U.S. patent application number 16/158744 was filed with the patent office on 2020-04-16 for computerized system and method for digital content extraction and propagation in html messages.
The applicant listed for this patent is OATH INC.. Invention is credited to Dotan Di CASTRO, Irena GRABOVITCH, Ian McCARTHY, Umang PATEL.
Application Number | 20200120054 16/158744 |
Document ID | / |
Family ID | 70056412 |
Filed Date | 2020-04-16 |
United States Patent
Application |
20200120054 |
Kind Code |
A1 |
GRABOVITCH; Irena ; et
al. |
April 16, 2020 |
COMPUTERIZED SYSTEM AND METHOD FOR DIGITAL CONTENT EXTRACTION AND
PROPAGATION IN HTML MESSAGES
Abstract
Disclosed are systems and methods for improving interactions
with and between computers in content providing, searching and/or
hosting systems supported by or configured with devices, servers
and/or platforms. The disclosed systems and methods provide a novel
framework for partitioning HTML content in electronic messages
based on the relative positions of the content's links within the
DOM hierarchy of the messages, and basing the propagation (e.g.,
display or communication) of such content therefrom. The disclosed
message partitioning and extraction framework can be applied
online, in real-time, at scale, without any pre-processing or
pre-learning/training.
Inventors: |
GRABOVITCH; Irena; (Haifa,
IL) ; Di CASTRO; Dotan; (Haifa, IL) ; PATEL;
Umang; (Sunnyvale, CA) ; McCARTHY; Ian;
(Sunnyvale, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
OATH INC. |
New York |
NY |
US |
|
|
Family ID: |
70056412 |
Appl. No.: |
16/158744 |
Filed: |
October 12, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 51/12 20130101;
H04L 51/22 20130101; H04L 51/08 20130101; H04L 51/26 20130101; G06F
40/14 20200101; H04L 51/18 20130101; H04L 51/20 20130101; H04L
51/10 20130101 |
International
Class: |
H04L 12/58 20060101
H04L012/58; G06F 17/22 20060101 G06F017/22 |
Claims
1. A method comprising the steps of: identifying, via a computing
device, a message addressed to a user, said message comprising
digital content associated with a third party entity; analyzing,
via the computing device, said identified message, and based on
said analysis, identifying the Document Object Model (DOM) of the
message; partitioning, via the computing device, said DOM, said
partitioning comprising analyzing links associated with the digital
content within the DOM, and based on said analysis, determining a
set of sub-trees within said DOM; analyzing, via the computing
device, the set of sub-trees, and based on said analysis,
determining a candidate set of sub-trees from said set of sub-trees
that comprise information indicating a type of sender; executing,
via the computing device, regular expression (regex) software on
said candidate set of sub-trees, and based on said regex execution,
identifying field information within the message that comprise
entity content specific to said sender type; extracting, via the
computing device, said entity content from said field information
of the message; and propagating, via the computing device, said
extracted entity content to the user.
2. The method of claim 1, further comprising: identifying an end of
the DOM; and performing said analysis of the DOM by traversing the
DOM in a bottom-up manner beginning at said end, said traversal of
the DOM comprising identifying nodes for each link in the DOM.
3. The method of claim 2, further comprising: determining, for each
identified node, a number of links associated therewith;
determining, based on said number of links, whether to siphon out a
sub-tree for the node, said sub-tree comprising the node as a
rooted link separate from the DOM.
4. The method of claim 3, wherein said siphoned out sub-tree is
part of the set of sub-trees.
5. The method of claim 1, wherein said analysis of the set of
sub-trees comprises: concatenating, for each sub-tree in said set
of sub-trees, alt attributes and textual nodes of a respective
sub-tree according to a character criteria, said character criteria
associated with the sender type, wherein said determination of said
candidate set of sub-trees is based on said concatenation.
6. The method of claim 1, further comprising: storing, in a
database associated with an inbox of the user, said extracted
entity content.
7. The method of claim 6, wherein said storage further comprises
storing said extracted entity content in a look-up table (LUT) in
association with said field information.
8. The method of claim 6, further comprising; receiving a request
to communicate content to a user from said sender type; searching,
based on said request, said database for said content, and based on
said searching, identifying said extracted entity content, wherein
said propagation is based on said request.
9. The method of claim 1, wherein said identified message is an
incoming message to an inbox of the user.
10. The method of claim 1, further comprising: identifying a set of
messages associated with an inbox of the user, wherein said steps
are performed on said set of messages.
11. The method of claim 1, further comprising: analyzing the
extracted entity content, and based on said analysis, identifying
entity content information for the user; causing communication,
over the network, of said entity content information to an
advertisement platform to obtain a digital content item comprising
digital advertisement content associated with said entity content
information; and communicating said identified digital content item
to said user for display in association with an interface of an
inbox.
12. A non-transitory computer-readable storage medium tangibly
encoded with computer-executable instructions, that when executed
by a processor associated with a computing device, performs a
method comprising the steps of: identifying, via the computing
device, a message addressed to a user, said message comprising
digital content associated with a third party entity; analyzing,
via the computing device, said identified message, and based on
said analysis, identifying the Document Object Model (DOM) of the
message; partitioning, via the computing device, said DOM, said
partitioning comprising analyzing links associated with the digital
content within the DOM, and based on said analysis, determining a
set of sub-trees within said DOM; analyzing, via the computing
device, the set of sub-trees, and based on said analysis,
determining a candidate set of sub-trees from said set of sub-trees
that comprise information indicating a type of sender; executing,
via the computing device, regular expression (regex) software on
said candidate set of sub-trees, and based on said regex execution,
identifying field information within the message that comprise
entity content specific to said sender type; extracting, via the
computing device, said entity content from said field information
of the message; and propagating, via the computing device, said
extracted entity content to the user.
13. The non-transitory computer-readable storage medium of claim
12, further comprising: identifying an end of the DOM; and
performing said analysis of the DOM by traversing the DOM in a
bottom-up manner beginning at said end, said traversal of the DOM
comprising identifying nodes for each link in the DOM.
14. The non-transitory computer-readable storage medium of claim
13, further comprising: determining, for each identified node, a
number of links associated therewith; determining, based on said
number of links, whether to siphon out a sub-tree for the node,
said sub-tree comprising the node as a rooted link separate from
the DOM, wherein said siphoned out sub-tree is part of the set of
sub-trees.
15. The non-transitory computer-readable storage medium of claim
12, wherein said analysis of the set of sub-trees comprises:
concatenating, for each sub-tree in said set of sub-trees, alt
attributes and textual nodes of a respective sub-tree according to
a character criteria, said character criteria associated with the
sender type, wherein said determination of said candidate set of
sub-trees is based on said concatenation.
16. The non-transitory computer-readable storage medium of claim
12, further comprising: storing, in a database associated with an
inbox of the user, said extracted entity content, said storing
further comprising storing said extracted entity content in a
look-up table (LUT) in association with said field information.
17. The non-transitory computer-readable storage medium of claim
16, further comprising; receiving a request to communicate content
to a user from said sender type; searching, based on said request,
said database for said content, and based on said searching,
identifying said extracted entity content, wherein said propagation
is based on said request.
18. The non-transitory computer-readable storage medium of claim
12, wherein said identified message is an incoming message to an
inbox of the user.
19. The non-transitory computer-readable storage medium of claim
12, further comprising: identifying a set of messages associated
with an inbox of the user, wherein said steps are performed on said
set of messages.
20. A computing device comprising: a processor; and a
non-transitory computer-readable storage medium for tangibly
storing thereon program logic for execution by the processor, the
program logic comprising: logic executed by the processor for
identifying, via the computing device, a message addressed to a
user, said message comprising digital content associated with a
third party entity; logic executed by the processor for analyzing,
via the computing device, said identified message, and based on
said analysis, identifying the Document Object Model (DOM) of the
message; logic executed by the processor for partitioning, via the
computing device, said DOM, said partitioning comprising analyzing
links associated with the digital content within the DOM, and based
on said analysis, determining a set of sub-trees within said DOM;
logic executed by the processor for analyzing, via the computing
device, the set of sub-trees, and based on said analysis,
determining a candidate set of sub-trees from said set of sub-trees
that comprise information indicating a type of sender; logic
executed by the processor for executing, via the computing device,
regular expression (regex) software on said candidate set of
sub-trees, and based on said regex execution, identifying field
information within the message that comprise entity content
specific to said sender type; logic executed by the processor for
extracting, via the computing device, said entity content from said
field information of the message; and logic executed by the
processor for propagating, via the computing device, said extracted
entity content to the user.
Description
[0001] This application includes material that is subject to
copyright protection. The copyright owner has no objection to the
facsimile reproduction by anyone of the patent disclosure, as it
appears in the Patent and Trademark Office files or records, but
otherwise reserves all copyright rights whatsoever.
FIELD
[0002] The present disclosure relates generally to improving the
performance of content hosting and providing devices, systems
and/or platforms by modifying the capabilities and providing
non-native functionality to such devices, systems and/or platforms
through a novel and improved framework for partitioning Hypertext
Markup Language (HTML) content in electronic messages based on the
relative positions of the content's links within the Document
Object Model (DOM) hierarchy of the messages, and basing the
propagation (e.g., display or communication) of such content
thereon.
BACKGROUND
[0003] The growth and usage of machine generated electronic mail
has seemingly become ubiquitous over the last few years.
Auto-generated content such as, for example, purchase receipts,
order confirmations, travel reservations, events and social
notifications, to name just a few examples, are routinely created
by commercial companies and organizations, and account for over 90%
of the non-spam Web mail traffic. In fact, on a daily basis, such
forms of electronic messages (i.e., emails) can amount to billions
of messages.
[0004] The task of precisely identifying key elements within this
form of digital content in a truly scalable manner is of great
importance to both users and service providers, and can be
leveraged for applications such as ad re-targeting, mail search,
and mail summarization.
[0005] However, conventional techniques employed by online parties
relies on complex clustering mechanisms. This has many technical
drawbacks, of which, for example, is the large amount of messages
that need to be pre-processed. That is, in order for conventional
systems to properly partition and identify key content links, items
or portions of messages, these systems need to be trained on large
sample sets of messages. This leads to large amounts of system
resources and network throughput being wasted by such systems
during the pre-processing steps in receiving, accepting or
identifying messages, then actually performing the analysis. Such
systems are wasting vital network and computing device (e.g.
server) resources by requiring any system that desires to perform
message extraction to devote large amounts of its processing power
and memory resources to the development of the system's
capabilities, which leads to a resource drain on the computing
devices executing the systems as well as the network infrastructure
they are operating on/within.
SUMMARY
[0006] The disclosed systems and methods provide a technical
solution to existing technical problems, especially those
highlighted above, by providing an improved message partitioning
and extraction framework that can be applied online, in real-time,
at scale, without any pre-processing or pre-learning/training. The
disclosed framework, according to some embodiments, partitions
identified, received or incoming HTML content in email messages
based on the relative positions of the links in the message's DOM
hierarchy. The partitioning is leveraged into identifying
meaningful entities within the messages. For example, if a message
constitutes a travel reservation, the resulting partition of the
message will entail the identification of the travel information
(e.g., travel dates, modes of transportation, traveler information,
and the like) from the specific fields of the message. According to
embodiments of the disclosed systems and methods, the effectiveness
of the disclosed framework is in the detection or identification of
the pertinent sections in these automatically generated email
messages.
[0007] The technical steps performed by the disclosed framework are
critical for many applications such as ad retargeting, mail search,
and mail summarization, and can be leveraged to enhance many
user-facing features, such as coupon clipping and travel alerts. As
opposed to the approaches of conventional techniques, the disclosed
framework embodies a novel, technically efficient approach that
leverages the analysis of HTML links within messages, as they play
a key role in identifying and extracting valuable information about
an email message. Thus, the disclosed systems and methods are
capable of automatically detecting key fragments in email messages
by focusing solely on the links and their locations in the
content.
[0008] Semantically distinct portions of an email message, or a web
page, tend to contain unique links that reference particular
content in the DOM. These links enable the disclosed framework
(e.g., the entity identification engine 300, as discussed in more
detail below) to partition the DOM tree of particular messages into
subtrees based on their contained normalized links (i.e., links
that point to the same content, landing page, or that indicate
different actions such as click location indicators). As discussed
in more detail below, especially with regard to FIGS. 3-5, these
subtrees are then analyzed so that particular entity types are
identified (e.g., commercial entity content), then the
corresponding entities are extracted for propagation to users on
their devices.
[0009] While the discussion herein will focus on commercial emails
and the commercial content disclosed herein (e.g., messages from
content providers that can include digital content associated with
coupons, advertisements and the like), it should not be construed
as limiting, as any type of content type or entity provider can
form the basis for the message analysis disclosed herein--for
example, purchased items, flights, and show tickets, and the like.
Indeed, the disclosure herein focuses on the analysis of email
messages; however, any type of message or form of content--for
example, web pages, can be analyzed according to similar techniques
without departing from the scope of the instant disclosure.
[0010] In accordance with one or more embodiments, the instant
disclosure provides computerized methods for a message partitioning
and extraction framework that can be applied online, in real-time,
at scale, without any pre-processing or pre-learning/training. The
disclosed framework, according to some embodiments, partitions
identified, received or incoming HTML content in email messages
based on the relative positions of the links in the message's DOM
hierarchy. The partitioning is leveraged into identifying
meaningful entities within the messages, from which the entities'
content is then propagated to users.
[0011] In accordance with one or more embodiments, the instant
disclosure provides a non-transitory computer-readable storage
medium for carrying out the above mentioned technical steps of the
framework's functionality. The non-transitory computer-readable
storage medium has tangibly stored thereon, or tangibly encoded
thereon, computer readable instructions that when executed by a
device (e.g., application server, email server, ad server, content
server and/or client device, and the like) cause at least one
processor to perform a method for a novel and improved framework
for partitioning HTML content in electronic messages based on the
relative positions of the content's links within the DOM hierarchy
of the messages, and basing the propagation (e.g., display or
communication) of such content therefrom.
[0012] In accordance with one or more embodiments, a system is
provided that comprises one or more computing devices configured to
provide functionality in accordance with such embodiments. In
accordance with one or more embodiments, functionality is embodied
in steps of a method performed by at least one computing device. In
accordance with one or more embodiments, program code (or program
logic) executed by a processor(s) of a computing device to
implement functionality in accordance with one or more such
embodiments is embodied in, by and/or on a non-transitory
computer-readable medium.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The foregoing and other objects, features, and advantages of
the disclosure will be apparent from the following description of
embodiments as illustrated in the accompanying drawings, in which
reference characters refer to the same parts throughout the various
views. The drawings are not necessarily to scale, emphasis instead
being placed upon illustrating principles of the disclosure:
[0014] FIG. 1 is a schematic diagram illustrating an example of a
network within which the systems and methods disclosed herein could
be implemented according to some embodiments of the present
disclosure;
[0015] FIG. 2 depicts is a schematic diagram illustrating an
example of client device in accordance with some embodiments of the
present disclosure;
[0016] FIG. 3 is a block diagram illustrating components of an
exemplary system in accordance with embodiments of the present
disclosure;
[0017] FIG. 4 is a block diagram illustrating a data flow of an
exemplary system in accordance with some embodiments of the present
disclosure;
[0018] FIG. 5 is an exemplary embodiment of the entity
identification analysis being performed within an electronic
message according to some embodiments of the present disclosure;
and
[0019] FIG. 6 is a block diagram illustrating a data flow of an
exemplary system in accordance with some embodiments of the present
disclosure.
DESCRIPTION OF EMBODIMENTS
[0020] The present disclosure will now be described more fully
hereinafter with reference to the accompanying drawings, which form
a part hereof, and which show, by way of non-limiting illustration,
certain example embodiments. Subject matter may, however, be
embodied in a variety of different forms and, therefore, covered or
claimed subject matter is intended to be construed as not being
limited to any example embodiments set forth herein; example
embodiments are provided merely to be illustrative. Likewise, a
reasonably broad scope for claimed or covered subject matter is
intended. Among other things, for example, subject matter may be
embodied as methods, devices, components, or systems. Accordingly,
embodiments may, for example, take the form of hardware, software,
firmware or any combination thereof (other than software per se).
The following detailed description is, therefore, not intended to
be taken in a limiting sense.
[0021] Throughout the specification and claims, terms may have
nuanced meanings suggested or implied in context beyond an
explicitly stated meaning. Likewise, the phrase "in one embodiment"
as used herein does not necessarily refer to the same embodiment
and the phrase "in another embodiment" as used herein does not
necessarily refer to a different embodiment. It is intended, for
example, that claimed subject matter include combinations of
example embodiments in whole or in part.
[0022] In general, terminology may be understood at least in part
from usage in context. For example, terms, such as "and", "or", or
"and/or," as used herein may include a variety of meanings that may
depend at least in part upon the context in which such terms are
used. Typically, "or" if used to associate a list, such as A, B or
C, is intended to mean A, B, and C, here used in the inclusive
sense, as well as A, B or C, here used in the exclusive sense. In
addition, the term "one or more" as used herein, depending at least
in part upon context, may be used to describe any feature,
structure, or characteristic in a singular sense or may be used to
describe combinations of features, structures or characteristics in
a plural sense. Similarly, terms, such as "a," "an," or "the,"
again, may be understood to convey a singular usage or to convey a
plural usage, depending at least in part upon context. In addition,
the term "based on" may be understood as not necessarily intended
to convey an exclusive set of factors and may, instead, allow for
existence of additional factors not necessarily expressly
described, again, depending at least in part on context.
[0023] The present disclosure is described below with reference to
block diagrams and operational illustrations of methods and
devices. It is understood that each block of the block diagrams or
operational illustrations, and combinations of blocks in the block
diagrams or operational illustrations, can be implemented by means
of analog or digital hardware and computer program instructions.
These computer program instructions can be provided to a processor
of a general purpose computer to alter its function as detailed
herein, a special purpose computer, ASIC, or other programmable
data processing apparatus, such that the instructions, which
execute via the processor of the computer or other programmable
data processing apparatus, implement the functions/acts specified
in the block diagrams or operational block or blocks. In some
alternate implementations, the functions/acts noted in the blocks
can occur out of the order noted in the operational illustrations.
For example, two blocks shown in succession can in fact be executed
substantially concurrently or the blocks can sometimes be executed
in the reverse order, depending upon the functionality/acts
involved.
[0024] For the purposes of this disclosure a non-transitory
computer readable medium (or computer-readable storage
medium/media) stores computer data, which data can include computer
program code (or computer-executable instructions) that is
executable by a computer, in machine readable form. By way of
example, and not limitation, a computer readable medium may
comprise computer readable storage media, for tangible or fixed
storage of data, or communication media for transient
interpretation of code-containing signals. Computer readable
storage media, as used herein, refers to physical or tangible
storage (as opposed to signals) and includes without limitation
volatile and non-volatile, removable and non-removable media
implemented in any method or technology for the tangible storage of
information such as computer-readable instructions, data
structures, program modules or other data. Computer readable
storage media includes, but is not limited to, RAM, ROM, EPROM,
EEPROM, flash memory or other solid state memory technology,
CD-ROM, DVD, or other optical storage, cloud storage, magnetic
cassettes, magnetic tape, magnetic disk storage or other magnetic
storage devices, or any other physical or material medium which can
be used to tangibly store the desired information or data or
instructions and which can be accessed by a computer or
processor.
[0025] For the purposes of this disclosure the term "server" should
be understood to refer to a service point which provides
processing, database, and communication facilities. By way of
example, and not limitation, the term "server" can refer to a
single, physical processor with associated communications and data
storage and database facilities, or it can refer to a networked or
clustered complex of processors and associated network and storage
devices, as well as operating software and one or more database
systems and application software that support the services provided
by the server. Cloud servers are examples.
[0026] For the purposes of this disclosure a "network" should be
understood to refer to a network that may couple devices so that
communications may be exchanged, such as between a server and a
client device or other types of devices, including between wireless
devices coupled via a wireless network, for example. A network may
also include mass storage, such as network attached storage (NAS),
a storage area network (SAN), a content delivery network (CDN) or
other forms of computer or machine readable media, for example. A
network may include the Internet, one or more local area networks
(LANs), one or more wide area networks (WANs), wire-line type
connections, wireless type connections, cellular or any combination
thereof. Likewise, sub-networks, which may employ differing
architectures or may be compliant or compatible with differing
protocols, may interoperate within a larger network.
[0027] For purposes of this disclosure, a "wireless network" should
be understood to couple client devices with a network. A wireless
network may employ stand-alone ad-hoc networks, mesh networks,
Wireless LAN (WLAN) networks, cellular networks, or the like. A
wireless network may further employ a plurality of network access
technologies, including Wi-Fi, Long Term Evolution (LTE), WLAN,
Wireless Router (WR) mesh, or 2nd, 3rd, 4.sup.th or 5.sup.th
generation (2G, 3G, 4G or 5G) cellular technology, Bluetooth,
802.11b/g/n, or the like. Network access technologies may enable
wide area coverage for devices, such as client devices with varying
degrees of mobility, for example.
[0028] In short, a wireless network may include virtually any type
of wireless communication mechanism by which signals may be
communicated between devices, such as a client device or a
computing device, between or within a network, or the like.
[0029] A computing device may be capable of sending or receiving
signals, such as via a wired or wireless network, or may be capable
of processing or storing signals, such as in memory as physical
memory states, and may, therefore, operate as a server. Thus,
devices capable of operating as a server may include, as examples,
dedicated rack-mounted servers, desktop computers, laptop
computers, set top boxes, integrated devices combining various
features, such as two or more features of the foregoing devices, or
the like.
[0030] For purposes of this disclosure, a client (or consumer or
user) device may include a computing device capable of sending or
receiving signals, such as via a wired or a wireless network. A
client device may, for example, include a desktop computer or a
portable device, such as a cellular telephone, a smart phone, a
display pager, a radio frequency (RF) device, an infrared (IR)
device an Near Field Communication (NFC) device, a Personal Digital
Assistant (PDA), a handheld computer, a tablet computer, a phablet,
a laptop computer, a set top box, a wearable computer, smart watch,
an integrated or distributed device combining various features,
such as features of the forgoing devices, or the like.
[0031] A client device may vary in terms of capabilities or
features. Claimed subject matter is intended to cover a wide range
of potential variations, such as a web-enabled client device or
previously mentioned devices may include a high-resolution screen
(HD or 4K for example), one or more physical or virtual keyboards,
mass storage, one or more accelerometers, one or more gyroscopes,
global positioning system (GPS) or other location-identifying type
capability, or a display with a high degree of functionality, such
as a touch-sensitive color 2D or 3D display, for example.
[0032] A client device may include or may execute a variety of
possible applications, such as a client software application
enabling communication with other devices. A client device may also
include or execute an application to communicate content, such as,
for example, textual content, multimedia content, or the like. A
client device may also include or execute an application to perform
a variety of possible tasks, such as messaging functionality,
browsing, searching, playing, streaming or displaying various forms
of content, including locally stored or uploaded messages, images
and/or video, or games (such as live broadcasts of professional
sporting events).
[0033] As discussed herein, reference to an "advertisement" should
be understood to include, but not be limited to, digital media
content embodied as a media item that provides information provided
by another user, service, third party, entity, and the like. Such
digital ad content can include any type of known or to be known
media renderable by a computing device, including, but not limited
to, video, text, audio, images, and/or any other type of known or
to be known multi-media item or object. In some embodiments, the
digital ad content can be formatted as hyperlinked multi-media
content that provides deep-linking features and/or capabilities.
Therefore, while some content is referred to as an advertisement,
it is still a digital media item that is renderable by a computing
device, and such digital media item comprises content relaying
promotional content provided by a network associated party.
[0034] The principles described herein may be embodied in many
different forms. The disclosed systems and methods provide advanced
mechanisms for partitioning HTML content in electronic messages
based on the relative positions of the content's links within the
DOM hierarchy of the messages, and basing the propagation (e.g.,
display or communication) of such content therefrom.
[0035] As discussed above, according to some embodiments, the
disclosed techniques for partitioning commercial emails into
meaningful entities is discussed through its applied effectiveness
in detecting coupons and promotions within message content provided
by service providers, commercial entities or other forms of third
party entities providing users or other entities with content,
services or products (e.g., referred to as commercial entities). As
opposed to recent extraction techniques for HTML content, the
disclosed methods and systems do not rely on complex clustering
mechanisms that require the processing of a large sample of
messages beforehand. Rather, the disclosed framework is scalable
and can be applied in a real-time online environment for handling
and processing new arriving messages without any additional
data.
[0036] As discussed herein, the disclosed framework can detect
references to products (or coupons or ads) in message HTML
messages. For purposes of this disclosure, reference to HTML
content in messages is describing both the HTML source code of the
message pages, as well as the HTML code of the message content, as
discussed in more detail below. Thus, according to the disclosed
systems and methods, any HTML message or page can be divided into
partitions such that each partition contains a single type of link
to a specific product landing page that the user is redirected to
after he clicked on a link. The partition typically corresponds to
a sub-tree in the original DOM, which is the hierarchical
representation of the HTML content, as discussed in more detail
below.
[0037] According to some embodiments, the algorithm embodied by the
disclosed methods and executed by the disclosed systems unfolds in
three stages. First, candidates for e-commerce sections are
identified by finding maximal sub-trees that contain at most a
single type link (note that the same link can be found more than
once in this sub-tree.). Then, the disclosed framework employs
heuristics to determine which of the candidates correspond to
e-commerce items. Finally, the disclosed framework extracts the
relevant fields from the messages (or pages) that correspond to the
respective products.
[0038] As discussed in more detail below at least in relation to
FIG. 6, according to some embodiments, information associated with,
derived from, or otherwise identified from, during or as a result
of the entity content extraction/identification, as discussed
herein, can be used for monetization purposes and targeted
advertising when providing, delivering or enabling such devices
access to content or services over a network. Providing targeted
advertising to users associated with such discovered content can
lead to an increased click-through rate (CTR) of such ads and/or an
increase in the advertiser's return on investment (ROI) for serving
such content provided by third parties (e.g., digital advertisement
content provided by an advertiser, where the advertiser can be a
third party advertiser, or an entity directly associated with or
hosting the systems and methods discussed herein).
[0039] Certain embodiments will now be described in greater detail
with reference to the figures. In general, with reference to FIG.
1, a system 100 in accordance with an embodiment of the present
disclosure is shown. FIG. 1 shows components of a general
environment in which the systems and methods discussed herein may
be practiced. Not all the components may be required to practice
the disclosure, and variations in the arrangement and type of the
components may be made without departing from the spirit or scope
of the disclosure. As shown, system 100 of FIG. 1 includes local
area networks ("LANs")/wide area networks ("WANs")--network 105,
wireless network 110, mobile devices (client devices) 102-104 and
client device 101. FIG. 1 additionally includes a variety of
servers, such as content server 106, application (or "App") server
108 and third party server 130.
[0040] One embodiment of mobile devices 102-104 may include
virtually any portable computing device capable of receiving and
sending a message over a network, such as network 105, wireless
network 110, or the like. Mobile devices 102-104 may also be
described generally as client devices that are configured to be
portable. Thus, mobile devices 102-104 may include virtually any
portable computing device capable of connecting to another
computing device and receiving information, as discussed above. As
such, mobile devices 102-104 typically range widely in terms of
capabilities and features.
[0041] Mobile devices 102-104 also may include at least one client
application that is configured to receive content from another
computing device. In some embodiments, mobile devices 102-104 may
also communicate with non-mobile client devices, such as client
device 101, or the like. In one embodiment, such communications may
include sending and/or receiving messages, searching for, viewing
and/or sharing photographs, digital images, audio clips, video
clips, or any of a variety of other forms of communications.
[0042] Client devices 101-104 may be capable of sending or
receiving signals, such as via a wired or wireless network, or may
be capable of processing or storing signals, such as in memory as
physical memory states, and may, therefore, operate as a
server.
[0043] Wireless network 110 is configured to couple mobile devices
102-104 and its components with network 105. Wireless network 110
may include any of a variety of wireless sub-networks that may
further overlay stand-alone ad-hoc networks, and the like, to
provide an infrastructure-oriented connection for mobile devices
102-104.
[0044] Network 105 is configured to couple content server 106,
application server 108, or the like, with other computing devices,
including, client device 101, and through wireless network 110 to
mobile devices 102-104. Network 105 is enabled to employ any form
of computer readable media or network for communicating information
from one electronic device to another.
[0045] The content server 106 may include a device that includes a
configuration to provide any type or form of content via a network
to another device. Devices that may operate as content server 106
include personal computers desktop computers, multiprocessor
systems, microprocessor-based or programmable consumer electronics,
network PCs, servers, and the like. Content server 106 can further
provide a variety of services that include, but are not limited to,
email services, instant messaging (IM) services, streaming and/or
downloading media services, search services, photo services, web
services, social networking services, news services, third-party
services, audio services, video services, SMS services, MMS
services, FTP services, voice over IP (VOIP) services, or the
like.
[0046] Third party server 130 can comprise a server that stores
online advertisements for presentation to users. "Ad serving"
refers to methods used to place online advertisements on websites,
in applications, or other places where users are more likely to see
them, such as during an online session or during computing platform
use, for example. Various monetization techniques or models may be
used in connection with sponsored advertising, including
advertising associated with user data. Such sponsored advertising
includes monetization techniques including sponsored search
advertising, non-sponsored search advertising, guaranteed and
non-guaranteed delivery advertising, ad networks/exchanges, ad
targeting, ad serving and ad analytics. Such systems can
incorporate near instantaneous auctions of ad placement
opportunities during web page creation, (in some cases in less than
500 milliseconds) with higher quality ad placement opportunities
resulting in higher revenues per ad. That is advertisers will pay
higher advertising rates when they believe their ads are being
placed in or along with highly relevant content that is being
presented to users. Reductions in the time needed to quantify a
high quality ad placement offers ad platforms competitive
advantages. Thus, higher speeds and more relevant context detection
improve these technological fields.
[0047] For example, a process of buying or selling online
advertisements may involve a number of different entities,
including advertisers, publishers, agencies, networks, or
developers. To simplify this process, organization systems called
"ad exchanges" may associate advertisers or publishers, such as via
a platform to facilitate buying or selling of online advertisement
inventory from multiple ad networks. "Ad networks" refers to
aggregation of ad space supply from publishers, such as for
provision en-masse to advertisers. For web portals like Yahoo!
.RTM., advertisements may be displayed on web pages or in apps
resulting from a user-defined search based at least in part upon
one or more search terms. Advertising may be beneficial to users,
advertisers or web portals if displayed advertisements are relevant
to interests of one or more users. Thus, a variety of techniques
have been developed to infer user interest, user intent or to
subsequently target relevant advertising to users. One approach to
presenting targeted advertisements includes employing demographic
characteristics (e.g., age, income, gender, occupation, etc.) for
predicting user behavior, such as by group. Advertisements may be
presented to users in a targeted audience based at least in part
upon predicted user behavior(s).
[0048] Another approach includes profile-type ad targeting. In this
approach, user profiles specific to a user may be generated to
model user behavior, for example, by tracking a user's path through
a web site or network of sites, and compiling a profile based at
least in part on pages or advertisements ultimately delivered. A
correlation may be identified, such as for user purchases, for
example. An identified correlation may be used to target potential
purchasers by targeting content or advertisements to particular
users. During presentation of advertisements, a presentation system
may collect descriptive content about types of advertisements
presented to users. A broad range of descriptive content may be
gathered, including content specific to an advertising presentation
system. Advertising analytics gathered may be transmitted to
locations remote to an advertising presentation system for storage
or for further evaluation. Where advertising analytics transmittal
is not immediately available, gathered advertising analytics may be
stored by an advertising presentation system until transmittal of
those advertising analytics becomes available.
[0049] In some embodiments, users are able to access services
provided by servers 106, 108 and/or 130. This may include in a
non-limiting example, authentication servers, search servers, email
servers, social networking services servers, SMS servers, IM
servers, MMS servers, exchange servers, photo-sharing services
servers, and travel services servers, via the network 105 using
their various devices 101-104.
[0050] In some embodiments, applications, such as a mail
application (e.g., Yahoo! Mail.RTM., Gmail.RTM., and the like),
blog, photo or social networking application (e.g., Facebook.RTM.,
Twitter.RTM. and the like), search application (e.g., Yahoo! .RTM.
Search), and the like, can be hosted by the application server 108
(or content server 106 and the like).
[0051] Thus, the application server 108, for example, can store
various types of applications and application related information
including application data and user profile information (e.g.,
identifying and behavioral information associated with a user). It
should also be understood that content server 106 can also store
various types of data related to the content and services provided
by content server 106 in an associated content database 107, as
discussed in more detail below. Embodiments exist where the network
105 is also coupled with/connected to a Trusted Search Server (TSS)
which can be utilized to render content in accordance with the
embodiments discussed herein. Embodiments exist where the TSS
functionality can be embodied within servers 106, 108 and/or
130.
[0052] Moreover, although FIG. 1 illustrates servers 106, 108 and
130 as single computing devices, respectively, the disclosure is
not so limited. For example, one or more functions of servers 106,
108 and/or 130 may be distributed across one or more distinct
computing devices. Moreover, in one embodiment, servers 106, 108
and/or 130 may be integrated into a single computing device,
without departing from the scope of the present disclosure.
[0053] FIG. 2 is a schematic diagram illustrating a client device
showing an example embodiment of a client device that may be used
within the present disclosure. Client device 200 may include many
more or less components than those shown in FIG. 2. However, the
components shown are sufficient to disclose an illustrative
embodiment for implementing the present disclosure. Client device
200 may represent, for example, client devices discussed above in
relation to FIG. 1.
[0054] As shown in the figure, Client device 200 includes a
processing unit (CPU) 222 in communication with a mass memory 230
via a bus 224. Client device 200 also includes a power supply 226,
one or more network interfaces 250, an audio interface 252, a
display 254, a keypad 256, an illuminator 258, an input/output
interface 260, a haptic interface 262, an optional global
positioning systems (GPS) receiver 264 and a camera(s) or other
optical, thermal or electromagnetic sensors 266. Device 200 can
include one camera/sensor 266, or a plurality of cameras/sensors
266, as understood by those of skill in the art. The positioning of
the camera(s)/sensor(s) 266 on device 200 can change per device 200
model, per device 200 capabilities, and the like, or some
combination thereof. Power supply 226 provides power to Client
device 200.
[0055] Client device 200 may optionally communicate with a base
station (not shown), or directly with another computing device.
Network interface 250 is sometimes known as a transceiver,
transceiving device, or network interface card (NIC).
[0056] Audio interface 252 is arranged to produce and receive audio
signals such as the sound of a human voice. For example, audio
interface 252 may be coupled to a speaker and microphone (not
shown) to enable telecommunication with others and/or generate an
audio acknowledgement for some action. Display 254 may be a liquid
crystal display (LCD), gas plasma, light emitting diode (LED), or
any other type of display used with a computing device. Display 254
may also include a touch sensitive screen arranged to receive input
from an object such as a stylus or a digit from a human hand.
[0057] Keypad 256 may comprise any input device arranged to receive
input from a user. Illuminator 258 may provide a status indication
and/or provide light.
[0058] Client device 200 also comprises input/output interface 260
for communicating with external. Input/output interface 260 can
utilize one or more communication technologies, such as USB,
infrared, Bluetooth.TM., or the like. Haptic interface 262 is
arranged to provide tactile feedback to a user of the client
device.
[0059] Optional GPS transceiver 264 can determine the physical
coordinates of Client device 200 on the surface of the Earth, which
typically outputs a location as latitude and longitude values. GPS
transceiver 264 can also employ other geo-positioning mechanisms,
including, but not limited to, triangulation, assisted GPS (AGPS),
E-OTD, CI, SAI, ETA, BSS or the like, to further determine the
physical location of Client device 200 on the surface of the Earth.
In one embodiment, however, Client device may through other
components, provide other information that may be employed to
determine a physical location of the device, including for example,
a MAC address, Internet Protocol (IP) address, or the like.
[0060] Mass memory 230 includes a RAM 232, a ROM 234, and other
storage means. Mass memory 230 illustrates another example of
computer storage media for storage of information such as computer
readable instructions, data structures, program modules or other
data. Mass memory 230 stores a basic input/output system ("BIOS")
240 for controlling low-level operation of Client device 200. The
mass memory also stores an operating system 241 for controlling the
operation of Client device 200
[0061] Memory 230 further includes one or more data stores, which
can be utilized by Client device 200 to store, among other things,
applications 242 and/or other information or data. For example,
data stores may be employed to store information that describes
various capabilities of Client device 200. The information may then
be provided to another device based on any of a variety of events,
including being sent as part of a header (e.g., index file of the
HLS stream) during a communication, sent upon request, or the like.
At least a portion of the capability information may also be stored
on a disk drive or other storage medium (not shown) within Client
device 200.
[0062] Applications 242 may include computer executable
instructions which, when executed by Client device 200, transmit,
receive, and/or otherwise process audio, video, images, and enable
telecommunication with a server and/or another user of another
client device. Applications 242 may further include search client
245 that is configured to send, to receive, and/or to otherwise
process a search query and/or search result.
[0063] Having described the components of the general architecture
employed within the disclosed systems and methods, the components'
general operation with respect to the disclosed systems and methods
will now be described below with reference to FIGS. 3-6.
[0064] FIG. 3 is a block diagram illustrating the components for
performing the systems and methods discussed herein. FIG. 3
includes entity identification engine 300, network 315 and database
320. The entity identification engine 300 can be a special purpose
machine or processor and could be hosted by an application server,
content server, social networking server, web server, email server,
search server, content provider, third party server, user's
computing device, and the like, or any combination thereof.
[0065] According to some embodiments, entity identification engine
300 can be embodied as a stand-alone application that executes on a
user device. In some embodiments, the entity identification engine
300 can function as an application installed on the user's device,
and in some embodiments, such application can be a web-based
application accessed by the user device over a network. In some
embodiments, the entity identification engine 300 can be installed
as an augmenting script, program or application (e.g., a plug-in or
extension) to another application (e.g., Yahoo! Mail.RTM.).
[0066] The database 320 can be any type of database or memory, and
can be associated with a content server on a network (e.g., content
server, a search server or application server) or a user's device
(e.g., device 101-104 or device 200 from FIGS. 1-2). Database 320
comprises a dataset of data and metadata associated with local
and/or network information related to users, services,
applications, content and the like. Such information can be stored
and indexed in the database 320 independently and/or as a linked or
associated dataset. As discussed above, it should be understood
that the data (and metadata) in the database 320 can be any type of
information and type, whether known or to be known, without
departing from the scope of the present disclosure.
[0067] According to some embodiments, database 320 can store data
for users, e.g., user data. According to some embodiments, the
stored user data can include, but is not limited to, information
associated with a user's profile, user interests, user behavioral
information, user attributes, user preferences or settings, user
demographic information, user location information, user biographic
information, and the like, or some combination thereof. In some
embodiments, the user data can also include user device
information, including, but not limited to, device identifying
information, device capability information, voice/data carrier
information, Internet Protocol (IP) address, applications installed
or capable of being installed or executed on such device, and/or
any, or some combination thereof. It should be understood that the
data (and metadata) in the database 320 can be any type of
information related to a user, content, a device, an application, a
service provider, a content provider, whether known or to be known,
without departing from the scope of the present disclosure.
[0068] According to some embodiments, database 320 can store data
and metadata associated with a user from an assortment of media
and/or service providers and/or platforms. For example, the
information can be related to, but not limited to, content type or
category, information associated with the sender or recipient(s) of
a message, information associated with content or text included in
a message, and any other type of known or to be known attribute or
feature associated with a message or content of a message, or some
combination thereof.
[0069] According to some embodiments, information related to,
derived from or otherwise determined from analysis of a user's
inbox can be stored in database 320 as n-dimensional vector (or
feature vector), where the information associated with each message
can be translated as a node on the n-dimensional vector for an
inbox. In some embodiments, each message can have its own vector
where the information included therein can be represented by the
nodes on a respective vector. In some embodiments, as messages are
sent/received, detected and/or tracked, information corresponding
thereto can also be stored in the database 320 in a similar
manner.
[0070] Database 320 can store and index inbox/message information
in database 320 as linked set of inbox/message data and metadata,
where the data and metadata relationship can be stored as the
n-dimensional vector. Such storage can be realized through any
known or to be known vector or array storage, including but not
limited to, a hash tree, queue, stack, VList, or any other type of
known or to be known dynamic memory allocation technique or
technology. It should be understood that any known or to be known
computational analysis technique or algorithm, such as, but not
limited to, cluster analysis, data mining, Bayesian network
analysis, Hidden Markov models, artificial neural network analysis,
logical model and/or tree analysis, and the like, and be applied to
determine, derive or otherwise identify vector information for
messages within an inbox.
[0071] For purposes of the present disclosure, as discussed above,
messages (which are stored and located in database 320) as a whole
are discussed within some embodiments; however, it should not be
construed to limit the applications of the systems and methods
discussed herein. That is, while reference is made throughout the
instant disclosure to messages (e.g., email messages), other forms
of messages (e.g., social media messages, Instant Messages (IMs))
and the content included therein, including, text, audio, images,
multimedia, RSS feed information, can be used without departing
from the scope of the instant application, which can thereby be
communicated and/or accessed and processed by the entity
identification engine 300 according to the systems and methods
discussed herein.
[0072] As discussed above, with reference to FIG. 1, the network
315 can be any type of network such as, but not limited to, a
wireless network, a local area network (LAN), wide area network
(WAN), the Internet, or a combination thereof. The network 315
facilitates connectivity of the entity identification engine 300,
and the database of stored resources 320. Indeed, as illustrated in
FIG. 3, the entity identification engine 300 and database 320 can
be directly connected by any known or to be known method of
connecting and/or enabling communication between such devices and
resources.
[0073] The principal processor, server, or combination of devices
that comprises hardware programmed in accordance with the special
purpose functions herein is referred to for convenience as entity
identification engine 300, and includes HTML analysis module 302,
DOM analysis module 304, sub-tree analysis module 306 and
extraction module 308. It should be understood that the engine(s)
and modules discussed herein are non-exhaustive, as additional or
fewer engines and/or modules (or sub-modules) may be applicable to
the embodiments of the systems and methods discussed. The
operations, configurations and functionalities of each module, and
their role within embodiments of the present disclosure will be
discussed below.
[0074] Turning to FIG. 4, an overall data flow is disclosed for
partitioning HTML content in electronic messages based on the
relative positions of the content's links within the DOM hierarchy
of the messages, and basing the propagation (e.g., display or
communication) of such content therefrom. Process 400 of FIG. 4
provides embodiments for devices (e.g., content servers, email
servers, user devices, and the like) to process data in a novel
manner, via the disclosed message partitioning and analysis,
applied heuristics and extraction, thereby leading to increased
efficiency and effectiveness in the protocol utilized for providing
users digital content.
[0075] According to some embodiments, Steps 402-404 of Process 400
are performed by the HTML analysis module 302 of the entity
identification engine 300; Step 406 is performed by the DOM
analysis module 304; Steps 408-412 are performed by the sub-tree
analysis module 306; and Steps 414-420 are performed by the
extraction module 308.
[0076] Process 400 begins with Step 402 where a message, or set of
messages, are identified. According to some embodiments, the
identified message(s) is an incoming message sent by a sender that
is directed to and received at a recipient's inbox. In some
embodiments, the steps of Process 400 can be performed for each
incoming message received in a recipient's inbox.
[0077] In some embodiments, the set of messages analyzed by Process
400 can be a set of messages identified from the user's inbox. The
identification of the messages can be based on a criteria such that
only a set of all the messages in the inbox are identified. Such
criteria can reference a type of message (e.g., commercial
messages), a time period, location associated with a message(s)
(e.g., where was the message sent from, what location does the
message reference, where was the message received, and the like),
an identity of a sender, other recipients of a message (e.g., if it
was a group message), which platform the message originated from
(e.g., was it a message from another messaging platform), is the
message unread, was the message acted upon (e.g., was it forwarded,
responded to, saved, categorized or deleted), or how was the
message checked/read (e.g., did the user open and read the message
from an application on his/her mobile device), and the like, or
some combination thereof.
[0078] Therefore, according to some embodiments, Step 402 involves
analyzing received or incoming messages according to the criteria
and identifying a message set (e.g., a single message or a
plurality of messages) that satisfy the criteria. This enables
engine 300 to efficiently analyze and process a smaller set of
messages, as those messages not satisfying the criteria are
filtered out, which alleviates the system from having to perform
the computationally draining clustering techniques conventional
systems employ.
[0079] For example, Step 402 can involve determining a set of
messages that are sent by commercial entities. For example, a user
receives 100 emails a day--therefore, according to Step 402, engine
300 can identify which of those 100 emails are from commercial
entities (e.g., Groupon.RTM., Walgreens.RTM., Walmart.RTM., and the
like). According to some embodiments, Step 402 can involve parsing
the inbox data of the user's inbox and identify each message in the
set therein (e.g., based on the criteria).
[0080] In Step 404, each message in the identified set of messages
from Step 402 is then parsed and analyzed such that the message
data and metadata included in each message is identified (or
extracted). Step 404 results in the identification of the DOM for
each message, which as discussed below, provides a structure or
model of the types of content and information the message is
referencing and/or includes. For example, if the message includes a
travel itinerary, the DOM includes links (or elements) referencing
the travel information, and the sender (e.g., was it booked through
a travel website or directly from an airline's portal).
[0081] In Step 406, the DOM for each message is partitioned such
that the sub-trees included within each DOM are identified.
According to some embodiments, the partitioning process of Step 406
(and Step 408) can be embodied and described according Algorithm
1:
TABLE-US-00001 Algorithm 1: GetCandidates(node r) input :A DOM tree
rooted in node r output:A set of nodes with disjoint maximal rooted
sub-trees, each containing a single and unique rooted link. /* Make
initial recursive call with root node. */ return
GetCandidatesRec(r) Subroutine GetCandidatesRec(node v)
v.rootedLinks .rarw. .0. if v contains a link l then v.rootedLinks
.rarw. {l} Candidates .rarw. .0. for u .di-elect cons. v.children
do Candidates .rarw. Candidates .orgate. GetCandidatesRec (u)
v.rootedLinks .rarw. v.rootedLinks .orgate. u.rootedLinks for u
.di-elect cons. v.children do if |v.rootedLinks| > 1 and
|u.rootedLinks| = 1 then Candidates .rarw. Candidates + u return
Candidates
[0082] According to some embodiments, Step 406 involves analyzing
the DOM tree structure for each message in a bottom-up manner.
Therefore, for each DOM, the bottom (or end) of the DOM structure
is identified, and the engine 300 begins its analysis there. That
is, beginning at the bottom of the tree structure and traversing
upwards, each of the nodes in the DOM are identified. This involves
determining whether the nodes are parents (i.e., is a rooted link)
or children to other nodes and how they are related (as discussed
in relation to FIG. 5 below). Step 406 involves, based on the
rooted link identification, identifying sub-tree structures within
the overall DOM structure.
[0083] In Step 408, the overall DOM tree structure for each message
is determined based on analysis of each node's relationship to
other nodes. That is, whenever a node in the DOM is a rooted link
to more than sub-tree with a unique link, those sub-trees are
filtered out (referred to as "pruning out") and treated as isolated
item-partitions (or separate sub-tree structures). Steps 406-408
halt when the root of the DOM tree for each message is reached via
the bottom-up analysis performed by engine 300.
[0084] FIG. 5 illustrates a non-limiting example embodiment of the
execution of Steps 406-408 through the depiction of HTML snippets
from a Groupon.RTM. message 502 referencing deals for two
e-commerce entities. FIG. 5 includes the HTML code snippets: item
502a and 502b. Item 502a references Groupon coupon content for
"Patriot Jet Boat Thrill Rides," and its DOM tree is represented in
item 504a. Item 502b references Groupon coupon content for "Street
Food Cinema," and its DOM tree is represented in item 504b.
[0085] In the DOM tree 504 for message 502, each of the subtrees
(items 504a and 504b) share the rooted link <tbody>. This is
identified by traversing the DOM 504 from the bottom up and
identifying <tbody> as being a rooted link for more than one
subtree (here, 2 subtrees). Therefore, as Process 400 proceeds, as
discussed more below, these subtrees 504a and 504b are pruned out
and treated as their own individual/independent tree
structure/model.
[0086] As a result of Steps 406-408, a candidate listing of
sub-trees for each DOM (for each message in the message set) is
identified.
[0087] In Step 410, the candidate listing of sub-trees are analyzed
in order to identify which sub-trees represent content from a
particular type of entity--for purposes of this disclosure,
commercial (or e-commerce) entity types are being used. This
identification (or determination) is made by concatenating the alt
attributes and textual nodes of each sub-tree in the candidate
listing according to character criteria that corresponds to
commercial entity content. Typically, commercial entity content
within messages or pages contain either a currency ("$") or
percentage ("%") sign (or both). Therefore, Step 410 involves
generating a textual representation of each sub-tree by
concatenating the textual values of its alt attributes and textual
nodes, and filtering-out any candidates for which their textual
representations do not contain a particular character--e.g., a
currency or percentage sign.
[0088] Thus, based on the analysis occurring in Step 410, Step 412
results in determining a set of sub-trees that represent digital
content associated with coupon or advertisement of a good, service
or a general e-commerce entity (e.g., the set of sub-trees being a
subset of the candidate listing of subtrees from Steps
406-408).
[0089] In Step 414, engine 300 executes software defined by a
regular expression (regex) algorithm on the sub-tree set identified
in Step 412. The regex algorithm can be any type of known or to be
known algorithm that identifies a sequence of characters, pattern
of characters, a target of characters, and the like, such as a
string searching algorithm. As a result of the regex software
execution occurring in Step 414, Step 416 involve identifying the
fields of each message that comprise entity content from commercial
entities.
[0090] According to some embodiments, for example, Steps 414-416
involve identifying the fields of a message (or page) that indicate
the specifics of the coupon or ad from the messages--such as, for
example, the original price, sale price, expiration date and the
textual description of the coupon. According to some embodiments,
for fields with values that have a predefined format (e.g., date
and price), Step 414 can involve engine 300 applying regex pattern
matching software. In some embodiments, for the textual field of
description, engine 300 executes a heuristic algorithm that uses
the textual representation of a sub-tree, and filters-out repeated
phrases and frequent phrases that appear in multiple candidate
sub-trees (e.g., "view deal", or "shop now" can be filtered out as
they are common to commercial types of messages).
[0091] In Step 418, the entity content for each identified field is
extracted and is stored in a database. The extracted content can be
stored in a look-up table (LUT) in association with the identified
field information from Step 4116, which enables a more efficient
search and retrieval of the entity content (as discussed in more
detail below in relation to Step 420). The database can be
associated with the user's inbox.
[0092] In Step 420, extracted entity content is propagated to a
user. According to some embodiments, such propagation can be based
on, or part of, coupon/ad clipping systems, coupon/ad
recommendation systems and/or coupon/ad summarization algorithms.
Conventional versions of such systems perform the computationally
draining task of performing the entity extraction before providing
the coupon/ad views they are configured for; therefore, by such
conventional systems utilizing the extracted entity content process
of Process 400, they can be provided with advanced and improved
functionality of utilizing the entity content that is already
extracted (thereby eliminating the need for them to perform such
task). For example, the number of CPU cycles can be reduced by such
systems, as they can focus on providing the content without having
to perform the computational tasks of extracting the content from
messages/pages. This saves on systems resources and improves the
performance of the devices hosting and/or executing such
systems.
[0093] FIG. 6 is a work flow example 600 for serving related
digital media content based on the information associated with an
extracted entity content item, as discussed above in relation to
FIGS. 3-5. In some embodiments, the content can be associated with
or comprising advertisements (e.g., digital advertisement content).
Such content, referred to as "entity content information" for
reference purposes only, can include or be based upon, but is not
limited to, information associated with an object a user received
in his/her mailbox (e.g., a message or piece of media included
within a message, for example), a context of a user's activity on a
network and the like (e.g., how did the user interact with the
message or extracted entity content item, and/or some combination
thereof.
[0094] As discussed above, reference to an "advertisement" should
be understood to include, but not be limited to, digital media
content that provides information provided by another user,
service, third party, entity, and the like. Such digital ad content
can include any type of known or to be known media renderable by a
computing device, including, but not limited to, video, text,
audio, images, and/or any other type of known or to be known
multi-media. In some embodiments, the digital ad content can be
formatted as hyperlinked multi-media content that provides
deep-linking features and/or capabilities. Therefore, while the
content is referred as an advertisement, it is still a digital
media item that is renderable by a computing device, and such
digital media item comprises digital content relaying promotional
content provided by a network associated third party.
[0095] In Step 602, entity content information is identified. As
discussed above, the entity content information can be based any of
the information utilized or generated from/during the partitioning,
analysis, identification and extraction outlined above with respect
to FIG. 4. For purposes of this disclosure, Process 600 will refer
to single extracted entity content item for serving additional
content; however, it should not be construed as limiting, as any
number of content items and messages, as well as programs used can
form such basis, without departing from the scope of the instant
disclosure.
[0096] In Step 604, a context is determined based on the identified
entity content information. This context forms a basis for serving
content related to the entity content information. In some
embodiments, the context can be in accordance with whether a user
interacted with the extracted entity content item, as discussed
above in relation to FIGS. 3-4. For example, a user just purchased
a plane ticket to Dallas, Tex., and received a confirmation
itinerary email in her inbox; therefore, the context identified in
Step 604 can be related to "travel" or, more specifically, "Dallas,
Tex.", and can be leveraged in order to identify digital content
related to such activity--e.g., a coupon for purchasing food at the
Dallas-Fort Worth airport. In some embodiments, the identification
of the context from Step 604 can occur before, during and/or after
the analysis detailed above with respect to Process 400, or it can
be a separate process altogether, or some combination thereof.
[0097] In Step 606, the determined context is communicated (or
shared) with a content providing platform comprising a server and
database (e.g., content server 106 and content database 107, and/or
advertisement server 130 and ad database). Upon receipt of the
context, the server performs (e.g., is caused to perform as per
instructions received from the device executing the visual
recognizer engine 300) a search for a relevant digital content
within the associated database. The search for the content is based
at least on the identified context.
[0098] In Step 608, the server searches the database for a digital
content item(s) that matches the identified context. In Step 610, a
content item is selected (or retrieved) based on the results of
Step 608. In some embodiments, the selected content item can be
modified to conform to attributes or capabilities of the page,
interface, message, platform, application or method upon which the
content item will be displayed, and/or to the application and/or
device for which it will be displayed. In some embodiments, the
selected content item is shared or communicated via the application
the user is utilizing to view, render and/or interact with a media,
content or object item. Step 612. In some embodiments, the selected
content item is sent directly to a user computing device for
display on the device and/or within the UI displayed on the
device's display. In some embodiments, the selected content item is
displayed within a portion of the interface or within an overlaying
or pop-up interface associated with a rendering interface displayed
on the device. In some embodiments, the selected content item can
be displayed as part of a coupon/ad clipping, coupon/ad
recommendation and/or coupon/ad summarization interface.
[0099] For the purposes of this disclosure a module is a software,
hardware, or firmware (or combinations thereof) system, process or
functionality, or component thereof, that performs or facilitates
the processes, features, and/or functions described herein (with or
without human interaction or augmentation). A module can include
sub-modules. Software components of a module may be stored on a
computer readable medium for execution by a processor. Modules may
be integral to one or more servers, or be loaded and executed by
one or more servers. One or more modules may be grouped into an
engine or an application.
[0100] For the purposes of this disclosure the term "user",
"subscriber" "consumer" or "customer" should be understood to refer
to a user of an application or applications as described herein
and/or a consumer of data supplied by a data provider. By way of
example, and not limitation, the term "user" or "subscriber" can
refer to a person who receives data provided by the data or service
provider over the Internet in a browser session, or can refer to an
automated software application which receives the data and stores
or processes the data.
[0101] Those skilled in the art will recognize that the methods and
systems of the present disclosure may be implemented in many
manners and as such are not to be limited by the foregoing
exemplary embodiments and examples. In other words, functional
elements being performed by single or multiple components, in
various combinations of hardware and software or firmware, and
individual functions, may be distributed among software
applications at either the client level or server level or both. In
this regard, any number of the features of the different
embodiments described herein may be combined into single or
multiple embodiments, and alternate embodiments having fewer than,
or more than, all of the features described herein are
possible.
[0102] Functionality may also be, in whole or in part, distributed
among multiple components, in manners now known or to become known.
Thus, myriad software/hardware/firmware combinations are possible
in achieving the functions, features, interfaces and preferences
described herein. Moreover, the scope of the present disclosure
covers conventionally known manners for carrying out the described
features and functions and interfaces, as well as those variations
and modifications that may be made to the hardware or software or
firmware components described herein as would be understood by
those skilled in the art now and hereafter.
[0103] Furthermore, the embodiments of methods presented and
described as flowcharts in this disclosure are provided by way of
example in order to provide a more complete understanding of the
technology. The disclosed methods are not limited to the operations
and logical flow presented herein. Alternative embodiments are
contemplated in which the order of the various operations is
altered and in which sub-operations described as being part of a
larger operation are performed independently.
[0104] While various embodiments have been described for purposes
of this disclosure, such embodiments should not be deemed to limit
the teaching of this disclosure to those embodiments. Various
changes and modifications may be made to the elements and
operations described above to obtain a result that remains within
the scope of the systems and processes described in this
disclosure.
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