U.S. patent application number 16/422605 was filed with the patent office on 2019-09-12 for intelligent offer for related products with preview and real time feedback.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Saritha Guntumadugu, Charles McGonigal, Mike Niemann, Srinivasa Ogireddy, Hemanth Puttaswamy, Russell Salsbury.
Application Number | 20190279274 16/422605 |
Document ID | / |
Family ID | 41427964 |
Filed Date | 2019-09-12 |
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United States Patent
Application |
20190279274 |
Kind Code |
A1 |
McGonigal; Charles ; et
al. |
September 12, 2019 |
INTELLIGENT OFFER FOR RELATED PRODUCTS WITH PREVIEW AND REAL TIME
FEEDBACK
Abstract
Recommendations for purchase are made based on customer behavior
across multiple sessions. Correlations used for recommendations
include: buy-to-buy (cross-session), view-to-view (same-session),
view-to-buy (same-session), and abandon-to-buy (same-session)
actions. A preview display allows a merchant to adjust
recommendation algorithm weightings to achieve a desired result. A
closed-loop system is provided with real-time feedback. The
recommendations can be based on various segments of other users,
including users of the same search engine.
Inventors: |
McGonigal; Charles; (Austin,
TX) ; Salsbury; Russell; (Cambria, CA) ;
Guntumadugu; Saritha; (Cupertino, CA) ; Niemann;
Mike; (Austin, TX) ; Puttaswamy; Hemanth;
(Fremont, CA) ; Ogireddy; Srinivasa; (San Mateo,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
41427964 |
Appl. No.: |
16/422605 |
Filed: |
May 24, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12615476 |
Nov 10, 2009 |
10304113 |
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16422605 |
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11748391 |
May 14, 2007 |
7636677 |
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12615476 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0631 20130101;
G06Q 30/0282 20130101; G06F 16/9535 20190101; G06Q 30/02
20130101 |
International
Class: |
G06Q 30/06 20060101
G06Q030/06; G06F 16/9535 20060101 G06F016/9535; G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method for recommending affinity products, the method
comprising: collecting, in a server system, data corresponding to
monitored actions on a web site; providing an identification of
affinity products based on said monitored actions relating to a
target product; providing a preview of an identified affinity
product; modifying the selection of said identified affinity
product; and providing an updated preview showing any change in
said affinity product due to said modifying.
2. The method as recited in claim 1 further comprising: providing a
formula having weightings for identifying affinity products based
on said monitored actions; providing a preview of an affinity
product identified according to said formula; varying said
weightings; and providing an updated preview showing any change in
said affinity product due to said varying of said weightings.
3. The method as recited in claim 2, wherein said formula includes
an exclusion of selected products.
4. The method as recited in claim 2, wherein said formula provides
at least two correlations of a browsing or buying action of a first
product by a first user with browsing, abandoning or buying actions
of a group of users who also browsed or brought said first
product.
5. The method as recited in claim 2, wherein said monitored actions
include keywords, and said affinity product is a product on which
action was taken by a group of users who used the same keyword.
6. The method as recited in claim 1 further comprising: providing a
plurality of affinity products for each target product.
7. The method as recited in claim 1, wherein said preview comprises
a listing of a plurality of target products, with at least one
affinity produce associated therewith.
8. A method for tracking web usage data in real time, the method
comprising: collecting, in a server system, real time data
corresponding to monitored actions on a web site, said monitored
actions including buying a recommended product; aggregating said
real time data into aggregate groups desired for display; storing
said aggregated real time data in a hierarchical structure in a RAM
in said server system; and providing said real time data from said
RAN to a client at a client computer.
9. The method as recited in claim 8 further comprising: providing
an identification of affinity products based on said monitored
actions relating to a target product; providing a preview of an
identified affinity product; modifying the selection of said
identified affinity product based on said real time data; and
providing an updated preview showing any change in said affinity
product due to said modifying.
10. A method for recommending affinity products, the method
comprising: collecting, in a server system, data corresponding to
monitored actions on a web site; providing a formula for
identifying affinity products based on said monitored actions;
tracking said monitored actions for an identified segment of users;
and identifying said affinity product from monitored actions of
said segment of users.
11. The method as recited in claim 10, wherein said segment is
selected from the group of segments comprising product market
segment, time related segment, user characteristic segment,
geographical segment and browsing action segment.
12. The method as recited in claim 10 further comprising: providing
an identification of affinity products based on said monitored
actions relating to a target product; providing a preview of an
identified affinity product; modifying the selection of said
identified affinity product; and providing an updated preview
showing any change in said affinity product due to said
modifying.
13. The method as recited in claim 10 further comprising:
collecting, in a server system, real time data corresponding to
said monitored actions on a web site; aggregating said real time
data into aggregate groups desired for display; storing said
aggregated real time data in a hierarchical structure in a RAM in
said server system; and providing said real time data from said RAM
to a client at a client computer.
Description
TECHNICAL FIELD
[0001] The present invention relates generally to user providing
recommendations for product purchases based on previous product
purchases or other behavior by a customer.
BACKGROUND
[0002] Neonics, Inc. U.S. Pat. No. 4,996,642, describes selectively
recommending to a user items such as movies sampled by other users.
The recommendations are weighted, based on scalar ratings of the
user being close to scalar ratings of other users for some product
both have reviewed.
[0003] MNI Interactive U.S. Pat. No. 5,583,763 describes a user
designating his or her preferred selections as entries in a user's
preference list. Entries in the user's list are compared with
entries in the other users' lists. When a significant number of
matches have been found between two lists, the unmatched entries of
the other user's preference list are extracted. Those unmatched
entries with a high correlation to the user's preference list are
presented to the user as selections in which the user is likely to
be interested.
[0004] Cendant Publishing U.S. Pat. No. 6,782,370 describes
allowing customers to submit goods or services to be used as filter
data when providing recommendations based on customer buying hi
story.
[0005] Amazon.com U.S. Pat. No. 6,266,649 describes a
recommendations service that recommends items to individual users
based on a set of items that are known to be of interest to the
user, such as a set of items previously purchased by the user. In
the disclosed embodiments, the service is used to recommend
products to users of a merchant's Web site. The real-time service
generates the recommendations using a previously-generated
(off-line) table which maps items to lists of "similar" items. The
similarities reflected by the table are based on the collective
interests of the community of users.
[0006] Amazon.com U.S. Pat. No. 6,912,505 describes determining
relationships between products by identifying products that are
frequently viewed by users within the same browsing session (e.g.,
products A and Bare related because a significant portion of those
who viewed A also viewed B). The resulting item relatedness data is
stored in a table that maps items to sets of related items. The
table may be used to provide personalized product recommendations
to users.
[0007] Amazon.com U.S. Pat. No. 7,113,917 is similar, relating to
items actually selected (e.g., in a shopping cart).
SUMMARY
[0008] The present invention provides the ability to make
recommendations to customers based on a variety of tracked customer
behaviors. In one embodiment, behavior by a customer can be tracked
across a session, and across multiple sessions, including Lifetime
Individual Visitor Experience Profiles (LIVE Profiles). The system
can track browsing, buying and abandoning actions. By correlating
these to behaviors of other customers, recommendations of affinity
products can be made. For example, the following correlations can
be used for recommendations: buy-to-buy (cross-session),
view-to-view (same-session), view-to-buy (same-session), and
abandon-to-buy (same-session) actions.
[0009] In one embodiment, a merchant is provided with a preview
display. The preview display shows the actual recommendations that
would be made based on the weightings applied to different
correlations in an algorithm. The merchant can thus adjust the
weightings, create exceptions or overrides, or take other action to
get the desired results. After such adjustments, the merchant can
export the results and affinity product data to the merchant's web
site for actual usage.
[0010] In another embodiment, a closed-loop system is provided. The
merchant is provided with a display providing real-time feedback on
the performance of the recommendation algorithm. The real-time
feedback shows the correlated products, and tracks the actual
sales, browsing and abandoning. The merchant can thus instantly see
the results of changes (different weightings, etc.) in the
recommendation algorithm.
[0011] In one embodiment, the feedback is provided in real time by
aggregating the monitored data by the web analytics server into
aggregate groups. The aggregate data is then stored in a
hierarchical structure in a RAM in the analytics server system. The
data is then provided from said RAM to a client at a client
computer.
[0012] In one embodiment, the recommendations can be based on
different segments of users. For example, the segment of users
whose behavior is used to generate the recommendations could be
users of the merchants' website, users of all merchants in a
particular market segment, user characteristics, users using the
same search engine, vertical or horizontal market segments,
etc.
[0013] The foregoing has outlined rather generally the features and
technical advantages of one or more embodiments of the present
invention in order that the detailed description of the present
invention that follows may be better understood. Additional
features and advantages of the present invention will be described
hereinafter which may form the subject of the claims of the present
invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] A better understanding of the present invention can be
obtained when the following detailed description is considered in
conjunction with the following drawings, in which:
[0015] FIG. 1 is a diagram of a system according to an embodiment
of the present invention.
[0016] FIGS. 2A-2B are diagrams of preview and export settings
screens according to one embodiment of the preview module of FIG.
1.
[0017] FIG. 3 is a diagram of a real-time reporter screen according
to one embodiment of the real-time reporter module of FIG. 1.
[0018] FIG. 4 is a diagram of one embodiment of the web analytics
server system of FIG. 1.
DETAILED DESCRIPTION
[0019] Overall System
[0020] FIG. 1 is a diagram of a system according to an embodiment
of the present invention. A merchant web site server 10 is visited
by users 12 over the Internet 14.
[0021] Traffic to the merchant web site is monitored by web
analytics server system 16. System 16 provides data over the
Internet to a merchant web site monitoring computer and a merchant
FTP (File Transfer Protocol) site 19.
[0022] Data on user traffic is stored in a database of database
engine 20 in web analytics server system 16. A sessionizer 17
organizes the data into user sessions, as described in co-pending
application Ser. No. 11/546,923, filed Oct. 11, 2006. The system
also includes a web reporter 125 and a real-time analytics
application 136. Merchant computer 18 includes a preview client
module 22 that interacts with web reporter 125, and a real-time
reporter client that interacts with real-time analytics application
136.
[0023] Weighted Algorithm
[0024] In one embodiment, analytics system 16 tracks lifetime user
behavior. The same user can be tracked over multiple sessions, and
also may be tracked at different merchant sites (of merchants
subscribing to the web analytics system). For example, when user A
browses a product, the system can determine the group of other
users who browsed the same product. Other products bought by those
other users can be determined, and the most common product bought
(and not bought by user A) can be provided as a recommendation to
user A. Alternately, the recommendation could be the next most
common product bought if, for example, the most common product is
already being discounted and the merchant wants to promote another
product. Alternately, the most common product browsed can be
recommended to user A. A variety of affiliations are possible based
on different user actions. These affiliations are described in an
algorithm, with different weightings applied by the merchant.
[0025] In one embodiment, four kinds of weights to specify
different probabilities to inter-relate various actions performed
by the customer across sessions while visiting the e-commerce
website. These weights combine the most common affiliated product
in buy-to-buy (BB, cross-session), view-to-view (VV, same-session),
view-to-buy (VB, same-session), and abandon-to-buy (AB,
same-session) actions. These weights can be interactively adjusted
for providing flexibility for users to control their
recommendations. Other embodiments use more scores, consisting of
these 3 attributes and add-to-carts. In one example, the merchant
can select the variables a, b, c and d between O and 100% to select
a recommended product according to the formula:
Recommendation=(a.times.BB)+(b.times.VV)+(c.times.VB)+(d.times.AB).
The formula can further compare the recommendation to products
already viewed or bought by a particular user, and can change to a
next most common affiliated product if the user has already viewed
or bought the first choice for a recommendation. Alternate formulas
could be used, such as including the second or third most common
affiliated product in each category (BB, VV, VB, AB).
[0026] Preview
[0027] FIG. 2A is a diagram of a preview screen according to one
embodiment of the preview module of FIG. 1. A window 26 displays
four relationships and their associated weightings. "Similar
Products" signifies that user 1 is viewing (browsing) product A,
and is being recommended a product X viewed by other users that
also viewed product A (where product X has not been viewed by user
1). "Final Product Choice" buy signifies that user 1 is viewing
product A, and is being recommended a product X bought by other
users that also viewed product A (where product X has not been
bought by user 1). "Products that Go Together" signifies that user
1 is buying product A, and is being recommended a product X bought
by other users that also bought product A (where product X has not
been bought by user 1). "Second Chance for Conversion" signifies
that user 1 is abandoning product A (put in the cart but not
bought) and being recommended a product X bought by other users
that also viewed product A (where product X has not been abandoned
or bought by user 1). It will be appreciated that many variations
of the algorithm are possible. For example, an abandon-buy
relationship can be used, or a buy-browse, or any other
combination. There could be more or less than four relationships
weighted.
[0028] Window 26 includes a weighting for each of the four
categories. These are shown as rating from 0 to 100 in the example.
The merchant can adjust these weighting, and see the effect in
preview window 28. The recommendation preview window 28 shows a
Target Product Name in the first column (such as a Model X laptop
computer A which can be browsed by user 1) and one or more affinity
products that will be recommended according to the formula and
weightings chosen. Three affinity products are shown in the example
of FIG. 2A. In one embodiment, the preview window shows a small
subset of all recommendations at any one time. The products
displayed in the first column can be either the most popular
browsed products, or other products the merchant selects. The
affinity products recommended in the second-fourth columns apply
the algorithm and weightings to the database engine 20 which
contains the browsing, buying, abandoning, etc. behavior of all
visitors to the merchant's website. The merchant can then adjust
the weightings in window 26 and see, in window 28, the effect on
what products are recommended.
[0029] The merchant can also exclude certain products or categories
of products from being recommended by entering them in exclusion
window 30. For example, the merchant may not want to recommend
products that are already selling well and are the most popular, or
products that are discounted or otherwise being marketed by
different means. This eliminates any bias towards new products or
popular products associated with events happening across the
World.
[0030] In one embodiment, additional options allow customizing the
number of recommendations according to e-commerce merchant needs.
This can be done with an Export Settings window as shown in FIG.
2B. It can cap the results at any number of target items or
unlimited, as well as capping per-target recommendations at any
number from 1 to 10. For example, the merchant can enter a cap on
the number of recommendations per product or keyword in selection
box 32. For example, the merchant may want only one or two products
recommended. The merchant can also cap the number of target
products from which recommendations will be drawn in selection box
34.
[0031] In another embodiment of the invention, recommendation
preview window 28 is used to show a preview of recommendations
based on keywords. This can be used for words searched by the user
either on the merchant's site, or on the search engine which led to
the merchant's site. The recommendations can be displayed to the
user on the merchant's web site. Alternately, if the merchant has
an advertising arrangement with a search engine, the
recommendations could be displayed along with the search results
when the user enters the key words in the search engine.
[0032] An alternate view of window 26 can be provided with
different keyword affinities. For example, keyword-browse,
keyword-abandon, keyword-buy. Further gradations can be specified,
such as whether the keyword or affinity product is on the merchant
site, the search engine, or any merchant site in a category. In one
embodiment, a weighting algorithm can combine both keywords and
products browsed, bought, etc.
[0033] Real-Time Monitor
[0034] FIG. 3 is a diagram of a real-time reporter screen according
to one embodiment of the real-time reporter module of FIG. 1. A
Historic window 40 shows the historic results with the product
being bought, viewed or abandoned in the first column, followed by
the recommended products in the following columns. Each recommended
product shows the conversion percentage in parenthesis. A second
window 42 provides real-time results, with the period of time
selectable by the merchant. This provides the merchant with real
time feedback on the success of the recommendations. If a product
is not selling well, the merchant can tweak the weightings, change
exclusions, etc. For example, if there is a high rate of
abandonment, a discount could be offered on that product. The real
time feedback is made possible using the system described
below.
[0035] In one embodiment, the feedback is provided in real time by
aggregating the monitored data by the web analytics server into
aggregate groups. The aggregate data is then stored in a
hierarchical structure in a RAM in the analytics server system. The
data is then provided from said RAM to a client at a client
computer.
[0036] FIG. 4 is a diagram of one embodiment of the web analytics
server system of FIG. 1, showing the real-time analytics module.
More details can be found in co-pending application Ser. No.
11/546,923, filed Oct. 11, 2006, the disclosure of which is hereby
incorporated herein by reference. A merchant web server 10 provides
web pages which are downloaded to a client (user) computer, and
include URLs 112 and Flash, Ajax, Java, or other local applications
114. Each of the components referred to has associated metadata
request elements 116 and 118, respectively, for tracking clicks by
the users 12. The metadata request elements collect the user click
information and transmit it over the internet 14 to a web analytics
or tracking server system 16.
[0037] Data is initially provided to a group of web servers, or
pixel servers, 123 as a log of click stream data. Multiple
collectors 126 pull the data, sort the data by session (using the
session ID), and provide the data in multiple messaging queues to
the sessionizers (transformers) 128. The data for the same session
is sent to the same sessionizer based on a hash ID algorithm. The
sessionizers organize the collected data as discussed below, then
provide it in different formats and based on various business and
statistical logic through a variety of different messaging systems
130 to different targets that include but are not limited to:
1-real time in-memory streaming for real time in-memory analytics;
2-real time in memory streaming through a variety of application
APIs for other applications; 3-used for long term database loading
or other storage media.
[0038] Any of these messaging systems 130 can pass on any number of
well-defined alerts coming from any external sources to the RAM
135. RAM 135 may also directly receive an RSS feed through the
internet. Thus, data from different sources including the session
data from the sessionizer, the alerts or other data types from
other external sources can be combined and processed, using any
business logic or statistical data analysis in the RAM and made
available for real time viewing to any target. Examples include,
for the same client, not only web data, but call center data,
bricks and mortar store data, giving a complete overview of
business models defined and represented using the data.
[0039] The data in RAM 135 is provided to a variety of web services
platforms 142, which are available for external vendors to pull
through any APIs for export streaming. Also, the data from RAM 135
is accessed by a real time browser based application 144. Real-Time
Analytics Application 136 includes RAM for storage 135 and RAM
based services 137. RAM based services 137 are programs stored in
the main memory of a server which controls the storing, processing,
aggregating, accessing, authenticating, authorizing, etc. of data
in the RAM. Such services include a de-serializing service, an
aggregator service, a localizer service, a security service, a
messaging service, a recovery service, and/or any other service
defined on the data in RAM.
[0040] Real time reporter 144 may reside on a client computer or
may be downloaded from a web analytic server, and can use Flash,
Ajax, a local application or other methods for requesting and
rendering reports. The data for the reports is requested from Web
Analytics Server 16 across the Internet 14. Independent modules
within the real time reporter program 144 will retrieve data in RAM
135 from real time analytics application 136 asynchronously using
interface module 140, through different protocols (HTTPs, Flash,
Ajax, etc.) for the real time interactions.
[0041] The system of FIG. 4 is designed to respond at the speed of
accessing the data in memory and processing the data in memory. It
can also handle data for a large number of clients across a large
number of geographically distant web servers. In one embodiment,
collectors 126 include a large numbers of servers, with associated
disk drive storage. There could typically be fewer servers for
sessionizers 128, and even fewer servers making up messaging system
130, all with associated disk drives. Loaders may include dozens of
servers and associated disk drives. RAM 135 could be a single or
multiple banks of RAMs.
[0042] In one embodiment, the real-time monitor can be used to
provide the last 7 days of data on the top 100 products of a
merchant. The merchant could vary the recommendations daily, based
on browsing of the top 100 products. Alternately, other numbers of
products or time periods could be used. The real-time monitor will
show the actual sales being made based on the recommendations soon
after they occur, allowing the merchant to adjust quickly to
changing conditions.
[0043] LIVE Profile Segmentation
[0044] In one embodiment, the recommendations can be based on
different segments of users. For example, the segment of users
whose behavior is used to generate the recommendations could be
users of the merchant's website. Alternately, because the web
analytics is typically provided by a third party to multiple
merchants, the segment can be users of all merchants in a
particular market segment. For example, if the merchant is a shoe
store, the segment could be all shoe stores, all clothing stores,
all women's shoe and/or clothing stores, all users in a particular
geographic area, or any combination. The recommendations can also
be segmented by any other information available, such as the net
worth of the users, the purchase volume or average price per
product bought by the users (e.g., high end users), users browsing
at the same time of day or seasons, users using the same search
engine, vertical or horizontal market segments, etc.
[0045] In one embodiment, behavior by a customer can be tracked
across a session, and across multiple sessions, including Lifetime
Individual Visitor Experience Profiles (LIVE Profiles) of
Coremetrics. The system can track browsing, buying and abandoning
actions for each user over the online lifetime of that user.
[0046] Search Engine Source
[0047] In one embodiment, the search engine used by customers to
reach the merchant's site is tracked. This can form the basis of
another segmentation, with recommendations being drawn from the
group of users that came to the merchant's site using the same
search engine. Additionally, based on this information,
recommendations can be made to the customers by providing the
recommendation before the customer even reaches the merchant's web
site, by providing a recommendation upon entry of certain key words
in the search engine. The merchant can accomplish this through an
advertising arrangement with the search engine provider.
Alternately, the recommendation can be made on the basis of the
keywords used to reach the merchant site, and displayed on the
merchant site.
[0048] Embodiments of the present invention thus provide a system
that offers and algorithm using conditional probability to
calculate the recommendations based on past history of product page
views and purchases by online customers. The system is generic
enough that it can be applied across any kind of retail ecommerce
site. It provides an interactive mechanism to fine tune the
recommendations at any time according to merchant's requirements.
The system can adaptively increase the efficiency of the
recommendations by tracking the effectiveness of the
recommendations presented. This information can be used in the
subsequent calculations of recommendations. The effectiveness can
be improved using real-time feedback.
[0049] It will be understood that modifications and variations may
be effected without departing from the scope of the novel concepts
of the present invention. For example, other actions of users could
be track and correlated or segmented, such as particular browsing
actions including dwell time on a particular web page, viewing of
particular news stories, amount of time spent surfing, etc.
Alternately, physical characteristics can be correlated or
segmented, such as those inferred from product purchases (shoe
size, dress size), etc. Also, although the term "products" has been
used herein, it is understood to include services, categories or
groupings of products and categories or groupings of services.
Additionally, the recommendations can include any sort of
qualification or terms, such as a discount for buying in the next
hour. Accordingly, the foregoing description is intended to be
illustrative, but not limiting, of the scope of the invention which
is set forth in the following claims.
[0050] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0051] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0052] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0053] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
[0054] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0055] These computer readable program instructions may be provided
to a processor of a computer, or other programmable data processing
apparatus to produce a machine, such that the instructions, which
execute via the processor of the computer or other programmable
data processing apparatus, create means for implementing the
functions/acts specified in the flowchart and/or block diagram
block or blocks. These computer readable program instructions may
also be stored in a computer readable storage medium that can
direct a computer, a programmable data processing apparatus, and/or
other devices to function in a particular manner, such that the
computer readable storage medium having instructions stored therein
comprises an article of manufacture including instructions which
implement aspects of the function/act specified in the flowchart
and/or block diagram block or blocks.
[0056] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0057] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be accomplished as one step, executed concurrently,
substantially concurrently, in a partially or wholly temporally
overlapping manner, or the blocks may sometimes be executed in the
reverse order, depending upon the functionality involved. It will
also be noted that each block of the block diagrams and/or
flowchart illustration, and combinations of blocks in the block
diagrams and/or flowchart illustration, can be implemented by
special purpose hardware-based systems that perform the specified
functions or acts or carry out combinations of special purpose
hardware and computer instructions.
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