of
well-known transfer protocols (e.g., hypertext transfer protocol
(HTTP)). Similarly, the instructions 716 may be transmitted or
received using a transmission medium via the coupling 772 (e.g., a
peer-to-peer coupling) to devices 770. The term "transmission
medium" shall be taken to include any intangible medium that is
capable of storing, encoding, or carrying instructions 716 for
execution by the machine 700, and includes digital or analog
communications signals or other intangible medium to facilitate
communication of such software.
Language
[0091] Throughout this specification, plural instances may
implement components, operations, or structures described as a
single instance. Although individual operations of one or more
methods are illustrated and described as separate operations, one
or more of the individual operations may be performed concurrently,
and nothing requires that the operations be performed in the order
illustrated. Structures and functionality presented as separate
components in example configurations may be implemented as a
combined structure or component. Similarly, structures and
functionality presented as a single component may be implemented as
separate components. These and other variations, modifications,
additions, and improvements fall within the scope of the subject
matter herein.
[0092] Although an overview of the inventive subject matter has
been described with reference to specific example embodiments,
various modifications and changes may be made to these example
embodiments without departing from the broader scope of example
embodiments of the present disclosure. Such example embodiments of
the inventive subject matter may be referred to herein,
individually or collectively, by the term "invention" merely for
convenience and without intending to voluntarily limit the scope of
this application to any single disclosure or inventive concept if
more than one is, in fact, disclosed.
[0093] The example embodiments illustrated herein are described in
sufficient detail to enable those skilled in the art to practice
the teachings disclosed. Other example embodiments may be used and
derived therefrom, such that structural and logical substitutions
and changes may be made without departing from the scope of this
disclosure. The Detailed Description, therefore, is not to be taken
in a limiting sense, and the scope of various example embodiments
is defined only by the appended claims, along with the full range
of equivalents to which such claims are entitled.
[0094] As used herein, the term "or" may be construed in either an
inclusive or exclusive sense. Moreover, plural instances may be
provided for resources, operations, or structures described herein
as a single instance. Additionally, boundaries between various
resources, operations, modules, engines, and data stores are
somewhat arbitrary, and particular operations are illustrated in a
context of specific illustrative configurations. Other allocations
of functionality are envisioned and may fall within a scope of
various example embodiments of the present disclosure. In general,
structures and functionality presented as separate resources in the
example configurations may be implemented as a combined structure
or resource. Similarly, structures and functionality presented as a
single resource may be implemented as separate resources. These and
other variations, modifications, additions, and improvements fall
within a scope of example embodiments of the present disclosure as
represented by the appended claims. The specification and drawings
are, accordingly, to be regarded in an illustrative rather than a
restrictive sense.
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