U.S. patent application number 16/183859 was filed with the patent office on 2019-03-21 for deterministic rules protocol to determine a score.
This patent application is currently assigned to American Express Travel Related Services Company, Inc.. The applicant listed for this patent is American Express Travel Related Services Company, Inc.. Invention is credited to ARNAB BOSE, VERNON MARSHALL, HOUMAN MOTAHARIAN, ASHISH KAPATIA SHARMA, SURABHI SINGHAL, CHAO YUAN.
Application Number | 20190087914 16/183859 |
Document ID | / |
Family ID | 58799179 |
Filed Date | 2019-03-21 |
United States Patent
Application |
20190087914 |
Kind Code |
A1 |
MARSHALL; VERNON ; et
al. |
March 21, 2019 |
Deterministic Rules Protocol To Determine A Score
Abstract
Systems and methods of improving the operation of a transaction
network and transaction network devices is disclosed. A SBO
identification network host may comprise various modules and
engines as discussed herein wherein the probability that a
cardholder is a small business owner may be evaluated for
establishing proper usage of differentiated transaction instruments
according to their proper purposes, marketing and cross-marketing
of differentiated transaction instruments, and provision of
value-added services. For instance, a probable SBO may be
identified, whereby the SBO identification network network may
tailor the handling of the transactions, such as by denying them,
whereby the transaction network may actively deter misuse of
transaction products, or tailor the handling of electronically
delivered advertisements, such as by targeting them, whereby the
SBO identification network more properly functions according to
approved parameters.
Inventors: |
MARSHALL; VERNON; (London,
GB) ; BOSE; ARNAB; (Jersey City, NJ) ;
MOTAHARIAN; HOUMAN; (Glen Rock, NJ) ; SHARMA; ASHISH
KAPATIA; (Jersey City, NJ) ; SINGHAL; SURABHI;
(Harrison, NJ) ; YUAN; CHAO; (Montclair,
NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
American Express Travel Related Services Company, Inc. |
New York |
NY |
US |
|
|
Assignee: |
American Express Travel Related
Services Company, Inc.
New York
NY
|
Family ID: |
58799179 |
Appl. No.: |
16/183859 |
Filed: |
November 8, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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14957121 |
Dec 2, 2015 |
10152754 |
|
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16183859 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 40/12 20131203;
G06Q 40/025 20130101 |
International
Class: |
G06Q 40/00 20120101
G06Q040/00; G06Q 40/02 20120101 G06Q040/02 |
Claims
1. A method comprising: creating, by a processor, a consumer group
by identifying consumers within a consumer base that have a
merchant relationship with a transaction account provider;
eliminating, by the processor, a subset of the consumers from the
consumer group that are associated with data elements that are
inconsistent and indicative that a subset of consumers are
simultaneously in a category and not in the category; determining,
by the processor, a score for each of the consumers according to a
deterministic rules protocol using a model directive; and
assigning, by the processor, the score to each of the
consumers.
2. The method of claim 1, further comprising extracting, by the
processor, the consumer base from the data elements, wherein the
data elements are from a data element source set associated with
the consumers, and wherein the data elements are from a node of a
distributed storage system.
3. The method of claim 1, wherein having the merchant relationship
with the transaction account provider includes the consumers having
completed transactions with at least one merchant using a
transaction account associated with the transaction account
provider.
4. The method of claim 1, further comprising computing, by the
processor, the model directive and a deterministic rules directive
for the deterministic rules protocol.
5. The method of claim 1, wherein the score comprises the model
directive in response to a deterministic rules directive for the
deterministic rules protocol not indicating a deterministic
outcome.
6. The method of claim 1, wherein the score comprises a
deterministic rules directive in response to the deterministic
rules directive for the deterministic rules protocol indicating a
deterministic outcome.
7. The method of claim 1, further comprising receiving, by the
processor, data representative of the consumers and representative
of a tag associated with the score.
8. The method of claim 1, further comprising electronically
indicating, by the processor, cross selling opportunities
associated with a tag associated with the score.
9. The method of claim 1, further comprising extracting, by the
processor, the consumer base from the data elements, wherein the
data elements are from a data element source set associated with
the consumers, wherein the data element source set comprises
prospect data comprising at least one of demographics, income,
tradeline history, family status, social media posting, or
employment data of prospective consumers.
10. The method of claim 1, further comprising extracting, by the
processor, the consumer base from the data elements, wherein the
data elements are from a data element source set associated with
the consumers, and wherein the data element source set comprises
click stream data comprising internet browsing history for the
consumers.
11. The method of claim 1, further comprising extracting, by the
processor, the consumer base from the data elements, wherein the
data elements are from a data element source set associated with
the consumers, and wherein the data element source set comprises
email data comprising text mining of email contents of the
consumers.
12. The method of claim 1, further comprising extracting, by the
processor, the consumer base from the data elements, wherein the
data elements are from a data element source set associated with
the consumers, and wherein the data element source set comprises
remittance data comprising banking data comprising at least one of
historical account balance, present account balance, or
transactions of the consumers.
13. The method of claim 1, wherein the score comprises a value
between zero and one indicative of a probability that the consumers
are small business owners.
14. The method of claim 1, further comprising comparing, by the
processor, the score to a scoring threshold.
15. The method of claim 1, further comprising determining, by the
processor, a deterministic rules directive for the deterministic
rules protocol in response to a rule set.
16. The method of claim 1, further comprising determining, by the
processor, a deterministic rules directive for the deterministic
rules protocol in response to a rule set, wherein the rule set
comprises deterministic rules indicative that the consumers are in
the category.
17. The method of claim 1, further comprising determining, by the
processor, a deterministic rules directive for the deterministic
rules protocol in response to a rule set, wherein the rule set
comprises deterministic rules indicative that the consumers are in
the category, and wherein the deterministic rules comprise: the
data elements depicting an active merchant relationship; the data
elements depicting a commercial credit report inquiry; and the data
elements depicting existing credit financials.
18. The method of claim 1, further comprising determining, by the
processor, a deterministic rules directive for the deterministic
rules protocol in response to a rule set, wherein the rule set
comprises deterministic rules indicative that the consumers are in
the category, and wherein the deterministic rules comprise at least
one of: the data elements depicting an active merchant
relationship; the data elements depicting a commercial credit
report inquiry; or the data elements depicting existing credit
financials.
19. An article of manufacture including a non-transitory, tangible
computer readable storage medium having instructions stored thereon
that, in response to execution by a processor, cause the processor
to perform operations comprising: creating, by the processor, a
consumer group by identifying consumers within a consumer base that
have a merchant relationship with a transaction account provider;
eliminating, by the processor, a subset of the consumers from the
consumer group that are associated with data elements that are
inconsistent and indicative that a subset of consumers are
simultaneously in a category and not in the category; determining,
by the processor, a score for each of the consumers according to a
deterministic rules protocol using a model directive; and
assigning, by the processor, the score to each of the
consumers.
20. A system comprising: a processor; and a tangible,
non-transitory memory configured to communicate with the processor,
wherein the tangible, non-transitory memory has instructions stored
thereon that, in response to execution by the processor, cause the
processor to perform operations; creating, by the processor, a
consumer group by identifying consumers within a consumer base that
have a merchant relationship with a transaction account provider;
eliminating, by the processor, a subset of the consumers from the
consumer group that are associated with data elements that are
inconsistent and indicative that a subset of consumers are
simultaneously in a category and not in the category; determining,
by the processor, a score for each of the consumers according to a
deterministic rules protocol using a model directive; and
assigning, by the processor, the score to each of the consumers.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of, claims priority to
and the benefit of, U.S. Ser. No. 14/957,121 filed Dec. 2, 2015,
entitled "SYSTEM AND METHOD FOR SMALL BUSINESS OWNER
IDENTIFICATION," which is hereby incorporated by reference in its
entirety.
FIELD
[0002] The present disclosure relates to data analytics for
transaction data.
BACKGROUND
[0003] Large data sets may exist in various sizes and with various
levels of organization. With big data comprising data sets as large
as ever, the volume of data collected incident to the increased
popularity of online and electronic transactions continues to grow.
Billions of rows and hundreds of thousands of columns worth of data
may populate a single table, for example. An example of the use of
big data is in identifying and categorizing business spending and
consumer spending, which is frequently a key priority for
transaction card issuers. In that regard, transactions processed by
the transaction card issuer are massive in volume and comprise
tremendously large data sets.
[0004] Large data sets may have challenges. For example,
cardholders may frequently hold a business-oriented transaction
card, but various merchants may or may not accept the
business-oriented transaction card. Similarly, cardholders may hold
a consumer-oriented transaction card, but may complete business
transactions using the card. These actions confuse and frustrate
the identification and categorization of transaction data, and
obscure the identity and categorization of real-world entities and
individuals behind transactions, while also hampering data
analytics.
SUMMARY
[0005] In accordance with various embodiments, a SBO identification
network host is disclosed. The SBO identification host may include
a decisioning engine in communication with the processor and
configured to accept data elements from a data element source set
associated with a cardholder from a node of a distributed storage
system and configured to compute a SBO score associated with the
cardholder according to a SBO identification methodology. The SBO
identification host may include a SBO tag decisioner in
communication with the processor and configured to receive a SBO
score from the decisioning engine and configured to assign a SBO
tag to a cardholder.
[0006] In various embodiments, the decisioning engine may include a
SBO model engine configured to compute an SBO model directive and a
deterministic rules engine configured to compute a deterministic
rules directive. The SBO score may include the SBO model directive,
in response to the deterministic rules directive not indicating a
deterministic outcome, and the SBO score may include the
deterministic rules directive, in response to the deterministic
rules directive indicating a deterministic outcome.
[0007] The SBO identification network host may include a SBO tag
receiver configured to receive data representative of the
cardholder and SBO tag and electronically indicate cross selling
opportunities associated with the SBO tag.
[0008] In various embodiments, the electronically indicated
cross-selling opportunities include at least one of transaction
products, value-added services, or financial products. The SBO tag
receiver may include a cross-selling targeting manager configured
to present electronically indicated cross selling opportunities
associated with the SBO tag to an electronic delivery network.
[0009] In various embodiments, the data element source set may
include prospect data including at least one of demographics,
income, tradeline history, family status, social media posting, or
employment data of a prospective cardholder. The data element
source set may include click stream data comprising the cardholder
internet browsing history. The data element source set may include
email data comprising text mining of email contents of the
cardholder. The data element source set may include remittance data
including banking data such as at least one of transaction data of
the cardholder, historical account balance, present account
balance, or transactions of the cardholder.
[0010] In various embodiments, the SBO score includes a value
between zero and one indicative of a probability that the
cardholder is a small business owner. In various embodiments, the
SBO tag decisioner is configured to compare the SBO score to a
scoring threshold and to associate a SBO tag with the cardholder in
response to the comparing. In various embodiments, the SBO tag
includes an affirmative SBO tag in response to the SBO score
exceeding the scoring threshold.
[0011] In various embodiments, the deterministic rules engine is
configured to determine a deterministic rules directive in response
to a SBO rule set. In various embodiments, the deterministic rules
directive includes a binary selection of 1 or 0 with 1 being
indicative of 100% probability that the entity is a SBO and 0 being
indicative of a 0% probability that the entity is a SBO. In various
embodiments, the SBO rule set includes three deterministic rules
indicative that a cardholder is a SBO, the deterministic rules
including the data elements depict an active merchant relationship,
the data elements depict a commercial credit report inquiry, and
the data elements depict existing credit financials.
[0012] The SBO identification network may include a SBO
identification network host configured to categorize a cardholder
according to a SBO identification methodology, wherein the SBO
identification network host directs data to be stored, a
distributed storage system having a plurality of nodes, the
distributed storage system configured to direct data to the SBO
identification network host, in response to the SBO identification
methodology of the SBO identification network host, and a
telecommunications transfer channel including a network logically
connecting the SBO identification network host to the distributed
storage system.
[0013] The SBO identification methodology may include extracting,
by a decisioning engine in communication with a processor and
configured to accept data elements from a data element source set
associated with a cardholder from a node of a distributed storage
system, an entire consumer base from a data element set,
identifying, by the decisioning engine, a consumer within the
entire consumer base associated with a merchant relationship with a
transaction account provider, creating, by the decisioning engine,
a clean database comprising consumers associated with a merchant
relationship with the transaction account provider, and
eliminating, by the decisioning engine, any consumers from the
clean database that are associated with data elements indicative
that the consumer is simultaneously a SBO and a non-SBO, and
determining, by the decisioning engine, a SBO likelihood.
[0014] The forgoing features and elements may be combined in
various combinations without exclusivity, unless expressly
indicated herein otherwise. These features and elements as well as
the operation of the disclosed embodiments will become more
apparent in light of the following description and accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The subject matter of the present disclosure is particularly
pointed out and distinctly claimed in the concluding portion of the
specification. A more complete understanding of the present
disclosure, however, may be obtained by referring to the detailed
description and claims when considered in connection with the
drawing figures, wherein like numerals denote like elements.
[0016] FIG. 1 illustrates an exemplary system for distributed
storage and distributed processing, in accordance with various
embodiments;
[0017] FIG. 2 illustrates an exemplary small business owner (SBO)
identification network host component of a system according to FIG.
1, in accordance with various embodiments;
[0018] FIG. 3 illustrates an exemplary mechanism for SBO
determination by a SBO identification network host component of
FIG. 2, in accordance with various embodiments;
[0019] FIG. 4 illustrates deterministic rules implemented by a
deterministic rules engine as illustrated in FIG. 2, in accordance
with various embodiments; and
[0020] FIG. 5 illustrates model variables implemented by a SBO
model engine as illustrated in FIG. 2, in accordance with various
embodiments; and
[0021] FIG. 6 illustrates an example method of the step of
determining a SBO likelihood as illustrated in FIG. 3, in
accordance with various embodiments.
DETAILED DESCRIPTION
[0022] The detailed description of various embodiments herein makes
reference to the accompanying drawings and pictures, which show
various embodiments by way of illustration. While these various
embodiments are described in sufficient detail to enable those
skilled in the art to practice the disclosure, it should be
understood that other embodiments may be realized and that logical
and mechanical changes may be made without departing from the
spirit and scope of the disclosure. Thus, the detailed description
herein is presented for purposes of illustration only and not of
limitation. For example, the steps recited in any of the method or
process descriptions may be executed in any order and are not
limited to the order presented. Moreover, any of the functions or
steps may be outsourced to or performed by one or more third
parties. Furthermore, any reference to singular includes plural
embodiments, and any reference to more than one component may
include a singular embodiment.
[0023] With reference to FIG. 1, system 100 for distributed data
storage and processing is shown, in accordance with various
embodiments. System 100 may comprise a SBO identification network
host 102. SBO identification network host 102 may comprise any
device capable of receiving and/or processing an electronic message
via telecommunications transfer channel 104. Telecommunications
transfer channel 104 may comprise a network. SBO identification
network host 102 may take the form of a computer or processor, or a
set of computers/processors, although other types of computing
units or systems may be used, including laptops, notebooks, hand
held computers, personal digital assistants, cellular phones, smart
phones (e.g., iPhone.RTM., BlackBerry.RTM., Android.RTM., etc.)
tablets, wearables (e.g., smart watches and smart glasses), or any
other device capable of receiving data over telecommunications
transfer channel 104.
[0024] As used herein, the term "network" includes any cloud, cloud
computing system or electronic communications system or method
which incorporates hardware and/or software components.
Communication among the parties may be accomplished through any
suitable communication channels, such as, for example, a telephone
network, an extranet, an intranet, Internet, point of interaction
device (point of sale device, personal digital assistant (e.g.,
iPhone.RTM., Blackberry.RTM.), cellular phone, kiosk, etc.), online
communications, satellite communications, off-line communications,
wireless communications, transponder communications, local area
network (LAN), wide area network (WAN), virtual private network
(VPN), networked or linked devices, keyboard, mouse and/or any
suitable communication or data input modality. Moreover, although
the system is frequently described herein as being implemented with
TCP/IP communications protocols, the system may also be implemented
using IPX, Appletalk, IP-6, NetBIOS, OSI, any tunneling protocol
(e.g. IPsec, SSH), or any number of existing or future protocols.
If the network is in the nature of a public network, such as the
Internet, it may be advantageous to presume the network to be
insecure and open to eavesdroppers. Specific information related to
the protocols, standards, and application software utilized in
connection with the Internet is generally known to those skilled in
the art and, as such, need not be detailed herein. See, for
example, DILIP NAIK, INTERNET STANDARDS AND PROTOCOLS (1998); JAVA
2 COMPLETE, various authors, (Sybex 1999); DEBORAH RAY AND ERIC
RAY, MASTERING HTML 4.0 (1997); and LOSHIN, TCP/IP CLEARLY
EXPLAINED (1997) and DAVID GOURLEY AND BRIAN TOTTY, HTTP, THE
DEFINITIVE GUIDE (2002), the contents of which are hereby
incorporated by reference.
[0025] A network may be unsecure. Thus, communication over the
network may utilize data encryption. Encryption may be performed by
way of any of the techniques now available in the art or which may
become available--e.g., Twofish, RSA, El Gamal, Schorr signature,
DSA, PGP, PKI, GPG (GnuPG), and symmetric and asymmetric
cryptography systems.
[0026] In various embodiments, SBO identification network host 102
may interact with distributed storage system 106 for storage and/or
processing of big data sets. As used herein, big data may refer to
partially or fully structured, semi-structured, or unstructured
data sets including millions of rows and hundreds of thousands of
columns. A big data set may be compiled, for example, from a
history of purchase transactions over time, from web registrations,
from social media, from records of charge (ROC), from summaries of
charges (SOC), from internal data, or from other suitable sources.
Big data sets may be compiled without descriptive metadata such as
column types, counts, percentiles, or other interpretive-aid data
points.
[0027] In various embodiments, distributed storage system 106 may
comprise one or more nodes 108. Nodes 108 may comprise computers or
processors the same as or similar to SBO identification network
host 102. Nodes 108 may be distributed geographically in different
locations, housed in the same building, and/or housed in the same
rack. Nodes 108 may also be configured to function in concert to
provide storage space and/or processing power greater than one of a
node 108 might provide alone. As a result, distributed storage
system 106 may collect and/or store the data 110. Data 110 may be
collected by nodes 108 individually and compiled or in concert and
collated. Data 110 may further be compiled into a data set and
formatted for use in SBO modeling method 400 of FIG. 3.
[0028] In various embodiments, data 110 may comprise a collection
of data including and/or originating from cardholder information,
transaction information, account information, record of sales,
account history, customer history, sensor data, machine log data,
data storage system, public web data, and/or social media. Data 110
may be collected from multiple sources and amalgamated into a big
data structure such as a file, for example. In that regard, the
data may be used as an input to generate metadata describing the
big data structure itself, as well as the data stored in the
structure. Data 110 may include all or a portion of a data element
source set 210 (FIG. 2) as discussed further herein.
[0029] The distributed storage system 106 may comprise a
transaction network. A SBO identification network host 102 may
comprise various modules and engines as discussed herein wherein
the probability that a transaction is executed by an individual or
entity comprising a small business owner (SBO) may be evaluated for
establishing proper usage of differentiated transaction instruments
according to their proper purposes. For instance, a probable SBO
transaction may be identified as being associated with a
transaction, whereby the transaction network may tailor the
handling of the transaction, such as by denying it, whereby the
transaction network may actively deter misuse of transaction
products not intended for SBOs, and/or such as by allowing it and
or delivering value-added services, such as electronically provided
advertisements and/or offers, and/or other credit and/or lending
products, whereby the transaction network more properly functions
according to approved parameters.
[0030] Moreover, such identifications enhance credit risk
discrimination, identification of businesses and consumers
associated with a business organization who may presently be
consumer cardholders, whereby business-oriented transactions cards
may be provided to them. Such identifications enable the promotion
of relevant merchants to relevant cardholders such as to promote
business-to-business relationship building and/or potential
business-to-business relationships.
[0031] In various embodiments, a SBO determination involves
multiple complex and interactive machine steps. For instance, by
evaluating the data 110 at a transaction level, assessing the
nature of a transaction at the individual transaction level
provides sufficient granularity. Data may be evaluated at the
transaction level and/or aggregated such as to determine if a
cardholder (or supplementary card holder, or a third-party merchant
with whom cardholders or supplementary card holders engage in
transactions, or any other transaction party) may be identified as
a SBO. Moreover, such identification may be combined with or
enhance the identification of such aspects as card product type,
merchant industry codes, transaction amounts, number of
transactions by an individual or a business in an industry (or at a
particular merchant in an industry), determination of gross sales,
removal of noise inducing transactions, and/or controlling for
transactions having similar profiles (such as to facilitate further
data processing).
[0032] Thus, it may be appreciated that in view of the preceding
discussion, SBO determinations may relate to three types of
cardholders: individual card holders, small business cardholders,
and large business cardholders. Each cardholder may be associated
with one or more card type, such as for example, a
consumer-oriented card and/or a business-oriented card. One may
further appreciate that SBO determinations may facilitate the
identification of credit risk, likelihood of fraud (for instance,
consumer-oriented transactions on a business oriented transaction
card indicating fraudulent use or business-oriented transactions on
a consumer oriented transaction card indicating
liquidity/access-to-capital problems and elevated default risk for
the business), identification and classification of
business-to-business transactions and counterparties to the
transactions to facilitate marketing, and tailoring of online ad
experiences. Even furthermore, cardholders having transactions
inconsistent with their card type can be cross-marketed other card
types and credit limits established. SBO determinations may further
leverage text mining on names and addresses to identify SBOs and
machine learning methodologies (e.g., gradient boosting decision
trees) to identify the non-linear patterns of behavior exhibited by
cardholders.
[0033] In various embodiments, and with reference to FIG. 2, a SBO
identification network host 102 is described in more particular
detail. For instance, a SBO identification network host 102 may
comprise various logical modules configured to perform various
operations and processes in accordance with methods disclosed
herein.
[0034] A SBO identification network host 102 may comprise a data
element source set 210. In further embodiments, the SBO
identification network host 102 does not comprise the data element
source set 210. Rather (with reference to FIGS. 1 and 2), the data
110 from nodes 108 of distributed storage system 106 comprises a
data element source set 210 from which data elements 212 are
provided to a SBO identification network host 102. A data element
source set 210 comprises a set of data sources configured to be
received and processed by a decisioning engine 220 of a SBO
identification network host 102 whereby "SBO scores" are
determined.
[0035] A SBO identification network host 102 may comprise a
decisioning engine 220. A decisioning engine 220 may be configured
to receive data elements 212 from a data element source set 210 and
may be configured to compute a SBO score 225 and provide a SBO
score 225 to an SBO tag decisioner 230. The decisioning engine 220
may be configured to compute an SBO score 225 indicative of a
probability that an individual or organization (collectively,
"entity") associated with a transaction is a small-business
owner.
[0036] A SBO identification network host 102 may comprise a SBO tag
decisioner 230. A SBO tag decisioner 230 may be configured to
receive a SBO score 225 indicative of a probability that an entity
associated with a transaction is a small-business owner, and may
prepare a SBO tag in response to the probability. Stated
differently, the SBO tag decisioner 230 may be said to "tag" the
entity with a binary yes or now tag indicating whether that entity
is a small business owner. As such, the SBO tag decisioner 230 may
evaluate the SBO score 225 and interpret the SBO score 225
according to a scoring threshold. For instance, the SBO tag
decisioner 230 may associate an affirmative SBO tag (e.g., "IS
SMALL BUSINESS OWNER" tag) with an entity having an SBO score 225
greater than a scoring threshold. The SBO tag decisioner 230 may
associate a negative SBO tag (e.g., "NOT A SMALL BUSINESS OWNER"
tag) with an entity having an SBO score 225 not greater than a
scoring threshold.
[0037] Finally, A SBO identification network host 102 may comprise
a SBO Tag Receiver 300. In further embodiments, the SBO
identification network host 102 does not comprises a SBO Tag
Receiver 300, but is in communication with a SBO Tag Receiver 300.
An SBO Tag Receiver 300 comprises at least one of a network,
device, and/or human-operable interface configured to receive data
representative of the entity with a SBO tag applied to it and
electronically indicate cross selling opportunities, such as
transaction products, value-added services, and/or financial
products applicable to a SBO and/or a non-SBO, depending on the SBO
score 225
[0038] Directing attention back to the data element source set 210,
a data element source set may comprise a plurality of data element
sources. For instance, any number of data element sources may be
contemplated. For example, a data element source set may comprise a
first data element source 211-1, a second data element source
211-2, a third data element source 211-3, a fourth data element
source 211-4, a fifth data element source 211-5, a sixth data
element source 211-6, a seventh data element source 211-7, and an
eighth data element source 211-8. In various embodiments, the data
element sources may comprise prospect data (e.g., data related to
prospective cardholders such as demographics, income, tradelines,
tradeline history, family status, social media posting, employment,
and/or the like). The data element sources may comprise clickstream
data (e.g., internet browsing history). The data element sources
may comprise SCORE platform data (e.g., card provider internal
data). The data element sources may comprise email data (e.g.,
interactions with the card member, text mining of email contents,
and/or the like). The internal data may comprise data from an
authorization system, for instance, data indicative of card member
spending patterns, card member security questions and/or the like.
The internal data may comprise bank remittance data (e.g., data
provided by banks regarding transaction data of the cardholder,
present and historical account balances, transactions, transaction
timing, bill payment, and/or the like). The internal data may
comprise account holder (e.g., cardmember) data, for example, name,
age, address, billing and payment habits, transaction patterns,
income, tradelines, tradeline history, family status, social media
posting, employment, demographics and/or the like. The internal
data may also comprise account monitoring data (e.g. credit bureau
inquiries).
[0039] Directing attention back to the decisioning engine 220, the
decisioning engine 220 may comprise an SBO model engine 223. An SBO
model engine 223 may perform various complex and interoperable
steps, such as according to a SBO modeling method 400 (See FIG. 3)
wherein the data elements 212 are ingested, and a SBO model
directive 290 generated. A SBO model directive 290 may comprise an
SBO score 225, or in various embodiments, may be superseded by a
deterministic rules directive 550 which comprises a SBO score 225,
as will be discussed further herein. Thus, the SBO model directive
290 may comprise a fraction between 1 and 0, with 1 being
indicative of 100% probability that the entity is a SBO and 0 being
indicative of a 0% probability that the entity is a SBO.
[0040] Directing attention back to the decisioning engine 220, the
decisioning engine 220 may comprise a deterministic rules engine
221. The deterministic rules engine 221 may perform various complex
and interoperable steps, such as according to a deterministic rules
protocol 500 (See FIG. 4) wherein the data elements 212 are
ingested and a deterministic rules directive 550 produced. A
deterministic rules directive 550 comprises a binary selection of 1
or 0 with 1 being indicative of 100% probability that the entity is
a SBO and 0 being indicative of a 0% probability that the entity is
a SBO. The decisioning engine 220 may override any SBO model
directive 290 in the event that a deterministic rules directive 550
comprising a 1 is produced. Thus, the SBO score comprises the SBO
model directive in response to the deterministic rules directive
not indicating a deterministic outcome, and wherein the SBO score
comprises the deterministic rules directive in response to the
deterministic rules directive indicating a deterministic
outcome.
[0041] Directing attention back to the SBO tag receiver 300, the
SBO tag receiver 300 may comprise a SCORE platform 310. A SCORE
platform 310 may comprise a data transfer facility configured to
facilitate transfer of data from Hadoop to LET configured to
receive an SBO score 225 and electronically indicate cross selling
opportunities, such as transaction products, value-added services,
and/or financial products applicable to a SBO and/or a non-SBO,
depending on the SBO score 225.
[0042] Similarly, the SBO tag receiver 300 may comprise a LET
platform 320. A LET platform 320 may comprise an execution system
configured to select eligible consumers for marketing campaigns and
configured to receive an SBO score 225 and electronically indicate
cross selling opportunities, such as transaction products,
value-added services, and/or financial products applicable to a SBO
and/or a non-SBO, depending on the SBO score 225.
[0043] Finally, the SBO tag receiver 300 may comprise a
cross-selling targeting manager 330. The cross-selling targeting
manager may receive electronically indicated cross selling
opportunities from the SCORE platform 310 and the LET platform 320
and transmit electronically indicated cross selling offers
presenting the electronically indicated cross selling opportunities
to an electronic delivery network such as for conveyance to the
entity.
[0044] Having discussed various aspects of a SBO network host 200,
attention is directed to FIG. 3, which depicts a SBO modeling
method 400 performed by a decisioning engine 220 (FIG. 2). Thus,
with reference to both FIGS. 2 and 4, a decisioning engine 220 may
receive data elements 212 comprising an entire consumer base
encompassed in the data element source set 210. The decisioning
engine 220 may extract the entire consumer base from the data
elements (Step 401). The decisioning engine 220 may identify
consumers within the consumer base that have merchant relationships
with the transaction account provider (e.g., are indicated to be
merchants equipped to accept transaction cards from the transaction
account provider and/or are indicated to have completed
transactions with at least one merchant utilizing a card of the
transaction account provider), thus being said to have a "merchant
relationship with the transaction account provider" (Step 403).
Thus, the decisioning engine 220 may create a clean database
comprising only those consumers associated with a merchant
relationship with a transaction account provider (Step 405). The
decisioning engine 220 may eliminate any consumer associated with
data elements from the data element set that indicate the customer
simultaneously appears to be both a SBO and a non-SBO (e.g., with
reference to FIG. 5, the data elements 212 comprise variables 213
that are inconsistent and/or contradictory) (Step 407). After
determining that the consumer does not share both SBO and non-SBO
traits, the decisioning engine may then determine a SBO likelihood
(Step 409A, 409B). The determination of an SBO likelihood may
comprise a determination according to the deterministic rules
protocol 500 (See FIG. 4) wherein the data elements 212 are
ingested and a deterministic rules directive 550 produced (e.g.,
Step 409B). The determination of an SBO likelihood may comprise a
determination according to an SBO model directive 290 comprising an
SBO score 225 (See FIGS. 2 and 5) (e.g., Step 409A).
[0045] For instance, and with additional reference to FIGS. 5 and
6, the step of determining a SBO likelihood 409A may include
creating dependent variables (Step 601). For example, data elements
212 may be ingested comprising variables 213. For instance, first
variable 213-1 through thirteenth variable 213-13 may be ingested.
The variables may comprise whether the consumer exists in a
commercial credit bureau. The variables may comprise a magnitude of
total business spending. The variables may comprise a percentage of
payments made through company checks. The variables may comprise a
costumer's "Prob-B" determination. For instance, various aspects of
co-owned pending patent application Ser. No. 14/954,430 (Docket No.
11655.14600/201511971), entitled "SYSTEM AND METHOD FOR DATA
ANALYTICS," and filed on Nov. 30, 2015, such as a so-called
"Prob-B" determination may be considered, such disclosure is
incorporated by reference herein in its entirety for all purposes.
The variables may comprise a count of commercial credit bureau hits
(e.g., D&B, Infogroup, Equifax, and/or the like). The variables
may comprise the number of active supplementary relationships
(e.g., association with transaction products of others) of the
consumer. The variables may include a unique email domain
indicator, such as may be held by small businesses. The variables
may include the number of commercial credit inquiries against the
consumers credit report, such as via Experian. The variables may
include the number of webpages visited by the consumer that are
affiliated with the transaction account provider and are business
(rather than consumer) oriented. The variables may include the
number of card members and/or small business owners living in the
customer's neighborhood. Moreover, the variables may further
comprise a third highest Prob-B score, second highest Prob-B score,
and first highest Prob-B score of the card holder, for instance, a
first second and third highest probability that a transaction is
for a business purpose. Moreover, the variables may comprise text
mining of data elements 212 to identify potential SBOs such as by
identifying titles (e.g., "owner," "CEO," "Founder," "Principal,"
and/or the like
[0046] Furthermore, the variables 213 may be ascribed differing
importance as indicated in FIG. 5, so that a model is created (Step
603). For instance, variables 213 may be ascribed differing weight
(e.g., importance) on a scale of 0 to 100, with 100 being
determinative. Moreover, the model may be scored (Step 605). In
other words, thus, the relatively more important variables may be
ascribed greater weight and an average may be computed. This
average may be divided by 100 to create a SBO model directive 290
comprising a number between zero and one.
[0047] With attention to FIG. 2 and FIG. 4, the step of determining
a SBO likelihood 409B may include that the deterministic rules
engine 221 may calculate a deterministic rules directive 550, which
as mentioned, may override the SBO model directive 290. Thus, in
various embodiments, for instance, a deterministic rules engine 221
may execute a deterministic rules protocol 500, wherein any
cardholder for whom the data elements 212 depict one or more of
three scenarios as determinatively an SBO, so that a deterministic
rules directive 550 (comprising an indication that the cardholder
is determinatively an SBO) is generated. For instance, the three
scenarios comprise wherein the data elements 212 include an active
merchant relationship 501 (e.g., the cardholder is a registered
merchant configured to receive payments via a transaction
instrument of the transaction account provider), or for whom the
data elements 212 depict a commercial credit report inquiry (e.g.,
a business trade line and/or credit reporting inquiry seeking a
business trade line) 503 as extant, or for whom cruse financials
(e.g., financial statements submitted by a customer) 505 exist.
[0048] Data, as discussed herein, may include "internal data."
Internal data may include any data a credit issuer possesses or
acquires pertaining to a particular consumer. Internal data may be
gathered before, during, or after a relationship between the credit
issuer and the transaction account holder (e.g., the consumer or
buyer). Such data may include consumer demographic data. Consumer
demographic data includes any data pertaining to a consumer.
Consumer demographic data may include consumer name, address,
telephone number, email address, employer and social security
number. Consumer transactional data is any data pertaining to the
particular transactions in which a consumer engages during any
given time period. Consumer transactional data may include, for
example, transaction amount, transaction time, transaction
vendor/merchant, and transaction vendor/merchant location.
Transaction vendor/merchant location may contain a high degree of
specificity to a vendor/merchant. For example, transaction
vendor/merchant location may include a particular gasoline filing
station in a particular postal code located at a particular cross
section or address. Also, for example, transaction vendor/merchant
location may include a particular web address, such as a Uniform
Resource Locator ("URL"), an email address and/or an Internet
Protocol ("IP") address for a vendor/merchant. Transaction
vendor/merchant and transaction vendor/merchant location may be
associated with a particular consumer and further associated with
sets of consumers. Consumer payment data includes any data
pertaining to a consumer's history of paying debt obligations.
Consumer payment data may include consumer payment dates, payment
amounts, balance amount, and credit limit. Internal data may
further comprise records of consumer service calls, complaints,
requests for credit line increases, questions, and comments. A
record of a consumer service call includes, for example, date of
call, reason for call, and any transcript or summary of the actual
call.
[0049] Any communication, transmission and/or channel discussed
herein may include any system or method for delivering content
(e.g. data, information, metadata, etc.), and/or the content
itself. The content may be presented in any form or medium, and in
various embodiments, the content may be delivered electronically
and/or capable of being presented electronically. For example, a
channel may comprise a website or device (e.g., Facebook,
YouTube.RTM., AppleTV.RTM., Pandora.RTM., xBox.RTM., Sony.RTM.
Playstation.RTM.), a uniform resource locator ("URL"), a document
(e.g., a Microsoft Word.RTM. document, a Microsoft Excel.RTM.
document, an Adobe .pdf document, etc.), an "ebook," an
"emagazine," an application or microapplication (as described
herein), an SMS or other type of text message, an email, Facebook,
twitter, MMS and/or other type of communication technology. In
various embodiments, a channel may be hosted or provided by a data
partner. In various embodiments, the distribution channel may
comprise at least one of a merchant website, a social media
website, affiliate or partner websites, an external vendor, a
mobile device communication, social media network and/or location
based service. Distribution channels may include at least one of a
merchant website, a social media site, affiliate or partner
websites, an external vendor, and/or a mobile device communication.
Examples of social media sites include Facebook.RTM.,
Foursquare.RTM., Twitter.RTM., MySpace.RTM., LinkedIn.RTM., and the
like. Examples of affiliate or partner websites include American
Express.RTM., Groupon.RTM., LivingSocial.RTM., and the like.
Moreover, examples of mobile device communications include texting,
email, and mobile applications for smartphones.
[0050] A "consumer profile," "customer data," or "consumer profile
data" may comprise any information or data about a consumer that
describes an attribute associated with the consumer (e.g., a
preference, an interest, demographic information, personally
identifying information, and the like).
[0051] In various embodiments, the methods described herein are
implemented using the various particular machines described herein.
The methods described herein may be implemented using the below
particular machines, and those hereinafter developed, in any
suitable combination, as would be appreciated immediately by one
skilled in the art. Further, as is unambiguous from this
disclosure, the methods described herein may result in various
transformations of certain articles.
[0052] For the sake of brevity, conventional data networking,
application development and other functional aspects of the systems
(and components of the individual operating components of the
systems) may not be described in detail herein. Furthermore, the
connecting lines shown in the various figures contained herein are
intended to represent exemplary functional relationships and/or
physical couplings between the various elements. It should be noted
that many alternative or additional functional relationships or
physical connections may be present in a practical system.
[0053] The various system components discussed herein may include
one or more of the following: a host server or other computing
systems including a processor for processing digital data; a memory
coupled to the processor for storing digital data; an input
digitizer coupled to the processor for inputting digital data; an
application program stored in the memory and accessible by the
processor for directing processing of digital data by the
processor; a display device coupled to the processor and memory for
displaying information derived from digital data processed by the
processor; and a plurality of databases. Various databases used
herein may include: client data; merchant data; financial
institution data; and/or like data useful in the operation of the
system. As those skilled in the art will appreciate, user computer
may include an operating system (e.g., Windows NT.RTM., Windows
95/98/2000.RTM., Windows XP.RTM., Windows Vista.RTM., Windows
7.RTM., OS2, UNIX.RTM., Linux.RTM., Solaris.RTM., MacOS, etc.) as
well as various conventional support software and drivers typically
associated with computers.
[0054] The present system or any part(s) or function(s) thereof may
be implemented using hardware, software or a combination thereof
and may be implemented in one or more computer systems or other
processing systems. However, the manipulations performed by
embodiments were often referred to in terms, such as matching or
selecting, which are commonly associated with mental operations
performed by a human operator. No such capability of a human
operator is necessary, or desirable in most cases, in any of the
operations described herein. Rather, the operations may be machine
operations. Useful machines for performing the various embodiments
include general purpose digital computers or similar devices.
[0055] In fact, in various embodiments, the embodiments are
directed toward one or more computer systems capable of carrying
out the functionality described herein. The computer system
includes one or more processors, such as processor. The processor
is connected to a communication infrastructure (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 skilled in the relevant art(s) how to implement various
embodiments using other computer systems and/or architectures.
Computer system can include a display interface that forwards
graphics, text, and other data from the communication
infrastructure (or from a frame buffer not shown) for display on a
display unit.
[0056] Computer system also includes a main memory, such as for
example random access memory (RAM), and may also include a
secondary memory. The secondary memory may include, for example, a
hard disk drive 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 well-known manner. 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.
[0057] In various embodiments, secondary memory may include other
similar devices for allowing computer programs or other
instructions to be loaded into computer system. Such devices 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 (such as an erasable programmable read only memory
(EPROM), or programmable read only memory (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 computer system.
[0058] Computer system may also include a communications interface.
Communications interface allows software and data to be transferred
between computer system and external devices. Examples of
communications interface may include a modem, a network interface
(such as an Ethernet card), a communications port, a Personal
Computer Memory Card International Association (PCMCIA) slot and
card, etc. Software and data transferred via communications
interface are in the form of signals which may be electronic,
electromagnetic, and optical or other signals capable of being
received by communications interface. These signals are provided to
communications interface via a communications path (e.g., channel).
This channel carries signals and may be implemented using wire,
cable, fiber optics, a telephone line, a cellular link, a radio
frequency (RF) link, wireless and other communications
channels.
[0059] The terms "computer program medium" and "computer usable
medium" and "computer readable medium" are used to generally refer
to media such as removable storage drive and a hard disk installed
in hard disk drive. These computer program products provide
software to computer system.
[0060] Computer programs (also referred to as computer control
logic) are stored in main memory and/or secondary memory. Computer
programs may also be received via communications interface. Such
computer programs, when executed, enable the computer system to
perform the features as discussed herein. In particular, the
computer programs, when executed, enable the processor to perform
the features of various embodiments. Accordingly, such computer
programs represent controllers of the computer system.
[0061] In various embodiments, software may be stored in a computer
program product and loaded into computer system using removable
storage drive, hard disk drive or communications interface. The
control logic (software), when executed by the processor, causes
the processor to perform the functions of various embodiments as
described herein. In various embodiments, hardware components such
as application specific integrated circuits (ASICs). Implementation
of the hardware to perform the functions described herein will be
apparent to persons skilled in the relevant art(s).
[0062] The various system components may be independently,
separately or collectively suitably coupled to the network via data
links which includes, for example, a connection to an Internet
Service Provider (ISP) over the local loop as is typically used in
connection with standard modem communication, cable modem, Dish
Networks.RTM., ISDN, Digital Subscriber Line (DSL), or various
wireless communication methods, see, e.g., GILBERT HELD,
UNDERSTANDING DATA COMMUNICATIONS (1996), which is hereby
incorporated by reference. It is noted that the network may be
implemented as other types of networks, such as an interactive
television (ITV) network. Moreover, the system contemplates the
use, sale or distribution of any goods, services or information
over any network having similar functionality described herein.
[0063] "Cloud" or "Cloud computing" includes a model for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, servers, storage,
applications, and services) that can be rapidly provisioned and
released with minimal management effort or service provider
interaction. Cloud computing may include location-independent
computing, whereby shared servers provide resources, software, and
data to computers and other devices on demand. For more information
regarding cloud computing, see the NIST's (National Institute of
Standards and Technology) definition of cloud computing at
http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf
(last visited June 2012), which is hereby incorporated by reference
in its entirety.
[0064] As used herein, "transmit" may include sending electronic
data from one system component to another over a network
connection. Additionally, as used herein, "data" may include
encompassing information such as commands, queries, files, data for
storage, and the like in digital or any other form.
[0065] The computers discussed herein may provide a suitable
website or other Internet-based graphical user interface which is
accessible by users. In one embodiment, the Microsoft Internet
Information Server (IIS), Microsoft Transaction Server (MTS), and
Microsoft SQL Server, are used in conjunction with the Microsoft
operating system, Microsoft NT web server software, a Microsoft SQL
Server database system, and a Microsoft Commerce Server.
Additionally, components such as Access or Microsoft SQL Server,
Oracle, Sybase, Informix MySQL, Interbase, etc., may be used to
provide an Active Data Object (ADO) compliant database management
system. In one embodiment, the Apache web server is used in
conjunction with a Linux operating system, a MySQL database, and
the Perl, PHP, and/or Python programming languages.
[0066] Any of the communications, inputs, storage, databases or
displays discussed herein may be facilitated through a website
having web pages. The term "web page" as it is used herein is not
meant to limit the type of documents and applications that might be
used to interact with the user. For example, a typical website
might include, in addition to standard HTML documents, various
forms, Java applets, JavaScript, active server pages (ASP), common
gateway interface scripts (CGI), extensible markup language (XML),
dynamic HTML, cascading style sheets (CSS), AJAX (Asynchronous
Javascript And XML), helper applications, plug-ins, and the like. A
server may include a web service that receives a request from a web
server, the request including a URL
(http://yahoo.com/stockquotes/ge) and an IP address
(123.56.789.234). The web server retrieves the appropriate web
pages and sends the data or applications for the web pages to the
IP address. Web services are applications that are capable of
interacting with other applications over a communications means,
such as the internet. Web services are typically based on standards
or protocols such as XML, SOAP, AJAX, WSDL and UDDI. Web services
methods are well known in the art, and are covered in many standard
texts. See, e.g., ALEX NGHIEM, IT WEB SERVICES: A ROADMAP FOR THE
ENTERPRISE (2003), hereby incorporated by reference.
[0067] Practitioners will also appreciate that there are a number
of methods for displaying data within a browser-based document.
Data may be represented as standard text or within a fixed list,
scrollable list, drop-down list, editable text field, fixed text
field, pop-up window, and the like. Likewise, there are a number of
methods available for modifying data in a web page such as, for
example, free text entry using a keyboard, selection of menu items,
check boxes, option boxes, and the like.
[0068] The system and method may be described herein in terms of
functional block components, screen shots, optional selections and
various processing steps. It should be appreciated that such
functional blocks may be realized by any number of hardware and/or
software components configured to perform the specified functions.
For example, the system may employ various integrated circuit
components, e.g., memory elements, processing elements, logic
elements, look-up tables, and the like, which may carry out a
variety of functions under the control of one or more
microprocessors or other control devices. Similarly, the software
elements of the system may be implemented with any programming or
scripting language such as C, C++, C#, Java, JavaScript, VBScript,
Macromedia Cold Fusion, COBOL, Microsoft Active Server Pages,
assembly, PERL, PHP, awk, Python, Visual Basic, SQL Stored
Procedures, PL/SQL, any UNIX shell script, and extensible markup
language (XML) with the various algorithms being implemented with
any combination of data structures, objects, processes, routines or
other programming elements. Further, it should be noted that the
system may employ any number of conventional techniques for data
transmission, signaling, data processing, network control, and the
like. Still further, the system could be used to detect or prevent
security issues with a client-side scripting language, such as
JavaScript, VBScript or the like. For a basic introduction of
cryptography and network security, see any of the following
references: (1) "Applied Cryptography: Protocols, Algorithms, And
Source Code In C," by Bruce Schneier, published by John Wiley &
Sons (second edition, 1995); (2) "Java Cryptography" by Jonathan
Knudson, published by O'Reilly & Associates (1998); (3)
"Cryptography & Network Security: Principles & Practice" by
William Stallings, published by Prentice Hall; all of which are
hereby incorporated by reference.
[0069] As will be appreciated by one of ordinary skill in the art,
the system may be embodied as a customization of an existing
system, an add-on product, a processing apparatus executing
upgraded software, a standalone system, a distributed system, a
method, a data processing system, a device for data processing,
and/or a computer program product. Accordingly, any portion of the
system or a module may take the form of a processing apparatus
executing code, an internet based embodiment, an entirely hardware
embodiment, or an embodiment combining aspects of the internet,
software and hardware. Furthermore, the system may take the form of
a computer program product on a computer-readable storage medium
having computer-readable program code means embodied in the storage
medium. Any suitable computer-readable storage medium may be
utilized, including hard disks, CD-ROM, optical storage devices,
magnetic storage devices, and/or the like.
[0070] The system and method is described herein with reference to
screen shots, block diagrams and flowchart illustrations of
methods, apparatus (e.g., systems), and computer program products
according to various embodiments. It will be understood that each
functional block of the block diagrams and the flowchart
illustrations, and combinations of functional blocks in the block
diagrams and flowchart illustrations, respectively, can be
implemented by computer program instructions.
[0071] These computer program instructions may be loaded onto a
general purpose computer, special purpose computer, or other
programmable data processing apparatus to produce a machine, such
that the instructions that execute on the computer or other
programmable data processing apparatus create means for
implementing the functions specified in the flowchart block or
blocks. These computer program instructions may also be stored in a
computer-readable memory that can direct a computer or other
programmable data processing apparatus to function in a particular
manner, such that the instructions stored in the computer-readable
memory produce an article of manufacture including instruction
means which implement the function specified in the flowchart block
or blocks. The computer program instructions may also be loaded
onto a computer or other programmable data processing apparatus to
cause a series of operational steps to be performed on the computer
or other programmable apparatus to produce a computer-implemented
process such that the instructions which execute on the computer or
other programmable apparatus provide steps for implementing the
functions specified in the flowchart block or blocks.
[0072] Accordingly, functional blocks of the block diagrams and
flowchart illustrations support combinations of means for
performing the specified functions, combinations of steps for
performing the specified functions, and program instruction means
for performing the specified functions. It will also be understood
that each functional block of the block diagrams and flowchart
illustrations, and combinations of functional blocks in the block
diagrams and flowchart illustrations, can be implemented by either
special purpose hardware-based computer systems which perform the
specified functions or steps, or suitable combinations of special
purpose hardware and computer instructions. Further, illustrations
of the process flows and the descriptions thereof may make
reference to user windows, webpages, websites, web forms, prompts,
etc. Practitioners will appreciate that the illustrated steps
described herein may comprise in any number of configurations
including the use of windows, webpages, web forms, popup windows,
prompts and the like. It should be further appreciated that the
multiple steps as illustrated and described may be combined into
single webpages and/or windows but have been expanded for the sake
of simplicity. In other cases, steps illustrated and described as
single process steps may be separated into multiple webpages and/or
windows but have been combined for simplicity.
[0073] The term "non-transitory" is to be understood to remove only
propagating transitory signals per se from the claim scope and does
not relinquish rights to all standard computer-readable media that
are not only propagating transitory signals per se. Stated another
way, the meaning of the term "non-transitory computer-readable
medium" and "non-transitory computer-readable storage medium"
should be construed to exclude only those types of transitory
computer-readable media which were found in In Re Nuijten to fall
outside the scope of patentable subject matter under 35 U.S.C.
.sctn. 101.
[0074] Systems, methods and computer program products are provided.
In the detailed description herein, references to "various
embodiments", "one embodiment", "an embodiment", "an example
embodiment", etc., indicate that the embodiment described may
include a particular feature, structure, or characteristic, but
every embodiment may not necessarily include the particular
feature, structure, or characteristic. Moreover, such phrases are
not necessarily referring to the same embodiment. Further, when a
particular feature, structure, or characteristic is described in
connection with an embodiment, it is submitted that it is within
the knowledge of one skilled in the art to affect such feature,
structure, or characteristic in connection with other embodiments
whether or not explicitly described. After reading the description,
it will be apparent to one skilled in the relevant art(s) how to
implement the disclosure in alternative embodiments.
[0075] Benefits, other advantages, and solutions to problems have
been described herein with regard to specific embodiments. However,
the benefits, advantages, solutions to problems, and any elements
that may cause any benefit, advantage, or solution to occur or
become more pronounced are not to be construed as critical,
required, or essential features or elements of the disclosure. The
scope of the disclosure is accordingly to be limited by nothing
other than the appended claims, in which reference to an element in
the singular is not intended to mean "one and only one" unless
explicitly so stated, but rather "one or more." Moreover, where a
phrase similar to `at least one of A, B, and C` or `at least one of
A, B, or C` is used in the claims or specification, it is intended
that the phrase be interpreted to mean that A alone may be present
in an embodiment, B alone may be present in an embodiment, C alone
may be present in an embodiment, or that any combination of the
elements A, B and C may be present in a single embodiment; for
example, A and B, A and C, B and C, or A and B and C. Although the
disclosure includes a method, it is contemplated that it may be
embodied as computer program instructions on a tangible
computer-readable carrier, such as a magnetic or optical memory or
a magnetic or optical disk. All structural, chemical, and
functional equivalents to the elements of the above-described
exemplary embodiments that are known to those of ordinary skill in
the art are expressly incorporated herein by reference and are
intended to be encompassed by the present claims. Moreover, it is
not necessary for a device or method to address each and every
problem sought to be solved by the present disclosure, for it to be
encompassed by the present claims.
[0076] Furthermore, no element, component, or method step in the
present disclosure is intended to be dedicated to the public
regardless of whether the element, component, or method step is
explicitly recited in the claims. No claim element herein is to be
construed under the provisions of 35 U.S.C. 112 (f) unless the
element is expressly recited using the phrase "means for." As used
herein, the terms "comprises", "comprising", or any other variation
thereof, are intended to cover a non-exclusive inclusion, such that
a process, method, article, or apparatus that comprises a list of
elements does not include only those elements but may include other
elements not expressly listed or inherent to such process, method,
article, or apparatus.
* * * * *
References