U.S. patent application number 17/308670 was filed with the patent office on 2021-10-21 for promotion system for determining and correcting for insufficiency of promotion data.
The applicant listed for this patent is Groupon, Inc.. Invention is credited to Amit Aggarwal, Kevin Chang.
Application Number | 20210326921 17/308670 |
Document ID | / |
Family ID | 1000005682137 |
Filed Date | 2021-10-21 |
United States Patent
Application |
20210326921 |
Kind Code |
A1 |
Chang; Kevin ; et
al. |
October 21, 2021 |
PROMOTION SYSTEM FOR DETERMINING AND CORRECTING FOR INSUFFICIENCY
OF PROMOTION DATA
Abstract
A promotion system for determining a deficiency in promotion
data and correcting for the deficiency is disclosed. Issuing offers
from a promotion program results in promotion data being generated.
The promotion data may be analyzed to determine an acceptance rate
of the offers. The promotion system may compare whether the
acceptance rate is above a predetermined threshold, but has a
confidence level that is less than a confidence rate threshold. In
that event, the promotion system may issue additional offers in
order to increase the confidence level associated with the
acceptance rate by a predetermined amount.
Inventors: |
Chang; Kevin; (Mountain
View, CA) ; Aggarwal; Amit; (Los Altos, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Groupon, Inc. |
Chicago |
IL |
US |
|
|
Family ID: |
1000005682137 |
Appl. No.: |
17/308670 |
Filed: |
May 5, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16437111 |
Jun 11, 2019 |
11037189 |
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17308670 |
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13839036 |
Mar 15, 2013 |
10360580 |
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16437111 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0242
20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1-28. (canceled)
29. A method for determining whether to present an offer from a
first promotion program to a consumer comprising: accessing, via a
processor, a database configured for storing promotion program data
from a plurality of promotion programs, feedback data received from
a plurality of consumer devices having previously received an
electronic communication comprising a prior offer, performance
data, and attribute data; wherein the promotion program data from
the plurality of promotion programs includes promotion program data
from the first promotion program and promotion program data from
one or more promotion programs other than the first promotion
program; wherein the attribute data includes one or more values of
a first attribute and wherein each consumer device is associated
with one specific value of the one or more values of the first
attribute; wherein the performance data includes acceptances of
offers from the plurality of promotion programs; wherein the
database compiles the feedback data received from each of the
plurality of consumer devices, wherein the feedback data is
comprised of an indication indicative of one value of the first
attribute; determining, via the processor, and using a first
predictive model, a historical predicted acceptance of an offer
from the first promotion program based on the value of the first
attribute and the performance data of the one or more promotion
programs other than the first promotion program in order to
correlate historical predicted acceptances of offers from the one
or more promotion programs other than the first promotion program
to respective values of the attribute; determining, via the
processor, and using a second predictive model, a promotion program
predicted acceptance of the offer from the first promotion program
based on performance data from previous offers of the first
promotion program in order to correlate the promotion program
predicted acceptance to respective values of the attribute;
determining, via the processor, a confidence level in the
performance data from the previous offers of the first promotion
program; adjusting, via the processor, the promotion program
predicted acceptance based on the confidence level; combining, via
the processor, the historical predicted acceptance and the adjusted
promotion program predicted acceptance to generate a predicted
acceptance of the offer; determining, via the processor, whether to
present an offer from the first promotion program to a consumer
based on the predicted acceptance of the offer; and providing, via
electronic communication, in real-time, the offer from the first
promotion program to a consumer device of the consumer.
30. The method of claim 29, wherein the database is organized and
correlated to one or more attribute values such that, for each of
the plurality of consumer devices having previously received the
electronic communication comprising the prior offer, each value of
a particular attribute is correlated to an indication of at least
one action taken by a particular consumer device of the plurality
of consumer devices in response to receive the electronic
communication comprising the prior offer.
31. The method of claim 29, wherein the confidence level in the
performance data from the previous offers of the first promotion
program is based on a number of the previous offers of the first
promotion program that include the value of the first attribute
associated with the consumer.
32. The method of claim 29, wherein the first attribute is
indicative of a distance of the consumer device to a location
associated with the offer.
33. The method of claim 29, wherein the second predictive model
uses the performance data from previous offers of the first
promotion program associated with the value of the first attribute
associated with the consumer.
34. The method of claim 29, wherein adjusting the promotion program
predicted acceptance based on the confidence level comprises
adjusting the promotion program predicted acceptance so that,
within a predetermined probability, an actual promotion acceptance
is greater than the adjusted predicted acceptance.
35. The method of claim 29, wherein the electronic communication
providing the offer from the first promotion program to the
consumer device of the consumer includes an additional offer from
an additional promotion program.
36. A computer program product comprising at least one
non-transitory computer-readable storage medium having
computer-executable program code instructions stored therein, the
computer-executable program code instructions comprising program
code instructions for: accessing, via a processor, a database
configured for storing promotion program data from a plurality of
promotion programs, feedback data received from a plurality of
consumer devices having previously received an electronic
communication comprising a prior offer, performance data, and
attribute data; wherein the promotion program data from the
plurality of promotion programs includes promotion program data
from a first promotion program and promotion program data from one
or more promotion programs other than the first promotion program;
wherein the attribute data includes one or more values of a first
attribute and wherein each consumer device is associated with one
specific value of the one or more values of the first attribute;
wherein the performance data includes acceptances of offers from
the plurality of promotion programs; wherein the database compiles
the feedback data received from each of the plurality of consumer
devices, wherein the feedback data is comprised of an indication
indicative of one value of the first attribute; determining, via
the processor, and using a first predictive model, a historical
predicted acceptance of an offer from the first promotion program
based on the value of the first attribute and the performance data
of the one or more promotion programs other than the first
promotion program in order to correlate historical predicted
acceptances of offers from the one or more promotion programs other
than the first promotion program to respective values of the
attribute; determining, via the processor, and using a second
predictive model, a promotion program predicted acceptance of the
offer from the first promotion program based on performance data
from previous offers of the first promotion program in order to
correlate the promotion program predicted acceptance to respective
values of the attribute; determining, via the processor, a
confidence level in the performance data from the previous offers
of the first promotion program; adjusting, via the processor, the
promotion program predicted acceptance based on the confidence
level; combining, via the processor, the historical predicted
acceptance and the adjusted promotion program predicted acceptance
to generate a predicted acceptance of the offer; determining, via
the processor, whether to present an offer from the first promotion
program to a consumer based on the predicted acceptance of the
offer; and providing, via electronic communication, in real-time,
the offer from the first promotion program to a consumer device of
the consumer.
37. The computer program product of claim 36, wherein the database
is organized and correlated to one or more attribute values such
that, for each of the plurality of consumer devices having
previously received the electronic communication comprising the
prior offer, each value of a particular attribute is correlated to
an indication of at least one action taken by a particular consumer
device of the plurality of consumer devices in response to receive
the electronic communication comprising the prior offer.
38. The computer program product of claim 36, wherein the
confidence level in the performance data from the previous offers
of the first promotion program is based on a number of the previous
offers of the first promotion program that include the value of the
first attribute associated with the consumer.
39. The computer program product of claim 36, wherein the first
attribute is indicative of a distance of the consumer device to a
location associated with the offer.
40. The computer program product of claim 36, wherein the second
predictive model uses the performance data from previous offers of
the first promotion program associated with the value of the first
attribute associated with the consumer.
41. The computer program product of claim 36, wherein adjusting the
promotion program predicted acceptance based on the confidence
level comprises adjusting the promotion program predicted
acceptance so that, within a predetermined probability, an actual
promotion acceptance is greater than the adjusted predicted
acceptance.
42. The computer program product of claim 36, wherein the
electronic communication providing the offer from the first
promotion program to the consumer device of the consumer includes
an additional offer from an additional promotion program.
43. An apparatus comprising at least one processor and at least one
memory including computer program code, the at least one memory and
the computer program code configured to, with the processor, cause
the apparatus to at least: access, via a processor, a database
configured for storing promotion program data from a plurality of
promotion programs, feedback data received from a plurality of
consumer devices having previously received an electronic
communication comprising a prior offer, performance data, and
attribute data; wherein the promotion program data from the
plurality of promotion programs includes promotion program data
from a first promotion program and promotion program data from one
or more promotion programs other than the first promotion program;
wherein the attribute data includes one or more values of a first
attribute and wherein each consumer device is associated with one
specific value of the one or more values of the first attribute;
wherein the performance data includes acceptances of offers from
the plurality of promotion programs; wherein the database compiles
the feedback data received from each of the plurality of consumer
devices, wherein the feedback data is comprised of an indication
indicative of one value of the first attribute; determine, via the
processor, and using a first predictive model, a historical
predicted acceptance of an offer from the first promotion program
based on the value of the first attribute and the performance data
of the one or more promotion programs other than the first
promotion program in order to correlate historical predicted
acceptances of offers from the one or more promotion programs other
than the first promotion program to respective values of the
attribute; determine, via the processor, and using a second
predictive model, a promotion program predicted acceptance of the
offer from the first promotion program based on performance data
from previous offers of the first promotion program in order to
correlate the promotion program predicted acceptance to respective
values of the attribute; determine, via the processor, a confidence
level in the performance data from the previous offers of the first
promotion program; adjust, via the processor, the promotion program
predicted acceptance based on the confidence level; combine, via
the processor, the historical predicted acceptance and the adjusted
promotion program predicted acceptance to generate a predicted
acceptance of the offer; determine, via the processor, whether to
present an offer from the first promotion program to a consumer
based on the predicted acceptance of the offer; and provide, via
electronic communication, in real-time, the offer from the first
promotion program to a consumer device of the consumer.
44. The apparatus according to claim 43, wherein the database is
organized and correlated to one or more attribute values such that,
for each of the plurality of consumer devices having previously
received the electronic communication comprising the prior offer,
each value of a particular attribute is correlated to an indication
of at least one action taken by a particular consumer device of the
plurality of consumer devices in response to receive the electronic
communication comprising the prior offer.
45. The apparatus according to claim 43, wherein the confidence
level in the performance data from the previous offers of the first
promotion program is based on a number of the previous offers of
the first promotion program that include the value of the first
attribute associated with the consumer.
46. The apparatus according to claim 43, wherein the first
attribute is indicative of a distance of the consumer device to a
location associated with the offer.
47. The apparatus according to claim 43, wherein the second
predictive model uses the performance data from previous offers of
the first promotion program associated with the value of the first
attribute associated with the consumer.
48. The apparatus according to claim 43, wherein adjusting the
promotion program predicted acceptance based on the confidence
level comprises adjusting the promotion program predicted
acceptance so that, within a predetermined probability, an actual
promotion acceptance is greater than the adjusted predicted
acceptance.
Description
FIELD OF THE INVENTION
[0001] The present description relates to offering promotions
associated with a product or a service. This description more
specifically relates to identifying whether feedback to promotion
offers are insufficient, and for correcting for the insufficiency
in the feedback.
DESCRIPTION OF THE RELATED ART
[0002] Merchants typically offer promotions to consumers from time
to time in order to generate more business. The promotions offered
may be in the form of discounts, deals, rewards or the like. Often
times, there are a multitude of promotions that may be offered to
the consumer, with the promotions potentially being of different
types (e.g., a restaurant promotion versus a spa promotion). The
consumers respond to the offers for the promotions in the form of
feedback. The feedback may include ignoring the promotion offer,
opening the promotion offer but not buying the promotion included
therein, or buying the promotion. It may be difficult to determine
whether the feedback received from the offers is insufficient and
how to correct for any perceived insufficiency.
SUMMARY OF THE INVENTION
[0003] An apparatus and method for analyzing collections of
promotions is disclosed.
[0004] According to one aspect, a method is provided for
determining whether and how many additional offers to make for a
promotion from a promotion program. The method includes: analyzing
feedback from previous offers sent to consumers for the promotion
program; determining, based on the analysis, an estimated
acceptance correlated to an attribute, the attribute comprising or
derived from one or both of a consumer attribute or a promotion
attribute; determining whether the estimated acceptance is above a
predetermined acceptance threshold; determining a number of
additional consumers having the attribute to send the promotion to
in order to increase confidence in the estimated acceptance; and
determining whether to send the promotion to some or all of the
number of additional consumers based on whether the estimated
acceptance is above the predetermined acceptance threshold.
[0005] According to another aspect, a method is provided for
determining whether to present an offer from a promotion program to
a consumer. The method includes: accessing a value of an attribute,
the attribute comprising or derived from a consumer attribute or a
promotion attribute; generating, using a historical predictive
model, a historical predicted acceptance of the offer, the
historical predictive model configured to input the value and to
output the historical predicted acceptance, the historic predictive
model using performance data of offers from different promotion
programs in order to correlate historical predicted acceptances to
respective values of the attribute; generating, using a promotion
program predictive model, a promotion program predicted acceptance
of the offer, the promotion program predictive model configured to
input the value and to output the promotion program predicted
acceptance, the promotion program predictive model using
performance data from previous offers from the promotion program to
correlate promotion program predicted acceptances to respective
values of the attribute; adjusting the promotion program predicted
acceptance based on confidence in the performance data from the
previous offers from the promotion program; combining the
historical predicted acceptance and the adjusted promotion program
predicted acceptance in order to generate a predicted acceptance of
the offer; and using the predicted acceptance in order to determine
whether to present an offer from the promotion program to the
consumer.
[0006] According to yet another aspect, a system is provided for
determining whether and how many additional offers to make for a
promotion from a promotion program. The system includes: one or
more memories configured to store a consumer attribute and a
promotion attribute; and a processor in communication with the one
or more memories. The processor is configured to: analyze feedback
from previous offers sent to consumers for the promotion program;
determine, based on the analysis, an estimated acceptance
correlated to an attribute, the attribute comprising or derived
from one or both of the consumer attribute or the promotion
attribute; determine whether the estimated acceptance is above a
predetermined acceptance threshold; determine a number of
additional consumers having the attribute to send the promotion to
in order to increase confidence in the estimated acceptance; and
determine whether to send the promotion to some or all of the
number of additional consumers based on whether the estimated
acceptance is above the predetermined acceptance threshold.
[0007] According to still another aspect, a system is provided for
determining whether to present an offer from a promotion program to
a consumer. The system includes: one or more memories configured to
store performance data from previous offers from the promotion
program and performance data of offers from different promotion
programs; and a processor in communication with the one or more
memories. The processor is configured to: access a value of an
attribute, the attribute comprising or derived from a consumer
attribute or a promotion attribute; generate, using a historical
predictive model, a historical predicted acceptance of the offer,
the historical predictive model configured to input the value and
to output the historical predicted acceptance, the historic
predictive model using the performance data of offers from
different promotion programs in order to correlate historical
predicted acceptances to respective values of the attribute;
generate, using a promotion program predictive model, a promotion
program predicted acceptance of the offer, the promotion program
predictive model configured to input the value and to output the
promotion program predicted acceptance, the promotion program
predictive model using the performance data from previous offers
from the promotion program to correlate promotion program predicted
acceptances to respective values of the attribute; adjust the
promotion program predicted acceptance based on confidence in the
performance data from the previous offers from the promotion
program; combine the historical predicted acceptance and the
adjusted promotion program predicted acceptance in order to
generate a predicted acceptance of the offer; and use the predicted
acceptance in order to determine whether to present an offer from
the promotion program to the consumer.
[0008] Other systems, methods, and features will be, or will become
apparent to one with skill in the art upon examination of the
following figures and detailed description. It is intended that all
such additional systems, methods, and features included within this
description, be within the scope of the disclosure, and be
protected by the following claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The present invention may be better understood with
reference to the following drawings and description. Non-limiting
and non-exhaustive descriptions are described with reference to the
following drawings. The components in the figures are not
necessarily to scale, emphasis instead being placed upon
illustrating principles. In the figures, like referenced numerals
may refer to like parts throughout the different figures unless
otherwise specified.
[0010] FIG. 1 shows an example of a system that includes an
analytical model, which identifies whether feedback from
performance offers is insufficient and at least partially corrects
for the insufficiency.
[0011] FIG. 2 shows an expanded block diagram of the analytical
model illustrated in FIG. 1.
[0012] FIG. 3 illustrates a flow chart identifying one or more
parts of a promotion program with potential but insufficient
feedback data, and determining a number of additional offers to
improve for the insufficient feedback data.
[0013] FIG. 4 illustrates an expanded flow chart of FIG. 3 in which
the number of additional offers is determined iteratively.
[0014] FIG. 5 illustrates a flow chart identifying a promotion
program that has different attributes or different attribute values
with potential but insufficient feedback data, determining a number
of additional offers to improve for the insufficient feedback data
for the different attributes or attribute values, and selecting
which of the different attributes or attribute values to send
additional offers.
[0015] FIG. 6 illustrates a flow chart for determining an
adjustment to a promotion conversion rate based on confidence in
the performance data used to generate the promotion conversion
rate.
[0016] FIG. 7 is a general computer system, programmable to be a
specific computer system, which may represent any of the computing
devices referenced herein.
DETAILED DESCRIPTION
[0017] The present invention as described herein may be embodied in
a number of different forms. Not all of the depicted components may
be required, however, and some implementations may include
additional, different, or fewer components from those expressly
described in this disclosure. Variations in the arrangement and
type of the components may be made without departing from the
spirit or scope of the claims as set forth herein.
[0018] A promotion program offering system 102 may offer promotions
from a promotion program. The promotion may include any type of
reward, discount, coupon, credit, deal, voucher or the like used
toward part or all of the purchase of a product or a service. The
promotion may be offered as part of a larger promotion program, or
the promotion may be offered as a stand-alone one time promotion.
In an effort to better distinguish and identify the promotion, the
promotion may include one or more attributes, such as the merchant
offering the promotion (e.g., merchant 1118, which may be
identified as "XYZ coffee shop"), the redemption location of the
promotion, the amount of the promotion, the category of the
promotion (such as a restaurant promotion, a spa promotion, a
travel promotion, a local promotion, etc.), the subcategory of the
promotion (such as a sushi restaurant), or the like.
[0019] As discussed below, the promotion program offering system
102 may present to a consumer an electronic communication with a
promotion. The electronic communication may comprise, without
limitation, an email, SMS text message, webpage inbox message, VOIP
voice message, real-time webpage content presentation, mobile push
notifications or other similar types of electronic correspondences.
In order to determine whether to send a promotion to a consumer,
one or more attributes of the consumer may be examined. Similar to
promotions, a consumer may be identified by one or more attributes.
As discussed in more detail below, the attributes for consumers may
be stored in respective consumer profiles within consumer profiles
database 112. The attributes may include, for instance, the name,
age, gender, addresses (e.g., home, work, addresses of interest),
occupation, educational background, previously accepted promotion
program offerings, previously rejected promotion program offerings,
and the like.
[0020] In addition to promotion attributes and consumer attributes,
other attributes may be derived from one or both of the promotion
attributes and the consumer attributes. For example, a distance
attribute is defined as the distance between the location of the
promotion and the location of the consumer (such as the home
location or work location of the consumer). The distance attribute
may thus be derived from the promotion location attribute and from
the consumer location attribute. As another example, a direction
attribute may indicate the direction of the consumer from the
promotion location (e.g., east, west, north, or south from the
promotion location). Attributes, such as promotion attributes,
consumer attributes, and derived attributes are discussed in U.S.
application Ser. No. 13/411,502 and U.S. Provisional Application
No. 61/695,857, both of which are incorporated by reference
herein.
[0021] In response to offering promotions, the promotion program
offering system 102 may receive feedback. The feedback may come in
one of several forms and may provide an indication of success of
offering the promotion. One form comprises acceptances (e.g.,
purchases) of promotions. Another form comprises access of the
promotion (e.g., an indication that the consumer activated a link
to a webpage describing the promotion). In this regard, the
feedback may be compiled as an indication of success or acceptance.
One example of an indication of success or acceptance of the
promotion is a conversion rate. The conversion rate is the rate by
which a consumer accepts a promotion that is offered, or the number
of purchases of the promotion divided by the number of times the
promotion is offered to consumers. Other indications of success or
acceptance are contemplated. The discussion below, while focused on
conversion rate, may be equally applied to any indication of
success or acceptance.
[0022] The feedback may be organized and correlated to one or more
attributes, such as correlated to one or more promotion attributes,
one or more consumer attributes and/or one or more derived
attributes. For example, one attribute may comprise distance of the
consumer from the promotion. Different values of distance of the
consumer to the promotion (e.g., 0-2 miles, 2-4 miles, etc.) may be
correlated to the conversion rate of consumers that meet this
attribute. As discussed in more detail below, different predictive
models may correlate attribute(s) with the organized feedback. One
example is a historical predictive model, which correlates
attribute(s) with conversion rates being based on feedback of
historical promotions, discussed in more detail below. Another
example is a promotion program predictive model, which correlates
attribute(s) with conversion rates, with the conversion rates being
based on feedback from offers for the promotion program.
[0023] As another example, multiple attributes may include the
category, the subcategory and the distance of the consumer from the
promotion. In the case of the historical predictive model, the
multiple attributes may be correlated to calculated conversion
rates for historical promotions that have the corresponding
multiple attributes. In this regard, the historical predictive
model may input values for the multiple attributes (such as
category=restaurant; subcategory=sushi; distance=0-2 miles) and
output the corresponding conversion rate.
[0024] Similar to the historical predictive model, the promotion
program predictive model may input values for one or more
attributes and output a conversion rate. As discussed above, the
conversion rate may indicate an estimated acceptance of the
promotion for the correlated attributes. In certain instances, the
conversion rate may be unreliable. In this regard, the conversion
rate, as an estimated acceptance of the promotion for the
correlated attributes, may deviate from the actual acceptance of
the promotion for the correlated attributes. In the example of the
promotion program predictive model correlating values of distance
to conversion rates, a value of 0-2 miles may indicate a conversion
rate of 10%. However, due to unreliability of the feedback upon
which the conversion rate of 10% is based, the actual conversion
rate for a distance of 0-2 miles is actually 5%.
[0025] One basis for unreliability of the conversion rate may be
the number of offers upon which the feedback (and in turn, the
conversion rate) is based. For example, the number of offers for
the promotion may be too low to provide a reliable conversion rate.
In one aspect, the promotion program offering system 102 is
configured to determine which conversion rates show promise, and
configured to determine a number of additional offers to increase
the reliability of the conversion rate to a predetermined level, as
discussed below. In another aspect, the promotion program offering
system 102 is configured to combine a historical conversion rate
(generated from the historical predictive model) and a promotion
program conversion rate (generated from the promotion program
predictive model) to generate a predicted conversion rate. Prior to
combining, the promotion program conversion rate may be adjusted
based on a confidence (e.g., a measure of the reliability or
unreliability) in the promotion program conversion rate, as
discussed in more detail below with respect to FIG. 6.
[0026] FIG. 1 shows an example of a system 100 for determining and
at least partly correcting for insufficiency of promotion data. The
system 100 includes a promotion program offering system 102, which
communicates via one or more networks 122 with one or more
consumers, such as consumer 1124, consumer N 126, and more. For
example, the promotion program offering system 102 may communicate
with consumers by sending electronic promotion correspondence to a
consumer device, such as a laptop computer used by consumer 1124, a
mobile telephone used by consumer N 126, or any other electronic
device that can receive electronic promotion correspondence. The
promotion program offering system 102 may communicate with one or
more merchants, such as the merchants labeled in FIG. 1 as merchant
1118 and merchant M 120.
[0027] The promotion program offering system 102 includes an
analytical model 104 that is in communication with databases 110,
112, 114, 116. The analytical model 104 may include one or more
components, logic, or circuitry for grouping a number of
promotions. The analytical model 104 may further include one or
more components, logic, or circuitry for generating electronic
promotion correspondence that includes one or more promotions.
[0028] A promotion may be characterized by a promotion score. The
analytical model 104 may generate a promotion score for a
promotion, including a consumer-specific promotion score based on
one or more attributes, historical data, or other characteristics
of the consumer and/or the promotion. In one implementation, the
promotion score of a promotion may be a probability indicator of
estimation that the particular consumer accepting (e.g.,
purchasing) the promotion.
[0029] To generate promotion scores, the analytical model 104 may
access data with respect to a particular consumer, a particular
promotion, or both. The analytical model 104 may communicate with
multiple databases of the promotion program offering system 102
such as a promotion program database 110, consumer profiles
database 112, historical data database 114 and dynamic data
database 116. With respect to the particular consumer, the
analytical model 104 may access the databases 110, 112, 114 and 116
in order to obtain specific attribute information on the particular
consumer and the various promotions being scored. As discussed
above, various attributes may be associated or assigned to a
promotion and a consumer in the promotion system 100. The
analytical model 104 may use obtained attribute information to
generate promotion scores for each promotion. An example of scoring
promotions is disclosed in U.S. application Ser. No. 13/411,502,
incorporated by reference herein in its entirety. An example for
scoring a grouping of promotions is disclosed in U.S. Provisional
Application No. 61/663,508, incorporated by reference herein in its
entirety.
[0030] The promotion programs database 110 may store data detailing
various promotions and promotion programs available for offer in
the promotion program offering system 102. In order to input
promotion program information into the promotions program database
110, merchants (e.g., merchant 1118) may communicate through the
communication networks 122 with the promotion program offering
system 102 to input the information detailing the various promotion
program offerings.
[0031] The consumer profiles database 112 may store consumer
profiles for consumers, such as consumer 1124 and consumer N 126.
The analytical model 104 may use one, some, or all of the
attributes of the consumer in managing the electronic
correspondence cadence of the consumer and/or determining whether
to send an electronic promotion correspondence to the consumer.
[0032] The historical data database 114 may store data of
previously offered promotion programs, such as performance
detailing the past performance of promotion program offerings
presented by the promotion program system 102. The historical data
database 114 may include, as examples, rates of acceptances of
specific promotion programs, attributes of consumers that accepted
or rejected specific promotion programs, and the like.
[0033] The dynamic data database 116 may store data of presently
active promotion programs, such as performance data of a promotion
program offering that is currently active in the promotion offering
system 102. While a promotion program referenced in the dynamic
data database 116 is currently active, the data stored in the
dynamic data database 116 may pertain to performance data of the
active promotion program from a previous time period.
[0034] Although FIG. 1 has been illustrated to show separate
databases 110, 112, 114 and 116, FIG. 1 has been illustrated for
demonstrative purposes only, and it is contemplated to have the
databases 110, 112, 114 and 116 arranged in any combination of one
or more memories/storage units.
[0035] FIG. 2 shows an expanded block diagram of the analytical
model 104 illustrated in FIG. 1. The analytical model 104 may be
segmented functionally as shown in FIG. 2 into a data confidence
engine 200, a promotion program predictive model 202, a historical
predictive model 204, and an additional offer engine 206. FIG. 2 is
provided for illustration purposes. The division of functionality
may differ from that illustrated in FIG. 2.
[0036] As discussed above, the promotion program predictive model
202 correlates attribute(s) with the conversion rates being based
on feedback from offers for the promotion program. The promotion
program predictive model 202 is configured to input values for the
attribute(s) and output the conversation rate correlated to the
attribute(s).
[0037] Likewise, the historical predictive model 204 correlates
attribute(s) with conversion rates, with the conversion rates being
based on feedback from offers for the historical promotion
programs. The historical predictive model 204 is configured to
input values for the attribute(s) and output the conversation rate
correlated to the attribute(s).
[0038] The data confidence engine 200 is configured to determine an
indication of confidence (such as a level of confidence) in the
conversion rate output by the promotion program predictive model
202. As discussed above, the conversion rate correlated to
particular values for attribute(s) is determined based on the
promotion offers that include the particular values for the
attribute(s). The indication of confidence may be based on the
number of the promotion offers that include the particular values
for the attribute(s). For example, a first conversion rate for a
first promotion correlated to a distance attribute value of 0-2
miles may comprise 10%, and is based on 10 offers (1 acceptance
from the 10 offers for the first promotion). A second conversion
rate for a second promotion correlated to a distance attribute
value of 0-2 miles may comprise 9.9%, and is based on 1000 offers
(99 acceptances from the 1000 offers for the second promotion). As
discussed in more detail below, even though the first conversion
rate is higher than the second conversion rate, the confidence in
the first conversion rate is lower than the second conversion
rate.
[0039] The additional offer engine 206 is configured to determine a
number of additional offers to present to consumers in order to
increase the confidence in the conversion rate (such as increase
the confidence by a predetermined amount).
[0040] FIG. 3 illustrates a flow chart 300 identifying one or more
parts of a promotion program with potential but with insufficient
feedback data, and determining a number of additional offers to
compensate for the insufficient feedback data. As discussed above,
offers are presented for a promotion program. The offers result in
feedback data that may be organized into the promotion program
predictive model 202. Parts of the promotion program predictive
model 202 may indicate potential success in the promotion. For
example, a promotion program predictive model 202 organized by
correlating values of attribute(s) with corresponding conversion
rates may indicate potential success for certain values of the
attribute(s).
[0041] At 302, it is determined whether the conversion rate is
above a predetermined conversion rate threshold (e.g., indicative
of potential success). For example, the predetermined conversion
rate threshold may comprise an absolute number (e.g., 10%
conversion rate). As another example, the predetermined conversion
rate threshold may comprise a threshold number that varies
depending on values of the attribute(s). In particular, in the
example of a promotion program predictive model 202 organized by
correlating values of distances between the consumer and the
promotion program with corresponding conversion rates, different
threshold numbers may be used depending on the value of the
distance (e.g., 10% for 0-2 miles, 8% for 2-4 miles, 6% for 4-6
miles). As still another example, the predetermined conversion rate
threshold may comprise a predetermined percentile for a value of
the attribute in a particular geographic region. Again, using the
promotion program predictive model 202 organized by correlating
values of distances between the consumer and the promotion program
with corresponding conversion rates, the predetermined conversion
rate for 0-2 miles may comprise the 50th percentile for promotion
program 0-2 miles in the same city as the promotion program. Other
indications of potential success of part of the promotion program
are contemplated.
[0042] At 304, it is determined whether confidence in the
conversion rate is below a predetermined confidence threshold. As
discussed in more detail below, confidence in the conversion rate
may be determined in several ways. Likewise, the predetermined
confidence threshold may be represented in one of several ways. One
way to measure confidence is to analyze the number of offers that
are used to generate the conversion rate. In this regard, the
predetermined confidence threshold may comprise an absolute number.
Another way to measure confidence in the conversion rate is to
analyze a potential distance of the conversion rate (as determined
by the offers that have been received) versus an actual conversion
rate. As discussed in more detail below, a promotion may have a
given number of conversions (X) from a given number of offers (Y),
which results in a conversion rate (cr). A measure of confidence
may be determined in the calculated conversion rate (cr). For
example, confidence intervals may be calculated such that there is
an M % that the conversion rate (cr) is within the confidence
intervals (e.g., there is a 95% confidence that the calculated
conversion rate is within the confidence interval). In this regard,
the confidence as represented by the confidence interval may vary
based on the width of the confidence interval. For example, the
wider the confidence interval (+/-20%), the lower the confidence in
the conversion rate. Conversely, the narrower the confidence
interval (+/-5%), the greater the confidence in the conversion
rate. The confidence intervals may generally narrow as more offers
are issued. Thus, the confidence interval for the conversion rate
may be determined and compared with a predetermined confidence
interval. In the event that the confidence interval is greater than
the predetermined confidence interval (e.g., the confidence in the
conversion rate is lower than a predetermined confidence
threshold), the flow chart 300 proceeds to 306.
[0043] At 306, a number of additional offers to achieve a
predetermined confidence level is determined. Additional offers may
be determined in one of several ways. A non-limiting example is
provided. A promotion program for certain value(s) of attribute(s)
has X acceptances for Y offers that have already been sent. Thus,
the empirical conversion rate is X/Y. Further, the total number of
impressions Y' may be calculated such that the empirical conversion
rate is within a predetermined percentage of the actual conversion
rate. For example, the following equation calculates Y' to have the
empirical conversion rate to be within 20% of the actual conversion
rate:
Y'=c_alpha.sup.2/(0.2.sup.2)/cr
[0044] where cr is the empirical conversion rate, and c_alpha is a
constant that depends on alpha;
[0045] where alpha in the equation above is the predetermined
probability that the conversion rate is within 20% of the actual
conversion rate.
[0046] In this regard, the number of additional offers to present
in order for empirical conversion rate to be within a predetermined
percentage of the actual conversion rate is: number of additional
offers=Y'-Y.
[0047] The 20% is merely for illustration purposes. Other
percentages are contemplated. In this regard, different values of
alpha may be used, such as 50%, so that the equation above, instead
of using 0.2 may use 0.5. Further, as shown in the equation
illustrated above, the number of additional offers may be dependent
on cr (the empirical conversion rate). More specifically, the
number of additional offers may be inversely related to the cr
(empirical conversion rate). In this regard, for a higher cr, a
lower number of additional offers may be needed for empirical
conversion rate to be within a predetermined percentage of the
actual conversion rate. Conversely, for a lower cr, a higher number
of additional offers may be needed for empirical conversion rate to
be within a predetermined percentage of the actual conversion
rate.
[0048] At 308, the consumers to receive the additional offers are
selected. The selection of consumers may be in accordance with the
disclosure in U.S. application Ser. No. 13/411,502, incorporated by
reference herein in its entirety. In the event that the number of
additional offers is large, throttling may be used, such as
disclosed in U.S. application Ser. No. 13/839,142 entitled
"Throttling System for Consumer Deals", incorporated by reference
herein in its entirety. Throttling may be used to meter the
additional offers over a series of several time periods, such as
over several days. In this regard, the determination as to the
number of additional offers may be repeated, such as repeated after
each day, as discussed in FIG. 4.
[0049] FIG. 3 illustrates a separate determination of the
confidence level for the conversion rate. In one embodiment, a
separate determination step may not be required. Instead, the
calculation of the number of additional offers may serve as an
indication of the confidence level. For example, in the event that
the number of additional offers is greater than zero, then this
serves as an indication that the confidence should be increased to
the predetermined rate (e.g., 20% as discussed above). As another
example, in the event that no additional offers are needed to have
the predetermined confidence in the conversion rate, this indicates
that the confidence level is at least the predetermined rate.
Further, different sequences than the sequence illustrated in FIG.
3 may be implemented. For example, the determination that the
conversion rate is above a predetermined conversion rate threshold
may be made after the calculation of the number of additional
offers.
[0050] FIG. 4 illustrates an expanded flow chart 400 of FIG. 3 in
which the number of additional offers is determined iteratively. As
discussed above, the number of additional offers may be determined.
The additional offers may be presented over one or more periods of
time. For example, in the instance where a period of time is one
day, the additional offers may be presented over a span of several
days. The offers presented in one period of time (such as the first
day) may be used to re-calculate the conversion rate and
re-calculate the number of additional offers to achieve the
predetermined confidence level. Output from 308 is feedback data
from some of the additional offers presented (such as for one
period of time). At 402, it may be determined whether to analyze
the feedback data to update the conversion rate. If so, the
conversion rate may be updated based on the feedback data from the
additional offers. Further, the flow chart 400 may loop back to 304
in order to update the number of additional offers to achieve the
predetermined confidence level. In this regard, a previous period's
additional offers (such as yesterday's additional offers) and the
subsequent purchases may affect the present calculations.
[0051] As discussed above, additional offers may be transmitted
over the course of several time periods, such as over several days.
In this regard, the confidence in the conversion rate may be
increased by issuing the additional offers. Further, the number of
additional offers may be readjusted periodically, such as each day
or after issuance of a predetermined number of additional offers
sent. In addition, the number of additional offers may be
readjusted based on the additional feedback data from the
additional offers transmitted.
[0052] FIG. 5 illustrates a flow chart 500 identifying a promotion
program that has different attributes or different attribute values
with potential but insufficient feedback data, determining a number
of additional offers to improve for the insufficient feedback data
for the different attributes or attribute values, and selecting
which of the different attributes or attribute values to send
additional offers. At 502, a first attribute or attribute value(s)
are selected. As discussed above, one or multiple attributes may
define a promotion program, such as the distance from the promotion
program (derived from consumer attributes and promotion
attributes), ages (e.g., 20-29, 30-39, 40-49, etc.), gender, etc.
Different attributes, such as distance, ages, gender, may be
examined. Also, different combinations of attributes may be
examined, such as location/age, location/gender,
location/age/gender. Further, different values within the examined
attribute (or combinations of attributes) may be examined. For
example, in examining the distance attribute, the different values
of distance, such as 0-2 miles, 2-4 miles, etc. may individually be
examined. In this regard, at 502, a first attribute or an attribute
value is selected.
[0053] Flow chart 500 iterates through determining whether the
selected attribute (or value of the attribute) is above a
predetermined conversion rate threshold at 504, determining whether
the confidence level is below a predetermined conversion rate
threshold at 506, determining a number of additional offers to
achieve a predetermined confidence level at 508, and correlate the
conversion rate/additional number of offers to the selected
attribute or selected attribute value at 510 (for further analysis
at 516).
[0054] At 512, it is determined whether there are additional
attributes or additional attribute values, and if so, the next
attribute or attribute value is selected at 514, and flow chart 500
loops back to 504. In this regard, different values for an
attribute (such as 0-2 miles, 2-4 miles, etc. may be evaluated for
the distance attribute), different values for multiple attributes
(such as 0-2 miles, 2-4 miles, etc. may be evaluated for the
distance attribute; 20-29, 30-39, 40-49, etc. may be evaluated for
the age attribute), different combinations of attributes (such as
0-2 miles/male, 0-2 miles/female, 2-4 miles/male, 2-4 miles/female,
etc. may be evaluated for the distance/gender attributes) may be
evaluated. The different values of attribute(s) and different
combinations of attributes are merely for illustration
purposes.
[0055] If there are no additional attributes or additional
attribute values, the conversion rate/additional number of offers
correlated at 510 may be evaluated. One example of evaluation is
illustrated in FIG. 5 at 516-510.
[0056] At 516, the conversion rate/additional number of offers are
scored for the different attribute(s) or attribute value(s). In
general terms, the evaluation or scoring attempts to determine an
effect in issuing additional offers for the attribute(s) or
attribute value(s). In other words, issuing additional offers may
increase the confidence in the determined conversion rate. In this
regard, the effect of the increase in confidence for the
attribute(s) or the attribute(s) under consideration may be
compared.
[0057] Various factors may be used to determine the effect of the
increase in confidence. Factors include, but are not limited to:
the penalty associated with the lack of confidence; and the number
of consumers affected by the penalty As discussed in more detail
below, a reduced confidence in a conversion rate results in a
penalty associated with the conversion rate. In the example of the
distance attribute of 0-2 miles indicating a conversion rate of
10%, a reduced confidence results in a penalty so that the
penalized or adjusted conversion rate is 2%, as discussed below
with regard to FIG. 6. Thus, the gap in confidence is 8%. The
effect of the 8% gap may be greater quantified by combining the gap
with the pool of users that include the attribute. In the example
of the distance attribute of 0-2 miles, the number of consumers
that meet this attribute (e.g., 100,000 consumers) multiplied by
the gap in confidence may be one example of the measure, or score.
In this regard, a gap of 8% for 100,000 consumers results in 8,000
potential additional conversions. Conversely, if the number of
consumers that meet the attribute are 100 consumers, the gap of 8%
for 100 consumers results in 8 potential additional conversions. In
this manner, the effect of the increase in confidence may be
quantified and compared. Other indications of the effect of the
increase in confidence are contemplated.
[0058] At 518, the scores are ranked. At 520, the top "N" scores
are selected to issue additional offers. In one example, "N"=1, so
that top score is selected. As another example, "N">1.
[0059] Though not illustrated in FIG. 5, once the top "N" scores
are selected, the consumers may be selected to receive the
additional offers. In this regard, the additional offers may be
sent over the course of several time periods. Thus, as discussed in
FIG. 4, the feedback from the additional offers may be used to
reevaluate the confidence level. For example, resulting from the
analysis illustrated in FIG. 5, the system determines that "Q"
additional offers be issued in 0-2 mile distance attribute and "R"
additional offers be issued in the 4-6 mile distance attribute.
Issuing some of the "Q" additional offers for the 0-2 mile distance
attribute results in feedback, which may affect the confidence
level in the conversion rate associated with the 0-2 mile distance
attribute, and in turn the number of additional offers (e.g.,
potentially resulting in a change from the initial decision to
issue "Q" additional offers).
[0060] FIG. 6 illustrates a flow chart 600 for determining an
adjustment to a promotion conversion rate based on confidence in
the performance data used to generate the promotion conversion
rate. In one embodiment, a promotion conversion rate may be
adjusted to penalize the conversion rate for reduced or low
confidence. In this regard, as the feedback data is increased (such
as by issuing additional offers), the confidence in the promotion
conversion rate may increase, thereby decreasing the penalty.
[0061] At 602, a consumer attribute or multiple consumer attributes
are accessed from a consumer profile. At 604, a promotion attribute
or multiple promotion attributes are accessed. At 606, the
historical conversion rate is accessed from the historical
predictive model 204. At 608, the promotion conversion rate is
accessed from the promotion program predictive model 202.
[0062] At 610, the promotion conversion rate is modified based on
the confidence in the data in the promotion program predictive
model. The modification may be performed in one of several ways.
One way is to determine modifying the promotion conversion rate
such that there is a predetermined probability that the actual
conversion rate is greater than the modified promotion conversion
rate. For example, if a promotion has X purchases (e.g.,
conversions) for Y offers, the modified conversion rate s is as
follows:
s=X/Y-c_alpha*X.sup.(0.5)/Y;
[0063] where c_alpha is a constant that depends on alpha; and
[0064] where alpha is the probability that the actual conversion
rate is greater than s.
[0065] Other ways are contemplated to modify the promotion
conversion rate, such as modifying the promotion conversion rate so
that there is an alpha probability that the actual conversion rate
is within a range of (rather than greater than) the modified
promotion conversion rate.
[0066] As discussed in examples above, the conversion rate may be
measured as 10%. However, due to the reduced confidence in the
conversion rate, using the equation listed above, a penalty may be
levied so that the conversion rate may be adjusted downward to 2%,
with the gap of 8% representing a gap in the confidence in the
conversion rate.
[0067] At 612, the historical conversion rate and the modified
promotion conversion rate are combined in order to score the
promotion. The combination of the historical conversion rate with
the modified promotion conversion rate may comprise weighting the
two rates and combining them (such as by multiplying both by 0.5
and adding them together).
[0068] FIG. 7 illustrates a general computer system 700,
programmable to be a specific computer system 700, which can
represent any server, computer or component, such as consumer 1
(124), consumer N (126), merchant 1 (118), merchant M (120), and
promotion program offering system 102. The computer system 700 may
include an ordered listing of a set of instructions 702 that may be
executed to cause the computer system 700 to perform any one or
more of the methods or computer-based functions disclosed herein.
The computer system 700 can operate as a stand-alone device or can
be connected, e.g., using the network 122, to other computer
systems or peripheral devices.
[0069] In a networked deployment, the computer system 700 can
operate in the capacity of a server or as a client-user computer in
a server-client user network environment, or as a peer computer
system in a peer-to-peer (or distributed) network environment. The
computer system 700 may also be implemented as or incorporated into
various devices, such as a personal computer or a mobile computing
device capable of executing a set of instructions 702 that specify
actions to be taken by that machine, including and not limited to,
accessing the Internet or Web through any form of browser. Further,
each of the systems described can include any collection of
sub-systems that individually or jointly execute a set, or multiple
sets, of instructions to perform one or more computer
functions.
[0070] The computer system 700 can include a memory 703 on a bus
710 for communicating information. Code operable to cause the
computer system to perform any of the acts or operations described
herein can be stored in the memory 703. The memory 703 may be a
random-access memory, read-only memory, programmable memory, hard
disk drive or any other type of volatile or non-volatile memory or
storage device.
[0071] The computer system 700 can include a processor 701, such as
a central processing unit (CPU) and/or a graphics processing unit
(GPU). The processor 701 may include one or more general
processors, digital signal processors, application specific
integrated circuits, field programmable gate arrays, digital
circuits, optical circuits, analog circuits, combinations thereof,
or other now known or later-developed devices for analyzing and
processing data. The processor 701 may implement the set of
instructions 702 or other software program, such as manually
programmed or computer-generated code for implementing logical
functions. The logical function or any system element described
can, among other functions, process and convert an analog data
source such as an analog electrical, audio, or video signal, or a
combination thereof, to a digital data source for audio-visual
purposes or other digital processing purposes such as for
compatibility for computer processing.
[0072] The computer system 700 can also include a disk or optical
drive unit 704. The disk drive unit 704 may include a
computer-readable medium 705 in which one or more sets of
instructions 702, e.g., software, may be embedded. Further, the
instructions 702 may perform one or more of the operations as
described herein. The instructions 702 may reside completely, or at
least partially, within the memory 703 or within the processor 701
during execution by the computer system 700. Accordingly, the
databases 110, 112, 114, or 116 may be stored in the memory 703 or
the disk unit 704.
[0073] The memory 703 and the processor 701 also may include
computer-readable media as discussed above. A "computer-readable
medium," "computer-readable storage medium," "machine readable
medium," "propagated-signal medium," or "signal-bearing medium" may
include any device that has, stores, communicates, propagates, or
transports software for use by or in connection with an instruction
executable system, apparatus, or device. The machine-readable
medium may selectively be, but not limited to, an electronic,
magnetic, optical, electromagnetic, infrared, or semiconductor
system, apparatus, device, or propagation medium.
[0074] Additionally, the computer system 700 may include an input
device 707, such as a keyboard or mouse, configured for a user to
interact with any of the components of system 700. It may further
include a display 706, such as a liquid crystal display (LCD), a
cathode ray tube (CRT), or any other display suitable for conveying
information. The display 706 may act as an interface for the user
to see the functioning of the processor 701, or specifically as an
interface with the software stored in the memory 703 or the drive
unit 704.
[0075] The computer system 700 may include a communication
interface 708 that enables communications via the communications
network 122. The network 122 may include wired networks, wireless
networks, or combinations thereof. The communication interface 708
network may enable communications via any number of communication
standards, such as 802.11, 802.17, 802.20, WiMax, 802.15.4,
cellular telephone standards, or other communication standards, as
discussed above. Simply because one of these standards is listed
does not mean any one is preferred.
[0076] Further, the promotion program offering system 102, as
depicted in FIG. 1 may comprise one computer system or multiple
computer systems. Further, the flow diagrams illustrated in FIGS.
3-6 may use computer readable instructions that are executed by one
or more processors in order to implement the functionality
disclosed.
[0077] The present disclosure contemplates a computer-readable
medium that includes instructions or receives and executes
instructions responsive to a propagated signal, so that a device
connected to a network can communicate voice, video, audio, images
or any other data over the network. Further, the instructions can
be transmitted or received over the network via a communication
interface. The communication interface can be a part of the
processor or can be a separate component. The communication
interface can be created in software or can be a physical
connection in hardware. The communication interface can be
configured to connect with a network, external media, the display,
or any other components in system, or combinations thereof. The
connection with the network can be a physical connection, such as a
wired Ethernet connection or can be established wirelessly as
discussed below. In the case of a service provider server, the
service provider server can communicate with users through the
communication interface.
[0078] The computer-readable medium can be a single medium, or the
computer-readable medium can be a single medium or multiple media,
such as a centralized or distributed database, or associated caches
and servers that store one or more sets of instructions. The term
"computer-readable medium" can also include any medium that can be
capable of storing, encoding or carrying a set of instructions for
execution by a processor or that can cause a computer system to
perform any one or more of the methods or operations disclosed
herein.
[0079] The computer-readable medium can include a solid-state
memory such as a memory card or other package that houses one or
more non-volatile read-only memories. The computer-readable medium
also may be a random access memory or other volatile re-writable
memory. Additionally, the computer-readable medium may include a
magneto-optical or optical medium, such as a disk or tapes or other
storage device to capture carrier wave signals such as a signal
communicated over a transmission medium. A digital file attachment
to an email or other self-contained information archive or set of
archives may be considered a distribution medium that may be a
tangible storage medium. The computer-readable medium is preferably
a tangible storage medium. Accordingly, the disclosure may be
considered to include any one or more of a computer-readable medium
or a distribution medium and other equivalents and successor media,
in which data or instructions can be stored.
[0080] Alternatively or in addition, dedicated hardware
implementations, such as application specific integrated circuits,
programmable logic arrays and other hardware devices, may be
constructed to implement one or more of the methods described
herein. Applications that may include the apparatus and systems of
various embodiments may broadly include a variety of electronic and
computer systems. One or more embodiments described herein may
implement functions using two or more specific interconnected
hardware modules or devices with related control and data signals
that may be communicated between and through the modules, or as
portions of an application-specific integrated circuit.
Accordingly, the present system may encompass software, firmware,
and hardware implementations.
[0081] The methods described herein may be implemented by software
programs executable by a computer system. Further, implementations
may include distributed processing, component/object distributed
processing, and parallel processing. Alternatively or in addition,
virtual computer system processing may be constructed to implement
one or more of the methods or functionality as described
herein.
[0082] Although components and functions are described that may be
implemented in particular embodiments with reference to particular
standards and protocols, the components and functions are not
limited to such standards and protocols. For example, standards for
Internet and other packet switched network transmission (e.g.,
TCP/IP, UDP/IP, HTML, and HTTP) represent examples of the state of
the art. Such standards are periodically superseded by faster or
more efficient equivalents having essentially the same functions.
Accordingly, replacement standards and protocols having the same or
similar functions as those disclosed herein are considered
equivalents thereof.
[0083] The illustrations described herein are intended to provide a
general understanding of the structure of various embodiments. The
illustrations are not intended to serve as a complete description
of all of the elements and features of apparatus, processors, and
systems that utilize the structures or methods described herein.
Many other embodiments can be apparent to those of skill in the art
upon reviewing the disclosure. Other embodiments can be utilized
and derived from the disclosure, such that structural and logical
substitutions and changes can be made without departing from the
scope of the disclosure. Additionally, the illustrations are merely
representational and cannot be drawn to scale. Certain proportions
within the illustrations may be exaggerated, while other
proportions may be minimized. Accordingly, the disclosure and the
figures are to be regarded as illustrative rather than
restrictive.
[0084] The above disclosed subject matter is to be considered
illustrative, and not restrictive, and the appended claims are
intended to cover all such modifications, enhancements, and other
embodiments, which fall within the true spirit and scope of the
description. Thus, to the maximum extent allowed by law, the scope
is to be determined by the broadest permissible interpretation of
the following claims and their equivalents, and shall not be
restricted or limited by the foregoing detailed description.
* * * * *