U.S. patent application number 17/572490 was filed with the patent office on 2022-04-28 for dynamic determination of localization source for web site content.
The applicant listed for this patent is MOTIONPOINT CORPORATION. Invention is credited to Eugene ALVAREZ, Fabio BELTRAMINI, Will FLEMING, Enrique TRAVIESO, Chuck WHITEMAN.
Application Number | 20220129949 17/572490 |
Document ID | / |
Family ID | 1000006078692 |
Filed Date | 2022-04-28 |
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United States Patent
Application |
20220129949 |
Kind Code |
A1 |
WHITEMAN; Chuck ; et
al. |
April 28, 2022 |
DYNAMIC DETERMINATION OF LOCALIZATION SOURCE FOR WEB SITE
CONTENT
Abstract
Method and system for localizing an element present in a piece
of content having a plurality of elements. A cost of localizing an
element with respect to each of one or more localization sources is
first computed. At least one criterion based on which a
localization source for localizing the element is to be determined
is obtained. A localization source for to the element is then
selected based on an assessment with respect to the at least one
criterion. The element of the content is then localized using the
selected localization source.
Inventors: |
WHITEMAN; Chuck; (Coconut
Creek, FL) ; ALVAREZ; Eugene; (Coconut Creek, FL)
; TRAVIESO; Enrique; (Davie, FL) ; FLEMING;
Will; (Boca Raton, FL) ; BELTRAMINI; Fabio;
(Boca Raton, FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MOTIONPOINT CORPORATION |
COCONUT CREEK |
FL |
US |
|
|
Family ID: |
1000006078692 |
Appl. No.: |
17/572490 |
Filed: |
January 10, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13840747 |
Mar 15, 2013 |
11222362 |
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17572490 |
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61752778 |
Jan 15, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0206 20130101;
G06Q 30/0249 20130101; G06Q 30/0269 20130101; G06Q 30/0254
20130101; G06Q 30/02 20130101; G06Q 30/0205 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method implemented on a compute having at least one processor,
storage, and communication platform for localizing a webpage having
plurality of elements, comprising the steps of: with respect to
each of the plurality of elements; determining one of one or more
categories into which the element is classified, selecting an
initial localization source from a plurality of localization
sources to localize the element based on known metrics
characterizing the category, replacing the element on the webpage
using an initial localized element generated by localizing the
element using the initial localization source, monitoring the
activities of visitors to the webpage directed at the initial
localized element: determining an updated localization source for
the initial localized element based on the monitored activities;
and replacing the initial localized element on the webpage with an
updated localized element generated by localizing the element with
the updated localization source.
2. The method of claim 1, wherein each of the one or more
categories comprises at least one piece of content; and each piece
of content in each category comprises one or more content
elements.
3. The method of claim 2, wherein content in the one or more
categories including at least one of: an article on a certain
topic; textual information about a product, wherein the textual
information includes at least one of a product description, a
listing price, and a detailed specification of the product; and
pictorial information.
4. The method of claim 1, wherein the plurality of localization
sources comprise at least one of: a copy editing, a professional
translation, a crowd translation, a human-edited machine
translation, and a machine translation.
5. The method of claim 1, wherein the step of selecting comprises:
estimating an initial value for the element in the category using
the known metrics characterizing the category; and determining the
initial localization source for the category based on the initial
value and costs associated with the respective plurality of
localization sources.
6. The method of claim 5, wherein the step of determining the
updated localization source comprises: updating the initial value
of the initial localized element based on one or more measures
computed based on the monitored activities to generate an updated
value; and selecting the updated localization source for the
initial localized element based on the updated value when a certain
criterion is met.
7. Machine readable and non-transitory medium having information
recorded thereon for localizing a webpage having plurality of
elements, wherein the information, when read by the machine, causes
the machine to perform the following steps: with respect to each of
the plurality of elements, determining one of one or more
categories into which the element is classified, selecting an
initial localization source from a plurality of localization
sources to localize the element based on known metrics
characterizing the category, replacing the element on the webpage
using an initial localized element generated by localizing the
element using the initial localization source, monitoring the
activities of visitors to the webpage directed at the initial
localized element; determining an updated localization source for
the initial localized element based on the monitored activities;
and replacing the initial localized element on the webpage with an
updated localized element generated by localizing the element with
the updated localization source.
8. The medium of claim 7, wherein each of the one or more
categories comprises at least one piece of content; and each piece
of content in each category comprises one or more content
elements.
9. The medium of claim 8, wherein content in the one or more
categories includes at least one of: an article on a certain topic;
textual information about a product, wherein the textual
information includes at least one of a product description, a
listing price, and a detailed specification of the product; and
pictorial information.
10. The medium of claim 7, wherein the plurality of localization
sources comprise at least one of: a copy editing, a professional
translation, a crowd translation, a human-edited machine
translation, and a machine translation.
11. The medium of claim 7, wherein the step of selecting comprises:
estimating an initial value for the element in the category using
the known metrics characterizing the category; and determining the
initial localization source for the category based on the initial
value and costs associated with the respective plurality of
localization sources.
12. The medium of claim 11, wherein the step of determining the
updated localization source comprises: updating the initial value
of the initial localized element based on one or more measures
computed based on the monitored activities to generate an updated
value; and selecting the updated localization source for the
initial localized element based on the updated value when a certain
criterion is met.
Description
TECHNICAL FIELD
[0001] The present teaching generally relates to the Internet, and
more particularly relates to localization of Internet content.
BACKGROUND
[0002] The Internet and the world-wide web have allowed consumers
to complete business transactions with organizations or individuals
located across continents. In an increasingly global marketplace,
it is becoming imperative for businesses/organizations to localize
their websites for visitors in other markets in order to expand
their customer base.
[0003] Local relevance is the degree to which a website contains
content and functionality that is useful to people within a local
market. Factors that determine a websites local relevance include
the language of the website, units of measure used on the site,
relevant marketing offers, relevant imagery, the currency used to
denominate the products and services offered on the site,
conventions used to format different types of data (for instance
dates and monetary amounts), the particular products and services
offered on the site, payment types that can be used to transact on
the site, shipping methods used to deliver products to customers,
local market regulatory compliance (such as duties & tariffs,
privacy policies, etc.) and access to local customer service.
[0004] Traditionally, much of the cost of localizing a website is
incurred by website owners prior to local visitors using the site
and generating a return for the owners of the site. Knowing what to
localize and how to do it can be very difficult. The incremental
return from localization is hard to estimate. Traditionally, the
ongoing cost of localizing is also difficult to estimate. The
ultimate size of a market in a locale and the pace with which it
will grow are difficult for companies to predict. All of these
factors make it difficult to determine or budget the amount of
investment needed in the localization of a website.
[0005] An organization's profit potential in each local market is
usually a function of market-specific factors such as population,
GDP, maturity of the industry, competition, shipping costs, tax
rates, etc. and often changes over time. Because of this, each
organization may make a localization decision based on an
assessment as to whether the localization will enhance the
profitability in each particular locale. In addition, different web
pages of a website may or often likely have different potential to
drive up the profits. For example, a job posting page of a company
is unlikely to directly contribute to profitability. At the same
time, a page posting products for sale, which allows a user to make
a purchase on that page, is more likely to play a role to enhance
the profitability. Given that, different web pages of a website
have different priorities in terms of localization. Furthermore,
the potential of each page, or even portions of a single page, to
drive the profitability likely changes over time. For example, the
popularities of products that are displayed on different web pages
(or different portions of a single page) may change from locale to
locale or over time. In this case, ideally, the localization
decision with respect to a particular locale should adapt according
to such changes or dynamics in the local market. Traditional
localization approaches do not consider such factors and do not
offer any solution for such needs.
[0006] There is another dimension to localization, When translating
a website to another language in order to localize it for a
specific market, an organization can choose from several
localization sources, which can include machine translation, human
edited machine translation, human translation and copy writing.
Machine translation is inexpensive, but the quality is inferior to
human translation. The editing of machine translation by a human
improves the quality of the machine translation at a higher cost
than machine translation alone, but because the starting point can
be a poor translation, the end result is typically inferior to
human translation. Human translation is more expensive than the
machine or human edited machine methods, yet results are in general
of a superior quality. However, even with human translation, there
are vastly different cost structures and associated quality of
translations. Professional human translators typically cost more
and produce higher quality translations than volunteers or crowd
sourced translations. Also, a copy writer goes beyond translation
by conveying different and ideally more relevant messages to the
website visitors, but it is typically the most expensive
approach.
[0007] The value derived from localizing a website is a function of
factors such as how many visitors will see it, how likely visitors
are to "transact" as a result of the localization, how much value a
"transaction" generates, and how much the localization costs.
Typically, an organization will choose one of the above
localization sources and apply it to the entire website without
taking into consideration the fact that different areas of the
website, or different pages of the website, or even different areas
of a single page, may give rise to different levels of significance
in terms of potential financial return to the organization.
Organizations rarely revisit their decisions in localization, as
circumstances change, nor do they consider the full range of
localization sources. This consequently impacts the value they can
derive from localization. An important reason that organizations do
not do that is because there is no efficient approach, system, or
tool to efficiently and dynamically allocate localization sources
to a website.
[0008] The type of localization source used will generate varying
degrees of online engagement and conversion rates (i.e., the
percentage of website visitors who take a desired action). In
addition, different areas or pieces of content within the website
have varying levels of importance for localization. For instance,
high visibility content and areas containing a call-to-action
typically generate greater return on localization investment than
other areas. As a result, applying a one-size-fits-all approach for
the entire website is not the most efficient or effective approach
because it results in organizations over-investing in localizing
less important content while under-investing in localizing more
important content.
[0009] Therefore a need exists to overcome the problems with the
prior art as discussed above.
SUMMARY
[0010] The methods, systems, and/or programming described herein
are related to content localization and particularly related to
determining content localization sources for content.
[0011] In one example, a method implemented on a computer having at
least one processor, storage, and communication platform for
localizing an element present in a piece of content comprising a
plurality of elements is disclosed. A cost of localizing an element
with respect to each of one or more localization sources is first
computed. At least one criterion based on which a localization
source for localizing the element is to be determined is obtained.
A localization source for localizing the element is then selected
based on an assessment with respect to the at least one criterion.
The element of the content is then localized using the selected
localization source.
[0012] In a different example, a method implemented on a computer
having at least one processor, storage, and communication platform
for determining a localization source for localizing a plurality of
elements present in a piece of content is disclosed. A cost of
localizing each of the elements in the content with respect to each
of one or more localization sources is first computed. At least one
criterion with respect to each element based on which a
localization source for localizing the element is to be determined
is obtained. A localization source for localizing each of the
elements in the content is then selected based on the at least one
criterion associated with the element. Each of the elements in the
content is then localized using its respectively selected
localization source. One or more measures for each element are
monitored and such measures are computed with respect to the at
least one criterion corresponding to the element. Based on such
monitored one or more parameters with respect to each clement, an
updated localization source for the element is dynamically
determined. Then each of the elements is localized based on its
corresponding update localization source.
[0013] In another example, a method implemented on a computer
having at least one processor, storage, and communication platform
for localizing an element present in a piece of content having
multiple elements is disclosed. A cost of localizing an element
with respect to one or more localization sources is first computed.
An initial localization source for localizing the element is
selected based on at least one criterion and the initial
localization source is used to localize the element. One or more
measures are monitored where the one or more measures are computed
with respect to the at least criterion. Based on such monitored one
or more parameters with respect to the element, an updated
localization source for the element is automatically determined.
Then the elements is localized based on its corresponding update
localization source.
[0014] In yet another example, a method implemented on a computer
having at least one processor, storage, and communication platform
for localizing an element present in a piece of content having
multiple elements is disclosed. A cost of localizing an element
with respect to one or more localization sources is first computed.
An initial localization source for localizing the element is
selected based on at least one criterion and the initial
localization source is used to localize the element. One or more
measures are monitored where the one or more measures are computed
with respect to the at least criterion. An adjustment to the
initial localization source is determined based on the one or more
measures. Such an adjustment includes either promotion or
demotion,
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The methods, systems, and/or programming described herein
are further described in terms of exemplary embodiments. These
exemplary embodiments are described in detail with reference to the
drawings. These embodiments are non-limiting exemplary embodiments,
in which like reference numerals represent similar structures
throughout the several views of the drawings, and wherein:
[0016] FIG. 1 is a block diagram illustrating the overall
architecture of the present teaching by showing its relationship to
a website, the localized version of the website and online
visitors, in one embodiment of the present invention;
[0017] FIG. 2 is a drawing that shows a product information page
with 2 elements;
[0018] FIG. 3 is a block diagram illustrating the system
architecture of the present invention, in one embodiment of the
present invention;
[0019] FIG. 4A shows an exemplary table that provides an example
list of small televisions for sale on an online retailer's
website;
[0020] FIG. 4B shows an exemplary table that provides another
example list of small televisions for sale on an online retailer's
website;
[0021] FIG. 5A is a table that shows an example of estimating the
value of a list of new articles present on an ad supported portal
or news website;
[0022] FIG. 58 is a table that shows an example of estimating the
value of the content of several products on a shopping engine's
website.
[0023] FIG. 6 contains an exemplary table that shows the cost of
localizing a detailed product description element using different
localization sources;
[0024] FIG. 7 is an operational flow diagram depicting an exemplary
general process of automatic determination of localization sources
for a website;
[0025] FIG. 8 is an operational flow diagram depicting an exemplary
process of automatic determination of a localization source for a
specific element;
[0026] FIG. 9 is an operational flow diagram depicting an exemplary
process of automatic determination of a localization source for a
specific element that starts by applying the least expensive
localization source;
[0027] FIG. 10A is an exemplary table that shows localization
source promotion decisions by TV based on traffic and visitor
behavior during a period of 30 days for the list of televisions
shown in FIGS. 4A and 4B:
[0028] FIG. 10B shows an exemplary computed contribution margin for
machine translation based on the table shown in FIG. 10A;
[0029] FIG. 11 is an operational flow diagram depicting an
exemplary process of automatic determination of a localization
source for a specific element using test groups with different
localization sources;
[0030] FIG. 12A is a table that shows the results of an example
test performed during a period of 30 days for a representative
sample of televisions for both human and machine translation
localization sources and determines an optimized localization
source for each TV based on the results;
[0031] FIG. 12B shows an exemplary computed contribution margin for
machine translation and an exemplary computed contribution margin
for human translation based on the table shown in FIG. 12A;
[0032] FIG. 13A is a table that shows the results of an example
test performed during a period of 30 days for a representative
sample of televisions for both human and machine translation
localization sources and determines an optimum category-wide
focalization source based on the results;
[0033] FIG. 13B shows an exemplary computed contribution margin for
machine translation and an exemplary computed contribution margin
for human translation based on the table shown in FIG. 13A;
[0034] FIG. 14 is an operational flow diagram depicting an
exemplary process of automatic determination of a localization
source for a specific element that starts by not applying a
localization source to the content;
[0035] FIG. 15A is an exemplary table that shows localization
source promotion decisions by TV based on traffic and visitor
behavior during a period of 30 days and on category specific
conversion lift percentages by localization source for the list of
televisions shown in FIGS. 4A and 4B;
[0036] FIG. 15B shows an exemplary computed contribution margin for
no localization based on the table shown in FIG. 15A;
[0037] FIG. 16A is a table that shows the results of an example
test performed during a period of 30 days for a representative
sample of televisions for various localization sources and
determines an optimum localization source for each TV based on how
the test results impact return on investment;
[0038] FIG. 16B shows an exemplary computed contribution margin for
no translation based on the table shown in FIG. 16A;
[0039] FIG. 17A is a table that shows the result of an exemplary
test on determining the optimum localization source for individual
elements via successive localization source promotions while
simultaneously considering other factors;
[0040] FIG. 17B shows an exemplary scheme for maximizing profit
across multiple products;
[0041] FIG. 17C shows an initial state before applying the scheme,
shown in FIG. 17B;
[0042] FIG. 17D shows final localization sources and other features
based on the scheme shown in FIG. 17B;
[0043] FIG. 18A shows more test results determining a localization
source for each category while simultaneously considering various
factors;
[0044] FIG. 18B shows the associated investment decisions and other
features in accordance with the results in FIG. 18A;
[0045] FIG. 18C shows the same information as FIG. 18B in a
graphical format;
[0046] FIG. 19A shows another test result determining a
localization source for groups of content while simultaneously
considering multiple factors;
[0047] FIG. 19B shows the net result of applying localization
source selection criteria to the example content of FIG. 19A;
[0048] FIG. 20 shows more example test results relating to
gradually localizing groups of content within a budget constraint
while adapting to the discrepancy between a predicted and the
actual amount of content to be localized;
[0049] FIG. 21A shows an example of producing updated
forward-looking estimates for expected amount of content to be
localized by taking an initial prediction and periodically updating
it;
[0050] FIG. 21B visualizes three distributions involved in the
Bayesian updating process;
[0051] FIG. 22 is a tree diagram that shows exemplary factors that
can be used in automatically determining a localization source for
an clement; and
[0052] FIG. 23 is an exemplary block diagram showing a computer
system useful for implementing the present invention;
DETAILED DESCRIPTION
[0053] All organizations have limited resources (e.g., budget
limitations), which makes it important to invest such limited
resources in ways that generate maximum return. Therefore, it is
important to determine a most suitable localization source for each
particular area or piece of content in a web site in order to
maximize the return on investment and the profit generated by the
localized website. As discussed above, to date, there is no
effective approach, system, or tool that enables organizations to
do so. The present teaching provides methods and systems that
facilitate organizations to individually localize different
portions of a website (be it different pages or areas of the same
page) using individually selected localization sources based on
criteria, such as profit maximization, and to adjust the same
dynamically over time to adapt to the changing surrounding dynamics
to allow organizations to balance smartly the cost of localization
and the return from the localization. The automated selection of a
localization source may be optimized based on criteria specific to
each organization so that the automatically selected localization
source may correspond to an optimum localization source with
respect to the criterion provided.
[0054] The present teaching involves individually localizing the
content of different elements contained in web pages, web sites,
documents, etc. The disclosed teaching is capable of automatically
determining a localization source for the content of each element
in, e.g., a web page, a web site, or a document, and automatically
routing the content to the selected localization source. It also
provides the capability of adjusting, dynamically, the localization
sources to be used to localize the content of individual elements.
Such dynamic adjustment may be made based on the surrounding
circumstances such as market dynamics, ongoing viewership or
economic return, which may be measured, e.g., with respect to the
investment put in the localization of each element. Based on such
monitored surrounding circumstances, the present teaching is
capable of automatically adjusting from one localization source to
another for, e.g., each of the elements in the content. The present
teaching is also capable of making dynamic adjustments based on,
e.g., a specified condition. For example, a selectable criterion
that can be used to control the dynamic adjustment of localization
of content of individual elements may be specified as, e.g.,
promote, demote, or no-changes. For example, based on the economic
return, the present teaching may promote (or demote) a localization
source to another higher cost/quality (or lower cost/quality)
localization source. The automatic determination, adjustment, and
routing to localization sources is done dynamically and can be
without human intervention, particularly when the system is
configured to run in real time. The disclosed system can also be
configured to allow human intervention, e.g., when human
intervention is needed, e.g., administrators need to confirm
machine automatically generated decisions related to localization
sources or approve the cost for localization. The system can be
configured to allow a human to participate in making confirmations
as to a decision or iteratively interface with the system to adjust
the process of making decisions related to localization sources.
For example, when the budgetary situation changes, a manager may be
allowed to specify different operational parameters such as the
budget so that the system can compute the optimal localization
sources based on such newly specified parameters.
[0055] It is often the case that not every element in content,
e.g., a web page, carries the same importance or potential to drive
up value. It follows that not every element of the content needs to
be localized using the same localization quality (or source). No
mechanism or method to date is able to individually localize the
content of different elements based on an estimated cost versus
benefit, using individually determined localization sources. in
addition, to ensure that localization of the content is cost
effective, no mechanism or method to date is capable of
individually monitoring the economic return with respect to the
content of each element and dynamically adjusting the localization
source to be used.
[0056] The methods, systems, and medium, disclosed in accordance
with the present teaching, overcome problems with the prior art by
providing dynamic determination of a localization source
individually for different portions of content. Such determined
localization source may be deemed as optimum in connection with
certain criteria.
[0057] FIG. 1 depicts a block diagram illustrating an exemplary
overall architecture of the present teaching showing its
relationship to a website, the localized version of the website and
online visitors, in one embodiment of the present invention. An
origin website 180 produces a web page 170 for a home market. The
origin website 180 is connected to a network 120, such as the
Internet, through which visitors 110 request and receive the web
pages 170. A localized version of the website 130 returns to
visitors localized versions of web pages 140 corresponding to the
web pages of the origin website 170. The localized website 130 can
be implemented via a Translation or Localization Server, such as
the one described in U.S. Pat. Nos. 7,580,960, 7,582,216,
7,627,479, 7,627,817, 7,996,417, and 8,065,294, as well as pending
U.S. pending patent applications with Ser. Nos. 13/096,464,
12/609,778, and 12/609,834, all assigned to the same assignee. The
localized website 130 is also coupled to a network, e.g., the same
network 120 as the origin website 180. Various types of public
visitors 110, such as users using mobile devices 110-a, vehicle
devices 110-b, laptops or tablets 110-c, and desktop computers
110-d, request and receive web pages 170 and localized web pages
140 via the network 120. A server 150 dynamically and automatically
determines the localization sources, e.g., optimal sources with
respect to different organization-based criteria, and routes
content to such determined localization sources, which are also
coupled to network 120, such as various available localizations
sources 160. The server 150 may use additional information from
external sources 190, also connected to the network 120, in the
process of determining the localization sources. An example of such
additional information includes market information.
[0058] Although FIG. 1 refers to a website and a localized website,
the methods, systems, and medium, disclosed in accordance with the
present teaching may be applied to other types of servers (not just
web servers) and applications. For example, these teachings can be
applied to online mobile applications that have a server and a
client component. The client component, typically referred to as a
mobile app, is installed on the mobile device. The server component
is installed on a server and communicates with the client component
via the network 120. An example of a server component is a web
service.
[0059] A common goal of a website is to trigger specific behaviors
from its online visitors. For example, the primary goal of an
online retailer's website is for its visitors to purchase items on
the site. The value of the website is driven by the behaviors it
promotes. Some example behaviors include: buying, consuming
content, locating a dealer, locating a store, requesting a quote,
subscribing to a list, requesting more information, downloading a
file, paying a bill, etc. The percentage of visitors who achieve a
desired behavior is also known as the percentage conversion.
[0060] For the purpose of this teaching, a website comprises
elements that contain content to be displayed. An example of an
element is a product information page on an online retailer's
website. An element of a website may also comprise multiple pages,
such as a checkout application. It may also be a subset of the
content in a web page, such as a detailed product description, or a
single image or button, such as an "add to cart" button. FIG. 2 is
a drawing that shows an exemplary product information page with 2
elements: a detailed product description 210 and an "add to cart"
button 220. The set of content that the detailed product
description element 210 contains comprises the descriptions of each
of the products available for sale on the online retailer's
website.
[0061] Elements that are important to a website are usually
associated with or intended to trigger specific behaviors. For
example, an "add to cart" button is associated with a buy behavior.
A detailed product description may also trigger buy behavior. The
commercial value of an element is directly related to the behavior
associated therewith. The process of estimating the value of an
element depends on the type of content that it contains, For
example, the value of a product information page element for a
specific product may be based on various considerations, such as
the retail price of the product, the percentage gross margin on the
sale of the product, the estimated number of visitors that will see
that page and the rate at which these visitors will purchase the
product. In contrast, the value of a "Locate Dealer" submission
element on a manufacturer site may be based on the amount a dealer
would pay for access to that lead or the rate at which that lead
later buys a product or service. The value of a visit to a site
which lacks a call to action that can be translated into a value
can be also quantified. For example, the value of such a visit
might be based on a post-visit survey regarding future purchase
intent, post-visit brand impression, or even deferred purchase
behavior,
[0062] FIG. 3 is a block diagram illustrating one embodiment of the
system architecture of the present invention. Localization source
determination server 150 depicts an exemplary architecture of the
component that automatically determines the localization sources
depicted in FIG. 1 and routes content to such optimum localization
sources. Various types of localization sources 160 may be
available, such as Copy Editing 160-a, Professional Translation
160-b, Crowd Translation 160-c, Human-edited Machine Translation
160-d and Machine Translation 160-c. The Localization Source
Determination Request 300 represents a request from an outside
entity, such as the Localized Website/Translation Server 130 of
FIG. 1, to determine the optimum localization source for the
content of an element, such as a detailed product description 210
shown in FIG. 2.
[0063] The Value Estimation Component 305 estimates the value of
content of an element on the website 180, or on the localized
website 130, based on, e.g., information present in the visitor
390-a, knowledge 390-b and financial 390-c databases. For example,
in the case of the product description 210, such information can
include the average sale price, gross margin percentage, item life
in months, projected views per month. projected conversion
percentage and a shop-online-buy-in-store factor for the actual
television product for sale. The value of the product description
210 of a television can then be estimated based on this
information. For example, a scheme for estimating the value of the
television description can involve multiplying the average sale
price, gross margin percentage, item life in months, projected
views per month, projected conversion percentage and an in-store
factor. Another example is advertisement (i.e., ad) supported
content, such as news articles, in which case the information
stored in the databases 390-a, 390-b and 390-c can include the
average advertisement revenue per mille and the projected
advertisement views for the lifetime of the articles. An exemplary
scheme for estimating the value of the content of a specific news
article involves multiplying the average ad revenue per mille by
the projected ad views for the lifetime of the article. The Value
Estimation Component 305 may store estimated values in the
financial 390-c database for, e.g., its own future use. use by
other components, and to maintain historical data. Such historical
data may also be aggregated and categorized to help draw
generalizations in the future.
[0064] The Localization Source Cost Computing Component 310
computes the cost of localizing the content of an element on the
website 180 using the various localization sources 160 based on
information present in the knowledge 390-b and financial 390-c
databases. Such information can include the cost per word by
localization source. For example, a scheme for computing the cost
of localizing the product description 210 of FIG. 2 using
professional (i.e., human) translation can involve multiplying the
number of words in the description by the cost per word. The
Localization Source Cost Computing Component 310 may store computed
costs into the financial database 390-c for, e.g., its own future
use, for use by other components and to maintain historical data.
Such historical data may also be aggregated and categorized to help
draw generalizations in the future.
[0065] The Contribution Computing Component 320 computes the
contribution of the content of an element on the localized website
130 in relation to a localization source. Contribution is a metric
that quantifies the impact of localizing the content of an clement.
Such impact is typically, but not necessarily, measured in terms of
profitability, such as margin, or profitability in relation to
cost, such as return on investment (ROI). The Contribution
Computing Component 320 can use information present in the visitor
390-a, knowledge 390-b and financial 390-c databases, as well as
information generated by the Localization Source Cost Computing
Component 310 and Value Estimation Component 305. It can also
obtain information from sources external to the system 150. For
example, in the case of the product description 210 of FIG. 2, such
information can include the cost of localizing product descriptions
using the various localization sources 160 and the value of the
product descriptions for the actual television products for sale.
The contribution of the product description 210 of a television can
then be computed based on this information. For example, a scheme
for computing the contribution as a profit margin of a specific
television description 210 with a human localization source can
involve subtracting the cost of localizing the product description
using human translation (e.g., as computed by the Localization
Source Cost Computing Component 310) from the value of the
television description (e.g., as estimated by the Value Estimation
Component 305). The Contribution Computing Component 320 may store
computed contributions into the financial database 390-c for, e.g.,
its own future use, for use by other components and to maintain
historical data. Such historical data may also be aggregated and
categorized to help draw generalizations in the future.
[0066] The Traffic Monitoring Component 330 may track the visitor
traffic to the localized web content, gathering statistics and
storing them in the Visitor Database 390-a. The Visitor Behavior
Monitoring Component 340 monitors visitor behavior (e.g., purchase
behavior) on the localized web content and stores statistics
related to the behavior in the Visitor Database 390-a. For example,
a scheme for tracking traffic to the product description 210 of
FIG. 2 may involve counting the number of times visitors view the
web page containing the product description. An exemplary scheme
for tracking visitor behavior for the "add to cart" button 220 of
FIG. 2 may involve counting the number of times visitors viewing
the page actually click on the button and calculating a percentage
conversion. Some tracked visitor data may have more relevance when
determining a target localization source or making a localization
source promotion decision and, as a result, may receive special
treatment, by, e.g., being used as a filter or given a different
weight. For example, visits from crawlers and other automated tools
(e.g., as reported by the user-agent header) may be discarded when
testing localizations sources. In another example, visitors with
specific language preferences (e.g., as reported by the browser) or
coming from specific locations (e.g., as reported by the visitor's
IP address) may be more relevant, and therefore given greater
weight than other visitors, when making a localization source
promotion decision. The Traffic Monitoring Component 330 and
Visitor Behavior Monitoring Component 340 may also analyze visitor
traffic and visitor behavior statistics and store results of the
analysis in the knowledge database 390-b for, e.g., its own future
use, for use by other components and to maintain historical data.
Such historical data may be aggregated and categorized to help draw
generalizations in the future. The category based views per month,
at 422 and category based conversion percentage at 423 of FIG. 4B
are examples of categorized historical data that may be stored in
the Visitor Database 390-a and used by the Value Estimation
Component 305.
[0067] One or more of the outputs of the Value Estimation Component
305, Localization Source Cost Computing Component 310, Contribution
Computing Component 320, Traffic Monitoring Component 330 and
Visitor Behavior Monitoring Component 340, along with budgeting
considerations and other factors 350, may be provided directly as
inputs to the Localization Source Determination Component 360,
and/or may be stored in the database 390 for use as inputs by the
Localization Source Determination Component 360. The Localization
Source Determination Component 360 determines the optimum
localization source 160 based on, e.g., one or more schemes with
respect to, e.g., one or more criteria that take into account
various inputs. For example, a criterion to determine the more
desirable localization source from two available localization
sources (e.g., human and machine translation) for a specific
television description 210 may be designed to select the
localization source with the highest contribution profit margin. An
example scheme to determine the localization source with the
highest contribution profit margin may involve computing the
contribution margin by subtracting the output of the Localization
Source Cost Computing Component 310 from the output of the Value
Estimation Component 305 for each of the two available localization
sources and selecting the contribution margin with the highest
value. Another exemplary criterion to determine a desired
localization source for the content of an element may be designed
to select a higher quality localization source (e.g., human
translation) for content on web pages that receive more traffic and
a lower quality localization source (e.g., machine translation) for
content on web pages that receive less traffic.
[0068] An exemplary scheme to determine a desirable localization
source for the content of an element between human and machine
translation based on traffic may involve establishing a traffic
threshold, based on the output of the Traffic Monitoring Component
330 to obtain, e.g., the amount of traffic received on each web
page with the content, and selecting human translation for the
content whose traffic meets or exceeds the threshold and machine
translation for all the other content of the element. The criterion
and scheme for determining a desirable localization source may also
use a combination of inputs and factors. For example, it may
involve selecting the localization source with the highest
contribution profit margin that clears a minimum contribution
return on investment hurdle and that has minimum traffic (e.g.,
visitor views) threshold.
[0069] The External Data Sources 190 represents any data external
to the system 150. Any of the components of FIG. 3 (e.g., the Value
Estimation Component 305, Localization Source Cost Computing
Component 310, Contribution Computing Component 320, Localization
Source Determination Component 360, etc.) may use data from
external sources as additional inputs when performing their
functions. The external data source may or may not be a third party
service. For example, the Value Estimation Component 305 may obtain
data from a market trends service to determine the popularity and
expected item life for a new product for which such data does not
exist in the database 390. Another example of an external data
source is an analytics server, which could provide, for example,
additional aggregated user information (e.g., the percentage of
users with a Chinese language preference that are located in the
United States). The external data source may also be information
available in an external database. For example, a website 180 owner
may have a database containing product and/or sales information,
such as the average sale price and gross margin percentage of
products for sale on the website, which could be used by the Value
Estimation Component 305 when estimating the value of such
products. Such external information may be used to reflect the
dynamics of the products in the marketplace so that such
information may be used by the teaching disclosed herein to learn
and adapt the localization to the changing dynamics of the
market.
[0070] The Localization Source Determination Component 360
automatically directs the Localization Source Router 370 to route
the translation of the content to the automatically selected
localization source. The Localization Source Determination
Component 360 may also direct the Localization Source
Promotion/Demotion Component 380 to automatically promote or demote
one localization source to another. The Localization Source
Promotion/Demotion Component 380 in turn directs the Localization
Source Router 370 to route translation to the new localization
source 160. The Localization Source Determination Component 360 may
also store decision information in the knowledge database 390-b
for, e.g., its own future use, for use by other components and to
maintain historical data. Such historical data may also be
aggregated and categorized to help draw generalizations in the
future.
[0071] It is important to note that the present teaching may be
implemented using embodiments other than those of FIGS. 1 and 3.
For example, the localization source determination server 150 that
determines the desired localization sources may not be implemented
as a separate server and may be instead integrated with the
localized website 130 of FIG. 1. The functions of server 150 may
also be split among multiple sewers, each of which may perform a
specialized function. For example, the traffic monitoring and
visitor behavior components may be implemented in a separate
analytics server.
[0072] The present teachings may be applied at the element level.
Elements within a web site 130 or 180 are handled or processed
independently of each other. Referring to the product information
page depicted in FIG. 2, determination of, and routing to, an
automatically selected localization source may be applied
individually to the detailed product description element 210 and to
the "add to cart" button element 220, even though both elements are
in the same page. As a result, traffic monitoring, visitor behavior
monitoring, value estimation, cost computation, contribution
computation, localization source determination, localization source
promotion/demotion and localization source routing may be performed
individually and independently on elements 210 and 220. The traffic
and visitor behavior monitoring schemes and the data monitored may
be different for the detailed product description element 210 than
for the "add to cart" button element 220. In addition, elements 210
and 220 may be dynamically configured to associate with different
value estimation, cost computation and contribution computation
schemes. The criterion and scheme for determining a desired
localization source may also be different for these 2 elements,
and, as a result, the selected localization source may be
different. For example, the selected localization source for the
detailed product description element 210 may be determined to be
machine translation, while the selected localization source for the
"add to cart" button element 220 may be determined to be human
translation.
[0073] Further, multiple elements may be grouped and classified
into categories. Elements assigned to the same category may reside
on the same page or on different pages on the web site 130 or 180.
In this case, traffic monitoring, visitor behavior monitoring,
value estimation, cost computation, contribution computation,
localization source determination, localization source
promotion/demotion and localization source routing may be performed
independently at the category level. Schemes for monitoring, value
estimation, cost computation, contribution computation,
localization source determination, localization source
promotion/demotion, and localization source routing may also be
defined at the category level so that all elements assigned to the
same category will use the criteria and schemes specified for the
category.
[0074] FIG. 4A shows an exemplary table that provides an example
list of small televisions for sale on an online retailer's website.
The televisions listed belong to the Small TVs category 410, which
are similar in terms of price range and features. The table shows
the average sale price 412, gross margin percentage 413, projected
item life in months 414, projected views per month 415. projected
conversion percentage 416, an in-store factor (to account for shop
online buy in-store behavior) 417, and a computed estimated value
418 for each television. The projected values and the in-store
factor are estimations based on historical data and experience. The
associated element on the website 130 for the televisions in this
example may be the detailed product description element 210 FIG. 2.
The scheme for computing the estimated value of the content of the
detailed product description element for each television in this
example involves multiplying the average sale price, gross margin
percentage, item life in months, projected views per month,
projected conversion percentage and an in-store factor.
[0075] A product may be new or some data, such as the gross margin
percentage or the lifespan, may not be available or may be
difficult to obtain or project. In that case, a category based or
similar item based equivalent figure may be used to compute the
estimated value of the content associated with the product. FIG. 4B
is a table that shows the same list of televisions as FIG. 4A.
However, the estimated value at 425 is computed using a category
based gross margin percentage at 420, a category based item life in
months 421, a category based views per month at 422, a category
based conversion percentage at 423, and a category based in-store
factor at 424. These category based figures can be computed using
averages of actual figures of existing similar products for which
this data exists. For example, for the small televisions category
410 the category based gross margin percentage can be computed as
the average of the gross margin percentage of all similar
televisions.
[0076] The estimated value of the content of an clement can also be
computed using other schemes and it may not be restricted to
products on an online retailer's website. The estimated value of
the content of an element may have a fixed value and can also be
based on the site owner's historical data and experience. The
specific scheme used may be determined based on the type of
content. For example, FIG. 5A shows an example of estimating the
value of the content of a news article on a portal or news site
that is supported by online advertising on the site. The table
shows the article category 510, the article title 511, the average
ad pay per mille (PPM) 512, and the projected lifetime views in
thousands 513 for each article. PPM 512 is a commonly used
measurement in advertising that represents the payment received by
a website owner from an advertiser for showing an ad to one
thousand viewers. It is also commonly referred to as cost per mille
(CPM). In this table, the PPM 512 shows the average revenue to the
portal or news site owner received from displaying the ads in each
article one thousand times to visitors. The projected lifetime
views in thousands 513 shows the estimated number of views that
each article will get during its expected lifetime on the site.
This projected value is an estimation based on historical data and
experience. For example, the "Manchester United IPO" article is
expected to be viewed 15,000 times by visitors before it is removed
from the site. The estimated value of the content of each article
514 in this example is computed by multiplying the average ad pay
per milk (PPM) 512 with the projected lifetime views in thousands
513.
[0077] There are many other ways that advertisers can pay a website
owner for displaying ads, which include for example, pay per click
(PPC), pay per action (PM), pay per lead (PPL) and pay per sale
(PPS). These terms are also commonly referred to as cost per click
(CPC), cost per action (CPA), cost per lead (CPL) and cost per sale
(CPS). In PPC, the site owner gets paid a certain amount for each
click of an ad. In PPA and PPL, the site owner gets paid for a
specific action (e.g., submitting a form, creating an account,
subscribing to a newsletter, signing up for a trial of a product or
service, etc.) that a visitor completes on the advertiser's site
after clicking on the ad. In PPS, the site owner gets paid when a
visitor completes a purchase of a product or service on the
advertiser's site after clicking on the ad. Payments to site owners
may be in the form of a fixed amount per click or action, or a
percentage commission of a purchase price. The scheme for computing
the estimated value described in FIGS. 5A and 5B can be applied to
any content that is supported by advertising and can be based on
any method of compensation to the website owner, including the PPC,
PPA, PPL and PPS methods just described.
[0078] Estimating the value of content may also be useful to
marketplaces (e.g., Amazon Marketplace, eBay Marketplace, etc.),
affiliates, comparison services (e.g., BizRate, Shopzilla, etc.),
shopping engines (e.g., Google Shopping, NextTag, PriceGrabber,
etc.) and product aggregator website owners that compile, promote
or offer products and services from other companies on their
websites. The other companies may be online merchants or
manufacturers. When a visitor clicks on a link on the website that
points to another company's product or service and, as a result, a
sale is made on the other company's website, the website owner gets
a commission on the sale amount or a flat fee. This is also
referred to as online revenue sharing. For example, FIG. 5B is a
table that shows an example of estimating the value of the content
of several products on a shopping engine's website. The shopping
engine provides links to the products on various online merchant
websites that the shopping engine is affiliated with. The table
shows the product category at 520, the item name at 521, the
average sales price of the item at 522, the commission percentage
that the website owner gets when the item is purchased by a visitor
at 523, the projected number of sales at 524, and the estimated
value of the item at 525. The projected number of sales at 524
shows the estimated number of sales that the website owner expects
to generate on the merchant's website during the expected lifetime
of the item on the site. The projected number of sales at 524 may
be based on the site owner's historical data and experience, and
may also be computed at the category level, so, for example, all
televisions belonging to the Small TVs category may have the same
number of projected sales. The estimated value of the content at
525 of each item in this example is computed by multiplying the
average sale price at 522, the commission percentage at 523, and
the projected number of sales at 524.
[0079] In addition to the estimated value of the content for an
element, it is also important to compute its cost of localization
(e.g., cost of translation) by localization source. FIG. 6 contains
an example table that shows the cost of the detailed product
description content element 210 of FIG. 2 for the televisions
previously listed in FIGS. 4A and 4B, for various localization
sources that include machine translation 615 & 616, human
translation 617 & 618 and copy editing 619 & 620. In
addition, a nominal cost for no translation (i.e., leaving the
content in the original language) 613 & 614 is computed. Note
that the cost for no localization may be zero. The cost is computed
by multiplying the number of words in the detailed description by
the cost per word of the applicable localization source.
[0080] FIG. 7 is a flow diagram depicting an exemplary general
process of determination of localization sources for a website 130
in a preferred embodiment of the current teaching. Such determined
localization sources may be optimum with respect to the specific
criteria associated with and provided by, e.g., the website owner
or the underlying organization. At 710, the website is classified
into different content elements. At 720, the elements to be
localized are selected. At 730, the cost to localize the content of
the selected elements is computed for the available localization
sources. Note that 730 may be performed at any time before the
contribution computation at 770. The cost may also be pre-computed
before this process starts. At 740, the content of the selected
elements is localized using an initial localization source. The
initial localization source may be, for example, the cheapest
available localization source. At 750, visitor traffic and behavior
associated with the localized content is tracked and analyzed on
the localized website 130. At 760, the estimated value of the
initial localization source and of other available localization
sources is obtained or computed based on the analysis of the
visitor traffic and behavior, and on historical data. At 770, the
contribution of the localized elements is computed based on the
computed cost and the determined estimated value for the initial
localization source and for the other available localization
sources. At 780, the localization source of the localized content
of the selected elements is promoted, demoted or left unchanged
based on a decision that takes into account the computed
contribution and other factors. At 790, the updated localization of
the elements just promoted or demoted is deployed to the localized
website.
[0081] Note that the process allows for further refinement, in
which case 790 goes back to 750 where traffic and visitor behavior
is tracked and analyzed based on the updated localization of the
elements. New estimated values of the updated localization are then
computed at 760, the contribution is also computed at 770, which
leads to 780 where promotion or demotion can occur again and to the
subsequent deployment of the updated localization at 790. Such
refinement can be continuous, in which case 790 always goes back to
750.
[0082] FIG. 8 is a flow diagram depicting an exemplary process of
determination of localization source for a specific clement in a
preferred embodiment of the current teaching. Such determined
localization sources may be optimum with respect to the specific
criteria associated with and provided by, e.g., the website owner
or the underlying organization. At 810, the cost to localize the
content of the element is computed for the available localization
sources. Note that 810 may be performed at any time before the
contribution computation at 860. The cost may also be pre-computed
before this process starts. At 820, the content of the element is
localized using an initial localization source and the localization
deployed to the localized website 130. The initial localization
source may be, for example, the cheapest available location source.
At 830, visitor traffic and behavior associated with the localized
content is tracked on the localized website 130. At 840, the
visitor traffic and behavior associated with the localized content
is analyzed. At 850, the estimated value of the initial
localization source and of other available localization sources is
computed based on the analysis of the visitor traffic and behavior,
and on historical data. At 860, the contribution of the localized
clement is computed based on the computed cost and estimated value
for the initial localization source and the other available
localization sources. At 870, the optimum localization source is
determined taking into account the computed contribution and other
factors. At 880, the localization source of the localized content
of the clement is promoted, demoted or left unchanged based on the
decision reached at 870. At 890, the updated localization of the
content of the element just promoted or demoted is deployed to the
localized website 130.
[0083] Same as in FIG. 7, the process of FIG. 8 allows for further
refinement, in which case 890 goes back to 830 and then 840, where
traffic and visitor behavior is tracked and analyzed based on the
updated localization of the element. The estimated value of the
updated localization is then computed at 850, the contribution
recomputed at 860 and the optimum localization source recomputed at
870. At 880, the promotion or demotion can occur again and if so, a
subsequent deployment of the updated localization is done at 890.
Such refinement can be continuous, in which case 890 always goes
back to 830.
[0084] In some embodiments of the present teaching, the process of
automatic determination of a localization source may start by using
the least expensive localization source for a specific element on
the localized website 130 for a specific type of content that the
element can display. FIG. 9 is an exemplary operational flow
diagram that depicts this process. The process in the diagram
starts at 910 by computing the cost of translation of the content
for each available localization source. Note that computation of
the cost does not have to be the first step and can be performed at
any time before 960 that computes the contribution of the content.
The cost may also be pre-computed before this process starts. At
920, the content is localized using the least expensive
localization source and deployed to the localized web site 130.
[0085] At 930, the traffic and visitor behavior associated with the
element localized with the least expensive localization is tracked
for a period of time. At 940, the traffic and visitor behavior
associated with the element localized with the least expensive
localization is analyzed. At 950, an estimated value is obtained or
computed for the content localized with the least expensive
localizations source based on the actual traffic and visitor
behavior. At 950, an estimated value is also obtained or computed
for each additional higher quality localization source available
based on historical information. At 960, the contribution of the
content is computed for the least expensive localization source
based on the estimated value and the computed cost of the least
expensive localization source. At 960, the contribution of the
content is also computed for each additional higher quality
localization source based on the estimated value and the computed
cost of those additional localization sources.
[0086] At 970, the localization source is determined based on the
computed contributions and other related information and factors,
such as a website owner specifications and available budget. Such
determined localization sources may be optimum with respect to the
specific criteria associated with and provided by, e.g., the
website owner or the underlying organization. If the selected
localization source is different than the least expensive
localization source 980, the control flows to 990 where the content
is promoted to the selected localization source. The updated
localization of the content is then deployed to the localized
website 130 at 995. If at 980 the least expensive localization
source turns out to be the selected localization source, then the
process ends or may go back to 930 for further analysis. After 995,
control may go back to 930 to further track and analyze the content
localized with the selected localization source to determine
whether additional promotions may be needed.
[0087] For example, the detailed product description element 210 of
FIG. 2 can be used as the element to localize and the type of
content can be the description of all the televisions listed in
FIGS. 4A and 4B. The detailed product description of all the listed
televisions is then translated using machine translation, which is
typically the least expensive localization source. FIG. 10A shows
exemplary traffic and visitor behavior during a period of 30 days
using machine translation for each product in the list of
televisions shown in FIGS. 4A and 4B. The table shows the average
sales price at 1012, gross margin percentage at 1013, in-store
factor at 1014, projected item life in months at 1015, the number
of views that the product description element received during the
30 day test period at 1016 and the conversion percentage of the
machine translation at 1017. The estimated value of machine
translation at 1018 is calculated, based on the scheme described in
FIGS. 4A and 4B, by, e.g., multiplying the average sale price at
1012, gross margin percentage at 1013, in-store factor at 1014,
projected item life in months at 1015, the actual number of views
in the test month at 1016 and the machine translation actual
conversion percentage at 1017. A category based human translation
conversion percentage 1019 is also shown, which is computed based
on historical conversion of similar items using human translation.
The estimated value of human translation at 1020 is also
calculated, based on the scheme described in FIGS. 4A and 4B, by,
e.g., multiplying the average sale price at 1012, gross margin
percentage at 1013, in-store factor at 1014, projected item life in
months at 1015, the actual number of views in the test month at
1016 and the category based human translation conversion rate at
1019.
[0088] FIG. 10B shows an exemplary computed contribution margin for
machine translation at 1054 calculated by subtracting the cost of
machine translation at 1053 from the estimated value of machine
translation at 1018, as computed in the table shown in FIG. 10A.
The table also shows the computed contribution margin for human
translation at 1057 calculated by subtracting the cost of human
translation at 1056 from the estimated value of human translation
at 1020, as computed in FIG. 10A. A promotion decision at 1058 is
then made to promote to human translation those TVs whose
contribution margin is larger for human translation. The table
shows that the detail product description for the first 3
televisions was promoted to human translation. The last 2
televisions were left with machine translation.
[0089] Automatic promotion of content to a better quality more
expensive localization source may be constrained by a specific
budget, such as a monthly limit on translation spend. In that case,
content is not promoted after the translation spend limit has been
reached. A fixed budget may apply to a single localized website or
spread across multiple localized websites, such as multiple
localized websites 130 corresponding to the same origin website
170. In this case, the limit on the combined expenditure on all the
affected localized websites needs to be managed so it does not
exceed the budget. Promotion may also be restricted by other
factors or preferences of the website owner. For example, a
promotion based on contribution margin may be restricted by a
minimum ROI hurdle. When promotion is restricted by a budget,
historical data maintained by the present teaching or external
sources on past content update trends and localization volumes can
be used to adjust the promotion process to attempt to maintain all
of the content translated using the most optimum localization
source while maintaining translation spend within the desired
budget. This is described in more detail in FIGS. 17, 19 and
20.
[0090] In another embodiment of the current teaching, the process
of automatic determination of a localization source is conditioned
on a test that determines how different localization sources affect
the behavior associated with an element. One way this can be done
is by testing and analyzing actual visitor behavior associated with
one or more representative sample elements of a type or category of
information using different localization sources. For example, a
test can be performed where the localization source is the
independent variable and the resulting percentage conversion the
dependent variable. Various controlled variables can also be used,
depending on the type of element. The data gathered from the test
is used to determine the optimum localization source for the set of
content in the element that the representative sample applies
to.
[0091] FIG. 11 is an exemplary operational flow diagram that
depicts this process. At 1110, the clement to test on the website
130 is selected. At 1120, the category, or type of information to
test, and the representative sample elements, or test group, are
selected, At 1130, the localization sources to test are selected
and deployed to the localized website 130. Once these variables are
selected, at 1140 a split test (or A/B test) is executed on the
test group for the desired test period. During the test, visitors
to the localized website 130 will be shown the sample elements
translated with each of the selected localization sources. At 1150,
visitor traffic and behavior associated with the test group is
tracked. At 1160, the visitor traffic and behavior associated with
the test group is analyzed. At 1170, the estimated value of each of
the of the sample elements in the test is obtained or computed for
each localization source based on the analysis of the visitor
traffic and behavior. At 1180, the contribution of each
localization source in the test group is computed based on the
estimated value and cost of each localization source. At 1190, a
localization source for the test group is determined taking into
account the contribution and other factors and by this process, the
selected localization source may be optimum with respect to the
contribution and considered factors. At 1195, the optimum
localization source is applied to the set of content belonging to
the category or type tested for the element tested.
[0092] For example, an online retailer that wants to localize the
description of all small TVs it sells on its site selects the
category of content to test as "Small TVs" and selects the list of
televisions shown in FIGS. 4A and 4B as the test group for all
small televisions. The corresponding element to localize for the
televisions is the detailed product description element 210 of FIG.
2. A test can be done on this element using the test group with two
localization sources: human and machine translation. FIGS. 12A and
12B show the results of an example test performed during a period
of 30 days for the test group for both human and machine
translation localization sources.
[0093] FIG. 12A shows exemplary traffic and visitor behavior during
a period of 30 days using both machine and human translation for
the televisions in the test group belonging to the "Small TVs"
category. In this example, a split test (i.e., AIR test) was
performed where each localization source was randomly shown to
visitors in equal percentages (50% of the visitors saw machine
translation and 50% of the visitors saw human translation) for each
television in the test group. The table shows the average sales
price at 1212, gross margin percentage at 1213, in-store factor at
1214, projected item life in months at 1215, the number of views
that the product description element received during the 30 day
test period at 1216, the actual conversion percentage for machine
translation during the test at 1217, and the actual conversion
percentage for human translation during the test at 1219. The
estimated value of machine translation at 1218 is calculated, based
on the scheme described in FIGS. 4A and 4B, using the machine
translation conversion percentage at 1217. The estimated value of
human translation at 1220 is also calculated, based on the scheme
described in FIGS. 4A and 4B, using the human translation
conversion percentage at 1219.
[0094] FIG. 12B shows exemplary computed contribution margin for
machine translation at 1254 calculated by subtracting the cost of
machine translation at 1253 from the estimated value of machine
translation at 1218, as computed in FIG. 12A. The table also shows
the computed contribution margin for human translation at 1257
calculated by subtracting the cost of human translation at 1256
from the estimated value of human translation at 1220, as computed
in FIG. 12A. A localization source is then determined based on
which localization source (machine or human) has the highest
contribution margin, which can optimize the choice of localization
source. FIG. 12B shows that human translation is the optimized
localization source for the detail product description of the first
television, and machine translation is the optimised localization
source for the detail product description of the last 4
televisions.
[0095] Based on the data in this table. these findings can be
generalized and an automatic determination made that because
machine translation is the optimum localization source for 80% of
the televisions in the test group, then the optimum localization
source to be applied to all televisions in the "Small TVs" category
that the online retailer sells is machine translation. FIG. 13A
shows that the same test described in FIG. 12A. However, in FIG.
13B, the average of the estimated value of machine translation at
1330 and the average cost of machine translation at 1331 is
computed for the set of TVs in the test group. The contribution
margin of machine translation at 1332 is then calculated by
subtracting the average cost of machine translation at 1331 from
the average estimated value of machine translation at 1330.
Finally, a contribution return on investment (ROI) is calculated
for machine translation at 1333 by dividing the contribution margin
of machine translation at 1332 by the average cost of machine
translation at 1331.
[0096] The same calculations are performed for human translation.
The average of the estimated value of human translation at 1334 and
the average cost of human translation at 1335 is computed for the
set of TVs in the test group. The contribution margin of human
translation at 1336 is then calculated by subtracting the average
cost of human translation at 1335 from the average estimated value
of human translation at 1334, Finally, a contribution return on
investment (ROI) is calculated for human translation at 1337 by
dividing the contribution margin of human translation at 1336 by
the average cost of human translation at 1335.
[0097] The optimum localization source at 1338 is then determined
based on which localization source (machine or human) has the
highest contribution return on investment (ROI). The table shows
that machine translation is the optimum localization source for the
set of small TVs in the test group. The finding is generalized and
machine translation is applied as the optimum localization source
for all small size televisions the online retailer currently sells
and to all new models of small size televisions that the retailer
will sell in the future.
[0098] The example described in FIGS. 13A and 13B uses contribution
return on investment (ROI) as the deciding factor when determining
the optimum localization source. The advantage of using
contribution ROI vs. the contribution margin used in previous
examples is that contribution ROI takes cost into consideration and
maximizes the return on whatever money is invested in localizing
content. Both contribution margin and contribution ROI may also be
taken into account, as described in FIGS. 15 and 16.
[0099] In another embodiment of the current teaching, the process
of automatic determination of a localization source may not start
with applying a localization source to the content for the specific
element on the localized website 130, so the content is not
translated and left in the original language. FIG. 14 is an
operational flow diagram that depicts this exemplary process. The
process in the diagram starts at 1410 by computing the cost of
translation for each available localization source. Note that
computation of the cost does not have to be the first step and can
be performed at any time before 1460 that computes the
contribution. The cost may also be pre-computed before this process
starts. At 1420, a localization source is not applied, so the
content is deployed on the localized website 130 without
localization.
[0100] At 1430, the traffic and visitor behavior associated with
the element that has not been localized is tracked for a period of
time. At 1440, the traffic and visitor behavior associated with the
element that has not been localized is analyzed. At 1450, an
estimated value is obtained or computed for the content that has
not been localized based on the actual traffic and visitor
behavior. Also at 1450 an estimated value is obtained or computed
for the content for each available localization source based on
historical information. At 1460, the contribution of the content
that has not been localized is computed based on the estimated
value and the computed cost (if any) of not localizing the content.
Also at 1460, the contribution of the content is computed for each
available localization source based on the estimated value and the
computed cost of each of the available localization sources.
[0101] At 1470, it is determined whether the content should be
localized, and if so, a localization source is determined based on
the computed contribution and other related information and
factors, such as a website owner specification and available
budget. If the content should be localized at 1480, then control
flows to 1490 where the content is promoted to the selected
localization source. At 1495, the selected localization source is
applied to the content on the localized website. If applying no
localization (i.e., not translating the content) turns out to be
the selected option, then the process ends. After 1495, control may
go back to 1430 to further track and analyze the content localized
with the selected localization source to determine whether
additional promotions may be needed. Control may also go back to
1430 from 1480 in the case where in 1480 it is decided not to apply
any localization to account for the possibility that a localization
may be needed in the future.
[0102] FIGS. 15A and 15B describe an example of the above process
that compares three different localization sources (machine, human
& copy edit) against the option of applying no localization
(i.e., performing no translation) for product descriptions. FIG.
15A shows exemplary traffic and visitor behavior during a period of
30 days using no localization for each product description in the
list of televisions shown in FIGS. 4A and 4B. FIG. 15A shows the
average sales price at 1512, gross margin percentage at 1513,
in-store factor at 1514, projected item life in months at 1515, the
number of views that the product description element received
during the 30 day test period at 1516 and the actual conversion
percentage of no localization at 1517. The estimated value of no
localization at 1518 is calculated using the actual conversion
percentage of no localization at 1517. A category based conversion
percentage lift for machine translation at 1519, human translation
at 1521 and copy editing at 1523 is also shown. These are
percentage lift figures derived from historical conversion of
similar items using these localization sources. A conversion rate
is computed for each localization source (machine, human & copy
edit) based on multiplying the lift percentage by the no
localization conversion rate. Actual historical conversion
percentages for machine translation, human translation and copy
editing may also be used instead of a conversion percentage lift.
The estimated value of machine translation at 1520, human
translation at 1522 and copy editing at 1524 is then calculated,
based on the scheme described in FIGS. 4A and 4B, using the
computed conversion rate for each localization source.
[0103] FIG. 15B shows an exemplary computed contribution margin for
no localization at 1534 calculated by subtracting the cost of no
localization at 1533 (which may be zero) from the estimated value
of no localization at 1518, as shown in FIG. 15A. The table in FIG.
15B shows the computed contribution margin for machine translation
at 1538, human translation at 1542 and copy editing at 1546
calculated by subtracting the cost of machine translation at 1537,
cost of human translation at 1541 and cost of copy editing at 1545
from the estimated value of machine translation at 1520, human
translation at 1522 and copy editing at 1524, as shown in FIG. 15A.
The table in FIG. 15B also shows a computed contribution return on
investment for no localization at 1535, machine translation at
1539, human translation at 1543 and copy editing at 1547. The
optimum localization source at 1548 is determined by selecting the
localization source or no localization with the highest
contribution ROI that clears the contribution ROI hurdle rate. The
contribution ROI hurdle rate is the minimum ROI that the online
retailer is willing to accept from the localization source. In this
example, the contribution ROI hurdle is 300%, meaning that if the
ROI is below 300%, then the localization source is not selected
even if it has a higher contribution margin than the other
localization sources. The table shows that the detail product
description for the first 2 televisions was promoted to copy
editing. The third television was promoted to human translation.
The fourth television was promoted to machine translation, and the
last television was left without localization in the original
language).
[0104] Similar to FIGS. 12A and 12B, FIGS. 16A and 16B show an
example table with the results of a 30 day split test performed to
determine how different localization sources affect the behavior
associated with the representative sample of televisions of FIGS.
4A and 48, Similar to the test described in FIGS. 12A and 12B, each
localization source was randomly shown to visitors in equal
percentages (25% of the visitors saw no localization, 25% saw
machine translation, 25% saw human translation and 25% saw copy
editing) for the product description of each television in the test
group. FIG. 16A shows the traffic and visitor behavior using no
translation, machine translation, human translation and copy
editing for each product in the list of televisions shown in FIGS.
4A and 4B. The table in FIG. 16A shows the average sales price at
1612, gross margin percentage at 1613, in-store factor at 1614,
projected item life in months at 1615, the number of views that the
product description clement received during the 30 day test period
at 1616, and the actual conversion percentage for no translation at
1617, machine translation at 1619, human translation at 1621 and
copy editing at 1623 during the test. The estimated value of no
translation at 1618, machine translation at 1620, human translation
at 1622 and copy editing at 1624 arc calculated, based on the
scheme described in FIGS. 4A and 4B, using their respective actual
conversion percentages.
[0105] FIG. 16B shows an exemplary computed contribution margin for
no translation at 1634, machine translation at 1638, human
translation at 1642 and copy editing at 1646, which is calculated
by subtracting the respective costs of localization from the
respective computed estimated values. The table in FIG. 16B also
shows a computed contribution return on investment (ROI) for no
translation at 1635, machine translation at 1639, human translation
at 1643 and copy editing at 1647. The optimum localization source
at 1648 is determined by selecting the localization source or no
localization with the highest contribution margin that clears the
contribution ROI hurdle rate of 300%. The table in FIG. 16B also
shows that the detail product description for the first television
was promoted to copy editing. The second and third televisions were
promoted to human translation. The fourth television was promoted
to machine translation, and the last television was not localized
(i.e., left without translation in the original language).
[0106] The examples discussed above provide exemplary criteria
applied in selecting the localization source of content examining
only the content in isolation. For example, in FIGS. 12A and 12B
the decision on the target localization source for each product is
made individually, without taking into consideration other
products. Similarly, in FIGS. 13A and 13B, the decision about the
target localization source for the Small TVs category is made
without taking into consideration the other product categories the
online retailer carries (e.g., Large TVs, Appliances, etc.). In
reality, to maximize the overall contribution (e.g., margin, ROI),
decisions need to be made about what investments to make in the
context of other investment options and limitations (e.g. other
content to be localized, fixed costs, overall budget, competing
external investment options). This may be done by comparing the
contribution of localization investment decisions across all
content to be localized.
[0107] FIG. 17A shows how to determine the localization source for
multiple products. Using the same example of FIGS. 16A and 16B,
each row in the table of FIG. 17A is a possible investment decision
defined by a combination of the product at 1711, an initial
localization source at 1712, and a promoted localization source at
1713. The initial localization source has an associated cost at
1714 and an associated contribution margin at 1715, and the
promoted localization source has an associated cost at 1716 and an
associated contribution margin at 1717. The costs and contribution
margins are calculated as described in FIGS. 16A and 16B. The
incremental cost of promotion at 1718 is calculated by subtracting
the cost of the initial localization source at 1714 from the cost
of the promoted localization source at 1716. The incremental
contribution margin of promotion at 1719 is calculated by
subtracting the contribution margin of the initial localization
source at 1715 from the contribution margin of the promoted
localization source at 1717. The incremental contribution ROI of
promotion at 1720 is calculated by dividing the incremental
contribution margin of promotion at 1719 by the incremental cost of
promotion at 1718. In the case of an incremental cost of zero, an
infinite or arbitrarily large ROI may be used.
[0108] An exemplary scheme for maximizing profit across multiple
products, or profit maximizing scheme, involves using the highest
ROI options available. To facilitate this, the table in FIG. 17B
takes the columns 1710, 1711, 1712, 1713, 1718, 1719, and 1720 from
the preceding table in FIG. 17A and sorts them in descending order
by incremental ROI of promotion at 1738. A maximum budget of $500
is specified at 1730 and an incremental ROI hurdle rate of $1,000%
is specified at 1731. The scheme used in this example to maximize
profit across multiple products involves repeatedly taking the
highest incremental ROI of the promotions available until the
budget at 1730 is spent or until the ROI hurdle rate at 1731 is
met, whichever happens first. FIG. 17C shows the initial state
before applying this scheme. All items are assigned an initial
localization source at 1762, which in this example is no
translation (the least costly). The total cost and total
contribution margin, as calculated in FIGS. 16A and 16B, are also
shown for each product at 1763 and 1764. A fixed cost at 1766, of
operating the localized website (e.g., a monthly hosting, bandwidth
and/or management fee incurred either internally by the website
owner or billed by a third party) may be added to the total cost
and subtracted from the total contribution margin. These values are
used to initialize the running total cost at 1755 and the running
total margin at 1756 in the initial state of FIG. 17B at column
1739.
[0109] FIG. 17B shows each progressive iteration at columns
1740-1745 and the state of each iteration in rows 1746-1756. The
iterations start with the first row in the table, since it has the
highest incremental ROI, and continue going down one row at a time
in order of decreasing ROI until the budget is reached or the ROI
hurdle rate is met. In the first iteration at 1740, the scheme's
process determines that the first highest ROI investment decision
is available because the current localization source at 1734 for
the item at 1733 matches the current localization source for that
item. Looking at the first iteration at column 1740, the promotion
with the highest ROI in this example is the Sony Bravia 25'' 3D
LCD, at 1746, being promoted from no translation ("No Trans") at
1747 to machine translation at 1748. The incremental cost of such
promotion of $14.70 is at 1749 and the incremental contribution
margin of such promotion of $20,922.54 is at 1750. The process
compares at 1751 the incremental ROI for this localization source
promotion option at 1738 against the ROI hurdle rate at 1731.
During the first iteration of this example, the incremental ROI for
the Sony Bravia 25'' 3D LCD is 142,330%, which is above the 1,000%
hurdle rate. In this way, an answer to the question "ROI above
Hurdle Rate?" at 1751 is "Yes". The process then compares at 1753
the previous running total cost plus the incremental cost of
localization under consideration at 1752 against the budget 1730.
During the first iteration of this example, the previous running
total is $1.56 (from the initial state at 1739) and the incremental
cost of promoting the localization source is $14.70, so the
"Running total cost+current cost:" at 1752 is calculated as the sum
of $16.26, which is below the budget of $500. Therefore the answer
to question "Within Budget?" at 1753 is "Yes". At 1754, if both the
criteria at 1751 and 1753 arc acceptable, the process assigns the
promoted source to the item's current source, updates the running
total cost at 1755, updates the running total margin at 1756, and
continues with its next iteration. During the first iteration of
this example, since the promotion being considered is above the ROI
hurdle rate and below budget, the answer to question "Apply
Promotion and Continue?" at 1754 is "Yes", the running total cost
at 1755 is updated to $16.26, the running total margin at 1756 is
updated to $57,437.01 and the process continues with the second
iteration at 1741.
[0110] During the second iteration at 1741 the process checks the
next two highest ROI options, but is unable to select these
localization options because the current state of the source of the
Sony Bravia 25'' 3D LCD item, as applied by the first iteration of
this scheme, is "Machine", which does not match any of the current
sources in the next two rows of 1734 ("No Trans") for that item. In
this case, the process determines that the fourth-highest ROI
investment decision is available because the current source at 1734
for the fourth item at 1733 ("No Trans") matches the current state
of the source for that item. Again, the incremental ROI from 1738
and running total cost plus cost of promotion at 1755 are compared
against the ROI hurdle rate at 1731 and budget at 1730, and the
process assigns the promoted source to the item's current source in
state, updates the running total cost at 1755, updates the running
total margin at 1756, and continues with its next iteration.
[0111] During the sixth iteration, at 1745 the process arrives at a
decision that the promotion being considered is below the ROI
Hurdle rate at 1751, the current iteration terminates and the
process stops. The current localization source for each item at the
time of termination becomes the final localization source that is
then applied to the content. If the process had terminated because
there was insufficient budget remaining to make the next
localization source promotion in full, then any remaining budget
may be invested into that localization source promotion to promote
a portion of that content. FIG. 17D shows the final localization
sources to be applied to each example item at 1772, as well as the
cost spent at 1773 and the contribution margin at 1774 and
contribution ROI at 1775 achieved for each item. The total cost
spent of $192.42 and the total contribution margin achieved for all
the products is shown at 1776. This example shows the optimal
(maximum) margin or profit that can be attained by selecting
localization sources with the given costs and contribution margins
given the budgetary and hurdle ROI constraints. A maximum overall
margin or profit can be obtained with this process by setting the
budgetary constraint to infinity and setting the ROI hurdle
constraint to 0%.
[0112] Although profit maximization is a common objective, a site
owner may wish to instead maximize the net ROI on his/her site
localization investment. The profit maximizing scheme described
above can also achieve this, by instead terminating investment
decisions once the net ROI (calculated as the running total margin
divided by the running total cost) begins decreasing. Given any
non-zero fixed cost of, for example, operating the localized
website (such as monthly hosting, bandwidth and/or management fees
incurred either internally by the website owner or billed by a
third party), the net ROI will initially be negative, and will
increase (as it is being combined with the initial
high-incremental-ROI decisions) until the incremental ROI drops
enough that the net ROI will begin decreasing as well. This point
happens before the profit maximizing point.
[0113] In the previous example shown in FIGS. 17A-17D, the process
determines the optimum localization sources for specific products
in a group while considering the contribution impact of localizing
all the other products in the group. This scheme may also be
applied to multiple groups of content (e.g. categories of products
on an ecommerce site). In this way, a website owner may make
localization investment decisions for a whole website while
simultaneously considering any costs not dependent on the current
investment decisions ("fixed costs") and an upper bound on money
available to invest ("budget").
[0114] FIG. 18A demonstrates how to do this at the product category
level. It shows example data for a website offering items for sate
that belong to just two product categories (Small TVs and Large
TVs) where three localization options are considered: no
translation, machine translation, and human translation. The cost
of these localization options at 1811, 1814 and 1817 respectively
have been provided, based on a per-word translation cost and
estimates of words present within the content. The value of these
localization options at 1812, 1815 and 1818 respectively have been
provided, based on an A/B test with a sample of this content
receiving the localization options and calculations as detailed in
earlier examples. The margin of each of these localization options
at 1813, 1816 and 1819 respectively is calculated as the difference
between the cost and the value.
[0115] FIG. 18B shows the associated investment decisions, defined
by combinations of the category at 1820, initial localization
sources at 1821, and a promoted localization sources at 1822. Each
row in the table represents a possible combination. The costs and
margins at 1823, 1824, 1825, and 1826 are taken from the table in
FIG. 18A. The incremental cost of promotion at 1827 is calculated
by subtracting, the cost of the initial localization source from
the cost of the promoted localization source. The incremental
margin of promotion at 1828 is calculated by subtracting the margin
of the initial localization source at 1824 from the margin of the
promoted localization source at 1826. The incremental ROI of
promotion at 1829 is calculated by dividing the incremental margin
of promotion at 1828 by the incremental cost of promotion at 1827.
In the case of where the incremental cost is zero, an infinite or
arbitrarily large ROI may be used.
[0116] FIG. 18C shows the same information as FIG. 18B, but in a
graphical rather than tabular format. Each investment decision from
FIG. 18B is represented as a vector in FIG. 18C with width (X axis)
equal to the cost of the investment, height (Y axis) equal to
margin of the investment, and slope equal to the ROI of the
investment. These vectors may be added to visualize the net ROI and
profit of multiple decisions.
[0117] FIG. 19A shows the same table listed in FIG. 18B with the
rows sorted in descending order by Incremental Contribution ROI of
Promotion at 1919. The chart in FIG. 19B shows the net result of
applying this localization source selection criteria to the example
content of FIG. 19A. In this example, there is a budget at 1920 of
$8,000, a fixed cost at 1921 of $2,000, and an ROI Hurdle rate at
1922 of 50%. The function of the budget and ROI hurdle rate has
been discussed in previous examples. The budget is represented in
the chart as a vertical line at 1930. The fixed cost is an
additional factor introduced in this example that represents the
fixed cost to the website owner. This may be the cost of operating
the localized website and may include, for example, hosting,
bandwidth and management fees incurred either internally or billed
by a third party.
[0118] The chart in FIG. 19B is generated by first plotting the
$2,000 fixed cost represented by the vector at 1932. Subsequent
vectors in the chart are plotted by applying the iterative scheme
described in FIGS. 17A-17D. The iterations start with the first row
in the sorted table of FIG. 19A, since it has the highest
incremental ROI (300%), and continues going down one row at a time
in order of decreasing ROI until the budget is reached or the ROI
hurdle is met. During the first iteration, the first row containing
the localization source promotion combination of no translation
("No Trans") initial source to machine translation ("Machine")
promoted source for Large TVs is applied and plotted in the chart
represented by the vector at 1933. The incremental ROI of this
first promotion is above the 50% ROI hurdle and after applying the
promotion the total cost remains within budget. During the second
iteration, the second row with the second highest ROI is then
skipped because it requires an initial localization source of no
translation for Large TVs, but the current localization source
applied to Large TVs in the first iteration was machine
translation, so this promotion is not available. The next row
having the third highest ROI (150%) can be applied and plotted in
the chart in the second iteration since it is for the Small TVs
category and the initial localization source of "No Trans" matches
the current source of the Small TVs category (since no
localizations have yet been applied). Further, the incremental ROI
of this promotion is above the 50% ROI hurdle and after applying
the promotion the total cost remains within budget. This
localization promotion is plotted in the chart at vector 1934.
During the third iteration, the fourth row containing machine
translation as the initial localization and human translation as
the promoted localization source for Large TVs, which has the next
highest ROI (150%), is considered and applied because the current
source for Large TVs is machine translation (as promoted in the
first iteration). Further, the. incremental ROI of this promotion
is above the 50% ROI hurdle and after applying the promotion the
total cost remains within budget. This localization promotion is
plotted in the chart at vector 1935. Finally, during the fourth and
final iteration, the fifth row in the table containing no
translation as the initial localization and human translation as
the promoted localization source for Small TVs is skipped because
it requires an initial localization source of no translation for
Small TVs, but the current localization source applied to Small TVs
in the second iteration was machine translation, so this promotion
is not available. The next and final row having the lowest ROI
(75%) is available since it is for the Small TVs category and the
initial localization source of "Machine" matches the current state
of the Small TVs category. Further, the incremental ROI of this
promotion is above the 50% ROI hurdle. However, the promotion
cannot be applied because after applying the promotion the total
cost will go over the $8,000 budget. This localization promotion is
plotted in the chart at vector 1936, the solid part stays within
budget, but the dotted line is above budget. In this case where the
promotion could not be applied in full to all products in the
category, the scheme may apply the promotion partially to only some
of the products in the category until the budget is reached in
full.
[0119] The profit maximizing scheme described in FIGS. 19A and 19B
takes into account that additional margin can be attained by making
additional investments at ROIs that are lower than the higher-ROI,
already-selected investments, and uses as much of the budget as
possible. Since all possible investment decisions (even across
different units of content) were sorted by descending ROI of the
investment decision or slope of the vectors, the net margin along
the vector path at any x-value is the maximum net margin that could
be achieved by selecting vectors/investments. Consequently, the
described scheme generates an optimal result when a budgetary
constraint is introduced. Also, since further investments into
promotions of content already selected for localization were also
considered, all investment opportunities are considered. In this
example, the end result of the localization decisions resulted in a
net margin (profit) of $7,750, and a net ROI of 97%
($7,75058,000).
[0120] Further, a determination of the optimum localization sources
for multiple groups of content, while simultaneously considering
the impact of localizing other groups, is not limited to categories
of products on an ecommerce site, as described in FIGS. 18 and 19.
The same process can be applied to any grouping of items. Grouping
could be defined based on logical arrangement of items, specific
needs or reasons, or even arbitrarily. For example, a news website
may group articles by news category, such as local, national,
international, politics, business, etc. and determine the optimum
localization source for each of these categories. Groups may also
be defined based on value (perceived or measured), margin or ROI,
even when the items within a group are not logically related. For
example, a website owner may decide, perhaps after performing some
split tests as described in FIGS. 11, 12 and 16, to group the
content on a website into 2 categories: High Value and Low Value,
and perform human translation on all High Value items and machine
translation on all Low Value items. The website owner may then
decide to put the navigation (e.g., menu items), headers, footers
and all content within the home page, company information pages and
services offering pages in the High Value category; and the news,
press releases, archives and support pages in the Low Value
category.
[0121] Additionally, the content to benefit from automatic
determination of localization source can be either existing content
or content that is yet to be generated. In this situation,
historical data about generation of similar content (e.g.
historical data about past localization activity by category,
number of products per month created in each category on an
ecommerce website, etc.), other data (e.g. trend adjustments,
release schedules, promotional/campaign schedules, etc.) and human
input can be combined to generate forward-looking estimates of
localization volumes, costs and margins. Such data may be tracked
and stored by the system described in this teaching (e.g., via the
Traffic Monitoring Component 330, Visitor Behavior Monitoring
Component 340, Budget & Other Factors 350, and Database 390 of
FIG. 3), or be external to the system (e.g., External Data Sources
190 of FIG. 3). For example, the current teaching may track all
localization performed and maintain historical data and trends
about the amount of content localized by each category, and store
it in the financial database 390-c. The estimates for a given
forward-looking time period (e.g. the upcoming month) are then
combined with a budget for that time period, and a profit
maximizing scheme can be applied, such as the ones previously
described in FIGS. 17 & 19, to arrive at a decision to how to
localize content as it is created during this time period as a
function of its category.
[0122] The above approach is effective for website owners who have
some degree of flexibility in their budget to adapt to the
discrepancy between the predicted amount of content to be localized
and the actual amount of content that is localized. For site owners
who require more precise control of their spending within budgetary
time periods (e.g., a monthly budget), a further refinement may be
applied. Any time (T.sub.current) during the budgetary time period
(from T.sub.initial to T.sub.final) that new content is available
for localization (e.g., once a day if a daily process is followed,
or hourly, or even immediately as new content becomes available if
sufficient computational resources are available), a profit
maximizing scheme may be reapplied. Instead of using the original
estimate of expected content from T.sub.initial to T.sub.fine, a
new estimate is generated as the current amount of content waiting
for localization at time T.sub.current plus a new expected amount
for the remainder of the time period (i.e., from T.sub.current to
T.sub.final). This updated content estimate is the basis for new
cost, value, margin, and ROI calculations when the profit
maximizing scheme is applied again. Accordingly, the budget that is
used as an input for the scheme is the remaining budget (i.e., the
original budget minus any costs incurred from T.sub.initial to
T.sub.current). This ensures that anytime new content is available
for localization, the investment decisions for that content take
into account the available budget. This is important, for example,
if early on in the time period, a greater than expected amount of
content was generated, since later on, the localization decision
would limit the investment based on the reduced budget.
[0123] While the above approach is effective in achieving a good
control on a budget, it does introduce the possibility for
systematic under-optimization of profit. For example, if a large
amount of low contribution ROI content is generated in the
beginning of the budgetary time period, it may consume an excessive
portion of the budget, and later high contribution ROI investments
may be unavailable due to reduced budget. This can be mitigated by
introducing a dynamic budget for each unit or category of content
that changes as the time period elapses. A profit maximizing scheme
continues to be applied using the overall remaining monthly budget
as an input, but the per-unit or per-category dynamic budget is
later applied to the localization process to cut-off localization
activity on a per-unit or per-category basis. This dynamic budget
may be computed several ways. Three exemplary methods for
calculating a dynamic budget with respect to p, the proportion of
the period elapsed, are presented below. The examples discuss
categories, but the same logic may be applied to individual units
of content, such as individual products. One example method for
calculating the dynamic budget for a category may be as a
proportion of the category budget equal to the proportion of the
billing period elapsed:
Dynamic Budget=b.sub.c.times.p
[0124] In the above formula, b.sub.C is the budget for the category
for the budgetary period or the expected future investment in this
category (i.e. the expected cost of localization for this
category), which is used as an input to a profit maximizing scheme,
in order for the scheme to determine the optimum localization
source. Notably, this may be 0, especially in the case where the
remaining overall budget is small or 0, and some or all categories
may not have been selected for localization. This method is
suitable for website owners who wish to control costs over the
course of the budgetary period but who still wish to ensure all
content is localized on a timely manner as long as there is budget
available irrespective of the contribution ROI of the content to be
localized, though this may come at the expense of having
unavailable budget later in the budgetary period for higher ROI
content.
[0125] A second exemplary method for calculating the dynamic budget
for a category may be as follows:
Dynamic .times. .times. Budget = b c .times. p 1 ROI category
##EQU00001##
[0126] In the above formula, b.sub.C is again the budget for the
category for the budgetary period or the expected future investment
in this category, p is the proportion of the period elapsed, and
ROI.sub.category is the ROI of localizing this category with the
selected localization source. The formula multiples b.sub.C by p
raised to the power of 1/ROI.sub.category. This method has the
benefit that it allows for the preferential localization of high
contribution ROI content towards the beginning of the budgetary
time period when there is still uncertainty about the future
content that will need to be localized. Specifically, the dynamic
budget places a greater restriction on localizing low-ROI content
towards the beginning of the time period, preventing such content
from exhausting available budget that could later be assigned to
localizing content with a higher-ROI, while still allowing for
localization to be made into content with a high-ROI if it is
generated earlier than expected.
[0127] A third exemplary method that might be used is very similar
to the previous approach, but adds normalization of each category's
ROI with respect to some baseline ROI (e.g., the median ROI among
the categories, the net ROI of the site, or some manually specified
value) using the formula below:
Dynamic .times. .times. Budget = b c .times. p ROI baseline ROI
category ##EQU00002##
[0128] This baseline is particularly useful when the various
category ROI's mostly tend to be skewed either below or above 100%.
With this method, even though an ROI may seem to be low or high in
isolation, it will be assigned a dynamic budget depending on the
extent to which it is higher or lower than the baseline ROI. For
example, half-way through a budgetary period (p=0.5), the dynamic
proportion for an ROI of 60% using the second method would be 31%,
calculated as 0.5 to the power of 1/(60%). Since this is a
relatively low ROI, it is receiving a dynamic proportion that is
less than the proportion of the month elapsed (i.e. 31%<50%).
This is desirable to allow for budget to be reserved for later,
higher-ROI localizations. However, on a website where all of the
localizations arc low in ROI, 60% may be a relatively high ROI and
it would not make sense to limit its budget in order to wait for
higher ROI opportunities. Specifically, if for example, the average
ROI of localizations available on a site were 40%, the present
method could be applied, resulting in a dynamic proportion of 62%,
calculated as 0.5 to the power of (40%)/(60%). This category is now
receiving a dynamic proportion that is higher than the proportion
of the month elapsed (i.e. 62%>50%), which is desirable since it
is among the higher ROI localizations given the baseline ROI of
40%.
[0129] FIG. 20 shows an example of this method applied over a week
time period to an example website with two product categories:
"BestSellers" and "Bargain". Note that this example uses a 7 day
period for simplicity of illustration. A more likely time period in
practice would be a month or a quarter, as monthly or quarterly
budgets are common; however, any time period from an hour to a day
to a year or more may be used. A fixed cost of $125 is given at
2010 and an overall localization budget for the week of $550 is
given at 2011. The fixed cost may be, for example, a hosting or
management fee for operating the localized website billed by a
third party. The cost-per-word rates for localization using each of
three localization sources (Human, Machine and No Translation) is
given at 2012. The contribution ROIs for each localization source
and category pair are given at 2013. For example, the contribution
ROI of localizing the BestSellers category using human translation
is 200%. These ROIs may be calculated as described in FIGS. 13, 15,
16 and 17.
[0130] The first row of table, labeled "Start", describes the state
before the first day of the period. In this example, after the last
day of the previous period there were 150 words left to localize in
the BestSellers category shown at 2033 labeled "Previously
Scheduled Words for Localization". The expected number of words to
be localized in the period is shown at column 2035 labeled
"Expected Words Remaining-in-Period". At the start of this example
period, it is expected that 1,400 words will need to be localized
in the BestSellers category and 10,000 words in the Bargain
category. The expected word counts may be, for example, based on
historical data about the amount of localization performed in past
periods for each category, which may be tracked by the present
system and maintained in the financial database 390-c. The last
column in the table at 2047 labeled "Remaining Budget" shows the
amount of money that remains in the budget after all localizations
are completed each day. At the start of the period, the full week
budget at 2011 of $550, minus the fixed cost at 2010 of $125, or
$425, is available since no localizations have been performed
yet.
[0131] The day of the week under consideration is shown at 2030,
and the proportion of the period elapsed, p, is calculated at 2031
as the day divided by 7, the total number of days in the period. On
day 1, the proportion of the period elapsed is calculated at 14% by
dividing day 1 over the 7 days in the period. As discussed above,
on day one there are 150 words left over for localization in the
BestSellers category from the previous period at 2033. Also given
are the number of words that were generated for localization that
day in each of the categories at 2034 labeled "Words Scheduled This
Day". This is the actual number of words that have been scheduled
for localization on that day, which, for example, can correspond to
new content posted on the website 180 that day which now requires
localization. On day one, 100 words were scheduled for localization
from the BestSellers category and 2,000 from the Bargain
category,
[0132] The remaining words that are expected to be generated for
localization in the remainder of the budgetary period is shown at
2035. For illustration purposes, this example uses a simple formula
to generate the updated expected number of words for the remainder
of the period. The formula determines the remaining number of words
by multiplying the expected number of words from the "Start" state
by the percentage of time left in the period. On day one, the
percentage of time left in the period is approximately 86%
(100%-14% at 2031) and the expected number of words from the
"Start" state is 1,400 for BestSellers and 10,000 for Bargain. This
results in 1,200 words remaining in the BestSellers category (86%
of 1,400) and 8,571 words remaining in the Bargain category (86% of
10,000). Note that the values of these estimates do not change the
logic of generating the dynamic budget. A more complex example that
uses using Bayesian inference for estimating the remaining words
that are expected to be generated for localization in the remainder
of the budgetary period is described in FIGS. 21A and 21B.
[0133] The three word counts at 2033, 2034, and 2035 are added
together to derive the total words remaining for localization for
the budgetary period for each category at 2036 labeled "Total Words
Remaining for Localization for Period". Also shown is the remaining
budget for the period at 2037, which is taken from column 2047 from
the previous day or the "Start" state. On day one, the remaining
budget of $425 is taken from the "Start" state and represents the
full budget since no money has yet been spent on any
localization.
[0134] The total words remaining at 2036 and the remaining budget
for the period at 2037, along with the cost-per-word rates at 2012
and the contribution ROIs at 2013 for each category, are provided
as inputs to a profit maximizing scheme which is run each day of
the period to determine the optimum localization source at that
point in time for each category. Example profit maximizing schemes
are described in FIGS. 17 & 19.
[0135] The selected localization source at 2038 shows the output of
the profit maximizing scheme (based on the inputs at 2036, 2037,
2012 and 2013) after it is run that day to determine the optimum
localization source for each category at that point in time. Based
on these updated inputs, the scheme selects a localization source
for each category such that localization investments remain within
budget while maximizing profit On day one, the profit maximizing
scheme selected human translation as the optimum localization
source for the BestSellers category and machine translation as the
optimum localization source for the Bargain category.
[0136] The cost of localizing the remaining number of words using
the selected localization source for each category is shown at
2039. This cost is calculated using the per word rate at 2012. For
example, on day one the cost of localizing the BestSellers category
using human translation at 2039 is $290 computed by multiplying the
1,450 remaining words at 2035 by the $0.20 per word rate of human
translation at 2012. The contribution ROI at 2040 is reproduced for
clarity from the appropriate localization source and category pair
shown at 2013.
[0137] The previously discussed dynamic budget calculation method
below is applied in this example to arrive at a dynamic proportion
at 2041 and a dynamic budget at 2042;
Dynamic .times. .times. Budget = b c .times. p 1 ROI category
##EQU00003##
[0138] For demonstrative purposes, the above formula is split up
into 2 parts: the budget or expected cost (b.sub.C) and the
"dynamic proportion" of the budget, as follows:
Dynamic Budget=b.sub.C.times.Dynamic Proportion
[0139] Where
Dynamic .times. .times. Proportion = p 1 ROI category
##EQU00004##
[0140] To compute the dynamic proportion at 2041, the proportion p
is taken from column 2031 and ROI.sub.category is taken from column
2040 for each category. This proportion represents the proportion
of the forward-looking investment that is expected for this
category that should be available for use during the current day.
To come up with the dynamic budget at 2042, this is then multiplied
by b.sub.C, taken from 2039. This dynamic budget places an upper
limit on how much can be invested on this day into each of the
given categories. In this example, because the localization of
"Bestsellers" has a higher ROI, the method assigns a greater
proportion of the expected cost as being available for use at the
beginning of the period (38% for BestSellers vs. 4% for Bargain for
day one). Although this example divides the budgetary period into
days, the same method could be applied on an hourly or continual
basis, as long as the expected words remaining in period at 2035
could be updated with each application of the method.
[0141] The words available for localization at 2043 are computed by
adding the previously scheduled words for localization at 2033 to
the words scheduled this day at 2034. The cost of localizing all
the words available using the selected localization source is shown
at 2044. This is computed by multiplying the words available at
2043 by the per word rate at 2012. Based on whichever is lower,
either the dynamic budget at 2042 or the cost of localizing all the
available words at 2044, the number of words that are actually
localized is given at 2045 with a corresponding cost at 2046, which
is equal to the lower of the dynamic budget or the cost of
localizing all available words. On day one, there are 250 words
available for localization in the BestSellers category; and since
the cost to localize all these words ($50) is less than the dynamic
budget of $109.61 for day one, then all 250 words are localized.
There are also 2,000 words available for localization in the
Bargain category on day one; but since the cost to localize all
these words ($20) is more than the dynamic budget of $4.13 for day
one, then only 412 words are localized to be able to stay within
the day's dynamic budget. That results in 1,588 words in the
Bargain category that cannot be translated on day one and therefore
carry over to day two as previously scheduled words at 2033. After
the localization costs are incurred, the remaining budget is shown
at 2047, which also carries over to the next day. Each subsequent
day, new values arc given for words scheduled this day at 2034 and
for expected words remaining in period at 2035.
[0142] On day 6, this example shows how a profit maximization
scheme would adapt to the reduced budget, Since a greater than
expected number of words were generated in the Bargain category (at
2034 from day 1 to day 5) early in the budgetary period, there is a
significant budget constraint. Applying both human translation to
the expected words remaining in period for Bestsellers (500 words
at 2036 multiplied by a per-word cost of $0.20 at 2012 for a cost
of $100 at 2039) and machine translation to the expected words
remaining in the period for Bargain (3,429 words at 2036 multiplied
by a per-word cost of $0.01 at 2012 for a cost of $34.29, not
shown) would have resulted in an localization investment decision
totaling $134.29, or more than the remaining budget of $125 at
2037. As a result, the profit maximizing scheme instead determined
that No Translation was the optimum localization source for the
remainder of the Bargain content, leaving budget available for
future higher-ROI localization investments, in particular human
Translation of the 200 expected future words of BestSellers content
shown at 2035 on Day 6.
[0143] One of the challenges in implementing a forward-looking
periodically-updated profit maximizing scheme, such as the one
described in FIG. 20, is the need to produce updated
forward-looking estimates for expected amount of content to be
localized. The example in FIG. 20 used a simple formula of
subtracting the content generated so far from the initial estimate
to come up with a remaining estimate. While intuitive, this
approach has shortcomings since it fully believes the initial
estimate without placing any importance on the data seen in the
current period. Considering a not-unusual example where the initial
estimate was 1,000 words to localize, and half-way through the
period 1,200 words have already been scheduled for localization,
this formula gives an unacceptable estimate of negative 200 words.
A better approach involves taking the initial estimate for the
period and periodically updating it as new content to localize
becomes available during the period. This may be done, for example,
by applying statistical inference, such as Bayesian inference,
which can be used to take the initial prediction and update it
using Bayesian updating with the data as it is observed.
[0144] To apply Bayesian inference, an initial belief or
probability distribution is required. This can be generated several
ways that may include, for example, (1) assuming that generation of
units of content occurs at a time that is independent of other
units of content (e.g., not specifically in batches, not with
extended amounts of downtime in which non-generation of one unit of
content strongly correlates with non-generation of another unit of
content), a Poisson distribution with mean equal to the number of
expected items can be used, and updated as products arc observed,
(2) a custom distribution based on historical content creation for
that website or another similar website, and (3) a normal
distribution.
[0145] When new content is made available for localization,
Bayesian updating can be applied to get a new estimate of future
content. To apply Bayesian updating, two inputs are required: a
prior distribution and a likelihood distribution; and one output
results: a posterior distribution. On the first application of
Bayesian updating (e.g. day 1), the prior distribution is the
initial distribution discussed in the previous paragraph, and on
all subsequent applications the posterior distribution from the
previous application becomes the new prior distribution. The
likelihood distribution is constructed as the probability density
of seeing the observed amount of new content that becomes available
for localization as a function of one of the prior distribution's
parameters (e.g. mean). The posterior distribution is generated by
multiplying the prior distribution and the likelihood distribution
and scaling the result such that its integral is one. A central
measure of this posterior distribution (such as its mean or median)
can be calculated using basic statistics and used as the updated
expected amount of content to be localized.
[0146] FIG. 21A shows an example of an initial expected number of
words to be localized that is updated each day during a one week
period based on the observed new content (i.e., new words scheduled
for localization) that becomes available for localization each day.
The resulting forecast of expected future words remaining in the
period for each day is then used (along with already scheduled
words still awaiting localization) to calculate the inputs to a
profit maximizing scheme, For each day at 2110, a belief about the
number of words expected per period is shown at 2111. On day 1,
this may be based on, for example, an automatically calculated
average from historical data. This is converted to a per day amount
at 2112 by dividing by the total number of days in the period (7 in
this example). The number of days remaining in the period at 2113
is then multiplied by this per-day expected number of words at 2112
to come up with the expected words in the remaining days of the
period at 2114. This can be added to the total number of words
scheduled for localization so far, shown at 2115, to obtain a total
expectation for the period, shown at 2116. The observation for the
day at 2117 shows the number of new words that are scheduled for
localization on that day, which is used to apply the Bayesian
updating.
[0147] FIG. 21B visualizes the three distributions involved in the
Bayesian updating process when new words become newly available
(i.e., are scheduled) for localization on day one, with the prior
distribution at 2131, the likelihood distribution at 2132, and the
posterior distribution at 2133. This example uses for its prior
distribution at 2131 a normal distribution with a mean and variance
both equal to 1,000, which is the expected number of words for the
period on day one at 2111. On subsequent days, the posterior
distribution from the previous day would be used instead. The
likelihood distribution at 2132 is given by the probability density
of seeing the observed number at 2117 as a function of the mean of
number of words created per week. The mode of this distribution
occurs at 700 (equivalent to 7 days multiplied by the number of
words observed). The two distributions are multiplied together and
the result is scaled such that its integral is one to arrive at the
posterior distribution at 2133. The median of this distribution is
used for the expected number of words per period at 2118 (though
other central measures such as mean or mode could be used as
well).
[0148] This is converted to a per day amount at 2119 by dividing by
the total number of days in the period (7). The number of days
remaining in the period after this date at 2120 is then multiplied
by this per-day expected number of words from 2119 to come up with
the expected words in the remaining days of the period at 2121.
This number at 2121 would be used in FIG. 20 at 2035, which is
added to the words scheduled on that day at 2034 and to any number
of words previously scheduled but not yet localized at 2033, to
arrive at the total words remaining for localization for the period
at 2036, which is in turn the input to a profit maximizing scheme
that determines the optimum localization source, as described in
more detail in FIG. 20. For illustrative purposes, the total words
observed so far at 2122 are calculated as the sum of the observed
values from the beginning of the period up to present day, and the
total expectation for the entire period at 2123 is calculated by
adding the total words observed so far from 2122 to the expected
words in days remaining from 2121. All 6 of these values from
2118-2123 are carried forward to the next day at 2111-2116, and the
posterior distribution from the Bayesian updating is used as the
prior distribution on the next day.
[0149] By applying Bayesian updating, the expected total number of
words to be localized for the period at 2111 changes from the
historical data based initial estimate of 1,000 words as new words
become available for localization each day. For example, after day
one, the number of words expected decreases to 960 based on the
fact that on day one a less than expected amount of new words
become available for localization (100 actual words vs. 143
expected words). Similarly, the number of words expected for the
remainder of the period at 2121 is dynamically adjusted based on
the number of new words that have become available for localization
up to the current day. For example, after day four, 463 words are
expected to remain to be localized in the final 3 days of the
period.
[0150] When testing how the different localization sources affect
the behavior associated with an element, the size of the samples
can vary widely. For example, an online retailer that offers for
sale on its website 100 different mobile phones may run a test
using a sample size of 10 phones. On the other hand, a retailer
that offers 1,000 different personal computers may run a test using
100 computers.
[0151] Although most of the examples provided herein are products
offered for sale on an online retailer website, the present
teaching is not limited to this type of content and can be applied
to any type of content so long as criteria can be provided and
taken into account in deciding how to localize each piece of
content. For example, the decision process related to, e.g.,
promoting, for a particularly piece of content, the current
localization source to a higher quality localization source and the
automatic determination of a localization source with respect to a
specific content can be applied to any type of content. In
addition, specific criteria described herein may also be applied to
any type of content, such as visitor traffic and/or the behavior of
a visitor. As a result the present teaching is not limited to
product content present on an online retailer's website. Criteria
to be used in deciding a localization source are not limited to the
exemplary ones described herein. Any criterion suitably developed
based on application needs may be applied in the process of
selecting localization sources for each element in content with
multiple elements without deviating from the spirit of the present
teaching.
[0152] Additional examples of content that can benefit from
automatic determination of localization sources include: (1)
advertisements supported content such as news, articles, blogs,
forums, reviews, ratings, etc.; (2) travel related content such as
hotels, flights, cruises, rentals, tours, etc.; (3) customer
support content such as knowledge bases, forums, questions and
answers, articles, manuals, guides, etc.; (4) archives or previous
years' content such as news, articles, blogs, investor reports,
government and regulatory filings, etc.; (5) user generated and
third party content, such as product reviews, store locators, job
boards, etc.; (6) locally relevant marketing and promotional
content that may be of limited value in other languages; and (7)
aggregated content, such as aggregators of products, news, reviews,
etc.
[0153] FIG. 22 is a tree diagram showing the hierarchical
organization of the factors that can be used in determining a
localization source of an element. The middle row of nodes
represents the main categories of factors that can be used to
determine a desired localization source. The leaf nodes at the
bottom provide specific examples for each category.
[0154] The present teaching may be realized in hardware, software,
firmware, or any combination thereof. A system according to one
embodiment of the present teaching can be realized in a centralized
fashion in one computer system or in a distributed fashion where
different elements are spread across several interconnected
computer systems. Any kind of computer system--or other apparatus
adapted for carrying out the methods described herein--is suited. A
typical combination of hardware, software, and firmware could be a
general-purpose computer system with a computer program that, when
being loaded and executed, controls the computer system such that
it carries out the methods described herein.
[0155] An embodiment of the present teaching can also be embedded
in a computer program product, which comprises all the features
enabling the implementation of the methods described herein, and
which--when loaded in a computer system--is able to carry out these
methods. Computer program means or computer program as used in the
present teaching indicates any expression, in any language, code or
notation, of a set of instructions intended to cause a system
having an information processing capability to perform a particular
function either directly or after either or both of the following
a) conversion to another language, code or, notation; and b)
reproduction in a different material form.
[0156] A computer system may include, inter alia, one or more
computers and at least a computer readable medium, allowing a
computer system, to read data, instructions, messages or message
packets, and other computer readable information from the computer
readable medium. The computer readable medium may include
non-volatile memory, such as ROM, Flash memory, Disk drive memory,
CD-ROM, and other permanent storage. Additionally, a computer
readable medium may include, for example, volatile storage such as
RAM, buffers, cache memory, and network circuits. Furthermore, the
computer readable medium may comprise computer readable information
in a transitory state medium such as a network link and/or a
network interface, including a wired network or a wireless network
that allow a computer system to read such computer readable
information,
[0157] FIG. 23 is a block diagram of an exemplary computer system
useful for implementing the different aspects of the present
teaching, such as value estimation, localization source computing,
traffic monitoring, visitor behavior, contribution computing,
localization source determination, localization source
promotion/demotion, localization source routing, etc. The computer
system includes one or more processors, such as processor 2320. The
processor 2320 is connected to a communication infrastructure 2310
(e.g., a communications bus, cross-over bar, or network). Various
software embodiments are described in terms of this exemplary
computer system. After reading this description, it will become
apparent to a person of ordinary skill in the relevant art(s) how
to implement the teaching using other computer systems and/or
computer architectures.
[0158] The computer system can include a display interface 2380
that forwards graphics, text, and other data from the communication
infrastructure 1602 (or from a frame buffer not shown) for display
on the display unit 2380. The computer system also includes a main
memory 2340, preferably random access memory (RAM), and may also
include a secondary memory. The secondary memory may include, for
example, a hard disk drive 2370 and/or a removable storage drive,
representing a floppy disk drive, a magnetic tape drive, an optical
disk drive, etc. The removable storage drive reads from and/or
writes to a removable storage unit in a manner well known to those
having ordinary skill in the art. Removable storage unit,
represents a floppy disk, magnetic tape, optical disk, etc. which
is read by and written to by removable storage drive. As will be
appreciated, the removable storage unit includes a computer usable
storage medium having stored therein computer software and/or
data.
[0159] In alternative embodiments, the secondary memory may include
other similar means for allowing computer programs or other
instructions to be loaded into the computer system. Such means may
include, for example, a removable storage unit and an interface.
Examples of such may include a program cartridge and cartridge
interface (such as that found in video game devices), a removable
memory chip 2330 (such as art EPROM, or PROM) and associated
socket, and other removable storage units and interfaces which
allow software and data to be transferred from the removable
storage unit to the computer system.
[0160] The computer system may also include a communications
interface 2350. Communications interface 2350 allows software and
data to be transferred between the computer system and external
devices. Examples of communications interface 2350 may include a
modem, a network interface (such as an Ethernet card), a
communications port, a PCMCIA slot and card, etc. Software and data
transferred via communications interface 1624 are in the form of
signals which may be, for example, electronic, electromagnetic,
optical, or other signals capable of being received by
communications interface 2350. These signals are provided to
communications interface 2350 via a communications path (i.e.,
channel). This channel carries signals and may be implemented using
wire or cable, fiber optics, a phone line, a cellular phone link,
an RE link, and/or other communications channels.
[0161] In this document, the terms "computer program medium,"
"computer usable medium," and "computer readable medium" are used
to generally refer to media such as main memory 2340 and secondary
memory 2330, removable storage drive, a hard disk installed in hard
disk drive 2370, and signals. These computer program products are
means for providing software to the computer system. The computer
readable medium allows the computer system to read data,
instructions, messages or message packets, and other computer
readable information from the computer readable medium. The
computer readable medium, for example, may include non-volatile
memory, such as Floppy, ROM, Flash memory, Disk drive memory,
CD-ROM, and other permanent storage. It is useful, for example, for
transporting information, such as data and computer instructions,
between computer systems. Furthermore, the computer readable medium
may comprise computer readable information in a transitory state
medium such as a network link and/or a network interface, including
a wired network or a wireless network, that allow a computer to
read such computer readable information.
[0162] Computer programs (also called computer control logic) are
stored in main memory 2340 and/or secondary memory 2330. Computer
programs may also be received via communications interface 2350.
Such computer programs, when executed, enable the computer system
to perform the features of the present teaching as discussed
herein. In particular, the computer programs, when executed, enable
the processor 2320 to perform the features of the computer system.
Accordingly, such computer programs represent controllers of the
computer system.
[0163] Although specific embodiments of the teaching have been
disclosed, those having ordinary skill in the art will understand
that changes can be made to the specific embodiments without
departing from the spirit and scope of the teaching. The scope of
the teaching is not to be restricted, therefore, to the specific
embodiments.
[0164] Other concepts relate to unique software for implementing
the different aspects of the present teaching, such as
determination of optimum localization source, translation server,
etc. A software product, in accord with this concept, includes at
least one machine-readable medium and information carried by the
medium. The information carried by the medium may be executable
program code data regarding web content translation and operational
parameters. When such information carried by the medium is read by
a machine, it causes the machine to perform programmed functions.
In one example, a translation server located connected with the
Internet executes instructions recorded on a medium and is capable
of receiving a request for content translation, to obtain content
in a first language from a publicly accessible source, analyzing
the content in the first language, performing necessary translation
based on the analysis, and forwarding, via a network, the
translated content in a second language to a party that is
requesting it.
[0165] The hardware elements, operating systems and programming
languages of such translation servers are conventional in nature,
and it is presumed that those skilled in the art arc adequately
familiar therewith. Of course, the server functions may be
implemented in a distributed fashion on a number of similar or even
different platforms, to distribute the processing load. Hence,
aspects of the methods of receiving web content translation
requests through a common communication port in a server or network
device from a variety of client applications, as outlined above,
may be embodied in programming.
[0166] Program aspects of the technology may be thought of as
"products" or "articles of manufacture" typically in the form of
executable code and/or associated data that is carried on or
embodied in a type of machine readable medium. Tangible
non-transitory "storage" type media include any or all of the
memory of the computers, processors or the like, or associated
modules thereof, such as various semiconductor memories, tape
drives, disk drives and the like, which may provide storage at any
time for the software programming.
[0167] All or portions of the software may at times be communicated
through the Internet or various other telecommunication networks.
Such communications, for example, may enable loading of the
software from one computer or processor into another, for example,
from a management server or host computer of the network operator
or carrier into the platform of the message server or other device
implementing a message server or similar functionality. Thus,
another type of media that may bear the software elements includes
optical, electrical and electromagnetic waves, such as those used
across physical interfaces between local devices, through wired and
optical landline networks and over various air-links. The physical
elements that carry such waves, such as wired or wireless links,
optical links or the like, also may be considered as media bearing
the software. As used herein, unless restricted to tangible
"storage" media, terms such as computer or machine "readable
medium" refer to any medium that participates in providing
instructions to a processor for execution.
[0168] Hence, a machine readable medium may take many forms,
including but not limited to, a tangible storage medium, a carrier
wave medium or physical transmission medium. Non-volatile storage
media include, for example, optical or magnetic disks, such as any
of the storage devices in any computer(s) or the like, such as may
be used to implement the data aggregator, the customer
communication system, etc. shown in the drawings. Volatile storage
media include dynamic memory, such as main memory of such a
computer platform. Tangible transmission media include coaxial
cables; copper wire and fiber optics, including the wires that
comprise a bus within a computer system. Carrier-wave transmission
media can take the form of electric or electromagnetic signals, or
acoustic or light waves such as those generated during radio
frequency (RF) and infrared (IR) data communications. Common forms
of computer-readable media therefore include for example: a floppy
disk, a flexible disk, hard disk, magnetic tape, any other magnetic
medium, a CD-ROM, DVD or DVD-ROM. any other optical medium, punch
cards paper tape, any other physical storage medium with patterns
of holes, a RAM, a PROM and EPROM, a FLASH-EPROM, any other memory
chip or cartridge, a carrier wave transporting data or
instructions, cables or links transporting such a carrier wave, or
any other medium from which a computer can read programming code
and/or data. Many of these forms of computer readable media may be
involved in carrying one or more sequences of one or more
instructions to a processor for execution.
[0169] Those skilled in the art will recognize that the present
teachings are amenable to a variety of modifications and/or
enhancements. For example, the determination of optimum
localization source server described above may be embodied in a
hardware device, or it can also be implemented as a software only
solution--e.g., requiring installation on an existing server. In
addition, the various servers and components as disclosed herein
can also be implemented as a firmware, firmware/software
combination, firmware/hardware combination, or
hardware/firmware/software combination.
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