U.S. patent application number 14/805143 was filed with the patent office on 2015-11-12 for instrument-based trigger events architecture.
The applicant listed for this patent is MasterCard Mobile Transactions Solutions, Inc.. Invention is credited to Mehul Desai, Nehal Maniar.
Application Number | 20150324786 14/805143 |
Document ID | / |
Family ID | 52691858 |
Filed Date | 2015-11-12 |
United States Patent
Application |
20150324786 |
Kind Code |
A1 |
Desai; Mehul ; et
al. |
November 12, 2015 |
INSTRUMENT-BASED TRIGGER EVENTS ARCHITECTURE
Abstract
A method is disclosed herein in accordance with an embodiment of
the present invention. The method may include deriving at a mobile
transaction platform a multi-dimensional context from one or more
user transactions and determining at least one life occurrence
based, at least in part, on the multi-dimensional context. The one
or more user transactions may be conducted through the mobile
transaction platform. The one or more user transactions may be
stored on a third-party source. In an aspect, the at least one life
occurrence has yet to occur. In another aspect of the invention,
the life occurrence has already occurred. The multi-dimensional
context may include at least one of user location information and
life occurrence location information. The multi-dimensional context
may include at least one of a time of life occurrence and a current
time.
Inventors: |
Desai; Mehul; (Oak Brook,
IL) ; Maniar; Nehal; (Oak Brook, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MasterCard Mobile Transactions Solutions, Inc. |
Purchase |
NY |
US |
|
|
Family ID: |
52691858 |
Appl. No.: |
14/805143 |
Filed: |
July 21, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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14317896 |
Jun 27, 2014 |
|
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14805143 |
|
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61841019 |
Jun 28, 2013 |
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Current U.S.
Class: |
705/41 |
Current CPC
Class: |
G06Q 30/0207 20130101;
G06Q 20/36 20130101; G06Q 30/0261 20130101; G06Q 20/386 20200501;
G06Q 30/0613 20130101; G06N 7/02 20130101; G06Q 20/308 20200501;
G06N 20/00 20190101; G06Q 30/0631 20130101; G06Q 20/322 20130101;
G06Q 20/382 20130101; G06N 5/046 20130101; G06Q 20/384 20200501;
G06Q 30/0255 20130101; G06N 3/0436 20130101 |
International
Class: |
G06Q 20/36 20060101
G06Q020/36; G06N 7/02 20060101 G06N007/02; G06Q 20/32 20060101
G06Q020/32 |
Claims
1. A non-transitory computer readable medium with an executable
program stored thereon, wherein the program instructs a
microprocessor to perform the following steps of an
instrument-based method of potential life occurrence alert,
comprising: determining possible resolution actions to take in
advance of a user's potential life occurrence based, at least in
part, on a multidimensional context derived from analysis of the
user's transactions performed with a mobile device via a mobile
transaction platform server and third-party sourced data related to
the user; determining trigger-event context, based on metadata that
describes aspects of the potential life occurrence, of
trigger-event conditions for each resolution action; monitoring
trigger-event context; when trigger-event conditions are met,
presenting in an electronic display of the user's mobile device
resolution actions that include life occurrence context that is
relevant to the user making a decision about accepting the
resolution action; preparing a digital instrument to facilitate
executing at least one of an action and a transaction for each of
the presented resolution actions; and adapting the digital
instrument to include metadata that identifies a transaction type
accessible by a server and user information required to use the
digital instrument when a resolution action is accepted by the
user.
3. The computer readable medium of claim 1, wherein the action is a
mobile device action.
4. The computer readable medium of claim 1, wherein preparing the
instrument comprises configuring the user's mobile device to access
an ecosystem service provider, an electronic wallet on the user's
mobile device, a secure element of the mobile device.
5. The computer readable medium of claim 1, wherein preparing the
instrument comprises configuring the user's mobile device to follow
user preferences for form of payment, receipt handling, and
delivery/contact details to facilitate service delivery that
effects the action/transaction without requiring user input for at
least one of the presented resolution actions.
6. The computer readable medium of claim 1, wherein the life
occurrence node is a mobile device.
7. The computer readable medium of claim 1, wherein the
trigger-event context sources comprise at least one of a GPS, a
clock, a calendar, an alert, an e-mail, a message, a call, and a
bookmark.
8. The computer readable medium of claim 1, wherein the life
occurrence container comprises at least one electronic wallet,
resolution action, context monitor, trigger event detector, and an
enabling layer.
9. The computer readable medium of claim 1, wherein the
trigger-event context sources comprise a time, a location, and at
least one of a transaction detail, an urgency, an importance, the
status of a credit card or account, mobile device use history,
payment source, wallet state, type of transaction, product/service,
vendor, delivery method, delivery arrangements, tax status,
transaction participant, user preferences, the presence of a
network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information.
10. A method comprising: determining possible resolution actions to
take in advance of a user's potential life occurrence based, at
least in part, on a multidimensional context derived from analysis
of the user's transactions performed with a mobile device
registered to the user via a mobile transaction platform server and
third-party sourced data related to the user; determining
trigger-event context, based on metadata that describes aspects of
the potential life occurrence, of trigger-event conditions for each
resolution action; monitoring trigger-event context; when
trigger-event conditions are met, presenting in an electronic
display of the user's mobile device resolution actions that include
life occurrence context that is relevant to the user making a
decision about accepting the resolution action; preparing a digital
instrument to facilitate executing at least one of an action and a
transaction for each of the presented resolution actions; and
adapting the digital instrument to include metadata that identifies
a transaction type accessible by a server and user information
required to use the digital instrument when a resolution action is
accepted by the user.
11. The method of claim 10, wherein the action is a mobile device
action.
12. The method of claim 10, wherein preparing the instrument
comprises configuring the user's mobile device to access an
ecosystem service provider, an electronic wallet on the user's
mobile device, a secure element of the mobile device.
13. The method of claim 10, wherein preparing the instrument
comprises configuring the user's mobile device to follow user
preferences for form of payment, receipt handling, and
delivery/contact details to facilitate service delivery that
effects the action/transaction without requiring user input for at
least one of the presented resolution actions.
14. The method of claim 10, wherein the life occurrence node is a
mobile device.
15. The method of claim 10, wherein the trigger-event context
sources comprise at least one of a GPS, a clock, a calendar, an
alert, an e-mail, a message, a call, and a bookmark.
16. The method of claim 10, wherein the life occurrence container
comprises at least one electronic wallet, resolution action,
context monitor, trigger event detector, and an enabling layer.
17. The method of claim 10, wherein the trigger-event context
sources comprise a time, a location, and at least one of a
transaction detail, an urgency, an importance, the status of a
credit card or account, mobile device use history, payment source,
wallet state, type of transaction, product/service, vendor,
delivery method, delivery arrangements, tax status, transaction
participant, user preferences, the presence of a network or a
particular account, user associations with a non-vendor
third-party, presence of vouchers and promotions, loyalty points,
third-party user-related data, social network information, and
calendar information.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to and is a continuation of
U.S. patent application Ser. No. 14/317,896 filed Jun. 27, 2014,
the entirety of which is incorporated herein by reference. U.S.
Ser. No. 14/317,896 claims the benefit of U.S. provisional
application Ser. No. 61/841,019 filed Jun. 28, 2013, the entirety
of which is incorporated herein by reference.
[0002] This application is related to the following co-owned U.S.
patent applications, the entirety of each is incorporated herein by
reference: Ser. No. 13/909,262 filed on Jun. 4, 2013; Ser. No.
11/539,024 filed on Oct. 5, 2006; and U.S. Ser. No. 10/284,676
filed on Oct. 31, 2002 now patented as U.S. Pat. No. 8,527,380.
[0003] This application is also related to the following co-owned
U.S. patent applications, the entirety of each is incorporated
herein by reference: U.S. Ser. No. 13/622,433 filed on Sep. 19,
2012; U.S. Ser. No. 13/622,462 filed on Sep. 19, 2012; and U.S.
Ser. No. 13/622,815 filed on Sep. 19, 2012.
[0004] Each patent, patent application and other document
referenced herein is hereby incorporated by reference in its
entirety.
BACKGROUND
[0005] 1. Field
[0006] This application relates to methods and systems of
electronic transactions and particularly relates to mobile secure
electronic transactions.
[0007] 2. Description of the Related Art
[0008] As the use of mobile devices for performing a wide range of
user-specific transactions, including healthcare, shopping,
financial, personal, business, and the like continues to rise, the
burden of managing most aspects of such transactions falls on the
mobile user, thereby increasing complexity of a mobile experience
for most users. However, the plethora of information available
through these transactions and other sources of user-related data
makes it possible to substantially ease the mobile experience. Yet
no integrated solution has been established that facilitates a
truly user-centric experience with the aim of fully integrating a
user's mobile experience with his/her lifestyle.
SUMMARY
[0009] A method is disclosed that may include deriving at a mobile
transaction platform a multi-dimensional context from one or more
user transactions and determining at least one life occurrence
based, at least in part, on the multi-dimensional context. The one
or more user transactions may be conducted through the mobile
transaction platform. The one or more user transactions are stored
on a third-party source. In an aspect, the at least one life
occurrence has yet to occur. In another aspect of the invention,
the life occurrence has already occurred. The multi-dimensional
context comprises at least one of user location information and
life occurrence location information. The multi-dimensional context
may include at least one of a time of life occurrence and a current
time.
[0010] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to derive at a mobile
transaction platform a multi-dimensional context from one or more
user transactions. The software may further include instructions to
determine at least one life occurrence based, at least in part, on
the multi-dimensional context. The one or more user transactions
may be conducted through the mobile transaction platform. The one
or more user transactions may be stored on a third-party source.
The at least one life occurrence has yet to occur, in an example.
The life occurrence has already occurred, in another aspect. The
multi-dimensional context may include at least one of user location
information and life occurrence location information. The
multi-dimensional context may include at least one of a time of
life occurrence and a current time.
[0011] A method is disclosed herein that may include receiving at a
mobile transaction platform a multi-dimensional context derived
from one or more user transactions. The method may further include
determining at least one life occurrence based, at least in part,
on the multi-dimensional context and generating at least one
trigger-event responsive to the at least one life occurrence. The
at least one trigger-event facilitates at least one user directed
mobile action. The one or more user transactions are conducted
through the mobile transaction platform. The one or more user
transactions may be stored on a third-party source. The at least
one life occurrence has yet to occur, in an aspect of the
invention. The life occurrence has already occurred, in another
aspect of the invention. The multi-dimensional context may include
at least one of user location information and life occurrence
location information. The multi-dimensional context may include at
least one of a time of life occurrence and a current time.
[0012] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to receive at a
mobile transaction platform a multi-dimensional context derived
from one or more user transactions. The software may further
include instructions to determine at least one life occurrence
based, at least in part, on the multi-dimensional context and
instructions to generate at least one trigger-event responsive to
the at least one life occurrence. The at least one trigger-event
facilitates at least one user directed mobile action. The one or
more user transactions may be conducted through the mobile
transaction platform. The one or more user transactions may be
stored on a third-party source. The at least one life occurrence
has yet to occur in an aspect of the invention. The life occurrence
has already occurred in another aspect. The multi-dimensional
context may include at least one of user location information and
life occurrence location information. The multi-dimensional context
may include at least one of a time of life occurrence and a current
time.
[0013] A method is disclosed herein that includes determining a
type classification for a life occurrence of an individual from
amongst a plurality of life occurrences based at least in part on a
multi-dimensional data set constructed by an expert engine that
receives analysis of transactions of the individual conducted
through a mobile transaction platform. The method may further
include generating a resolution path that resolves a life
occurrence aspect that is common to life occurrences of the
determined life occurrence type classification. The at least one of
determining and generating utilizes fuzzy logic in an aspect. The
resolution path may be adapted to be executed on a mobile device.
The steps of determining and generating may be performed on the
mobile device. The step of determining may include associating life
occurrences with resolution paths utilizing fuzzy logic. The step
of generating may include associating life occurrences with
resolution paths utilizing fuzzy logic.
[0014] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to determine a type
classification for a life occurrence of an individual from amongst
a plurality of life occurrences based at least in part on a
multi-dimensional data set constructed by an expert engine that
receives analysis of transactions of the individual conducted
through a mobile transaction platform. The software may further
include instructions to generate a resolution path that resolves a
life occurrence aspect that is common to life occurrences of the
determined life occurrence type classification. The at least one of
determining and generating may utilize fuzzy logic. The resolution
path may be adapted to be executed on a mobile device. The steps of
determining and generating may be performed on the mobile device.
The step of determining may include associating life occurrences
with resolution paths utilizing fuzzy logic. The step of generating
may include associating life occurrences with resolution paths
utilizing fuzzy logic.
[0015] A method is disclosed herein that may include determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set and generating a resolution path
adapted to address the life occurrence via a life occurrence node.
In an aspect, at least one of the determining and generating may be
performed according to a rule administered by a rules engine that
relates life occurrence types with available resolution paths and
that applies rules to data for the individual in the
multidimensional data set. The multidimensional data set may be
formed via a mobile transaction platform through which the life
occurrence node addresses the life occurrence. The life occurrence
node may include a mobile phone. The determining and generating may
be performed on the mobile phone. The rule may relate one of a type
of life occurrence to one of a plurality of resolution paths. The
method may further include applying the rule to the
multidimensional data set.
[0016] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to determine a type
of life occurrence of an individual based, at least in part, on a
multidimensional data set constructed in connection with a mobile
transaction platform (MTP) through which the individual conducts
transactions instructions to generate a resolution path adapted to
address the life occurrence via a life occurrence node. In an
aspect, at least one of the determining and generating is performed
according to a rule administered by a rules engine that relates
life occurrence types with available resolution paths and that
applies rules to data for the individual in the multidimensional
data set. The multidimensional data set is formed via a mobile
transaction platform through which the life occurrence node
addresses the life occurrence. The life occurrence node may include
a mobile phone. The determining and generating may be performed on
the mobile phone. The rule may relate one of a type of life
occurrence to one of a plurality of resolution paths. The software
may further include instructions to apply the rule to the
multidimensional data set.
[0017] A method is disclosed herein that may include determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set and generating a resolution action
that, when activated by the individual triggers invocation of a
resolution path adapted to address the life occurrence via a life
occurrence node. In an aspect, at least one of the determining and
generating is performed according to a rule administered by a rules
engine that relates life occurrence types with available resolution
paths and that applies rules to data for the individual in the
multidimensional data set.
[0018] A method is disclosed herein that includes determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set constructed in connection with a
mobile transaction platform (MTP) through which the individual
conducts transactions and generating a resolution path adapted to
address the life occurrence via a life occurrence node. The
determining the type of life occurrence may be based, at least in
part, on the application of a neural network. The at least one
input to the neural network may include data of the
multidimensional data set. The at least one feedback to the neural
network may include a plurality of known life occurrences. The
neural network may operate to infer a life occurrence from the
multidimensional data set. The multidimensional data set may be
formed via a mobile transaction platform. The life occurrence node
may include a mobile phone.
[0019] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to determine a type
of life occurrence of an individual based, at least in part, on a
multidimensional data set and instructions to generate a resolution
path adapted to address the life occurrence via a life occurrence
node. The determining the type of life occurrence is based, at
least in part, on the application of a neural network. The at least
one input to the neural network comprises data of the
multidimensional data set. The at least one feedback to the neural
network may include a plurality of known life occurrences. The
neural network may operate to infer a life occurrence from the
multidimensional data set. The multidimensional data set may be
formed via a mobile transaction platform. The life occurrence node
may include a mobile phone.
[0020] A method is disclosed herein that includes determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set constructed in connection with a
mobile transaction platform (MTP) through which the individual
conducts transactions. The method may further include generating a
resolution action that, when activated by the individual triggers
invocation of a resolution path adapted to address the life
occurrence via a life occurrence node. The determining the type of
life occurrence is based, at least in part, on the application of a
neural network.
[0021] A method is disclosed herein that includes determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set constructed in connection with a
mobile transaction platform (MTP) through which the individual
conducts transactions and generating a resolution path adapted to
address the life occurrence via a life occurrence node. The step of
generating the resolution path may be based, at least in part, on
the application of a neural network and wherein at least one
feedback to the neural network may include at least one outcome for
at least one individual having undertaken a resolution path for a
the determined type of life occurrence. The at least one input to
the neural network comprises data of the multidimensional data set.
The at least one feedback to the neural network may include a
plurality of known life occurrences. The neural network may operate
to infer a life occurrence from the multidimensional data set. The
multidimensional data set may be formed via a mobile transaction
platform. The life occurrence node may include a mobile phone.
[0022] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to determine a type
of life occurrence of an individual based, at least in part, on a
multidimensional data set constructed in connection with a mobile
transaction platform (MTP) through which the individual conducts
transactions and instructions to generate a resolution path adapted
to address the life occurrence via a life occurrence node. The step
of generating the resolution path is based, at least in part, on
the application of a neural network and wherein at least one
feedback to the neural network may include at least one outcome for
at least one individual having undertaken a resolution path for a
the determined type of life occurrence. The at least one input to
the neural network may include data of the multidimensional data
set. The at least one feedback to the neural network may include a
plurality of known life occurrences. The neural network may operate
to infer a life occurrence from the multidimensional data set. The
multidimensional data set may be formed via a mobile transaction
platform. The life occurrence node may include a mobile phone.
[0023] A method is disclosed herein that includes determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set constructed in connection with a
mobile transaction platform (MTP) through which the individual
conducts transactions and generating a resolution action that, when
activated by the individual triggers invocation of a resolution
path adapted to address the life occurrence via a life occurrence
node. The step of generating the resolution action is based, at
least in part, on the application of a neural network and wherein
at least one feedback to the neural network may include at least
one outcome for at least one individual having undertaken a
resolution path for a the determined type of life occurrence.
[0024] A method is disclosed herein that includes determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set and generating a resolution path
adapted to address the life occurrence via a life occurrence node.
In an aspect, at least one of the determining and generating is
based, at least in part, on the application of an algorithm and
wherein at least one feedback to the algorithm may include at least
one of an appropriateness of a prior generated resolution path and
a correctness of a previously determined life occurrence. The life
occurrence node may include a mobile phone. The multidimensional
data set may be formed, at least in part, via operation of a mobile
transaction platform. The mobile transaction platform may be
resident on the life occurrence node. The life occurrence node may
include a mobile phone. The at least one feedback to the algorithm
is among fuzzy logic and neural network elements performing the
algorithm.
[0025] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to determine a type
of life occurrence of an individual based, at least in part, on a
multidimensional data set and instructions to generate a resolution
path adapted to address the life occurrence via a life occurrence
node. In an aspect, at least one of the determining and generating
is based, at least in part, on the application of an algorithm and
wherein at least one feedback to the algorithm comprises at least
one of an appropriateness of a prior generated resolution path and
a correctness of a previously determined life occurrence. The life
occurrence node may include a mobile phone. The multidimensional
data set may be formed, at least in part, via operation of a mobile
transaction platform. The mobile transaction platform may be
resident on the life occurrence node. The life occurrence node may
include a mobile phone. The at least one feedback to the algorithm
is among fuzzy logic and neural network elements performing the
algorithm.
[0026] A method is disclosed herein that includes determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set constructed in connection with a
mobile transaction platform (MTP) through which the individual
conducts transactions and generating a resolution action that, when
activated by the individual triggers invocation of a resolution
path adapted to address the life occurrence via a life occurrence
node. In an aspect, at least one of the determining and generating
is based, at least in part, on the application of an algorithm and
wherein at least one feedback to the algorithm comprises at least
one of an appropriateness of a prior generated resolution path and
a correctness of a previously determined life occurrence.
[0027] A method is disclosed herein that includes determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set and generating a resolution path
adapted to address the life occurrence via a life occurrence node.
The multidimensional data set is formed, in part, utilizing data
generated from a mobile transaction platform via which an
individual conducts mobile transactions. The method may further
includes utilizing data from a third party analytics source. The
mobile transaction platform may be resident at least in part on the
life occurrence node. The life occurrence node may include a mobile
phone.
[0028] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to determine a type
of life occurrence of an individual based, at least in part, on a
multidimensional data set and generate a resolution path adapted to
address the life occurrence via a life occurrence node. The
multidimensional data set may be formed, in part, utilizing data
generated from a mobile transaction platform via which an
individual conducts mobile transactions. The method further
includes utilizing data from a third party analytics source. The
mobile transaction platform may be resident at least in part on the
life occurrence node. The life occurrence node may include a mobile
phone.
[0029] A method is disclosed herein that may include determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set and generating a resolution action
that, when activated by the individual triggers invocation of a
resolution path adapted to address the life occurrence via a life
occurrence node. The multidimensional data set may be formed, in
part, utilizing data generated from a mobile transaction platform
via which an individual conducts mobile transactions.
[0030] A method is disclosed herein that includes determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set and generating a resolution path
adapted to address the life occurrence via a life occurrence node.
The step of generating the resolution path is based, at least in
part, on a context of an individual that comprises data from a
mobile transaction platform via which the individual conducts
mobile transactions, data from a third party source relating to the
individual, and location data of the individual at a point in time.
The mobile transaction platform may be resident on the life
occurrence node. The life occurrence node may include a mobile
phone. The resolution path may be generated utilizing a pre-learned
preference from a past transaction of the individual, a change in a
pattern of the individual, and at least one of a level of loyalty
to a customer loyalty program, an account status, and a credit card
status. The resolution path may be generated utilizing data
indicative of a purchase by the individual. The resolution path may
include at least one trigger related to a level of loyalty
points.
[0031] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to determine a type
of life occurrence of an individual based, at least in part, on a
multidimensional data set and instructions to generate a resolution
path adapted to address the life occurrence via a life occurrence
node. The step of generating the resolution path is based, at least
in part, on a context of an individual that comprises data from a
mobile transaction platform via which the individual conducts
mobile transactions, data from a third party source relating to the
individual, and location data of the individual at a point in time.
The mobile transaction platform may be resident on the life
occurrence node. The life occurrence node may include a mobile
phone. The resolution path may be generated utilizing a pre-learned
preference from a past transaction of the individual, a change in a
pattern of the individual, and at least one of a level of loyalty
to a customer loyalty program, an account status, and a credit card
status. The resolution path may be generated utilizing data
indicative of a purchase by the individual. The resolution path may
include at least one trigger related to a level of loyalty
points.
[0032] A method is disclosed herein that includes determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set and generating a resolution action
that, when activated by the individual triggers invocation of a
resolution path adapted to address the life occurrence via a life
occurrence node. The step of generating the resolution action is
based, at least in part, on a context of an individual that
comprises data from a mobile transaction platform via which the
individual conducts mobile transactions, data from a third party
source relating to the individual, and location data of the
individual at a point in time.
[0033] A method is disclosed herein that includes determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set constructed, at least in part, via
interaction with a mobile transaction platform by which an
individual conducts at least one transaction. The method further
includes generating a resolution path adapted to address the life
occurrence via a life occurrence node. The resolution path may be
based, at least in part, on a combination of an outcome predicted
for the individual and an assessment of a risk imposed by the
resolution path on a third party service provider that supports, at
least in part, the resolution path. The assessment of risk may
include an assessment of a cumulative risk of the service provider
with respect to the individual. In an aspect, the assessment of
risk may include an assessment of a risk of the individual across a
plurality of service providers. The mobile transaction platform may
be resident on the life occurrence node. The life occurrence node
may include a mobile phone. The at least one user transaction may
be stored on a third-party source.
[0034] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to determine a type
of life occurrence of an individual based, at least in part, on a
multidimensional data set constructed, at least in part, via
interaction with a mobile transaction platform by which an
individual conducts at least one transaction and generate a
resolution path adapted to address the life occurrence via a life
occurrence node. The resolution path may be based, at least in
part, on a combination of an outcome predicted for the individual
and an assessment of a risk imposed by the resolution path on a
third party service provider that supports, at least in part, the
resolution path. The assessment of risk may include an assessment
of a cumulative risk of the service provider with respect to the
individual. The assessment of risk may include an assessment of a
risk of the individual across a plurality of service providers. The
mobile transaction platform may be resident on the life occurrence
node. The life occurrence node may include a mobile phone. The at
least one user transaction may be stored on a third-party
source.
[0035] A method is disclosed herein that may include determining a
type of life occurrence of an individual based, at least in part,
on a multidimensional data set constructed, at least in part, via
interaction with a mobile transaction platform by which an
individual conducts at least one transaction and generating a
resolution action that, when activated by the individual triggers
invocation of a resolution path adapted to address the life
occurrence via a life occurrence node. The resolution path may be
based, at least in part, on a combination of an outcome predicted
for the individual and an assessment of a risk imposed by the
resolution path on a third party service provider that supports, at
least in part, the resolution path.
[0036] A method is disclosed herein that may include analyzing one
or more mobile transactions processed by a mobile transaction
platform, life occurrence metadata and user related data derived,
at least in part, from third party data sources in order to
determine a plurality of resolution actions in response to a life
occurrence and presenting the plurality of resolution actions to a
user. The method may further include pre-configuring at least one
mobile transaction to facilitate an execution of the plurality of
resolution actions in response to a user selection of the at least
one of the plurality of resolution actions. The method may further
include pre-configuring at least one mobile transaction to
facilitate an execution of the plurality of resolution actions; and
performing the at least one mobile transaction. The method may
further include performing the at least one mobile transaction does
not require user selection of a transaction. The step of performing
the at least one mobile transaction may not require user selection
of a resolution action. The resolution action when activated by the
individual triggers invocation of a resolution path adapted to
address the life occurrence via a life occurrence node.
[0037] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to analyze one or
more mobile transactions processed by a mobile transaction
platform, life occurrence metadata and user related data derived,
at least in part, from third party data sources in order to
determine a plurality of resolution actions in response to a life
occurrence and instructions to present the plurality of resolution
actions to a user. The computer readable storage medium may further
include instructions to pre-configure at least one mobile
transaction to facilitate an execution of the plurality of
resolution actions in response to a user selection of the at least
one of the plurality of resolution actions. The computer readable
storage medium may further include instruction to pre-configure at
least one mobile transaction to facilitate an execution of the
plurality of resolution actions; and to perform the at least one
mobile transaction. The step of performing the at least one mobile
transaction may not require user selection of a transaction. The
step of performing the at least one mobile transaction may not
require user selection of a resolution action. The resolution
action when activated by the user triggers invocation of a
resolution path adapted to address the life occurrence via a life
occurrence node.
[0038] A method is disclosed herein that may include analyzing one
or more mobile transactions processed by a mobile transaction
platform, life occurrence metadata and user related data derived,
at least in part, from third party data sources in order to
determine a plurality of resolution actions in response to a life
occurrence and configuring a plurality of mobile transactions to
facilitate the execution of the plurality of resolution actions.
The method may further include presenting the plurality of mobile
transactions to a user in response to a detection of at least one
trigger event associated with the life occurrence. The life
occurrence may be an event in the user's life that has not yet
occurred. In another aspect, the life occurrence may be a
user-related event that occurred in the past.
[0039] A computer readable storage medium having data stored
therein representing software executable by a computer is disclosed
herein. The software may include instructions to analyze one or
more mobile transactions processed by a mobile transaction
platform, life occurrence metadata and user related data derived,
at least in part, from third party data sources in order to
determine a plurality of resolution actions in response to a life
occurrence and to configure a plurality of mobile transactions to
facilitate the execution of the plurality of resolution actions.
The software may further include instructions to present the
plurality of mobile transactions to a user in response to a
detection of at least one trigger event associated with the life
occurrence. The life occurrence is an event in the user's life that
has not yet occurred in an aspect. The life occurrence is a
user-related event that occurred in the past in another aspect.
[0040] A mobile transaction platform (MTP) is disclosed herein that
may include a transactional analytics facility that analyzes at
least one user transaction conducted with the MTP and creates at
least one of a user profile, a dynamic profile of the user, and a
multidimensional context for use by an expert engine. The MTP
further include the expert engine that determines a life occurrence
based on the multidimensional context and user-related data from
third-party sources, and generates a resolution path of resolution
actions that are responsive to one or more action trigger-events
for resolving one or more aspects of the life occurrence. The MTP
further includes at least one life occurrence container deployed on
a life occurrence node. The life occurrence container may alert a
user of the life occurrence node to the resolution path, gather a
user response to the alert, and generate one or more life
occurrence node-based transactions matched to the resolution path.
The life occurrence container may be in electronic communication
with the mobile transaction facility to maintain currency of life
occurrences, trigger-events, and resolution actions. The
transaction facility and the expert engine exchange resolution
trigger-events, static user profiles, and dynamic user profiles.
The expert engine may determine a life occurrence using a
combination of at least two of fuzzy logic, machine learning, and
neural networks. The expert engine and the transaction facility may
access one or more ecosystem resources when determining and
analyzing through an enterprise service bus or a utility resource
access switch. The ecosystem resources may include at least one
each of third party analytics, a social network interface, a
context driver, an offer, a value added service, a trusted service
manager (TSM), a certificate authoritie (CA), and a database. The
life occurrence node may be a mobile device. The mobile device may
be used to select one of the life occurrence node-based
transactions. In an aspect, a personalized instrument may be
configured to securely cause the life occurrence node-based
transaction matched to the resolution path to be executed by a
server. The user transactions and user-related data from
third-party sources may be stored in a multi-dimensional database.
The analysis by the transactional analytics facility may produce
transactional analytics data. The expert engine may be configured
to consolidate the transactional analytics data with data from a
third party source of user data and with a current context in
determining the life occurrence. The context may include vendor
personalization of a widget executing in the container and at least
one context item selected from a list of context items consisting
of: a time, a location, a transaction detail, an urgency, an
importance, the status of a credit card or account, mobile device
use history, payment source, wallet state, type of transaction,
product/service, vendor, delivery method, delivery arrangements,
tax status, transaction participant, user preferences, the presence
of a network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information. The at least one user
profile or dynamic profile may also used be in determining the life
occurrence.
[0041] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program instructs a
microprocessor to perform the steps of determining and resolving a
life occurrence. The steps may include analyzing at least one user
transaction conducted with a mobile transaction platform (MTP) to
create at least one of a user profile, a dynamic profile of the
user, and a multidimensional context, determining a life occurrence
based on at least one of the multidimensional context, the user
profile, and the dynamic profile, and user-related data from
third-party sources, generating a resolution path of one or more
action trigger-events for resolving one or more aspects of the life
occurrence, alerting a user, using a life occurrence container
deployed on a life occurrence node, to the resolution path,
gathering a user response to the alert, and generating one or more
life occurrence node-based transactions matched to the resolution
path. The step of determining a life occurrence may involve using a
combination of at least two of fuzzy logic, machine learning, and
neural networks. The step of determining and analyzing may involve
accessing one or more enterprise resources through at least one of
an enterprise service bus and a utility resource access switch. The
ecosystem resources may include at least one each of third party
analytics, a social network, a context driver, an offer, a value
added service, a trusted service manager (TSM), a certificate
authority (CA), and a database. The life occurrence node may be a
mobile device. The mobile device may be used to select one of the
life occurrence node-based transactions. The steps may further
include providing a personalized instrument configured to securely
cause the life occurrence node-based transaction matched to the
resolution path to be executed by a server. The user transactions
and user-related data from third-party sources may be stored in a
multi-dimensional database. The context may include vendor
personalization of a widget executing in the container and at least
one context item selected from a list of context items consisting
of: a time, a location, a transaction detail, an urgency, an
importance, the status of a credit card or account, mobile device
use history, payment source, wallet state, type of transaction,
product/service, vendor, delivery method, delivery arrangements,
tax status, transaction participant, user preferences, the presence
of a network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information.
[0042] A mobile transaction platform (MTP) is disclosed herein that
may include a transactional analytics facility that analyzes at
least one user transaction conducted with the MTP and creates at
least one of a user profile, a dynamic profile of the user, and a
multidimensional context for use by an expert engine. The MTP may
further include the expert engine that determines a life occurrence
based on the multidimensional context and user-related data from
third-party sources, and generates a resolution path of one or more
resolution actions for resolving one or more aspects of the life
occurrence; and at least one life occurrence container deployed on
a life occurrence node, wherein the life occurrence container
executes at least one transaction of at least one resolution action
of the resolution path.
[0043] A mobile transaction platform (MTP) is disclosed herein that
may include a multidimensional data set of transaction details of
transactions conducted by a user through the MTP and a
transactional analytics facility for analyzing the multidimensional
data set to produce a context for at least one of a life occurrence
determination and a resolution of at least one aspect of a life
occurrence. The platform may further include an expert engine that
uses the context to determine a life occurrence and at least one
resolution path for the life occurrence. The step of determining
may involve using a combination of at least two of fuzzy logic,
machine learning, and neural networks. The context may include
vendor personalization of a widget executing in a life occurrence
enabled container of a mobile device of the user and at least one
context item selected from a list of context items consisting of: a
time, a location, a transaction detail, an urgency, an importance,
the status of a credit card or account, mobile device use history,
payment source, wallet state, type of transaction, product/service,
vendor, delivery method, delivery arrangements, tax status,
transaction participant, user preferences, the presence of a
network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information. The data in the
multidimensional data set may at least be one of client specific
data and service provider specific data. The multidimensional data
set may be a user database. The transactional analytics facility
may analyze the data in the context of other users to establish a
weighting. The transactional analytics facility may analyze the
data in the context of similar or interested vendors to establish a
weighting. The platform may further include an expert engine
configured to consolidate the context with at least one third party
source of user data in determining the life occurrence.
[0044] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform the steps of determining a
context for determining a life occurrence. The steps may include
gathering transaction details of transactions conducted by a user
through a mobile transaction platform into a multidimensional data
set and analyzing, using a transactional analytics facility. The
multidimensional data set may produce a context for at least one of
a life occurrence determination and a resolution. The step of
determining may involve using a combination of at least two of
fuzzy logic, machine learning, and neural networks. The context may
include vendor personalization of a widget executing in a life
occurrence enabled container of a mobile device of the user and at
least one context item selected from a list of context items
consisting of: a time, a location, a transaction detail, an
urgency, an importance, the status of a credit card or account,
mobile device use history, payment source, wallet state, type of
transaction, product/service, vendor, delivery method, delivery
arrangements, tax status, transaction participant, user
preferences, the presence of a network or a particular account,
user associations with a non-vendor third-party, presence of
vouchers and promotions, loyalty points, third-party user-related
data, social network information, and calendar information. The
data in the multidimensional data set may at least be one of client
specific and service provider specific. The multidimensional data
set may be a user database. The analysis of the data may be done in
the context of other users to establish a weighting. The analysis
of the data may be done in the context of similar or interested
vendors to establish a weighting. The computer readable medium may
further store instructions to perform consolidating the context
with at least one third party source of user data in determining
the life occurrence.
[0045] An instrument-based mobile transaction platform is disclosed
herein that may include a transaction facility that handles
transactions of a plurality of personal mobile devices registered
to perform transactions with the transaction facility and
configured with at least one life occurrence capable executable
container, analyzes the transactions, and populates a
multidimensional context with output from the analysis. The
platform may further include an expert engine that determines life
occurrences based on the multidimensional context and third-party
sources of user-related data and that generates a resolution path
for resolving one or more aspects of the life occurrence, the
resolution path having a series of resolution actions that are
responsive to trigger-events related to the life occurrence and
that lead to resolution of the life occurrence. The platform may
further include an enterprise service bus for facilitating access
by the expert engine and the transaction facility to one or more
ecosystem resources and at least one life occurrence container
deployed on a life occurrence node. The life occurrence container
may alert a user of the life occurrence node to resolution actions
available for addressing an aspect of the life occurrence, gathers
a user response to the alert, and provides a personalized
instrument configured to securely cause a life occurrence-based,
mobile transaction matched to the resolution action to be executed
by a server. The life occurrence container may be in electronic
communication with the transaction facility to maintain currency of
life occurrences, trigger-events, and resolution actions. The
transaction facility and the expert engine may exchange resolution
trigger-events, static user profiles, and dynamic user profiles. In
an aspect, at least one static user profile or at least one dynamic
user profile may also used be in determining the life occurrence.
The expert engine may determine life occurrences using a
combination of at least two of fuzzy logic, machine learning, and
neural networks. The ecosystem resources may include at least one
each of third party analytics, a social network, a context driver,
an offer, a value added service, a trusted service manager (TSM), a
certificate authority (CA), and a database. The life occurrence
node may be a mobile device. The user transactions and user-related
data from third-party sources may be stored in a multi-dimensional
database. The analysis by the transactional analytics facility may
produce transactional analytics data. The expert engine may be
configured to consolidate transactional analytics data with at
least one of a third party source of user data and a current
context in determining the life occurrence. The context may include
vendor personalization of a widget executing in the container and
at least one context item selected from a list of context items
consisting of a time, a location, a transaction detail, an urgency,
an importance, the status of a credit card or account, mobile
device use history, payment source, wallet state, type of
transaction, product/service, vendor, delivery method, delivery
arrangements, tax status, transaction participant, user
preferences, the presence of a network or a particular account,
user associations with a non-vendor third-party, presence of
vouchers and promotions, loyalty points, third-party user-related
data, social network information, and calendar information.
[0046] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program instructs a
microprocessor to perform steps of determining and resolving a life
occurrence. The steps may include analyzing at least one user
transaction conducted with a mobile transaction platform (MTP) to
create a static user profile, a dynamic profile of the user, and a
multidimensional context comprising data representing aspects of a
plurality of user-specific life occurrences, determining a life
occurrence based on user-related data from third-party sources and
at least one of the multidimensional context, the static user
profile, and the dynamic profile, generating a resolution path of
one or more action trigger-events for resolving one or more aspects
of the life occurrence, alerting a user, using a life occurrence
container deployed on a life occurrence node, to the resolution
path, gathering a user response to the alert; and providing a
personalized instrument configured to securely cause a life
occurrence-based, mobile transaction matched to the resolution path
to be executed cooperatively with a server. The step of determining
involves using a combination of at least two of fuzzy logic,
machine learning, and neural networks. The ecosystem resources may
include at least one each of third party analytics, a social
network, a context driver, an offer, a value added service, a
trusted service manager (TSM), a certificate authority (CA), and a
database. The life occurrence node may be a mobile device. The user
transactions and user-related data from third-party sources may be
stored in a multi-dimensional database. The context may include
vendor personalization of a widget executing in the container and
at least one context item selected from a list of context items
consisting of: a time, a location, a transaction detail, an
urgency, an importance, the status of a credit card or account,
mobile device use history, payment source, wallet state, type of
transaction, product/service, vendor, delivery method, delivery
arrangements, tax status, transaction participant, user
preferences, the presence of a network or a particular account,
user associations with a non-vendor third-party, presence of
vouchers and promotions, loyalty points, third-party user-related
data, social network information, and calendar information.
[0047] An instrument-based life occurrence transaction platform is
disclosed herein that may include a transaction facility for
handling transactions of a personal mobile device, analyzing the
transactions, and extracting a multidimensional context from the
analysis, the multidimensional context comprising data representing
aspects of a plurality of user-specific life occurrences. The
platform may further include an expert engine that determines
user-specific life occurrences based on the multidimensional
context and third-party sources of user-related data, and generates
a resolution path for resolving one or more aspects of the
occurrence, the resolution path having a series of resolution
actions that are performed based on occurrences of trigger-events
leading to resolution of the life occurrence. The platform may
further include an enterprise service bus for facilitating access
by the expert engine and the transaction facility to one or more
ecosystem resources and at least one life occurrence container
deployed on a life occurrence node that administers selection of at
least one resolution action for addressing an aspect of the life
occurrence, wherein the at least one resolution action comprises
providing a personalized instrument configured to securely cause a
life occurrence-based, mobile transaction matched to the resolution
action to be executed cooperatively with a server. The life
occurrence container may be in electronic communication with the
transaction facility to maintain currency of life occurrences,
trigger-events, and resolution actions. The transaction facility
and the expert engine may exchange resolution trigger-events,
static user profiles, and dynamic user profiles. The at least one
static profile or at least one dynamic profile may also be used in
determining the life occurrence. The expert engine may use a
combination of at least two of fuzzy logic, machine learning, and
neural networks. The ecosystem resources may include at least one
each of third party analytics, a social network, a context driver,
an offer, a value added service, a trusted service manager (TSM), a
certificate authority (CA), and a database. The life occurrence
node may be a mobile device. The user transactions and user-related
data from third-party sources may be stored in a multi-dimensional
database. The analysis by the transactional analytics facility may
produce transactional analytics data. The expert engine may be
configured to consolidate transactional analytics data with at
least one of a third party source of user data and a current
context in determining the life occurrence. The context may include
vendor personalization of a widget executing in the container and
at least one context item selected from a list of context items
consisting of: a time, a location, a transaction detail, an
urgency, an importance, the status of a credit card or account,
mobile device use history, payment source, wallet state, type of
transaction, product/service, vendor, delivery method, delivery
arrangements, tax status, transaction participant, user
preferences, the presence of a network or a particular account,
user associations with a non-vendor third-party, presence of
vouchers and promotions, loyalty points, third-party user-related
data, social network information, and calendar information. The
instrument may include metadata that identifies a transaction type
accessible by a server and user/wallet/device information required
to execute the transaction on behalf of the user. The instrument
may be a coupon.
[0048] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps of determining and
resolving a life occurrence. The steps may include analyzing at
least one user transaction conducted with a mobile transaction
platform (MTP) to create at least one each of a user profile, a
dynamic profile of the user, and a multidimensional context
comprising data representing aspects of a plurality of
user-specific life occurrences, determining a life occurrence based
on user-related data from third-party sources and at least one of
the multidimensional context, the user profile, and the dynamic
profile, generating a resolution path for resolving one or more
aspects of the life occurrence and providing a personalized
instrument configured to securely cause a life occurrence-based,
mobile transaction matched to the resolution action to be executed
cooperatively with a server. The step of determining involves using
a combination of at least two of fuzzy logic, machine learning, and
neural networks. The ecosystem resources may include at least one
each of third party analytics, a social network, a context driver,
an offer, a value added service, a trusted service manager (TSM), a
certificate authority (CA), and a database. The life occurrence
node may be a mobile device. The user transactions and user-related
data from third-party sources may be stored in a multi-dimensional
database. The context may include vendor personalization of a
widget executing in the container and at least one context item
selected from a list of context items consisting of: a time, a
location, a transaction detail, an urgency, an importance, the
status of a credit card or account, mobile device use history,
payment source, wallet state, type of transaction, product/service,
vendor, delivery method, delivery arrangements, tax status,
transaction participant, user preferences, the presence of a
network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information. The instrument may include
metadata that identifies a transaction type accessible by a server
and user/wallet/device information required to execute the
transaction on behalf of the user. The instrument may be a
coupon.
[0049] An instrument-based life occurrence transaction platform is
disclosed herein that may include a transaction facility for
handling transactions of a personal mobile device, analyzing the
transactions, and providing the analysis to an expert engine as
multidimensional context comprising data representing aspects of a
plurality of user-specific life occurrences. The platform may
further include the expert engine that determines life occurrences
based on the multidimensional context and third-party sources of
user-related data, and generates a resolution path for resolving
one or more aspects of the occurrence, the resolution path having a
plurality of resolution actions that are optionally performed based
on occurrences of trigger-events leading to resolution of the life
occurrence. The platform may further include a utility access
switch for facilitating access by the expert engine and the
transaction facility to one or more ecosystem resources and at
least one life occurrence container deployed on a life occurrence
node that administers selection of at least one resolution action
for addressing an aspect of the life occurrence. The at least one
resolution action comprises providing a personalized instrument
configured to securely cause a life occurrence-based, mobile
transaction matched to the resolution action to be executed
cooperatively with a server. The life occurrence container may be
in electronic communication with the transaction facility to
maintain currency of life occurrences, trigger-events, and
resolution actions. The transaction facility and the expert engine
may exchange resolution trigger-events, static user profiles, and
dynamic user profiles. The at least one static profile or at least
one dynamic profile may also be used in determining the life
occurrence. The expert engine may use a combination of at least two
of fuzzy logic, machine learning, and neural networks. The
ecosystem resources may include at least one each of third party
analytics, a social network, a context driver, an offer, a value
added service, a trusted service manager (TSM), a certificate
authority (CA), and a database. The life occurrence node may be a
mobile device. The user transactions and user-related data from
third-party sources may be stored in a multi-dimensional database.
The analysis by the transactional analytics facility may produce
transactional analytics data. The expert engine may be configured
to consolidate transactional analytics data with at least one of a
third party source of user data and a current context in
determining the life occurrence. The context may include vendor
personalization of a widget executing in the container and at least
one context item selected from a list of context items consisting
of: a time, a location, a transaction detail, an urgency, an
importance, the status of a credit card or account, mobile device
use history, payment source, wallet state, type of transaction,
product/service, vendor, delivery method, delivery arrangements,
tax status, transaction participant, user preferences, the presence
of a network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information. The instrument may include
metadata that identifies a transaction type accessible by a server
and user/wallet/device information required to execute the
transaction on behalf of the user. The instrument may be a
coupon.
[0050] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps of determining and
resolving a life occurrence that may include analyzing at least one
user transaction conducted with a mobile transaction platform (MTP)
to create at least one each of a user profile, a dynamic profile of
the user, and a multidimensional context comprising data
representing aspects of a plurality of user-specific life
occurrences. The steps may further include determining a life
occurrence based on at least one of the multidimensional context,
the user profile, and the dynamic profile, and user-related data
from third-party sources, generating a resolution path for
resolving one or more aspects of the life occurrence and providing
a personalized instrument configured to securely cause a life
occurrence-based, mobile transaction matched to the resolution
action to be executed cooperatively with a server. The step of
determining may involve using a combination of at least two of
fuzzy logic, machine learning, and neural networks. The ecosystem
resources may include at least one each of third party analytics, a
social network, a context driver, an offer, a value added service,
a trusted service manager (TSM), a certificate authority (CA), and
a database. The life occurrence node may be a mobile device. The
user transactions and user-related data from third-party sources
may be stored in a multi-dimensional database. The context may
include vendor personalization of a widget executing in the
container and at least one context item selected from a list of
context items consisting of: a time, a location, a transaction
detail, an urgency, an importance, the status of a credit card or
account, mobile device use history, payment source, wallet state,
type of transaction, product/service, vendor, delivery method,
delivery arrangements, tax status, transaction participant, user
preferences, the presence of a network or a particular account,
user associations with a non-vendor third-party, presence of
vouchers and promotions, loyalty points, third-party user-related
data, social network information, and calendar information. The
instrument may include metadata that identifies a transaction type
accessible by a server and user/wallet/device information required
to execute the transaction on behalf of the user. The instrument
may be a coupon.
[0051] An expert engine is disclosed herein that may include a
processor that uses one or more algorithms to consolidate various
transactional analytics from a mobile transaction platform (MTP)
with data from third party sources to produce a multidimensional
data set comprising data representing aspects of a plurality of
user-specific life occurrences. The expert engine may further
include the processor further programmed with a high-speed
algorithm to determine a type of life occurrence of an individual
among a set of possible life occurrences based at least in part on
the multidimensional data set in real-time or near real-time, a
resolution path generation facility that generates a plurality of
resolution paths having a series of action events leading to
resolution of at least one life occurrence of the determined type
of life occurrence and a communications interface between the MTP
and the expert engine that facilitates the sharing of responses to
the action events between the MTP and expert engine, wherein at
least one of determining the type of life occurrence and generating
the plurality of resolution paths is based on the shared responses.
The high-speed algorithm may determine using at least one of
temporal data, spatial data and risk assessment. The response to
the action events may be via use of a life occurrence node. The
life occurrence node may be a mobile device. The processor may
further generate a multidimensional context used by the high-speed
algorithm in determining a life occurrence. The context may include
vendor personalization of a widget executing in a life occurrence
enabled container of a mobile device of the user and at least one
context item selected from a list of context items consisting of: a
time, a location, a transaction detail, an urgency, an importance,
the status of a credit card or account, mobile device use history,
payment source, wallet state, type of transaction, product/service,
vendor, delivery method, delivery arrangements, tax status,
transaction participant, user preferences, the presence of a
network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information.
[0052] An expert engine is disclosed herein that may include a
processor that is programmed with a high-speed algorithm to
determine a type of life occurrence of an individual among a set of
possible life occurrences based at least in part on a
multidimensional data set comprising data representing aspects of a
plurality of user-specific life occurrences. The expert engine may
further include a resolution path generation facility that
generates a resolution path having a series of action events
leading to resolution at least one life occurrence aspect that is
common to the life occurrences in the determined type of the life
occurrence and a communications interface between the MTP and the
expert engine that facilitates the sharing of responses to the
action events between the MTP and expert engine, wherein at least
one of determining the type of life occurrence and generating the
resolution path is based on the shared responses.
[0053] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps of determining a life
occurrence and generating a resolution path. The steps may include
determining, using an algorithm deployed on a processor, a type of
life occurrence of an individual among a set of possible life
occurrences based at least in part on a multidimensional data set
comprising data representing aspects of a plurality of
individual-specific life occurrences, generating, using a
resolution path generation facility, a resolution path having a
series of action events leading to resolution of the life
occurrence, and sharing responses to the action events from a life
occurrence node with the processor and resolution path generation
facility, wherein at least one of determining the type of life
occurrence and generating the resolution path is based on the
shared responses. The algorithm may determine using at least one of
temporal data, spatial data and risk assessment. The life
occurrence node may be a mobile device. The steps may further
include generating a multidimensional context used by the algorithm
in determining a life occurrence. The context may include vendor
personalization of a widget executing in a life occurrence enabled
container of a mobile device of the user and at least one context
item selected from a list of context items consisting of: a time, a
location, a transaction detail, an urgency, an importance, the
status of a credit card or account, mobile device use history,
payment source, wallet state, type of transaction, product/service,
vendor, delivery method, delivery arrangements, tax status,
transaction participant, user preferences, the presence of a
network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information.
[0054] A transactional analytics facility is disclosed herein that
may include a processor that analyzes user transactions conducted
through a mobile transaction platform (MTP) and third-party sources
of user-related data to generate a static user profile and a memory
for storing the static user profile where it can be accessed by an
expert engine in determining a life occurrence based on
multidimensional context comprising data representing aspects of a
plurality of user-specific life occurrences. The aspects may be
derived from analysis of the static user profile, and current
context. The current context may include at least one of time,
space, and user input. The multidimensional context may include a
time, a location, a transaction detail, and at least one of an
urgency, an importance, the status of a credit card or account,
mobile device use history, payment source, wallet state, type of
transaction, product/service, vendor, delivery method, delivery
arrangements, tax status, transaction participant, user
preferences, the presence of a network or a particular account,
user associations with a non-vendor third-party, presence of
vouchers and promotions, loyalty points, third-party user-related
data, social network information, and calendar information. The
current context may include a risk assessment. The transactional
analytics facility may be in electronic communication with a mobile
transaction platform (MTP). The facility may further include a user
interface that allows a user to limit which user transactions and
third-party sources of user-related data can be used to generate
the static user profile.
[0055] A transactional analytics facility is disclosed herein that
may include a processor that analyzes user transactions conducted
through a mobile transaction platform (MTP) and third-party sources
of user-related data to generate a static user profile and a memory
for storing the static user profile where it can be accessed by an
expert engine in determining a life occurrence based on
multidimensional context comprising data representing aspects of a
plurality of user-specific life occurrences. The aspects may be
derived from analysis of the static user profile, and current
context. The facility may further include a user interface that may
allow a user to limit which user transactions and third-party
sources of user-related data can be used to generate the static
user profile. The current context may include at least one of time,
space, and user input. The multidimensional context may include a
time, a location, a transaction detail, and at least one of an
urgency, an importance, the status of a credit card or account,
mobile device use history, payment source, wallet state, type of
transaction, product/service, vendor, delivery method, delivery
arrangements, tax status, transaction participant, user
preferences, the presence of a network or a particular account,
user associations with a non-vendor third-party, presence of
vouchers and promotions, loyalty points, third-party user-related
data, social network information, and calendar information. The
current context may include a risk assessment. The transactional
analytics facility may be in electronic communication with a mobile
transaction platform (MTP).
[0056] A mobile transaction platform (MTP) is disclosed herein that
may include a transactional analytics facility that creates a
static profile of the user for use by an expert engine of the MTP
and the expert engine that determines a life occurrence based on
multidimensional context derived from analysis of user transactions
associated with the MTP and third-party sources of user-related
data, and that generates at least one resolution path for resolving
one or more aspects of the life occurrence. The resolution path may
include a series of action trigger events leading to resolution of
the life occurrence. The transaction facility and the expert engine
may exchange resolution trigger-events, static user profiles, and
dynamic user profiles. The expert engine may use a combination of
at least two of fuzzy logic, machine learning, and neural networks.
The expert engine and the transaction facility may access one or
more ecosystem resources when determining and analyzing through an
enterprise service bus. The ecosystem resources may include at
least one each of third party analytics, a social network, a
context driver, an offer, a value added service, a trusted service
manager (TSM), a certificate authority (CA), and a database. The
platform may further include at least one life occurrence container
deployed on a life occurrence node. The life occurrence container
may alert a user of the life occurrence node to the resolution
path, gather a user response to the alert, and generate one or more
life occurrence node-based transactions matched to the resolution
path. The life occurrence node may be a mobile device. The mobile
device may be used to select one of the life occurrence node-based
transactions. A personalized instrument may be configured to
securely cause the life occurrence node-based transaction matched
to the resolution path to be executed by a server. The user
transactions and user-related data from third-party sources may be
stored in a multi-dimensional database. The context may include
vendor personalization of a widget executing in a life occurrence
enabled container of a mobile device of the user and at least one
context item selected from a list of context items consisting of: a
time, a location, a transaction detail, an urgency, an importance,
the status of a credit card or account, mobile device use history,
payment source, wallet state, type of transaction, product/service,
vendor, delivery method, delivery arrangements, tax status,
transaction participant, user preferences, the presence of a
network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information. The platform may further
include at least one life occurrence container deployed on a life
occurrence node that administers selection of at least one
resolution action for addressing an aspect of the life occurrence.
The at least one resolution action may include providing a
personalized instrument configured to securely cause a life
occurrence-based, mobile transaction matched to the resolution
action to be executed cooperatively with a server. The life
occurrence node may be a mobile device.
[0057] A mobile transaction platform (MTP) is disclosed herein that
may include a transactional analytics facility that creates a
dynamic profile of the user for use by an expert engine of the MTP
and the expert engine that determines a life occurrence based on
multidimensional context derived from analysis of user transactions
associated with the MTP and third-party sources of user-related
data, and that generates at least one resolution path for resolving
one or more aspects of the life occurrence, the resolution path
having a series of action trigger events leading to resolution of
the life occurrence. The transaction facility and the expert engine
may exchange resolution trigger-events, static user profiles, and
dynamic user profiles. The expert engine may use a combination of
at least two of fuzzy logic, machine learning, and neural networks.
The expert engine and the transaction facility may access one or
more ecosystem resources when determining and analyzing through an
enterprise service bus. The ecosystem resources may include at
least one each of third party analytics, a social network, a
context driver, an offer, a value added service, a trusted service
manager (TSM), a certificate authority (CA), and a database. The
platform may further include at least one life occurrence container
deployed on a life occurrence node. The life occurrence container
may alert a user of the life occurrence node to the resolution
path, gather a user response to the alert, and generate one or more
life occurrence node-based transactions matched to the resolution
path. The life occurrence node may be a mobile device. The mobile
device may be used to select one of the life occurrence node-based
transactions. A personalized instrument may be configured to
securely cause the life occurrence node-based transaction matched
to the resolution path to be executed by a server. The user
transactions and user-related data from third-party sources may be
stored in a multi-dimensional database. The context may include
vendor personalization of a widget executing in a life occurrence
enabled container of a mobile device of the user and at least one
context item selected from a list of context items consisting of: a
time, a location, a transaction detail, an urgency, an importance,
the status of a credit card or account, mobile device use history,
payment source, wallet state, type of transaction, product/service,
vendor, delivery method, delivery arrangements, tax status,
transaction participant, user preferences, the presence of a
network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information. The platform may further
include at least one life occurrence container deployed on a life
occurrence node that administers selection of at least one
resolution action for addressing an aspect of the life occurrence.
The at least one resolution action comprises providing a
personalized instrument configured to securely cause a life
occurrence-based, mobile transaction matched to the resolution
action to be executed cooperatively with a server.
[0058] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps that may include
creating a dynamic profile of the user for use by an expert engine
of a mobile transaction platform (MTP), determining a life
occurrence based on multidimensional context derived from analysis
of user transactions associated with the MTP and third-party
sources of user-related data and generating at least one resolution
path for resolving one or more aspects of the life occurrence, the
resolution path having a series of action trigger events leading to
resolution of the life occurrence. The step of determining may
involve a combination of at least two of fuzzy logic, machine
learning, and neural networks. The steps may further include
deploying at least one life occurrence container on a life
occurrence node. The life occurrence container may alert a user of
the life occurrence node to the resolution path, gather a user
response to the alert, and generate one or more life occurrence
node-based transactions matched to the resolution path. The life
occurrence node may be a mobile device. The mobile device may be
used to select one of the life occurrence node-based transactions.
A personalized instrument may be configured to securely cause the
life occurrence node-based transaction matched to the resolution
path to be executed by a server. The steps may further include
deploying at least one life occurrence container on a life
occurrence node that administers selection of at least one
resolution action for addressing an aspect of the life occurrence.
The at least one resolution action may include providing a
personalized instrument configured to securely cause a life
occurrence-based, mobile transaction matched to the resolution
action to be executed cooperatively with a server.
[0059] A transactional analytics facility is disclosed herein that
may include a communications facility that gathers multidimensional
life occurrence context from a mobile transaction platform (MTP)
and a processor that analyzes user transactions conducted through
the MTP. The multidimensional life occurrence context and
third-party sources of user-related data may generate a risk
profile of a user, trigger-events, third-parties, resolution
actions, life occurrences, and potential transactions. The risk
profile may be used for determining if one or more resolution
actions are suitable for presenting to the user. The risk profile
may be used to rank resolution actions. The risk profile may relate
to the risk of a transaction for a vendor.
[0060] A mobile transaction platform (MTP) is disclosed herein that
may include a lifestyle container deployed on a life occurrence
node that gathers multidimensional life occurrence context, a
transactional analytics facility that analyzes data extracted from
a plurality of user transactions by the MTP, third-party sources of
user-related data, and the multidimensional life occurrence context
to generate a risk profile of a user, trigger-events,
third-parties, resolution actions, life occurrences, and potential
transactions and an expert engine that uses the risk profile to
perform a risk-based ranking of resolution actions. The risk
profile may further be used by the expert engine for determining if
one or more resolution actions are suitable for presenting to the
user. The risk profile may relate to the risk of a transaction for
a vendor. The life occurrence node may be a mobile device.
[0061] A mobile transaction platform (MTP) is disclosed herein that
may include a lifestyle container deployed on a life occurrence
node that gathers multidimensional life occurrence context, a
transactional analytics facility that analyzes user transactions
conducted by the life occurrence node through the MTP, third-party
sources of user-related data, and the multidimensional life
occurrence context to generate a risk profile of a user,
trigger-events, third-parties, resolution actions, life
occurrences, and potential transactions and an expert engine that
uses the risk profile to determine if one or more resolution
actions are suitable for presenting to the user. The risk profile
may relate to the risk of a transaction for a vendor. The expert
engine may further use the risk profile to perform a ranking of
resolution actions. The life occurrence node may be a mobile
device.
[0062] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps that may include
gathering multidimensional life occurrence context from a mobile
transaction platform (MTP) and analyzing user transactions
associated with the MTP. The multidimensional life occurrence
context and third-party sources of user-related data may generate a
risk profile of a user, trigger-events, third-parties, resolution
actions, life occurrences, and potential transactions. The risk
profile may be used for determining if one or more resolution
actions are suitable for presenting to the user. The risk profile
may be used to rank resolution actions. The risk profile may relate
to the risk of a transaction for a vendor.
[0063] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps that may include
gathering multidimensional life occurrence context, analyzing user
transactions associated with the MTP, third-party sources of
user-related data, and the multidimensional life occurrence context
to generate a risk profile of a user, trigger-events,
third-parties, resolution actions, life occurrences, and potential
transactions, and ranking resolution actions based on the risk
profile. The steps may further include determining if one or more
resolution actions are suitable for presenting to the user based on
the risk profile. The risk profile may relate to the risk of a
transaction for a vendor.
[0064] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps that may include
gathering multidimensional life occurrence context, analyzing user
transactions associated with the MTP, third-party sources of
user-related data, and the multidimensional life occurrence context
to generate a risk profile of a user, trigger-events,
third-parties, resolution actions, life occurrences, and potential
transactions and determining if one or more resolution actions are
suitable for presenting to the user based on the risk profile. The
risk profile may relate to the risk of a transaction for a vendor.
The steps may further include performing a ranking of resolution
actions based on the risk profile.
[0065] A method for configuring an eco-system enabled life
occurrence container operating on a mobile device to address a life
occurrence is disclosed herein that may include. The method may
include developing and storing on a non-transient computer readable
medium a context for trigger-events based, at least in part, on
life occurrence time data, user and life occurrence location data,
transaction analytics of transactions conducted through a mobile
transaction platform of the eco-system, and third-party
user-related data, monitoring the trigger-event context to detect
at least one trigger-event indicative of a life occurrence,
deploying on the mobile device at least one personalized widget
available in the eco-system that facilitates delivery of a
third-party provided service for addressing the life occurrence,
associating at least one resolution action presented to a user in
response to a detected trigger-event with preconfigured mobile
transactions for executing the at least one resolution action in
response to a user acceptance of the presented action, and
pre-configuring mobile transactions that are executed via the
personalized widgets to effect delivery of the third-party service
that satisfies an aspect of the life occurrence. The method may
further include updating the trigger-event context through an
enabling layer operable on the mobile device. The enabling layer
may provide access to at least one trigger-event context source.
The at least one trigger-event context source comprises at least
one of a GPS data source, a clock and a calendar. The third-party
user-related data may include at least one of social data, calendar
data and family associations.
[0066] A computer readable storage medium having data stored
therein representing software executable by a computer to configure
an eco-system enabled life occurrence container operating on a
mobile device to address a life occurrence is disclosed herein. The
software may include instructions to develop and store on a
non-transient computer readable medium a context for trigger-events
based, at least in part, life occurrence time data, user and life
occurrence location data, transaction analytics of transactions
conducted through a mobile transaction platform of the eco-system,
and third-party user-related data, instructions to monitor the
trigger-event context to detect at least one trigger-event
indicative of a life occurrence, instructions to deploy on the
mobile device at least one personalized widget available in the
eco-system that facilitates delivery of a third-party provided
service for addressing the life occurrence, instructions to
associate at least one resolution action presented to a user in
response to a detected trigger-event with preconfigured mobile
transactions for executing the at least one resolution action in
response to a user acceptance of the presented action and
instructions to pre-configure mobile transactions that are executed
via the personalized widgets to effect delivery of the third-party
service that satisfies an aspect of the life occurrence. The steps
may further include updating the trigger-event context through an
enabling layer operable on the mobile device. The enabling layer
may provide access to at least one trigger-event context source.
The at least one trigger-event context source may include at least
one of a GPS data source, a clock and a calendar. The third-party
user-related data may include at least one of social data, calendar
data and family associations.
[0067] A mobile device configured for life occurrence resolution is
disclosed herein that may include a life occurrence container
operable on a life occurrence node operable to coordinate the
operation of at least two of a detection of at least one
trigger-event, a use of at least one personalized widget, a
presentation of at least one resolution action and an execution of
preconfigured actions to facilitate addressing a life occurrence.
The mobile device may further include at least one personalized
widget for facilitating service delivery associated with a
preconfigured transaction with a vendor that is determined from
analysis of mobile transactions processed through a mobile
transaction platform, life occurrence metadata, and user-related
data derived from third party user data sources. The mobile device
may further include an enabling layer operable on the mobile device
for facilitating interoperation of the life occurrence container
and life occurrence node resources comprising at least one of a
user interface, communications and secure element access and at
least one electronic wallet operable on the life occurrence node
that the personalized widget is authorized to access for
facilitating service delivery. The life occurrence node may be the
mobile device. The preconfigured actions may include mobile
transactions. The life occurrence may be predicted based, at least
in part, on user-specific mobile transactions processed through a
mobile transaction platform and user-related data derived from
third party user data sources. The service delivery may be
facilitated via a service layer of a platform for secure
personalized transactions.
[0068] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps that may include
configuring an eco-system-enabled life occurrence container that is
operable on a life occurrence node to facilitate coordinating
detection and monitoring of trigger-events for addressing a life
occurrence. The steps of configuring may include generating context
at an expert engine for trigger-events based on at least one of a
life occurrence time, a life occurrence location, transaction
analytics of user-specific transactions conducted through a mobile
transaction platform, third-party user-related data, and a risk to
a vendor of a transaction between a user and the vendor and
programming the life occurrence container to monitor the
trigger-event context for detection of trigger-events. The program
may instruct a microprocessor to perform the steps that may further
include synchronizing the life occurrence container with at least
one of the expert engine and a mobile transaction platform (MTP)
through which transactions are conducted on behalf of a user via a
life occurrence node to maintain a current context for the
trigger-events. The step of synchronizing may include updating the
trigger-event context of the life occurrence container through an
enabling layer operable on the life occurrence node. The enabling
layer may provide access to trigger-event context sources. The life
occurrence node may be a mobile device. The trigger-event context
sources may include at least one of a GPS, a clock, a calendar, an
alert, an e-mail, a message, a call, and a bookmark. The life
occurrence container may include at least one widget, electronic
wallet, resolution action, context monitor, trigger event detector,
and an enabling layer. The trigger-event context sources may
include a time, a location, a transaction detail, and at least one
of an urgency, an importance, the status of a credit card or
account, mobile device use history, payment source, wallet state,
type of transaction, product/service, vendor, delivery method,
delivery arrangements, tax status, transaction participant, user
preferences, the presence of a network or a particular account,
user associations with a non-vendor third-party, presence of
vouchers and promotions, loyalty points, third-party user-related
data, social network information, and calendar information.
[0069] A sync architecture is disclosed herein that may include an
eco-system-enabled life occurrence container, that is operable on a
life occurrence node, and is configured to facilitate coordinating
monitoring and detection of trigger-events for addressing a life
occurrence. The architecture may include an expert engine that
generates context for trigger-events based on at least one of time,
a location, transaction analytics, third-party user-related data,
and a risk and a communications facility for synchronizing the life
occurrence container with at least one of the expert engine and a
mobile transaction platform (MTP) through which transactions are
conducted on behalf of a user via a life occurrence node to
maintain a current context for the trigger-events. The step of
synchronizing may include updating the trigger-event context of the
life occurrence container through an enabling layer operable on the
life occurrence node. The enabling layer may provide access to
trigger-event context sources. The life occurrence node may be a
mobile device. The trigger-event context sources may include at
least one of a GPS, a clock, a calendar, an alert, an e-mail, a
message, a call, and a bookmark. The life occurrence container may
include at least one widget, electronic wallet, resolution action,
context monitor, trigger event detector, and an enabling layer. The
trigger-event context sources may include a time, a location, a
transaction detail, and at least one of an urgency, an importance,
the status of a credit card or account, mobile device use history,
payment source, wallet state, type of transaction, product/service,
vendor, delivery method, delivery arrangements, tax status,
transaction participant, user preferences, the presence of a
network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information.
[0070] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps that may include
configuring an eco-system-enabled life occurrence container that is
operable on a life occurrence node to facilitate coordinating
monitoring and detection of trigger-events for addressing a life
occurrence. The step of configuring may include generating context
at an expert engine for trigger-events based on at least one of
time, a location, transaction analytics, third-party user-related
data, and a risk. The program may instruct a microprocessor to
perform steps that may further include communicating among the life
occurrence container, the expert engine and a mobile transaction
platform (MTP) through which a user conducts transactions via the
life occurrence node to maintain current context for the
trigger-events. The step of communicating may include updating the
trigger-event context of the life occurrence container through an
enabling layer operable on the life occurrence node. The enabling
layer may provide access to trigger-event context sources. The life
occurrence node may be a mobile device. The trigger-event context
sources may include at least one of a GPS, a clock, a calendar, an
alert, an e-mail, a message, a call, and a bookmark. The life
occurrence container may include at least one widget, electronic
wallet, resolution action, context monitor, trigger event detector,
and an enabling layer. The trigger-event context sources may
include a time, a location, and at least one of a transaction
detail, an urgency, an importance, the status of a credit card or
account, mobile device use history, payment source, wallet state,
type of transaction, product/service, vendor, delivery method,
delivery arrangements, tax status, transaction participant, user
preferences, the presence of a network or a particular account,
user associations with a non-vendor third-party, presence of
vouchers and promotions, loyalty points, third-party user-related
data, social network information, and calendar information.
[0071] An enhanced communications architecture is disclosed herein
that may include an eco-system-enabled life occurrence container,
that is operable on a life occurrence node, and is configured to
facilitate coordinating monitoring and detection of trigger-events
for addressing a life occurrence. The architecture may include an
expert engine that generates context for trigger-events based on at
least one of time, a location, transaction analytics, third-party
user-related data, and a risk and a communications facility for
communicating among the life occurrence container, the expert
engine and a mobile transaction platform (MTP) through which a user
conducts transactions via the life occurrence node to maintain
current context for the trigger-events. The step of communicating
may include updating the trigger-event context of the life
occurrence container through an enabling layer operable on the life
occurrence node. The enabling layer may provide access to
trigger-event context sources. The life occurrence node may be a
mobile device. The trigger-event context sources may include at
least one of a GPS, a clock, a calendar, an alert, an e-mail, a
message, a call, and a bookmark. The life occurrence container may
include at least one widget, electronic wallet, resolution action,
context monitor, trigger event detector, and an enabling layer. The
trigger-event context sources may include a time, a location, and
at least one of a transaction detail, an urgency, an importance,
the status of a credit card or account, mobile device use history,
payment source, wallet state, type of transaction, product/service,
vendor, delivery method, delivery arrangements, tax status,
transaction participant, user preferences, the presence of a
network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information.
[0072] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform the steps of a life occurrence
alert that may include taking metadata that describes a future
potential life occurrence, determining possible resolution actions
beneficial to take in advance of the future life occurrence based
on multidimensional context derived from analysis of transactions
performed on behalf of a user with a life occurrence node via a
mobile transaction platform and third-party sources of user-related
data, and determining context of trigger-event conditions for each
resolution action, monitoring trigger-event context. When
trigger-event conditions are met, the steps may include presenting
resolution actions that include life occurrence context that is
relevant to a user making a decision about accepting the resolution
action. The steps may further include preparing an action for each
resolution action and adapting the action based on
action/transaction context when a resolution action is accepted by
the user. The action may be at least one of a mobile device action
and a transaction. The preparing of the action may include
configuring a widget to access an ecosystem service provider, an
electronic wallet on the user's mobile device, a secure element of
the mobile device, and to optionally trigger other widgets to
execute on the mobile device. The preparing of the action may
include configuring one or more widgets that follow user
preferences for form of payment, receipt handling, and
delivery/contact details to facilitate service delivery that
effects the action/transaction without requiring user input. The
life occurrence node may be a mobile device. The trigger-event
context sources may include at least one of a GPS, a clock, a
calendar, an alert, an e-mail, a message, a call, and a bookmark.
The life occurrence container may include at least one widget,
electronic wallet, resolution action, context monitor, trigger
event detector, and an enabling layer. The trigger-event context
sources may include a time, a location, and at least one of a
transaction detail, an urgency, an importance, the status of a
credit card or account, mobile device use history, payment source,
wallet state, type of transaction, product/service, vendor,
delivery method, delivery arrangements, tax status, transaction
participant, user preferences, the presence of a network or a
particular account, user associations with a non-vendor
third-party, presence of vouchers and promotions, loyalty points,
third-party user-related data, social network information, and
calendar information.
[0073] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps of a life occurrence
alert that may include taking metadata that describes a future
potential life occurrence, determining possible resolution actions
beneficial to take in advance of the future life occurrence based
on multidimensional context derived from analysis of transactions
performed on behalf of a user with a life occurrence node via a
mobile transaction platform and third-party sources of user-related
data, and determining context of trigger-event conditions for each
resolution action, monitoring trigger-event context. When
trigger-event conditions are met, the steps may include determining
if one or more resolution actions are suitable for presenting to
the user. The steps may further include presenting suitable
resolution actions that include life occurrence context that is
relevant to a user making a decision about accepting the resolution
action, preparing an action for each resolution action, adapting
the action based on action/transaction context when a resolution
action is accepted by the user. The action may be at least one of a
mobile device action and a transaction. The step of preparing the
action may include configuring a widget to access an ecosystem
service provider, an electronic wallet on the user's mobile device,
a secure element of the mobile device, and to optionally trigger
other widgets to execute on the mobile device. The step of
preparing the action may include configuring one or more widgets
that follow user preferences for form of payment, receipt handling,
and delivery/contact details to facilitate service delivery that
effects the action/transaction without requiring user input. The
life occurrence node may be a mobile device. The trigger-event
context sources may include at least one of a GPS, a clock, a
calendar, an alert, an e-mail, a message, a call, and a bookmark.
The life occurrence container may include at least one widget,
electronic wallet, resolution action, context monitor, trigger
event detector, and an enabling layer. The trigger-event context
sources may include a time, a location, and at least one of a
transaction detail, an urgency, an importance, the status of a
credit card or account, mobile device use history, payment source,
wallet state, type of transaction, product/service, vendor,
delivery method, delivery arrangements, tax status, transaction
participant, user preferences, the presence of a network or a
particular account, user associations with a non-vendor
third-party, presence of vouchers and promotions, loyalty points,
third-party user-related data, social network information, and
calendar information.
[0074] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein. The program may
instruct a microprocessor to perform steps of an instrument-based
method of life occurrence alert. The steps may include taking
metadata that describes a potential life occurrence, determining
possible resolution actions beneficial to take in advance of the
potential life occurrence based on multidimensional context derived
from analysis of user transactions performed with a mobile device
via a mobile transaction platform and third-party sources of
user-related data, and determining context of trigger-event
conditions for each resolution action, monitoring trigger-event
context. When trigger-event conditions are met, the steps may
include presenting resolution actions that include life occurrence
context that is relevant to a user making a decision about
accepting the resolution action. The steps may further include
preparing an instrument to facilitate executing at least one of an
action and a transaction for each resolution action and adapting
the instrument based on context when a resolution action is
accepted by the user. The instrument may include metadata that
identifies a transaction type accessible by a server and
user/wallet/device information required to execute the transaction
on behalf of the user. The action may be a mobile device action.
The steps of preparing the instrument may include configuring a
widget to access an ecosystem service provider, an electronic
wallet on the user's mobile device, a secure element of the mobile
device, and to optionally trigger other widgets to execute on the
mobile device. The step of preparing the instrument may include
configuring one or more widgets that follow user preferences for
form of payment, receipt handling, and delivery/contact details to
facilitate service delivery that effects the action/transaction
without requiring user input. The life occurrence node may be a
mobile device. The trigger-event context sources may include at
least one of a GPS, a clock, a calendar, an alert, an e-mail, a
message, a call, and a bookmark. The life occurrence container may
include at least one widget, electronic wallet, resolution action,
context monitor, trigger event detector, and an enabling layer. The
trigger-event context sources may include a time, a location, and
at least one of a transaction detail, an urgency, an importance,
the status of a credit card or account, mobile device use history,
payment source, wallet state, type of transaction, product/service,
vendor, delivery method, delivery arrangements, tax status,
transaction participant, user preferences, the presence of a
network or a particular account, user associations with a
non-vendor third-party, presence of vouchers and promotions,
loyalty points, third-party user-related data, social network
information, and calendar information.
[0075] A non-transitory computer readable medium with an executable
program stored thereon is disclosed herein that may include. The
program may instruct a microprocessor to perform steps of an
instrument-based method of life occurrence alert. The steps may
include taking metadata that describes a potential life occurrence,
determining possible resolution actions beneficial to take in
advance of the potential life occurrence based on multidimensional
context derived from analysis of user transactions performed with a
mobile device via a mobile transaction platform and third-party
sources of user-related data, and determining context of
trigger-event conditions for each resolution action, monitoring
trigger-event context. When trigger-event conditions are met, the
steps may include determining if one or more resolution actions are
suitable for presenting to the user. The steps may further include
presenting suitable resolution actions that include life occurrence
context that is relevant to a user making a decision about
accepting the resolution action, preparing an instrument to
facilitate executing at least one of an action and a transaction
for each resolution action, and adapting the instrument based on
context when a resolution action is accepted by the user. The
instrument may include metadata that identifies a transaction type
accessible by a server and user/wallet/device information required
to execute the transaction on behalf of the user. The action may be
a mobile device action. The step of preparing the instrument may
include configuring a widget to access an ecosystem service
provider, an electronic wallet on the user's mobile device, a
secure element of the mobile device, and to optionally trigger
other widgets to execute on the mobile device. The step of
preparing the instrument may include configuring one or more
widgets that follow user preferences for form of payment, receipt
handling, and delivery/contact details to facilitate service
delivery that effects the action/transaction without requiring user
input. The life occurrence node may be a mobile device. The
trigger-event context sources may include at least one of a GPS, a
clock, a calendar, an alert, an e-mail, a message, a call, and a
bookmark. The life occurrence container may include at least one
widget, electronic wallet, resolution action, context monitor,
trigger event detector, and an enabling layer. The trigger-event
context sources may include a time, a location, a transaction
detail, and at least one of an urgency, an importance, the status
of a credit card or account, mobile device use history, payment
source, wallet state, type of transaction, product/service, vendor,
delivery method, delivery arrangements, tax status, transaction
participant, user preferences, the presence of a network or a
particular account, user associations with a non-vendor
third-party, presence of vouchers and promotions, loyalty points,
third-party user-related data, social network information, and
calendar information.
[0076] A method for initiating on-boarding for a user is disclosed
herein. The method may include inputting user related information
from a lifestyle container for a specific user ID, registering at
least one intelligent appliance with the specific user ID, applying
at least one rule on the user related information to determine
profile of the user using at least one of machine learning, fuzzy
logic and neural network, and displaying information on a display
interface corresponding to the lifestyle container. The method may
further include accessing at least one external source to derive
information related to at least to user behavior, user profile on a
social networking site, transaction history for a merchant, travel
information, and health related information, wherein the derived
information is used for determining the profile of the user. The
displaying of information on the display interface may include
displaying a welcome message on creating the user profile at a
mobile transaction platform. The method may further include
analyzing the user profile and determining offers, notification and
messages based on the analyses of the user profile. The method may
further include displaying the offers, notification and messages on
the display interface corresponding to the lifestyle container.
[0077] A system for initiating on-boarding for a user is disclosed
herein. The system may include a lifestyle container configured to
receive input information from a user having a user ID, an at least
one intelligent appliance associated with the user ID, an expert
engine of a mobile transaction platform, wherein the expert engine
is configured to process the user related information and
facilitate communication with at least one external source to
retrieve information corresponding to the user, at least one rule
for determining profile of the user using at least one of machine
learning, fuzzy logic and neural network, and a display interface
configured to display information for the user. The expert engine
may be configured to communicate with the at least one external
source using an enterprise service bus. The lifestyle container may
be configured to receive offers, notification and messages from the
mobile transaction platform. The lifestyle container may be
configured to receive offers, notification and messages from the
mobile transaction platform during synchronization process. The
mobile transaction platform may be configured to utilize push
message to transmit the offers, notification and messages to the
lifestyle container.
[0078] A method for facilitating shopping transaction for a user is
disclosed herein. The method may include receiving a first shopping
list from a lifestyle container, determining at least one
trigger-event corresponding to the first shopping list, wherein the
at least one trigger-event is associated with at least one life
occurrence of the user, modifying the first shopping list based on
the behavior pattern of the user, transmitting the modified first
shopping list to at least one merchant via an enterprise service
bus, transmitting the shopping list from the merchant to the user
for facilitating selection of at least one shopping item, and
performing a shopping transaction based on the selection of the at
least one shopping item. The method may further include configuring
the expert engine to determine the transaction behavior of the user
from the shopping transaction that is based on the selection of the
at least one shopping item. The method may further include adding
at least one offer for the at least one shopping item based on the
transaction behavior of the user. The receiving of a first shopping
list from a lifestyle container may include receiving a manual
selection of the at least one shopping item from the user. The
receiving of a first shopping list from a lifestyle container may
include automatically selecting at least one offer saved on the
lifestyle container associated with the user, in another aspect.
The at least one trigger-event may be at least one of a temporal
event and a location event associated with the life occurrence of
the user.
[0079] A system for facilitating shopping transaction for a user is
disclosed herein that may include a lifestyle container configured
to receive a first shopping list, and a mobile transaction platform
configured to determine at least one trigger-event corresponding to
the first shopping list. The at least one trigger-event may be
associated with at least one life occurrence of the user. The
system may further include an expert engine configured to modify
the first shopping list based on the behavior pattern of the user,
to transmit the modified first shopping list to at least one
merchant via an enterprise service bus and to transmit the shopping
list from the merchant to the user for facilitating selection of at
least one shopping item, and an enabled ecosystem configured to
performing a shopping transaction based on the selection of the at
least one shopping item. The at least one trigger-event may be at
least one of a temporal event and a location event associated with
the life occurrence of the user. The expert engine may be
configured to determine the user behavior using fuzzy logic,
machine learning and neural network. The expert engine may be
configured to determine the user behavior using data corresponding
to the shopping transaction being performed by the enabled
ecosystem.
[0080] A method is disclosed herein for facilitating parking
arrangements for a user. The method may include determining time
and location associated with the parking requirements of the user,
monitoring user behavior for the at least one parking preference,
and generating a list of parking requirements for the user. The
list of the parking requirements may include at least one parking
requirement in accordance with the at least one parking preference
of the user. The method may further include transmitting the list
of parking requirements to at least one parking provider, and
communicating the status of the parking requirement to the user in
response to a life occurrence. The step of determining time and
location associated with the parking requirements of the user may
include monitoring at least one event that may be extracted from at
least one of a calendar application and travel booking information
of the user. The monitoring of user behavior for the at least one
parking preference may include monitoring the user behavior using
at least one of a machine learning, fuzzy logic and neural network.
The method may further include determining an availability of
pre-booking facility from the at least one parking provider. The
method may further booking a parking space in accordance with the
at least one parking requirement of the user when the at least one
parking provider support pre-booking of the parking space. The
method may further include generating a reference number for the at
least one parking requirement when the at least one parking
provider does not support the pre-booking of the parking space. The
method may further include communicating live parking updates to
the user for the reference number, wherein the live parking updates
may include at least one of: parking prices and an availability of
the parking space in accordance with the at least one parking
requirement of the user.
[0081] A system is disclosed herein for facilitating parking
arrangement for a user. The system may include a lifestyle
container configured to determine time and location associated with
the parking requirements of the user, a transaction platform
configured to store the parking requirements of the user, an expert
engine configured to monitor user behavior for the at least one
parking preference, to generate a list of parking requirements for
the user. The list of the parking requirements may include at least
one parking requirement in accordance with the at least one parking
preference of the user and to transmit the list of parking
requirements to at least one parking provider. The system may
further include an enabled ecosystem configured to communicate the
status of the at least one parking requirement to the user in
response to an life occurrence. The transaction platform may be
further configured to facilitate synchronizing parking requirements
of the user with the lifestyle container. The expert engine may be
further configured to monitor the user behavior using at least one
of: a machine learning, fuzzy logic and neural network.
[0082] These and other systems, methods, objects, features, and
advantages of a life occurrence management platform will be
apparent to those skilled in the art from the following detailed
description of the preferred embodiment and the drawings. All
documents mentioned herein are hereby incorporated in their
entirety by reference.
BRIEF DESCRIPTION OF THE FIGURES
[0083] The methods and systems of life occurrence management and
the following detailed description of certain embodiments thereof
may be understood by reference to the following figures:
[0084] FIG. 1 depicts a high-level system diagram including a
mobile transaction platform (MTP) expert engine (EE) configured to
determine life occurrences from a plurality of data sources, some
of which are accessible to the mobile transaction platform.
[0085] FIG. 2 depicts a high-level system diagram including an MTP
expert engine (EE) configured to determine types of life
occurrences of an individual and to generate candidate resolution
paths for resolving one or more aspects of the life occurrence.
[0086] FIG. 3 depicts a high-level system diagram including an
MTP-EE configured to generate context-based resolution paths using
temporal data and spatial data.
[0087] FIG. 4 depicts a high-level system diagram including an
MTP-EE configured to determine resolution actions for presenting to
a user in response to a determined life occurrence.
[0088] FIG. 5 depicts a high-level system diagram including an
MTP-EE configured to determine resolution actions using user
profiles.
[0089] FIG. 6 depicts a high-level system diagram including an
MTP-EE configured to communicate via a utility resource access
switch to access ecosystem resources.
[0090] FIG. 7 depicts a high-level system diagram including an MTP
expert engine configured to generate a resolution path having a
series of action trigger-events.
[0091] FIG. 8 depicts a high level system diagram including a life
occurrence container configured to facilitate shopping transactions
based on action trigger-events.
[0092] FIG. 9 depicts a high-level system diagram including a life
occurrence container that may facilitate coordinating detection of
trigger-events for addressing a life occurrence.
[0093] FIG. 10-16 depicts various tables that may include a
plurality of data types and corresponding attributes of the data
types.
[0094] FIG. 17A-D illustrates an example embodiment of a method for
facilitating on boarding of a user.
[0095] FIG. 18A-D depicts an example embodiment of method for
facilitating shopping transaction for the user in the mobile
transaction platform.
[0096] FIG. 19A-D depicts an example embodiment of a method for
facilitating parking arrangements for the user in response to a
life occurrence.
[0097] FIG. 20 depicts a block diagram of an embodiment of a life
occurrence determination and service system.
[0098] FIG. 21 depicts a block diagram of an embodiment of a life
occurrence determination and service system with risk scoring.
[0099] FIG. 22 depicts a functional and flow diagram of
communications among functional blocks of a life occurrence
determination and service system.
[0100] FIG. 23 depicts a flow diagram of MTP to expert engine
communications.
[0101] FIG. 24 depicts a block diagram of an embodiment of a MTP
for life occurrence determination and servicing.
[0102] FIG. 25 depicts guiding principles for a user centric life
occurrence determination and servicing capability.
[0103] FIG. 26 depicts three views of a life occurrence
determination and handling user interface.
[0104] FIGS. 27 through 40 depict various user interfaces for
facilitating interactions of a user with the lifestyle system
through a life occurrence node, in accordance with various
embodiments of the present invention.
DETAILED DESCRIPTION
[0105] A mobile lifestyle that leverages a mobile transaction
ecosystem to provide a range of life occurrence services that
maximize usability of the ecosystem while minimizing intrusions on
the user may enable a managed process of performance and risk
driven escalation of alerts, actions, and transactions to resolve
aspects of life occurrences. With the premise that a mobile
lifestyle experience should revolve around the mobile user, thereby
establishing a user-centric experience, an intuitive interface that
allows a user to view alerts, tokens, notifications and the like is
provided. A mobile lifestyle environment may also provide a
seamless experience with applications driving transactions for a
wide range of life occurrences covering finance, retail, health,
personal, business, government, and the like. To facilitate a
seamless low-intrusion experience, such a platform may handle
interfacing to all payment and transaction channels by applying a
proactively intelligent capability based around an expert
engine-like environment that accepts and utilizes inputs from
users, rules, behavioral analytics, all forms of electronic
user-related content (e.g. social media), and the like. The result
is a mobile user-centric experience that works to deliver
actionable alerts that relate to what a user wants to do rather
than just what the user has to do. Driving such actionable alerts
is multi-dimensional context derived from time, location, user
mobile uses history, transaction history, third party sourced data,
and the like. These actionable alerts are time, location, and
context aware while including sufficient flexibility to adjust to a
user's reaction to suggestions, and/or recommendations in real
time. The result being an intuitive system that focuses on simple,
seamless, contextual experience that brings all personalized
applications into a user lifestyle interface while facilitating all
necessary secure transactions behind the scenes thereby not
requiring the user to deal with these complexities.
[0106] Life occurrence management services may be extended well
beyond direct user mobile device interaction to include any
network-connected computing-capable device that is related to or
can facilitate resolution of user-related life occurrences through
a secure electronic transaction ecosystem. While determining and
resolving some life occurrences may involve presentation of
resolution path options to a user on the user's mobile or other
computing device, life occurrences may be resolved without any need
for user notification or interaction, thereby minimizing intrusion
on a user, while addressing the resolution actions that the user
desires. Such automatic life occurrence resolution services may be
initiated by any life occurrence node (e.g. intelligent electronic
device) or may simply be initiated by an expert engine that handles
determination of and resolution of life occurrences for users.
Resolution of aspects of life occurrences, via a secure electronic
transaction ecosystem, may include communicating resolution actions
among life occurrence devices and the ecosystem.
[0107] A life occurrence node may be any of a wide range of types
of machines, appliances, toys, equipment, packaging, service
facilities, healthcare devices, and the like that are provided with
computing and networking. A few such examples include laundry
appliances, drug administration devices, self-powered toys, air
conditioning units, on-road and off-road vehicles, automated
package delivery vehicles, industrial equipment, home-making
appliances, personal health monitoring wearable devices, and the
like. Each exemplary life occurrence node and any other computing
and network-capable device may be configured with life occurrence
servicing functionality to generate and/or contribute to the
generation of alerts, resolution actions, life occurrence servicing
transactions, and the like.
[0108] The methods and systems of life occurrence management
described herein may be optionally activated for a user through an
affirmative consent by the user. Confidentiality of user data, such
as user transactional data at any level of detail, is of paramount
of importance when considering the embodiments of life occurrence
management described herein. Any user may choose to register with a
platform providing life occurrence management services as described
herein. An example of such user-chosen registration is depicted in
FIG. 17 described later herein. Without such an explicit user
choice to participate, user information, including confidential
information available to a mobile transaction platform will remain
secure.
[0109] The systems and methods of life occurrence management
disclosed herein may comprise a mobile transaction platform (MTP)
expert engine. Such an engine may be used to determine life
occurrences based on multidimensional context derived from user
transactions. Such transactions may be handled either through the
mobile transaction platform, or third-party sources of user-related
data. Life occurrences may be an event that has not yet occurred.
Life occurrences may be based on at least one user-related event
that has occurred in the past. The MTP expert engine may use the
derived multidimensional context in order to generate resolution
action triggers to resolve such life occurrences by facilitating
user directed mobile actions. In order to facilitate such
resolution actions, the expert engine may include preconfigured
mobile transaction capabilities to facilitate execution of the
resolution actions in response to a user selection of a certain
resolution action. Each life occurrence may also be preconfigured
with various triggers, which may facilitate a user's ability to
review and take action to the resolution action. Such triggers may
also be associated with certain life occurrences so as to alert
users to the events and allow users to select certain resolution
paths.
[0110] As generally used herein, a resolution path may comprise one
or more resolution actions that lead to a resolution related to a
life occurrence, wherein the resolutions actions may be performed
individually. A resolution action that is part of a resolution path
may influence the direction of the path (e.g. cause a branching of
the resolution path). Alternatively, a resolution action may
comprise one or more resolution paths that when executed lead to a
resolution related to a life occurrence. A resolution action that
comprises at least one resolution path may be used to activate the
resolution path. Likewise a user may be provided with more than one
resolution action and based on which resolution action the user
chooses, the resolution path associated with the selected
resolution action will be taken to resolve an aspect of the life
occurrence.
[0111] The MTP may facilitate a secure mobile transaction. The data
associated with the transaction, such as the date, time, location,
payment source, wallet state, type of transaction, vendor, product
type, among others, may be harvested, categorized, aggregated, and
processed with other transaction data for the user. The data
collected may also be sent to a data repository from which the
expert engine can determine life occurrences and generate
resolution paths.
[0112] An expert engine may use mobile transaction data sources for
generating action triggers. Such an expert engine may be coupled
with a mobile transaction platform (MTP) and may use a
multidimensional context derived from user mobile-based
transactions handled through the MTP to generate action triggers
for resolving life occurrences by facilitating user directed mobile
device actions.
[0113] The expert engine may determine a type of life occurrence of
an individual among a set of possible life occurrences. The
determination may be based at least in part on a multidimensional
data set constructed in connection with a MTP through which the
individual conducts transactions. The expert engine may then
generate a resolution action, that when activated, triggers
invocation of a resolution path in order to address or resolve the
life occurrence. Such a resolution path may be generated to operate
via use of a life occurrence node, such as a user's mobile device
and the like. The expert engine may use numerous sources and
methods to determine the type of occurrence and subsequent
generation of a resolution path. The expert engine occurrence
detection and resolution generation is based on fuzzy logic
associating life occurrences with resolution paths.
[0114] The expert engine may determine the type of life occurrence
and subsequent generation of a resolution path according to a rule
administered by a rules engine. The rules engine may relate life
occurrence types with available resolution paths and apply the
rules to the data for the individual associated with the
multidimensional data set.
[0115] An expert engine, based on neural networking, may facilitate
determining life occurrences. Such an expert engine may determine a
type of life occurrence of an individual among a set of possible
life occurrences based at least in part on a multidimensional data
set constructed in connection with a mobile transaction platform
(MTP) through which the individual conducts transactions. The
expert engine may also generate a resolution path for resolving one
or more aspects of the occurrence via uses of a life occurrence
node, such as a user's mobile device and the like. Determining the
type of life occurrence may be performed based on the application
of a neural network the inputs for which include data of the type
contained in the multidimensional data set and feedback for which
includes a set of known life occurrences by which the neural
network may learn to infer a life occurrence from the occurrence of
data in the multidimensional data set.
[0116] The determination of the type of life occurrence is
performed based on the application of a neural network. The inputs
for the neural network may include data of the type contained in
the multidimensional data set. The feedback for the
multidimensional data set may include a set of known life
occurrences by which the neural network may learn to infer a life
occurrence from the occurrence of data in the multidimensional data
set. The feedback for the multidimensional data set may also
include outcomes for sets of individuals having undertaken
different resolution paths for different types of life
occurrences.
[0117] The generation of a resolution path to resolve one or more
aspects of the life occurrence detected is based on user feedback.
Users may provide feedback indicating whether the determination of
the life occurrence is correct or whether the resolution path
offered is appropriate. Such user feedback may then be incorporated
into the expert engine's algorithms, rules sets, fuzzy logic set,
neural network, or other decision-generating engine. Such feedback
may be shared between the MTP and the expert engine.
[0118] The expert engine may determine a type of life occurrence of
an individual among a set of possible life occurrences based at
least in part on consolidated analytics that may be based on
transaction and non-transaction data. The consolidated analytics
may be derived from a multidimensional data set that may be
constructed by using data from the mobile transaction platform
through which the individual conducts mobile transactions, data
from third party analytics data sources, and/or location data for
the individual at a particular point in time. Such data may also be
used to generate resolution paths, which are contextual and may be
used in conjunction with other data points such as, but not limited
to, when the determination of the resolution path is made or where
the user is at the time of determination. Other data points that
may add to the contextual determination of a resolution path may be
data such as pre-learned preferences from past transactions, past
patterns, change in patterns, levels of loyalty to customer loyalty
programs, account status or credit card status, urgency or
importance of the occurrence, among others. For example, the expert
engine may observe from third party analytics that a user has begun
to purchase certain products, such as diapers, or in certain
stores, such as Home Depot. Such changes may be indicative of the
user becoming a parent or buying a house, respectively.
[0119] The expert engine may generate triggers related to a level
of loyalty points. By analyzing transactions, the MTP can ascertain
if a user's level of loyalty points is high or low. Such
information is more than just knowledge of the mere membership of a
user in a loyalty program. Once such information is consolidated by
the expert engine into a multidimensional context, the expert
engine may generate triggers to propose offers which are especially
attractive when the user redeems some of his loyalty points, or
where an extra amount of loyalty points can be collected.
[0120] The status of a credit card or account may comprise a
context driver. For example, if the expert engine knows about how
`strained` a certain credit card already is, then, depending on the
amount to be paid, it might propose another card. Also, the user
might have a preference to pay for expensive goods (or
travel-related things) with a specific credit card, because it
offers some additional insurance that may be beneficial in that
situation.
[0121] In addition to the time/date element that is part of the
occurrence descriptor, one might add an `urgency+importance`
attribute to the occurrence descriptor, and this
`urgency+importance` attribute is likely to be very personal for
each user (and the weight might change over time), and the expert
engine can learn such preferences and make appropriate proposals.
For example, one user may like to pay all bills and taxes
absolutely on time, so the closer the due date of this kind of
transaction comes the more a certain element of the screen would
come to the top of the list, increase in size or change in color,
or have a nagging UI dialogue. A different user may not be so
focused on the bills, but more on relationships. For such a user, a
friend's birthday will be more important as a reminder, because she
needs to find the perfect present.
[0122] The expert engine may be sensitive to transaction risk for
service providers. The expert engine may generate a resolution path
based on a combination of the outcome predicted for an individual
and an assessment of the risk that would be imposed by the
generated resolution path on a third party service provider that
may support the resolution path. By evaluating the risk of a
resolution path while generating the resolution path, the expert
engine may adjust the generation of the resolution path to mitigate
at least a portion of the risk. In this way, risk may be
dynamically evaluated and does not have to be predetermined,
although it can be based on predetermined risk factors. Such an
assessment of risk may be based on the cumulative risk to the
service provider with respect to the individual user or an
assessment may be based on an assessment of the cumulative risk the
individual places across multiple service providers. Resolution
path risk for third parties may be generated outside of the expert
engine. The expert engine, or an alternate facility may assess this
externally generated risk as part of generating a resolution path.
In addition to resolution path-based risk, the expert engine when
attempting to resolve a life occurrence may also assess resolution
action risk to third parties. Resolution actions that pose a high
degree of risk may be discarded rather than being presented to a
user or otherwise enacted in response to a determination of a life
occurrence. Risk generally may be mitigated by adjusting aspect of
the resolution path (e.g. change vendors), the resolution action
(e.g. propose an alternate action), preconfigured mobile
transactions for resolving the life occurrence (e.g. adjust the
transaction to use a card with more favorable vendor protection
terms), and the like.
[0123] An expert engine may facilitate determination and use of
resolution action to facilitate resolving aspects of a user's life
occurrence. Such an expert engine may determine a plurality of
resolution actions for presenting to a user in response to a life
occurrence. The resolution actions may be determined by analyzing
combinations of mobile transactions processed through a mobile
transaction platform, life occurrence metadata, and user-related
data derived from third-party user data sources. The expert engine
may further facilitate preconfiguring mobile transactions to
facilitate execution of the resolution actions in response to a
user selection of the resolution actions. In addition, triggers may
be associated with the life occurrence to facilitate enabling the
user to review and take action on the resolution actions.
[0124] An expert engine may facilitate configuring a plurality of
mobile transactions for facilitating execution of a plurality of
resolution actions that are presented to a user in response to
detection of at least one trigger associated with a life
occurrence. The resolution actions may be determined from analyzing
combinations of mobile transactions processed through the mobile
transaction platform, life occurrence metadata, and user-related
data derived from third-party user data sources. The life
occurrence is an event that has not yet occurred and is based on at
least one user-related event that occurred in the past.
[0125] FIG. 1 depicts a high-level diagram of a potential
embodiment of a life occurrence management platform 100 including
an expert engine configured to determine life occurrences using a
plurality of data sources, including data sources accessible to a
mobile transaction platform (MTP) 102. The life occurrence
management platform 100 may comprise a MTP expert engine (EE) input
device 104 that may be used to facilitate determining life
occurrences 108 based on multi-dimensional context 110 derived
from, among other things, user transactions. The life occurrences
108 may be an event that has not yet occurred or may be based on at
least one user-related event that has occurred in the past.
[0126] The MTP expert engine 104 may use the derived
multi-dimensional context 110 in order to generate resolution
action trigger-events 112 to resolve such life occurrences 108 by
facilitating user-directed mobile actions. In order to facilitate
resolving the life occurrence 108 via the generated resolution
action trigger-events 112, the MTP 102 and/or the expert engine 104
may prepare preconfigured mobile-device compatible transactions to
facilitate execution of the resolution actions and/or present the
user-directed mobile actions in response to detected trigger-events
112. In an example, a corresponding preconfigured mobile
transaction may be executed in response to a user selection of a
certain resolution action. Each life occurrence 108 may also be
associated with various trigger-events 112, so that a user is able
to review and select a resolution action. Such trigger-events 112
may also be associated with certain life occurrences 106 so as to
alert users to the events and allow users to select certain
resolution paths 114.
[0127] The multi-dimensional context 110 may comprise location
information associated with resolving a life occurrence, such as
user location, resolution path and/or resolution action location
information, and the like. For example, the current location of the
user may be determined using any location determination
technologies such as global positioning system (GPS) and the like.
The current location associated with the specific life occurrence
may be a location that is not the user's current location (e.g.
another person's home, any of a plurality of waypoints, and the
like) The multi-dimensional context 110 may comprise at least one
of time of life occurrence and current time.
[0128] The MTP expert engine 104 may determine a type of life
occurrence of an individual among a set of possible life
occurrences based at least in part on consolidated analytics that
may be based on transaction and non-transaction data. The
consolidated analytics may be derived from a multi-dimensional
database 118 that may be constructed by using data from the mobile
transaction platform through which the individual conducts mobile
transactions, data from third party analytics data sources,
location data for the individual at a particular point in time, and
a wide range of other data from a range of sources, such as social
media, calendars, user contacts, prior life occurrences, user
relationships, and the like.
[0129] The multi-dimensional database 118 may be used to store
attributes related to clients, client devices, services, service
providers, merchants, merchant systems, transactions, payments,
tokens, receipts, and other items. The multi-dimensional database
118 may store such information in more than one dimension, so that
it can be accessed by different applications or for different
purposes. In an embodiment, the multi-dimensional database includes
three database dimensions (namely, user mobile transactions, user
calendar, and user preferences), it should be appreciated that the
number of dimensions may be one, two, three, or any whole number
greater than three.
[0130] The units of a first dimension of the multi-dimensional
database 118 may correspond to an attribute of user mobile
transactions, such as names of service providers, cash values of
transactions, types of transactions, date of transactions,
quantities of items in transactions, sources of items in a
transaction, or any other attribute of a mobile transaction.
[0131] The units of a second dimension of the multi-dimensional
database 118 may correspond to an attribute of the user calendar,
wherein this attribute may without limitation comprise names of
meeting attendees, types of the calendar events, geographic
location of the calendar events, and so forth.
[0132] The units of a third dimension of the multi-dimensional
database 118 may correspond to an attribute of user preferences for
mobile transactions, wherein this attribute may without limitation
comprise maximum amounts for automatic payment of bills, names of
preferred payers of bills, name of preferred payees for certain
items, and so forth. The multi-dimensional database structure may
be associated with the mobile transaction platform.
[0133] A structure of the multi-dimensional database 118 may be
designed to support various functional aspects of the MTP 102 such
as a user-centric interface, a user-centric engine, security
aspects, transmission aspects, hardware and/or software
infrastructure that may be associated with the MTP 102, an expert
system, a self-learning and self-scaling system, a secure
web-services protocol, distributed infrastructure services and
other functions of the MTP 102.
[0134] Information from multiple sources may be populated in the
multi-dimensional database 118 in such a way that the attributes of
the data may be set in multiple dimensions, including relationships
among data items across different dimensions. This enables querying
data in different ways for different purposes. For example, the
multi-dimensional database 118 supports the user-centric engine
whereby various data relating to various services, service
providers, domains, devices and systems are stored to allow a user
to access services that use such data. The multi-dimensional
database 118 allows the life occurrence management platform to sift
through data more efficiently, employing different dimensions that
are optimized for particular retrieval tasks. For example, an
element of data may be transaction-related. Another dimension may
relate to how data is evaluated. A third element of the data might
allow static profiles or entries. A fourth element may allow
external entities to enter data associated with the data. Data may
include data related to financial transactions such as billings,
data related to service providers, data related to content items,
or a host of other kinds of data. Storing data in the
multi-dimensional database 118 may assist with application
throughput, as data may be stored in a fashion that allows
efficient retrieval of data according to a user's specific needs.
For example, a learning algorithm or the MTP expert engine 104 as
described herein may learn which services a user tends to use in
which circumstances and the MTP expert engine 104 may push data
from the multi-dimensional database 118 to, for example, a client
device to improve performance of such services.
[0135] A user user-centric engine may look at data of the
multi-dimensional database 118 to attain advantage from the one or
more dimensions. For example, if a user flies into London, the
platform may be aware of that fact, be aware of past transactions
(such as meetings the user had with various people in the past),
and look at different dimensions of data to propose various
transactions. Similarly, the engine may propose multiple
transactions to the user, enabled by the data in the
multi-dimensional database 118.
[0136] The multi-dimensional database 118 data may also be used to
generate resolution paths 114 which are contextual and may be used
in conjunction with other data points such as, but not limited to,
when the determination of the resolution path 114 is made or where
the user is at the time of determination. Other data points that
may add to the contextual determination of a resolution path 114
may be data such as pre-learned preferences from past transactions,
past patterns, change in patterns, levels of loyalty to customer
loyalty programs, account status or credit card status, urgency or
importance of the occurrence, among others. For example, the MTP
expert engine 104 may observe from third party analytics that a
user has begun to purchase certain products, such as diapers, or
has begun making purchases in certain stores, such as Home Depot.
Such changes may be indicative of the user becoming a parent or
buying a house, respectively.
[0137] The MTP expert engine 104 may generate trigger-events
related to a level of loyalty points. By analyzing transactions,
the MTP expert engine 104 may ascertain if a user's level of
loyalty points is high or low. Such information is more than just
knowledge of the mere membership of a user in a loyalty program.
Once such information is consolidated by the expert engine into the
multi-dimensional context 110, the MTP expert engine 104 may
generate trigger-events 112 to propose offers which are especially
attractive when the user redeems some of his loyalty points, or
where an extra amount of loyalty points can be collected.
[0138] The status of a credit card or account may comprise a
context driver. For example, if the MTP expert engine 104 knows
about how `strained` a certain credit card already is, then,
depending on the amount to be paid, it might propose another card.
Also, the user might have a preference to pay for expensive goods
(or travel-related things) with a specific credit card, because it
offers some additional insurance that may be beneficial in that
situation.
[0139] In addition to time/date elements that may be part of a life
occurrence descriptor, a life occurrence may include an attribute
related to urgency or importance that may be very personal for each
user (and a weighting of such attributes might change over time).
The MTP expert engine 104 can learn such preferences and thereby
incorporate them into appropriate proposals, such as unsolicited
offers, resolution actions, and the like. For example, one user may
like to pay all bills and taxes absolutely on time. This preference
may place a corresponding level of importance on life occurrences
related to bill and tax payment-related. Therefore, the closer the
current date is to a due date of this kind of transaction, the more
the weighting for resolution actions or resolution paths determined
to resolve this life occurrence. Such an increase in weighting may
impact how a user may be notified of the life occurrence resolution
options prepared by the expert engine 104. In an example of a
visual user interface for allowing a user to interact with the life
occurrence methods and systems described herein, certain elements
of such a user interface display on a user's device screen may come
to the top of a life occurrence resolution action list.
Alternatively, elements in the user interface related to this
increasingly urgent and important life occurrence might change
visually, such as with an increase in size or change in color, or
have a nagging UI dialogue. A different user may not be so focused
on the bill payment life occurrences, but more on relationships and
the life occurrences related thereto. For such a user, a friend's
birthday may become an even more important reminder as the current
date creeps closer to the friend's birthday, perhaps because she
needs to find the perfect present.
[0140] The life occurrence management platform 100 enables a user
experience through a life occurrence node 120. Examples of a life
occurrence node 120 may include a user's mobile device that
facilitates presenting notifications of triggered life occurrences
derived from a robust multi-dimensional context 110 with associated
consolidated resolution actions. A life occurrence node 120 may be
any networkable device with a basic processing capability, not just
a mobile device. Examples of life occurrence nodes 120 are
described elsewhere herein. The life occurrence management platform
100 may facilitate communication between the life occurrence node
120 and the external entities 122 via an enterprise service bus
(ESB) 124. While the MTP 102 operates to facilitate mobile
transactions between the external entities 122 and the life
occurrence node 120, it facilitates passing data between the
external entities 122 and the life occurrence node 120 without
substantively altering their content. However, the MTP 102 does
acquire and collect for storage in, for example, a transactional
analytics database 128, information and metadata related to various
attributes of the transactions enabled by the MTP 102. For example,
the MTP 102 may store in the transactional analytics database 128
information related to transaction times, transaction amounts,
service provider identifiers, life occurrence-related trigger, user
action(s) to effect the transaction, and the like.
[0141] In communication with the MTP 102, the MTP expert engine 104
operates to consolidate various transactional analytics received
from the MTP 102 with one or more third party sources 130 to create
the multi-dimensional context 110 that is suitable for determining
life occurrences, developing and maintaining occurrence action
triggers, generating resolution paths that resolve an aspect of a
life occurrence via uses of the mobile device, and the like. In
operation, the MTP expert engine 104 may employ one or more
algorithms to consolidate various transactional analytics from the
MTP 102 with data from the third party sources 130 to produce the
multi-dimensional context 110 from which trigger-events may be
produced. Such algorithms may further order and prioritize the
display of life occurrence-related alerts to a user of the mobile
device.
[0142] The MTP expert engine 104 of the life occurrence management
methods and systems described herein may be configured to determine
services that may be offered to clients and service institutions by
using data from the plurality of sources including the
multi-dimensional context database 118. The MTP expert engine 104
may be configured to process multi-dimensional information from the
plurality of data sources that may include direct input specific
instructions from a client and consolidated input from a plurality
of service institutions and vendors. For instance, user or client
life occurrence management registration information may be compiled
based on surveys and interviews. A complete client profile may be
compiled using information from external agencies, the direct
information provided by the clients, and other sources of data
suitable for a robust client profile. Various transaction analysis
and records analysis may be conducted by a transaction service
provider and may be offered as a service to the client in a desired
form and format. The transaction service provider may feed
transaction analysis and records analysis into the MTP expert
engine 104 to form input to determine at least a portion of overall
service offerings suitable for presenting to the clients by a wide
range of service providers, such as retailers, banks, merchants,
service consolidators, and other aspects of a life occurrence
management platform. The direct input by the clients and specific
flag information from them may form preferences that may be
outlined by the clients to facilitate rapid use of the preferences
in an electronic transaction-oriented computing environment. The
consolidated input from the service institutions may include
information about various vendors that may affect overall client
profiles, and consequently the services offered to the clients. An
output of the MTP expert engine 104 may be utilized to determine
the services being offered to the clients and the service
Institutions. Further, the MTP expert engine 104 may be preferably
isolated from other processes to ensure confidentiality of
user/client information designated as confidential by the clients,
or as determined by the MTP expert engine to be confidential in
light of similarities of a data item to data items designated as
confidential by the clients. In this way, a user/client may not
have to designate each item of data as confidential, yet gain the
benefits of confidentiality of relevant information. In an example,
the MTP expert engine 104 may analyze data that the current user or
any other user or groups of users have designated as confidential
and determine properties of such confidential information. When the
MTP expert engine 104 encounters new information that has not yet
been designated as confidential, it may determine if the new
information has critical properties that are similar to properties
of designated confidential information and may thereby treat this
new information as confidential. A user may be alerted, based on
user preferences, as to this designation of confidentiality.
Alternatively, a user may not be alerted if the similarity of the
new information properties to properties of confidential
information is a high degree of similarity. In such a situation, a
user may receive a report of new information that has been
designated as confidential by the MTP expert engine 104.
[0143] The MTP expert engine 104 of a life occurrence management
platform may be configured to perform vendor data consolidation.
The MTP expert engine 104 may collect and assemble a complete
profile on the service institutions and vendors through various
sources. The complete profile may include profile information,
products & services information, marketing & advertising
information, information in terms of future releases of products
and services (e.g., "Future Attractions"). Such profile information
may further be utilized to determine the services offered to the
client, service institutions and vendors. Vendor and service
institution profile information may be accessible to the functional
elements described herein, such as the MTP and the MTP-expert
engine, and may be stored in the multi-dimensional database
described herein.
[0144] The MTP expert engine 104 may facilitate in providing users
seamless user-centric life experiences for the plurality of life
occurrences. Some seamless user-centric life experience examples
are provided herein without limitations. One such example is a
flight-based travel scenario, in which when a user's plane lands,
the MTP expert engine 104, having maintained context of the user
and events related to the user may already be aware of the flight
status and may arrange for a taxi pickup of the user automatically.
A life occurrence management platform may not require the user to
click a phone, select a coupon, etc. The life occurrence management
platform may automatically book the taxi and bother the user only
if something may not work the way the user wants it to.
[0145] The MTP expert engine 104 may be configured to determine
life occurrences associated with life occurrence nodes that
correspond to a user, such as intelligent devices that include some
form of processing capability and accordingly, generate resolution
paths including one or more resolution actions in accordance with
the life occurrences that are associated with the intelligent
devices. For example, an intelligent washing machine embodiment of
a life occurrence node may facilitate activating a resolution path
that includes a laundry detergent purchase context. The washing
machine may link up to a user's mobile device so that the user can
be given the option of being a trigger for the purchase of laundry
detergent. Alternatively, the purchase of laundry detergent may be
automated so that the washing machine effectively may start
ordering detergent by itself through an enabled secure ecosystem.
Determining whether to automatically order laundry detergent may be
based on what the MTP expert engine 104 may learn from analysis of
user context across a wide range of user transactions. In this way
the life occurrence management platform may adapt its actions based
on it's learning from the varying information.
[0146] FIG. 2 is a high-level diagram of a potential embodiment of
a life occurrence management platform including the MTP expert
engine 104 that may be configured to determine a type of life
occurrence of an individual and to generate a resolution path for
resolving one or more aspects of the occurrence. As discussed above
in conjunction with FIG. 1, the MTP expert engine 104 may be
configured to determine life occurrences using one or more data
sources of the MTP 102. The MTP expert engine 104 for example may
be configured for determining a type of life occurrence of an
individual among a set of life occurrences based at least in part
on a multi-dimensional database 118 constructed in connection with
the MTP 102 through which the individual conducts transaction. The
MTP expert engine 104 may be further configured to access a
resolution path generator 202 to generate a resolution path for
resolving one or more aspects of the occurrence via uses of the
life occurrence node 120. In various aspects, the determination of
the various life occurrences 108 and their resolution paths may
occur by using a set of automated algorithms, artificially
intelligent systems, and or contextually-controlled actions that
may operate in conjunction or within the MTP expert engine 104.
[0147] For example, as shown in FIG. 2, in an aspect, the MTP
expert engine 104 may determine a type of the life occurrence 108
of an individual among a set of possible life occurrences and
generate the resolution path by using a fuzzy logic 204 that
associates life occurrences with available resolution paths. The
fuzzy logic 204 may perform supervisory functions to let the MTP
expert engine 104 learn from contextual information and allow it to
make a decision regarding the life occurrence type and/or the
appropriate resolution path for a particular type of life
occurrence. In an example, fuzzy rules may be generated from
information contained in the multi-dimensional data set so that an
output of the fuzzy logic 204 may be indicative of the information
received from the multi-dimensional database 118 which may include
data related to user historical transactions in association with
defined spatial, temporal, or other constraints and the like. Fuzzy
logic 204 may be enabled through fuzzy systems and processors that
may be configured to allow processing of contextual knowledge and
project behavioral patterns, and the like. As an example, if a
student heads toward his school for examination and a temporal
contextual information indicates that the examination may be about
to start and the student may be running late, the fuzzy logic 204
may facilitate the expert engine to interpret the context and make
a decision regarding indicating a resolution path that suggests the
student about the nearest taxi stand for hiring a taxi so that the
student reaches the school on time.
[0148] The MTP expert engine 104 may determine the type of the life
occurrence of the individual among the set of possible life
occurrences 108 using a neural network 208. The neural network 208
may process data contained in the multi-dimensional database 118
and utilize the feedback from the fuzzy logic 204. The feedback may
include a set of known life occurrences by which the neural network
208 may learn to infer a life occurrence from the occurrence of
data in the multi-dimensional database 118. Further, the resolution
path generator 202 may generate the resolution path based on the
application of the neural network 208 such that the neural network
208 may process the data from the multi-dimensional database 118 in
conjunction with the feedback, which includes outcomes for sets of
individuals having undertaken different resolution paths for
different types of life occurrence.
[0149] The MTP expert engine 104 may determine a type of life
occurrence of an individual among a set of possible life
occurrences and generate the resolution based on rules administered
by a rules engine 210 that may be configured to relate life
occurrence types with available resolution paths. The rules engine
210 may apply one or more rules to the data for the individual in
the multi-dimensional database 118 to determine one or more
resolution paths for the determined type of the life
occurrence.
[0150] The MTP expert engine 104 may determine the type of life
occurrence and generate the resolution path based on feedback among
fuzzy logic 204 and neural network 208 and from users, sensors,
another system, etc. as to at least one of the accuracy of the
determination of the life occurrence and the appropriateness of the
resolution path.
[0151] FIG. 3 is a high-level system diagram depicting the MTP
expert engine 104 configured to generate the resolution path 114
based on temporal data 308 and spatial data 302. The resolution
path 112 may be based on an overall context of the individual that
includes the point in time at which the determination is made, data
from a mobile transaction platform (MTP) through which the
individual conducts mobile transactions, data from a third party
source relating to the individual, and location data for the
individual at the point in time. For example, the MTP expert engine
104 may determine present location and time of the individual that
may access the life occurrence node 120. Based on the current
location and time, the MTP expert engine 104 may access
multi-dimensional database 118 or third party sources to determine
past actions of the individual. Accordingly, the MTP expert engine
104 may utilize the resolution path generator 202 to determine one
or more resolutions paths from the contextual data. With regard to
time-based actions, the MTP expert engine 104 may determine
resolution paths that may include one or more actions such as
display of notifications, alerts, suggestions and the like.
Similarly, the MTP expert engine 104 may utilize location-based
information to determine the resolution path 114. For example, the
MTP expert engine 104 may extract information from the
multi-dimensional database 118 or third party sources to determine
past actions (e.g., visiting a restaurant) when the individual was
present at the current location. Accordingly, the resolution path
generator 202 may generate resolution path based on the past
actions of the user at the current location.
[0152] The MTP expert engine 104 may perform risk assessment 304 to
determine the resolution path 114. In such a case, the resolution
path 114 may be based on a combination of the outcome predicted for
the individual and an assessment of the risk imposed by the
resolution path 114 on a third party service provider associated
with the resolution path 114. The risk assessment 304 may include
an assessment of the cumulative risk of the service provider with
respect to the individual. Alternatively, the risk assessment 304
may include an assessment of the cumulative risk of the individual
across multiple service providers. The risk assessment 304 may
determine risk scores for each of the resolution paths 114
corresponding to at least one life occurrence 108 of the
individual. Based on risk score threshold for an individual, the
resolution path generator 202 may generate the one or more
resolution paths that may have the risk score greater than the
threshold score. As illustrated, the risk assessment 304 may be
disposed within the MTP expert engine 104. However, other
embodiments are envisioned, such as the risk assessment may be
independent of the expert engine 104, may be accessible through the
enterprise bus, and the like.
[0153] FIG. 4 is a high-level system diagram depicting an MTP
expert engine configured to determine resolution actions for
presenting to a user in response to a life occurrence. A resolution
action determination 402 may be based on analysis that may be
obtained from analyzing mobile transactions processed through the
MTP 102, life occurrence metadata, user-related data derived from
third party user data sources, and the like. The MTP 102 may
include a transaction pre-configuration module 404 that may
facilitate execution of the resolution actions in response to a
user selection of the resolution actions. The transaction
pre-configuration module 404 may facilitate execution of the
resolution actions and based on the analysis, the transaction
pre-configuration module 404 may perform at least one transaction
without requiring user selection of a transaction or a resolution
action.
[0154] The transaction pre-configuration module 404 may access
third-party (e.g. Internet search based) resources for available
offers. The transaction pre-configuration module 404 may analyze
the available offers in combination with the multi-dimensional
context 110 to select one or more offers that may be suitable for a
transaction in a life occurrence resolution action. The transaction
pre-configuration module 404 may configure a plurality of mobile
transactions for facilitating execution of a plurality of
resolution actions that are presented to a user in response to
detection of at least one trigger-event associated with a life
occurrence. The resolution actions are determined from analyzing
mobile transactions processed through a mobile transaction
platform, life occurrence metadata, and/or user-related data
derived from third party user data sources.
[0155] The life occurrence node 120 may include a lifestyle
container 408 that may, inter alias, facilitate alerting a user of
the life occurrence node 120 to resolution paths available for
addressing an aspect of the life occurrence. The lifestyle
container 408 may also cause mobile transactions matched to the
resolution path based on a user's response to a life occurrence
alert. The lifestyle container 408 may synchronize with the mobile
transaction facility to maintain currency of occurrences,
trigger-events, and resolution actions.
[0156] The systems and methods disclosed herein may comprise a
mobile transaction platform (MTP). The mobile transaction platform
may comprise an expert engine for determining life occurrences
based on multidimensional context. The multidimensional context may
be derived from an analysis of user transactions associated with
the mobile transaction platform and third party sources of user
related data. The ecosystem of resources available to the MTP may
include third party analytics, social networks, context drivers at
networks and gateways, offers and value added services, host
systems, trusted service managers, certificate authorities, and
databases, among others. The engine may also generate resolution
paths for resolving the aspects of the occurrence, where the
resolution path has a series of action triggers leading to the
resolution. The engine may be based on a combination of fuzzy
logic, machine learning, and neural networks.
[0157] The platform may also comprise a transactional analytics
facility. The facility may analyze the transactions conducted
within the MTP. The data derived from this facility may be
incorporated into a dynamic profile of the user for use by the
expert engine. The transaction facility and the expert engine may
exchange resolution triggers, static user profiles, and dynamic
user profiles. Static user profiles may be used in conjunction with
current context data such as time, space, and user input for the
expert engine in order to determine life occurrences.
[0158] The MTP may comprise a lifestyle container. The lifestyle
container may be deployable on a mobile device and may facilitate
alerting a user of the mobile device to resolution paths available
for addressing an aspect of the life occurrence. The lifestyle
container may also cause mobile transactions matched to the
resolution path based on a user's response to a life occurrence
alert. The lifestyle container may synchronize with the mobile
transaction facility to maintain currency of occurrences, triggers,
and resolution actions.
[0159] A mobile transaction platform (MTP) may be enhanced to
include a multidimensional data set of transaction details of
transactions conducted by a user through the MTP that are framed in
context of life occurrences and linked to third-party user-related
data. The MTP may be further enhanced with an analytics facility
for analyzing the multidimensional data set to produce context for
life occurrence determination and resolution. The multidimensional
data set may be a user database.
[0160] The MTP may be token-based. Such a token based MTP may
comprise an expert engine for determining life occurrences based on
multidimensional context derived from analysis of user transactions
associated with a mobile transaction platform (MTP) and third-party
sources of user-related data. Similarly, the expert engine may
generate a resolution path for such occurrences. The resolution
paths may have a series of action triggers leading to resolution of
the life occurrence. The token-based MTP may comprise a transaction
facility for handling transactions of a personal mobile device,
analyzing the transactions, and providing the analysis to the
expert engine. The token-based MTP may comprise additionally an
enterprise service bus for facilitating access by the expert engine
and the transaction facility to ecosystem resources. The
token-based MTP may also comprise lifestyle containers that are
deployable on a mobile device for facilitating alerting a user of
the mobile device to resolution actions available for addressing an
aspect of life occurrence. Based on a user's response to an alert,
the token-based MTP may provide a personalized token configured to
securely cause a mobile transaction matched to the resolution
action to be executed by a server.
[0161] An MTP as described herein facilitates among other things
secure mobile transactions (purchase, top-up, inquiry, etc.). The
data associated with such a transaction (date, time, location,
payment source, wallet state, type of transaction, product/service,
vendor, portions of the platform used by the vendor (e.g.
personalization, etc.) delivery method and arrangements, tax
status, widget used, direct transaction or through transaction
server, etc.) may be harvested, categorized, aggregated, and
processed with other transaction data for the user by an analytics
facility of the MTP. Such valuable data and context may also be fed
to a data repository from which the expert engine can determine
triggers, transactions, life occurrences, resolution paths, and the
like.
[0162] Transaction data may be analyzed by the MTP in context of
other users, similar or interested vendors, etc. to establish some
sort of weighting, importance, etc. This analysis might result in
determination of a new trigger, action, or occurrence. By itself it
might be sufficient for such determination for the user, or it
might be combined with other user data to determine an action.
Example of setting an occurrence and action: Transaction data might
indicate that the user has placed an order for a new rug that has
been purchased by other users who also bought bedroom furniture
(therefore the expert engine might determine that the rug might be
suitable for a bedroom). A new action might be generated for the
user to purchase bedroom furniture. The expert engine can then
generate this action for the user once an occurrence and/or trigger
related to the ordered rug is detected (e.g. setting the delivery
date for the rug). Lead-time for the furniture, rug, etc. might
also be factored into when the occurrence trigger(s) should be
scheduled.
[0163] FIG. 5 is a high-level system diagram depicting a MTP expert
engine 104 configured to determine resolutions action using profile
of a user. The secure transaction platform may comprise a
transactional analytics facility 128 that may analyze the
transactions conducted within the MTP 102. The data derived from
the transactional analytics facility 128 may be incorporated into a
static user profile 502 and/or a dynamic profile 504 of the user
for use by the MTP expert engine 104. The transaction data may be
analyzed by the MTP 102 in context of other users, similar or
interested vendors, etc to establish some sort of weighting,
importance, etc. This analysis may result in determination of a new
trigger-event, action, or occurrence. The transactional analytics
facility 128 and the MTP expert engine 104 may exchange resolution
trigger-events, static user profiles 502, and dynamic user profiles
504. The static user profiles 502 may be used in conjunction with
current context data such as time, space, and user input for the
MTP expert engine 104 in order to determine life occurrences
108.
[0164] The MTP 102 may receive user data from a source, such as an
external entity 122, and such as via the ESB 124. The MTP 102 may
then transmit the user data to a user such as a user operating a
mobile device executing the lifestyle container 408. Further, the
MTP 102 may transmit the user data, static user profile 502, and
the dynamic user profile 504 to the MTP expert engine 104. The MTP
expert engine 104 may determine life occurrences based on the
multi-dimensional context 110, and the profile related data. The
MTP expert engine 104 may further generate the resolution path 114
for resolving one or more aspects of the occurrence. The resolution
path 114 may comprise a series of action trigger-events leading to
resolution of the life occurrence. The MTP expert engine 104 may
generate the resolution path using any of the fuzzy logic 204,
neural network 208, machine learning 508 or any combination
thereof.
[0165] The MTP expert engine 104 may communicate one or more
resolution actions to the MTP 102, which in turn may transmit the
one or more resolution actions to the life occurrence node 120. For
example, the static user profile 502 and the dynamic profile 504
may indicate that the user has placed an order for a new rug that
has been purchased by other users who also bought bedroom
furniture. Accordingly, the MTP expert engine 104 may determine
that the rug may be suitable for the bedroom and generate a new
action for the user to purchase bedroom furniture. The life
occurrence node 120 equipped with the lifestyle container 408 may
execute the one or more resolution actions based on the selection
of the user. In addition, the MTP expert engine 104 may communicate
a variety of types of data and perform a range of functions with
the MTP 102. The variety of data may include notification, alerts,
suggestions, time, location, and the like. The functions may
include trigger bus exchange, synchronization, reconciling
temporal/spatial windows for contextual consistency. On receiving
the one or more resolution actions, the lifestyle container 408 may
facilitate alerting a user of the mobile device to resolution paths
available for addressing an aspect of life occurrence, and based on
user response to an alert, the lifestyle container 408 may cause
life occurrence node-based (e.g. mobile) transactions matched to
the resolution paths.
[0166] The MTP 102 may be configured to communicate with a
personalized instrument 510 (e.g., washing machine) that may be
configured to securely cause a life occurrence-based/mobile
transaction matched to the resolution action to be executed by a
server 512. The MTP expert engine 104 may determine life
occurrences based on the multi-dimensional context 110 derived from
analysis of user transactions associated with the MTP 102 and
third-party sources of user-related data. The MTP expert engine 104
may generate a resolution path for resolving one or more aspects of
the occurrence. The resolution path may include a series of action
trigger-events leading to resolution of the life occurrence. The
life occurrence management platform may include the transactional
analytics facility 128 for handling transactions of a personal
mobile device, analyzing the transactions, and providing the
analysis to the MTP expert engine 104. The ESB 124 may facilitate
access by the MTP expert engine 104 and the transactional analytics
facility 128 to ecosystem resources. The lifestyle container 408
deployable on the life occurrence node 120 may facilitate alerting
a user of the life occurrence node (e.g. mobile device) to
resolution actions available for addressing an aspect of life
occurrence, and based on user response to an alert, the lifestyle
container 408 may provide the personalized instrument 510 to
securely cause a life occurrence-based/mobile transaction matched
to the resolution action to be executed by the server 512.
[0167] The life occurrence node 120 may facilitate administering
selection of at least one resolution action for addressing an
aspect of the life occurrence. The resolution action may comprise
providing the personalized instrument 510 to securely cause a life
occurrence-based/mobile transaction matched to the resolution
action to be executed cooperatively with a server 512.
[0168] Referring to FIG. 6, a utility access switch 602 is
depicted. The utility access switch 602 may facilitate access to
the ecosystem resources such as third party analytics, social
networks, context drivers and at least one of networks and
gateways, offers and value added services, host systems, trusted
service managers (TSM), certificate authorities (CA), and databases
for the MTP expert engine 104 and the transactional analytics
facility 128.
[0169] An expert engine may facilitate determining a type of life
occurrence of an individual among a set of possible life
occurrences based at least in part on a multidimensional data set
constructed in connection with a mobile transaction platform (MTP)
through which the individual conducts transactions and generating a
resolution path having a series of action triggers leading to
resolution of the life occurrence via uses of the mobile device.
Determining the type of life occurrence and/or generating the
resolution path is based on feedback shared between the MTP and the
expert engine that is derived from user responses to the action
triggers as to at least one of the accuracy of the determination of
the life occurrence and the appropriateness of the resolution
path.
[0170] A transactional analytics facility may facilitate analyzing
user transactions associated with a mobile transaction platform
(MTP) and third-party sources of user-related data to generate a
static user profile for use by an expert engine for determining
life occurrences based on multidimensional context derived from
analysis of the static user profile and current context including
time, space, and user input.
[0171] A mobile transaction platform may include an expert engine
for determining life occurrences based on multidimensional context
derived from analysis of user transactions associated with a mobile
transaction platform (MTP) and third-party sources of user-related
data. The expert engine may generate a resolution path for
resolving one or more aspects of the occurrence. The resolution
path may have a series of action triggers leading to resolution of
the life occurrence. In addition, a transactional analytics
facility may be employed for analyzing the transactions conducted
with the MTP and creating a dynamic profile of the user for use by
the expert engine for at least one of determining life occurrences
and generating a resolution path.
[0172] FIG. 7 depicts a high-level system diagram including an MTP
expert engine 104 configured to generate a life occurrence aspect
resolution path having a series of resolutions actions, wherein the
resolution path is responsive to one or more trigger-events
associated with a life occurrence. As further described elsewhere
herein, an individual may conduct transactions through the MTP 102,
thereby making a wide range of transaction-related information
available to the MTP 102. As illustrated, various forms of data,
some of it transaction-specific, may be exchanged between the
expert engine 104 and the MTP 102 including, but not limited to,
trigger-event data, sync data, notifications, alerts, suggestions,
temporal data, static profiles, dynamic profiles, risk profiles,
generic user profiles, and the like. The MTP 102 may be connected
to a user life occurrence node, such as a user's mobile device, to
exchange important context related information, such as life
occurrences, trigger-events, resolution actions, preconfigured
mobile transactions, and the like.
[0173] A life occurrence services platform may participate in risk
mitigation through risk assessment and management. A risk
assessment capability 304 may be developed in conjunction with the
expert engine 104. Alternatively risk assessment capabilities may
be configured as separate risk assessment services 304b that may be
provided by third parties that may interface with the platform
through an enterprise service bus, such as ESB 124.
[0174] The expert engine may be sensitive to transaction risk for
service providers. The expert engine may generate a resolution path
based on a combination of the outcome predicted for an individual
and an assessment of the risk imposed by the generated resolution
path on a third party service provider that may support the
resolution path. Such an assessment of risk may be based on the
cumulative risk to the service provider with respect to the
individual user or an assessment may be based on an assessment of
the cumulative risk the individual places across multiple service
providers.
[0175] The expert engine 104 may utilize risk as a context driver
when generating triggers and attendant resolution actions. For
example, as the MTP 102 executes one or more transactions in
response to a user's inputs in response to an alert of a trigger,
the MTP 102 may dynamically identify one or more attributes of the
transactions as amounting to an unacceptable risk. In response, the
MTP 102 may alert the user to, for example, chose a different mode
of payment or another vendor. For example, a user may be provided
multiple payment options for proceeding with the mobile
transaction. The user may further have defined a default credit
card for mobile transactions that may be selected in the event that
no other form of payment is selected and/or if a chosen form of
payment is not acceptable. In this way, the MTP, in cooperation
with the container 106 and/or other MTP resources on the mobile
device, may automatically switch forms of payment and/or vendors in
response to detection of an unacceptable level of risk. A
non-limiting example of risk management, the user may buy neckties
and the expert engine 108 may identify matching cufflinks and
suggest to the user to purchase the matching cufflinks at an
identified vendor with the vendor's issued credit card. The user
agrees and lifestyle container 408 is updated to facilitate
presenting a consolidated view of the transaction. However, expert
engine 104 determines that an aspect of risk of this transaction is
unacceptable (e.g. payment terms of the vendor's credit card are
onerous) and suggests to the user the choice of using a different
credit card that is accessible in the user's mobile wallet via the
mobile device instead of the store card, even though the user will
lose out on some vendor-specific loyalty points.
[0176] A life occurrence management platform may include or be
communicatively coupled with a central ID management system. A
central ID management capable life occurrence management platform
may be used to implement life occurrence servicing as described
herein. A Profile and ID management and authentication capability
may be separated out from core life occurrence management platform
elements into a separate system that may communicate over a
transaction support bus, such as the ESB 124 to facilitate handling
ID-related functions associated with life occurrence servicing.
Alternatively, a switching and brokering interface may be provided
between the life occurrence handling elements (e.g. MTP, expert
engine, and the like) and a separate ID management system rather
than the ESB. ID related functions, such as security functions
(e.g. cryptography, etc.) may also be pulled out in a separate
system coupled with a life occurrence management platform such as
for security, trust models, approaches for authentication and the
like. An ID-aware container may be provided for life occurrence
nodes using ID functions to securely execute mobile applications
and other types of applications.
[0177] ID may be used as a handle to the profile in a life
occurrence management platform and the profile can be of various
types. These profiles may comprise static profile that may
encompass a person or a user, a dynamic profile, a risk profile,
and the like. ID may be associated with triggers and events. ID
life occurrence management platform may interact with the risk
system.
[0178] Feedback responses may be shared between the MTP 102 and the
MTP expert engine 104. The feedback responses may be derived from
user responses to presented resolution action and may be useful to
determine at least one of a measure of accuracy of a life
occurrence determination by the expert engine and a degree of
appropriateness of a particular resolution path suggested by the
expert engine.
[0179] A transactional analytics facility 128 may populate and
maintain a transactional analytics data store by analyzing the user
transactions associated with the MTP 102 and the third-party
sources of user-related data, thereby generating at least a static
user profile. The MTP expert engine 104 may utilize the static user
profile for determining life occurrences based on the
multi-dimensional context 110. The multi-dimensional context 110
may be derived from analysis of the static user profile 502 and
current context including temporal data 308, spatial data 302 and
user input.
[0180] A transactional analytics facility may also analyze the
transactions conducted with the MTP 102 and create a portion of a
dynamic profile of the user. A dynamic user profile may be used by
the MTP expert engine 104 for at least one of determining life
occurrences and generating a resolution path.
[0181] The transactional analytics facility 128 may analyze the
user transactions associated with the MTP 102 and third-party
sources of user-related data to generate a risk profile of users.
The risk profile of the users of the MTP expert engine 104 may be
used to generate trigger-events, resolution actions, life
occurrences, potential transactions, and the like based on
multi-dimensional life occurrence context. The risk profile may be
useful for determining if one or more resolution actions are
suitable for presenting to a user. The risk profile may be used for
ranking resolution actions.
[0182] In the context of risk profiling, a life occurrence
management platform may utilize user risk profiling to allow
merchants to maximize checkout conversion rates while also
decreasing fraud on transactions through use of a risk based
authentication system and dynamic multi-factor authentication
methods. Exemplary use cases of risk profile based transactions,
without limitations, are presented herein below.
[0183] In an example, a merchant may be able to choose a type of
Checkout during Account Management to accomplish a preferred degree
of risk management. The life occurrence management platform may
re-use existing functionality for merchants to opt into advanced
checkout and onboard 3D Security authentication information. As
part of account management, merchants may be able to opt in to
different levels of authentication. The life occurrence management
platform may provide a way for Merchant Service Providers to make
choices on behalf of their merchants. The life occurrence
management platform may be able to capture necessary data fields as
part of bulk merchant upload file, including without limitations a
channel of a merchant, Merchant Category Code (MCC/aka card
acceptor business code), Merchant ID (MID), merchant account
information at a payment gateway in order to process/accept
payments, Acquirer ID, and the like. The life occurrence management
platform may be able to update content on existing Advanced
Checkout placements to reflect additional functionality.
[0184] In an example, a user may want to be able to choose how he
would like his cards to be treated in advanced and verified
Checkout while making transactions through a life occurrence
management platform. The life occurrence management platform may
maintain a batch file, which may contain Card Issuer preferences at
the Bank Identification Number (BIN) level.
[0185] In an example, a life occurrence management platform may be
able to run JavaScript to capture device details during checkout.
The life occurrence management platform may be able to capture
device ID and redirect checkout experience, including eCom, mobile
web, and native app implementations and the like. The life
occurrence management platform may be able to capture device ID in
an API-based one click during checkout (full Primary Account Number
(PAN) requested), including eCom, mobile web, and native app
implementations. The life occurrence management platform may be
able to capture device ID outside of checkout in cases where not
all data will be present. This may include API pairing, Account
Management, or future functionality. This may include eCom, mobile
web, and native app implementations and the like.
[0186] In an example, the life occurrence management platform may
determine the type of log in method for each session and normalize
the selection. The life occurrence management platform may be able
to create, assign and edit a `login method` for each wallet. The
life occurrence management platform may be able to execute an API
one-click checkout that may be assigned a unique login method and
may override the wallet value. Each API one-click checkout may be
assigned its own value. The life occurrence management platform may
create a mapping table that matches all the discrete login method
values to generic values supported by the system. The life
occurrence management platform may be able to create, assign, or
edit for login methods and their mappings outside of a quarterly
release, including new values on either end.
[0187] In an example, a life occurrence management platform may
record how each card of a user has been previously authenticated
for transaction by the system, and update the value on subsequent
authentications. The life occurrence management platform may track
the strongest authentication method used for every PAN within each
wallet account. The life occurrence management platform may update
the card authentication value during card add and edit or during
checkout, and the like. The life occurrence management platform may
create ability to add new verification methods (or break into more
granular types) or re-order strength of each method for example
Unverified methods, Card Verification Code (CVC) validation,
Address Verification Schemes (AVS) w/ CVC validation, Camera/Video
capture, SecureKey NFC, 3DS, 3DS-One Time Passcode (OTP), Direct
Provisioned where card issuer=wallet issuer, or other methods. The
life occurrence management platform may update card verification
status to an equal, or stronger method and may authenticate it
successfully. If a card fails explicit authentication, its
verification method may be downgraded to Unverified by a life
occurrence management platform. The life occurrence management
platform may identify if a PAN is on a current `fraud` list and add
flag such as On System to Avoid Fraud Effectively (SAFE) list, High
chargeback rate, On Issuer Provided Account Status List, and the
like.
[0188] In an example, the life occurrence management platform may
provide card verification status during checkout for certain cards
as an API wallet. The life occurrence management platform may add
card verification status as a checkout parameter for API wallets.
If API wallet provides card verification status for a PAN during
checkout, the life occurrence management platform may for example
use provided value only if card issuer=wallet issuer. The life
occurrence management platform may create BIN table to determine if
wallet issuer=wallet issuer. For API wallet, if card issuer=/wallet
issuer, then the life occurrence management platform may ignore the
provided value and update the card verification status for the card
only after successful step-up authentication. In an example, the
life occurrence management platform may be able to normalize card
verification methods to Risk Based Decisioning (RBD) supported
generic values. The life occurrence management platform may create
a mapping table between each card verification method and the
generic values supported by a RBD system. The mappings may be able
to be added or changed outside of a quarterly release.
[0189] In an example, the life occurrence management platform may
assign a confidence interval to indicate if account owner is likely
a fraudster or a legitimate customer and normalize it as a generic
value for the RBD. In an example, the confidence interval may be
the strongest card verification method of a card in a wallet. The
confidence interval may be mapped to the RBD generic value (e.g.
strong, medium, weak). In an example, the life occurrence
management platform may create rules that may combine the card
verification methods for multiple cards in the wallet with the
level of contact information verification, and then map to the
confidence interval RBD supported values (e.g. 2 cards with medium
verification methods+email verified+phone verified=high confidence
interval). The life occurrence management platform may identify the
strongest card verification method used for the cards in the wallet
account. The life occurrence management platform may update the
wallet account status after every card add, edit, or delete
selections. The life occurrence management platform may update also
after email or phone validation. The life occurrence management
platform may identify whether the account's contact details have
been validated for example it may determine if email address and
mobile phone is validated. In an example, the life occurrence
management platform may determine whether to request a trust score
from an RBD platform so as to minimize costs. The life occurrence
management platform may create logic for this purpose.
[0190] In an example, the life occurrence management platform may
collect and aggregate data from all wallet types to provide it to
the RBD platform during either a recommendation request or a
data-contribution only request. The life occurrence management
platform may determine whether to submit a full recommendation
request, which requires RBD to return a recommendation and score or
a data-only contribution request, which does not require a
recommendation or score.
[0191] In an example, the life occurrence management platform may
collect data from all wallet types via API-based checkout requests
to provide it to the RBD platform as a contribution or
recommendation request. In an example, the life occurrence
management platform may determine whether to invoke step-up
authentication based on different preferences. The life occurrence
management platform may create ability for a wallet to be flagged
for example as Pre-authenticated and the like. Based on RBD
recommendation or based on defined criteria, step up authentication
may be bypassed. Otherwise if the RBD recommendation is bad, the
life occurrence management platform may not continue with user flow
and follow recovery path defined by each user experience. If RBD
recommendation is a challenge, the life occurrence management
platform may determine what step-up method may be presented to the
user. If the life occurrence management platform does not receive a
response from the RBD system that is sufficiently high enough for
authentication, the life occurrence management platform may set
default to challenge response. In an example, the life occurrence
management platform may follow special step-up rules when wallet is
pre-authenticated and the card issuer is same as wallet issuer or
other preset conditions. The life occurrence management platform
may maintain rules to determine which step up authentication method
to use in different situations and user preferences. The life
occurrence management platform may send data to a server to
generate an Account Address Verification (AAV) for transactions.
Authorizations may be appropriately flagged by the life occurrence
management platform to indicate level of, and reason for,
authentication. The life occurrence management platform may want
authentication and transaction data to be collected so that
reporting, billing, and analytics capabilities are improved.
[0192] The life occurrence management platform may store billing
and tracking data. The life occurrence management platform may have
data for tracking and to calculate possible future billing events
which may made available to a billing system and/or to a customer
reporting system and/or and to an internal reporting system. This
data may comprise data for each processed transaction such as date
and time, type of checkout used, Merchant ID, Wallet ID, PAN (may
be kept encrypted for security reasons--and only made available on
a need to have basis), transaction amount (in USD plus EUR/BRL,
depending on the issuer country), Card brand
(MasterCard/Maestro/Other), Card type
(Debit/Charge-or-Credit/Prepaid/ not known), Issuer configuration
options that are applied to a transaction, Merchant configuration
options that are applied to a transaction (Basic, Verified,
Advanced), step-up action taken (no step-up without valid Account
Address Verification (AAV); no step-up with valid AAV; step-up; not
applicable), step-up result (AAV received; no AAV received; not
applicable), transaction risk level for a transaction, type of
post-back received (successful, not successful, none), Check-out
phase reached by a transaction (successful login; card selected;
sent to 3DS; control handed back to merchant; post-back received),
and the like. The life occurrence management platform may be able
to provide check out quality data to Issuers, including failed
authentication rate of 3D secure, failed authentication rate of
check-out and the key components of such rate that may be
calculated in various ways, failed authentication rate of verified
check-out, failed authentication rate of basic check-out,
fraud-related authorization decline rate, and the like. The life
occurrence management platform may be able to provide general data
to issuers and may provide data to merchants.
[0193] The methods and systems disclosed herein may comprise an
ecosystem enabled lifestyle container. The lifestyle container may
be implemented via a mobile device. The lifestyle container may
allow a user to facilitate coordinating detection of triggers,
use/execution of personalized widgets, presentation of resolution
actions, and execution of preconfigured mobile transactions to
facilitate addressing a life occurrence. Context for triggers may
be developed based on time, location, transaction analytics,
third-party user-related data such as social networks, calendars,
family associations, etc. and adapting the container to monitor
trigger context for detection of triggers. The trigger context may
be updated through an enabling layer operable on the mobile device
that provides access to trigger context sources such as GPS, clock,
calendar, among others. The lifestyle container may comprise
personalized widgets available to the eco-system that may be
identified and configured to facilitate service delivery to address
certain life occurrences. The lifestyle container may be
preconfigured with mobile transactions that may be executed via the
configured personalized widgets to effect service delivery that
satisfies an aspect of the life occurrence. Such preconfigured
mobile transactions may be associated with resolution actions
presented to the user in response to a detected trigger. Users may
then accept and execute the transactions or actions. The lifestyle
container may communicate with the MTP or the expert engine to
maintain current context for triggers. Trigger context may be
synchronized to maintain current context for the triggers.
Synchronization may include updating the trigger context of the
lifestyle container through an enabling layer operable on the
mobile device that provides access to trigger context sources.
[0194] FIG. 8 depicts a high-level system diagram including a life
occurrence container configured to facilitate execution of mobile
transactions for satisfying an aspect of a life occurrence. The
life occurrence node 120 may be a user's mobile device or any other
network connected device. When a life occurrence node 120 indicates
a transaction is required to satisfy an aspect of a life
occurrence, the MTP expert engine 104 may identify merchants,
determine coupons, utilize loyalty points and perform the
transaction. The MTP 102 may facilitate a secure, enabled
ecosystem, with personalized transactions involving multiple
providers, and disparate domains.
[0195] The life occurrence node 120 may be configured to include a
lifestyle container 156 for facilitating transactions for
satisfying an aspect of a life occurrence in the secure mobile
transaction platform. The lifestyle container 408 may include
widgets 802, electronic wallet 804, resolution actions 808, context
monitor 810, trigger event detector 812, and an enabling layer 814.
The trigger-events detector 812 may detect one or more
trigger-events associated with a life occurrence. The
trigger-events may be temporal based trigger-events, spatial based
trigger-events, or other types of trigger-events. The temporal
based trigger-events can be explicit in nature (e.g., user may
define such trigger-events) or may be implicit in nature (e.g., the
trigger-events detector 812 may detect such time based events from
the database, from social networking sites associated with the
user, and the like). The location-based trigger-events may get
activated when a life occurrence node associated with the user is
detected in specific spatial location.
[0196] The MTP expert engine 104 may include a context generator
816 that may develop context for the trigger-events based on time,
space [location], transaction analytics, third-party user-related
data [social n/w, calendar, associations (e.g. family), etc]. The
context monitor 810 of the lifestyle container 408 may monitor the
trigger-events context for detection of trigger-events.
Subsequently, the lifestyle container 408 may facilitate updating
of the trigger-event context through the enabling layer 814 that
may be operable on the mobile device that provides access to
trigger-event context sources (GPS, CLOCK, CALENDAR, etc.). The
lifestyle container 408 may further identify and configure the
widgets 802 that may be available in the eco-system that facilitate
service delivery for addressing the life occurrence. Accordingly,
lifestyle container 408 may associate resolution actions 808 that
may be presented to the user in response to a detected
trigger-event with preconfigured mobile transactions for executing
the actions in response to a user acceptance of the actions.
[0197] The MTP 102 may include a pre-configuring module 818 that
may pre-configure mobile transactions that may be executed via the
configured personalized widgets to effect service delivery that
satisfies an aspect of the life occurrence.
[0198] The life occurrence may be predicted based on user-specific
mobile transactions processed through a mobile transaction platform
and user-related data derived from third party user data sources.
Accordingly, the widget 802 may facilitate service delivery via a
service layer of a platform for secure personalized mobile
transactions. Such personalized mobile transactions may be
associated with a vendor that may be determined from analysis of
mobile transactions processed through a mobile transaction
platform, life occurrence metadata, and user-related data derived
from third party user data sources. The enabling layer 814 operable
on the lifestyle container 408 may facilitate interoperation of the
lifestyle container 408 and the life occurrence node 120 (e.g.
mobile device) resources including user interface, communications,
and secure element access.
[0199] An eco-system enabled lifestyle container may be configured
for a user of a mobile device to facilitate coordinating detection
of triggers for addressing a life occurrence determined by an
expert engine via uses of the mobile device. Context for developing
triggers may include triggers based on time, space [location],
transaction analytics, third-party user-related data [social n/w,
calendar, associations (e.g. family), etc.]. The container may be
adapted to monitor trigger context for detection of triggers. The
lifestyle container may be synchronized with at least one of the
expert engine and a mobile transaction platform (MTP) through which
a user conducts transactions via the mobile device to maintain
current context for the triggers. Synchronizing may include
updating the trigger context of the lifestyle container through an
enabling layer operable on the mobile device that provides access
to trigger context sources [GPS, CLOCK, CALENDAR, etc.].
[0200] Configuring an eco-system enabled lifestyle container for a
user of a mobile device to facilitate coordinating detection of
triggers for addressing a life occurrence determined by an expert
engine via uses of the mobile device may include a few steps. At
least one step may include developing context for triggers based on
time, space [location], transaction analytics, third-party
user-related data [social n/w, calendar, associations (e.g.
family), etc.] and adapting the container to monitor trigger
context for detection of triggers. Another step may include
communicating among the lifestyle container, the expert engine and
a mobile transaction platform (MTP) through which a user conducts
transactions via the mobile device to maintain current context for
the triggers.
[0201] A lifestyle container as described above and elsewhere
herein may execute on a mobile device in a way that is similar to a
container that is described in U.S. patent application Ser. No.
13/651,028 filed Oct. 12, 2012.
[0202] Configuring the lifestyle container may include
communicating over a wireless signal (e.g. mobile network) between
a mobile device that stores the container and networked connected
resources, such as other mobile devices, servers, point of sale
devices, and the like.
[0203] The systems and methods disclosed herein may comprise a
method of alerting a user to a life occurrence. The creation of a
life occurrence alerts may comprise taking metadata that describes
a future life occurrence. Possible resolution actions beneficial to
take in advance of the occurrence of the life occurrence may then
be determined based on multidimensional context derived from
analysis of user transactions performed with a mobile device via a
mobile transaction platform and third-party sources of user-related
data. Additionally, context of trigger conditions for each
resolution action may be determined and the trigger context
monitored. When trigger conditions are met, the resolution actions
may be presented. Such resolution actions may include life
occurrence context that is relevant to a user making a decision
about accepting the resolution action. Each resulting action or
transaction for the contextual resolution action may be prepared on
the mobile device. When the user accepts a resolution action, the
mobile device's action or transaction may be processed. Preparation
of the mobile device for the action or transaction may include
configuring a widget to access an ecosystem service provider, an
electronic wallet on the user's mobile device, a secure element of
the mobile device, and to optionally trigger other widgets to
execute on the mobile device. Additionally, preparation of the
mobile device may include configuring one or more widgets that
follow user preferences for form of payment, receipt handling,
delivery/contact details, etc. to facilitate service delivery that
effects the action/transaction without requiring user input.
[0204] The systems and methods disclosed herein may comprise a
token-based method of life occurrence alert. The method may
comprise taking metadata that describes a future life occurrence
and determining possible resolution actions beneficial to take in
advance of the occurrence of the life occurrence. Such a resolution
actions may be determined based on multidimensional context derived
from analysis of user transactions performed with a mobile device
via a mobile transaction platform and third-party sources of
user-related data. The method may comprise determining context of
trigger conditions for each resolution action and monitoring the
trigger context. When trigger conditions are met, the user may be
presented resolution actions with appropriate context so that the
user may make a decision. The method may comprise preparing a token
to facilitate executing an action or transaction for each
resolution action and adapting the token based on context when the
user accepts a resolution action. The token may include metadata
that identifies a transaction type accessible by a server and
user/wallet/device information required to execute the transaction
on behalf of the user.
[0205] The systems and methods disclosed herein may comprise a
mobile device configured for life occurrence resolution. The device
may comprise a lifestyle container operable on a mobile device that
coordinates detection of triggers, use/execution of personalized
widgets, presentation of resolution actions, and execution of
preconfigured mobile transactions to facilitate addressing a life
occurrence. Such life occurrences may be predicted based on
user-specific mobile transactions processed through a mobile
transaction platform and user-related data derived from third party
user data sources. The device may comprise a personalized widget
for facilitating service delivery. The personalized widget may
facilitate such service delivery via a service layer of a platform
for secure personalized mobile transactions. The widget may be
associated with a preconfigured mobile transaction vendor that is
determined from analysis of mobile transactions processed through a
mobile transaction platform, life occurrence metadata, and
user-related data derived from third party user data sources. The
lifestyle container may comprise an enabling layer operable on the
mobile device for facilitating interoperation of the lifestyle
container and mobile device resources including user interface,
communications, secure element access. Additionally, the lifestyle
container may comprise an electronic wallet operable on the mobile
device that the personalized widget is authorized to access for
facilitating service delivery.
[0206] FIG. 9 depicts a high-level system diagram including a life
occurrence container that may facilitate coordinating detection of
trigger-events for addressing a life occurrence. In addition to
functionalities description in FIG. 8, the life occurrence
management platform may synchronize the lifestyle container 408
with at least one of the MTP expert engine 104 and the MTP 102
through which transactions are conducted on behalf of a user via
the life occurrence node 120 (e.g. a mobile device) to maintain
current context for the trigger-events. The synchronizing may
include updating the trigger-event context of the life occurrence
container through the enabling layer 814.
[0207] An eco-system enabled lifestyle container 408 may be
configured to facilitate coordinating detection of trigger-events
for addressing a life occurrence determined by an expert engine via
uses of a life occurrence node, such as a mobile device. The
context may be developed for trigger-events based on time, space
(e.g., location), transaction analytics, third-party user-related
data (e.g., social n/w, calendar, associations (e.g. family), etc),
risk profiles and the lifestyle container 408 may be adapted to
monitor trigger-event context for detection of trigger-events.
Further, the communication among the life occurrence container, the
expert engine and a mobile transaction platform (MTP) may be
enabled to maintain current context for the trigger-events.
[0208] A method of life occurrence alert may include taking
metadata that may describe a future potential life occurrence.
Various possible resolution actions that may be beneficial may be
determined in advance of the future life occurrence. The resolution
actions may be determined using multi-dimensional context that may
be derived from analysis of transactions performed on behalf of a
user with the life occurrence node 120 (e.g. a mobile device) via a
mobile transaction platform and third-party sources of user-related
data. The context of trigger-event conditions for each resolution
action may be determined and trigger-event context may be
monitored. The method may present the resolution actions to the
user when the trigger-event conditions are met. Such resolution
actions may include life occurrence context relevant to a user
making a decision about accepting the resolution action. The method
may prepare a mobile device action/transaction for each resolution
action and may adapt the mobile device action/transaction based on
action/transaction context when a resolution action is accepted by
the user.
[0209] A widget may be configured to access an ecosystem service
provider, an electronic wallet on the user's mobile device, a
secure element of the mobile device, and to optionally trigger
other widgets to execute on the mobile device. Additionally, one or
more widgets that follow user preferences for form of payment,
receipt handling, delivery/contact details, etc may be configured
to facilitate service delivery that effects the action/transaction
without requiring user input.
[0210] An instrument-based method of life occurrence alert is
disclosed. The method may include taking metadata describing a
potential life occurrence and determining possible resolution
actions beneficial to take in advance of the potential life
occurrence. The possible resolution actions may be based on
multi-dimensional context derived from analysis of user
transactions performed with a mobile device via a mobile
transaction platform and third-party sources of user-related data.
The method may determine the context of trigger-event conditions
for each resolution action and monitor trigger-event context.
Further, the method may include presenting the resolution actions
when trigger-event conditions are met. Such resolution actions may
include life occurrence context relevant to a user making a
decision about accepting the resolution action. Further, the method
may include preparing an instrument to facilitate executing an
action/transaction for each resolution action and adapt the
instrument based on context when a resolution action is accepted by
the user. The instrument may include metadata that may identify a
transaction type accessible by a server and user/wallet/device
information required to execute the transaction on behalf of the
user.
[0211] The life occurrence management platform may facilitate
interactions with the user through various user interfaces that are
configured to display a plurality of moving panels for performing
different trigger actions. Such user interfaces and the moving
panels are discussed herein in conjunction with various figures
related to the user interfaces associated with a life occurrence
management platform. In an example, the user interfaces may be
presented on a mobile device, cell phone, on a washing machine
display panel. In other examples no user interfaces on life
occurrence devices at all and a life occurrence management platform
may simply run a series of transactions and settle wherever it
needs to settle.
[0212] A node or a mobile device may be used for triggering a
transaction. In other cases, a mobile phone with a lifestyle
interface may be used to trigger the transaction. In some cases,
the transaction triggering may be done without any device at all.
The node may be a, tablet device, a mobile phone, or smart phone
that may be configured for lifestyle mobile application for
shopping or other life occurrences. In some cases, the node may be
any other networked device such as a washing machine that may be
connected with a secure, enabled ecosystem of payment providers,
service providers, and the like.
[0213] A life occurrence management platform may be implemented
through a lifestyle layer that may service the nodes including an
endpoint device that may order things as well as act as an
application on the node. For example, the nodes may be capable of
performing shopping tasks. These nodes may comprise a washing
machine, mobile phone, vending machine, or any other networked
device. For example, if kids are returning from vacation, a person
may need more supplies and the washing machine may also require
more detergents. Therefore, based on the contextual information,
the vending machine or washing machine may order more supplies
accordingly based on derived contextual information about the user
and his kids.
[0214] In a healthcare related example, the life occurrence
management platform may facilitate refilling of medications based
on information from sensors as end nodes. The life occurrence
management platform may facilitate in purchasing pills for a user
or manage hospital experiences such as including without
limitations annual checkup, taking care of co-pay, insurance, etc.
through the MTP. In an example, sensors may be provided with the
nodes that may take signals e.g., embedded signals that may send
out signals constantly, e.g., insulin levels for diabetics and the
like. The sensors may be FDA approved sensors emanating signals.
The life occurrence management platform may get the signals and
guide routine hospital experiences accordingly. A life occurrence
management platform may provide lifestyle experience on a user's
phone such as walking him through whole hospital process and
guiding him through the route etc. for the hospital and about
parking arrangements and prepaid card that the user may use in the
hospital and the like.
[0215] The life occurrence management platform may facilitate in
applications such as a diet application that may constantly monitor
dietary conditions, and may include consumer lifestyle utilities
and the like wherein the user may log what he may eat. The logged
information may be retrieved by a life occurrence management
platform so as to take care of the healthcare and dietary aspects
of the user. A user's FITNESS PAL.TM. may further take manual entry
of calories, etc and the calories information may be used by a life
occurrence management platform.
Utility/Usability
[0216] An entire transaction spectrum, starting from providers that
are part of the secure enabled ecosystem, through the life
occurrence management platform infrastructure, to the nodal
devices, may be separated into a set of capabilities that may form
an overall Utility, and, a set of capabilities that may form
Usability. A life occurrence management functional layer may be
provided that may intervene between the nodal points and the entire
secure enabled ecosystem. A part of a life occurrence management
platform may interact with the secure enabled ecosystem. Another
portion of a life occurrence management platform may interact with
the nodes. A life occurrence management platform may be enabled
through several layers. These may include a utility layer and a
usability layer. The utility layer may be enabled by the devices
that may have capabilities that provide utility for conducting
secure transactions. The utility layer may represent various
portions of the life occurrence management platform as modular
infrastructure elements of a utility. The various modules may be
presented as infrastructure services sets that may be presented as
utilities upon which applications may be made. The usability layer
may be enabled through applications that may provide a layer on the
usability side.
[0217] The utility and usability as discussed herein may be defined
as tiers of capabilities, wherein at one end of the tiers will be
features or building blocks that may comprise user, device, and
application agnostic, and, at the other end may be capabilities
which may be highly customized and personalized for users, devices,
and applications and the like. The aspects of usability and utility
may facilitate in building necessary linkages among various modules
of a life occurrence management platform and their coupling for
efficiency and scalability. It must be appreciated that utility
components may be evolved and governed over time whereas usability
components may be best suited to a relatively unrestricted and open
development environment of a life occurrence management platform.
It must be appreciated that certain features or capabilities of a
life occurrence management platform may be part of a utility-tier,
and certain features or capabilities may be part of a
Usability-tier.
[0218] FIGS. 10-16 depict various tables that may include a
plurality of data types and corresponding attributes of the data
types that may be suitable for use in a multi-dimensional
context-based database. The plurality of the data types may be
indicative of the respective dimensions of the multi-dimensional
database 118. The data of the multi-dimensional database 114 may be
stored within the plurality of data tables as described herein. A
table 1 depicts a category table that may be utilized for storing
information associated with the type of the products for which the
transaction may be carried out on detection of the life occurrence.
The category table 1 may include category ID, name of the category
and status of the category. A sub-category for each of the category
may be utilized for storing information associated with the
products corresponding to the sub-category of the main category. A
table 2 depicts an exemplary embodiment of the sub-category table.
For example, an electronics category in the table 1 includes two
sub-categories, namely phone and laptops as depicted in the table
2.
[0219] A table 3 depicts a merchant table that may be configured to
store information associated with the merchants that may be
available to the user in the mobile transaction platform. The table
3 may be used for storing information such as a merchant ID, name
of the merchant, types of the services being offered by the
merchant, web address for the merchant websites, any image
associated with the merchant and other related information. Such
information may be displayed to the user or may be used by the MTP
expert engine 104 so as to select a specific merchant from the
plurality of stored merchants.
[0220] A table 4 depicts an example of a product table that is
generated/referenced when performing a comparative shopping
activity with lifestyle. The product table may be utilized for
storing information such as product ID, product name, model number,
sub category associated with the product (e.g., as indicated in
table 2, information related to manufacturer, manufacturing date,
price and other information associated with the product. A table 5
depicts an example of an inventory table for the products that be
offered to the user while performing a shopping activity with the
lifestyle container. The inventory table may be used to store
information such as validity period of the offer, specifics of the
offer, minimum adjustment amount of the offer, discount percentage
for the user, available quantity and the status of the available
offers. The MTP expert engine 104 may use these data so as to
determine an offer to the user while generating a resolution path
for the life occurrence.
[0221] A table 6 may include the example of plurality of units that
the MTP expert engine 104 may support while determining the type of
life occurrences and for generating a resolution path for resolving
one or more aspects of the life occurrences. A table 7 depicts an
example of an event table that may have been learnt by the MTP
expert engine 104 so as to determine a candidate resolution action
for resolving the one or more aspects of the life occurrences the
life occurrence. The event table may include events such as a
wedding date, birthday date, graduation ceremony, valentine day and
other important days or time on occurrence of which the user may
like to perform an action using the lifestyle container.
[0222] A table 8 depicts an example product offer table, which may
be available to the user so as to perform shopping for the products
available in the product offer table. A table 9 depicts an example
of a loyalty table that may be generated for a specific user
depending on the type of the user. The MTP expert engine 104 may
determine the user behavior and analyze the transactions of the
user to generate the loyalty offer for the user. The user register
himself for the loyalty offers so that on detection of the life
occurrence the MTP expert engine 104 may suggest a best possible
loyalty offer to the user.
[0223] A table 10 is an example of a store message table, which may
be used for storing messages for a specific merchant. The store
message table may include information that may be harvested by,
learned by, and/or pushed to the MTP expert engine 104 for use in
the multi-dimensional context and resolution actions. A table 11 is
an example of a product suggestion table that may store information
associated with the suggestion for allowing mapping of one or more
compatible products with a particular product. A table 12 is an
example of a merchant _user table that may be used for storing
information such as loyalty of the user for a specific merchant,
any corresponding loyalty card related information, and merchant
related information. The MTP expert engine 104 may utilize the
table to suggest offers to the users depending on the loyalty of
the user for the specific merchant.
[0224] A table 13 is an example user behavior table that may be
indicative of the behavior of the user on the mobile transaction
platform. The MTP expert engine 104 may be configured to analyze
the user behaviors and make predictions for the patterns for the
user using machine learning techniques, fuzzy logic or neural
networks. Further, a table 14 is an example of initial behavior -1
for the user that may include list of life occurrences of the user,
which need to be managed. Similarly, a table 15 is an example of
other version of the behavior of the user. The MTP expert engine
104 may be configured to perform the behavior analysis of the user
so that the MTP expert engine 104 may be learn the user engagement
tendencies. Such type of behavior analysis may enable the MTP
expert engine 104 to provide better grading of results that may be
presented to the user. A table 16 is an example of a user alert
table that may be generated for preparing a dynamic list of user
lifestyle occurrence specific alerts. For example, when a user is
planning to travel, the MTP expert engine 104 may determine the
status of the flight of the user and accordingly, may alert the
user.
[0225] The multi-dimensional database 118 may be configured to
store various forms of the data in the form of tables. For example,
a table 17 is an example of a payment card table that may include
information associated with the payment card of the user. The
payment card table may include information regarding the wallet ID
that may be associated with the specific payments card. When the
user may perform any transaction with the wallet, the user may
select the associated payment card. The multi-dimensional database
114 may include a store table 18 that may include information
associated with the available stores and corresponding merchants.
The multi-dimensional database 118 may include a table 19 for
storing address related information for the user. For example, for
each user ID, a shipping address may be stored so that the MTP
expert engine 104 may complete the shopping transaction and may
dispatch the products to the shipping address as stored in the
address table of the user.
[0226] The multi-dimensional database 114 may include transaction
table 20 for storing information corresponding to the past
transactions of the user. The transaction table 20 may include
information corresponding to wallet ID, transaction ID, point of
sale terminal ID, merchant ID and other related information. A
table 21 may be used for transaction object related information for
each transaction. The transaction table 20 and the transaction
object table 21 may be used by the MTP expert engine 104 to
determine transaction behavior analysis of the user so as to
generate a resolution path for the one or more aspects of the life
occurrence.
[0227] The multi-dimensional database 118 may include other
information such as data related to inventory of the products
associated with the merchant in an inventory table 22, user profile
related information in a user profile table 23 and widget related
information in the widget table 24.
[0228] FIG. 17 illustrates an example embodiment of a method for
facilitating on boarding of a user. At step 1702, the user may
onboard the lifestyle container to accept the initial terms and
conditions. The lifestyle container may be closed when the user may
reject the terms and conditions required to access the lifestyle
container to perform one or more transactions. Otherwise, the
details such as a mobile number, an email ID, and other user
related information might be added. Further, various intelligent
appliances such as washing machine, vending machine and the like
may be registered with the lifestyle container and the lifestyle
container is registered with the MTP 102.
[0229] At step 1704, a determination is made as to whether the
lifestyle container requires additional processing. The MTP expert
engine 104 may apply rules and logic to the received content when
the lifestyle container may require additional processing.
Otherwise, information associated with the lifestyle container may
be persisted and stored in the multi-dimensional database 118. At
step 1708, the method may include determining requirement of
connecting with other systems. The MTP expert engine 104 may
connect with the other networks/infrastructure via the enterprise
service bus and information such as user profile from external
sites, shopping history from merchants, travel information; health
related information and social networking profile of the user might
be retrieved. The retrieved information may be sent back to the MTP
102 for further processing.
[0230] At step 1710, the MTP expert engine 104 may process the
content received from the external networks or from the internal
database and generate a user profile at the MTP expert engine 104.
The MTP expert engine 104 may communicate the profile generation
information with the MTP 102 via MTP EE interfaces and
subsequently, the MTP 102 may mark the user as a registered user
with the system. At step 1712, a message corresponding to a
successful registration of the user may be displayed on a display
screen of the life occurrence node 120 such as the mobile device of
the user.
[0231] At step 1714, the enabled ecosystem may generate offers,
notifications and messages for the user to the MTP 102 via the ESB
124. Subsequently, the MTP 102 and the MTP expert engine 104 may
processes these offers, notification and messages in accordance
with profile of the user. At step 1718, the processed offers,
notification and messages may be sent to the lifestyle container
either through push messages or through synchronization methods.
Subsequently, the offers, notification and messages may be
displayed for the user.
Shopping Flow
[0232] FIG. 18 depicts an example embodiment of method for
facilitating shopping transaction for the user in the mobile
transaction platform. The method may include accessing the
lifestyle container for initiating the transaction on determining
the life occurrences. The user may have the lifestyle container on
a life occurrence node 120 such as the mobile device as a lifestyle
app. In another example, a washing machine or any other utility
device might also be set up for the lifestyle ecosystem. At step
1802, the lifestyle container may facilitate the user to add
manually the shopping list and send the shopping list to the MTP
102. The lifestyle container may allow the user to tag the earlier
saved offers in the shopping list and send the saved offers as
items of the shopping list to MTP 102.
[0233] At step 1804, a trigger for the shopping list is generated
and sent to the MTP expert engine 104. The trigger may be a
time-based trigger or a location based trigger. For example, on
detection of arrival of an event (e.g., birthday event), a
time-based trigger is generated. In an example, Facebook birthday
event may trigger possible birthday shopping and birthday itself
may trigger a shopping flow. A Facebook application may remember
that the user bought a tie for a friend last year, so it could
inject a past purchase (cufflink) into the shopping flow to search
for something else. The usability component may inject past history
into the utility component at that time. The life occurrence
management platform may collect information from third party data
such as that the user's friend returned the tie last year. In
another example, depending on the location of the user, a
location-based trigger may be generated. At step 1808, the triggers
thus generated may be sent to the MTP expert engine 104.
[0234] At step 1810, the MTP expert engine 104 may be configured to
access the knowledge database and list of the triggers. The
knowledge database may include initial behavior of the user so that
the MTP expert engine 104 may utilize the behavior of the user to
generate the shopping list that may be sent to the merchants. The
behavior may include a plurality of shopping preferences such as
sizes for example waist, dress, shoe, sizes and the like, brands,
styles, genres, loyalty information, and the like. Other shopping
attributes that may be extracted from the behavior analysis of the
user may include merchant specific attributes across various
merchants, transactions-related attributes including of other
users, and the like.
[0235] For example, when a user goes to a merchant (couponing
system) and he buys something, the user may get a coupon from the
back end/cloud that may get pushed to the user. The user may get an
instrument (coupon) as a result of that transaction. In such
scenario, pushing out a coupon to a user's wallet might be a
natural example in a life occurrence management platform. The
coupon may be designed for anything. For example, if the user buys
toothpaste, he instantly may get a coupon for a toothbrush. If
every Friday Bob buys bread, the user might send a coupon from the
couponing system, transfer it to the MTP and the MTP may push out
the coupon to the user's wallet. The MTP expert engine 104 may
recognize charity and coupons as a result of transactions but the
user might want to generate a coupon based on a life occurrence
analysis. For example, a user may buy bread on Fridays, so the life
occurrence management platform may push a coupon on Thursday usable
for buying the bread. The life occurrence management platform
pushes coupons and the like that complements a transaction. The MTP
expert engine 104 may for example intercept a coupon and know
whether the user really needs a toothbrush. The life occurrence
management platform may shop it around or find out whether that is
the right coupon or whether the user needs something else. The life
occurrence management platform finds out if the user is entitled to
a discount on something he already bought.
[0236] The shopping list generated for the merchants may be sent to
the merchants through the enterprise service bus. At step 1812, the
enabled ecosystem may facilitate the merchants to add best
offers/prices to the shopping list and may send the shopping list
tagged with the best offers to the MTP expert engine 104. The life
occurrence management platform may apply merchant rules. The rules
may be user-dependent, may be based on a type of user, volume
requirements e.g., across multiple users, and the like. An example
of a merchant rule may be that Costco takes Amex but no other
cards. If Costco is the best place to fulfill a user's complete
order, then the user may not use his United MasterCard or other
card. If the user uses a particular merchant like Costco, he can't
use his MasterCard. The consumer might want to know what is going
on before shopping at a place that won't take his card. Another
example may be that Wal-Mart got customers to use PIN/Debit instead
of credit cards. Merchant rules may be derived from the enabled
ecosystem. They may be derived in through the Enterprise Service
Bus through an API that allows specifying rules, conditions, etc.
so that the expert engine may consume those rules. Merchant rules
may be consulted when a bid is placed to see what rules apply to
what that bid.
[0237] At step 1814, the MTP expert engine 104 may process the
shopping list including the offers from the merchants to the MTP
102 and further to the lifestyle container for presenting the
shopping list to the user. At step 1816, the user may decide to go
with the shopping list and at step 1818, the shopping list is sent
to the merchant via the ESB so that the merchant may process the
shopping list. The processing of the shopping list may include
generating the receipt and packaging the final order. The receipt
is transmitted to the MTP 102 via the ESB and further to the user
whereas, the order may be dispatched to the user. The MTP expert
engine 104 may access the shopping list that may be processed by
the merchant and may apply rules and fuzzy logic to build the user
behavior.
[0238] In an example, shopping types may include just finding
matching items, comparison shopping, Bidding, Reverse auction,
expert using an expert shopping engine that may shop price line,
hotwire and others. The life occurrence management platform may
facilitate applying for any offers and coupons in a user's
repository through lifestyle container on user's wallet or
otherwise in his data repository. The life occurrence management
platform may consult rules for the user and for example link them
to the rules engine/expert engine). The expert engine may apply
learning stuff to figure out what bids are the best for a user. The
expert engine may for example perform refinement of a bid because
it senses from the user behavior that the user is not using cards
at target anymore. The life occurrence management platform may
consult rules regarding extent of desired intervention. A user may
dial up or dial down the degree of intervention. The
dial-up/dial-down may be based on sectors such as control in health
care may be more relative to shopping. Within each sector, the life
occurrence management platform may offer different sets of
interventions. The life occurrence management platform may offer
granular control for some cases and automated intervention for
others. The expert engine may apply rules to general bids. For
example, the life occurrence management platform may apply the
rules to responses from step of shopping the shopping list. The
rules engine may be connected with a fuzzy system. The rules engine
may serve as a learning engine that may learn for example that
someone likes a certain thing in a given circumstance.
Parking Flow
[0239] FIG. 19 depicts an example embodiment of a method for
facilitating parking arrangements for the user in response to a
life occurrence. At step 1902, the method may include identifying a
location at which the user may be interested for discovering a
parking space. The user may add appointment related information in
the calendar of the lifestyle container. The lifestyle container
may be configured to detect the timing and location from the
calendar application so that the parking arrangement may be made
for the user. The lifestyle container may determine the timing and
location information from the travel bookings that may have been
done by the user. FIG. 19 illustrates an example embodiment in
which the life occurrence management platform may identify the
timing and location required for making the parking arrangements
from the calendar application. As depicted, the calendar available
at the lifestyle container is synchronized with the cloud calendar
available at the MTP 102. The cloud calendar available at the MTP
102 may also include information from the company calendar and the
travel booking related information received from the enabled
ecosystem.
[0240] At step 1904, the MTP 102 may share the cloud calendar with
the MTP expert engine 104 so that the MTP expert engine 104 may
process it to generate a parking list. The MTP expert engine 104
may be configured to determine user behavior that may be available
in the knowledge database accessible to the MTP expert engine 104.
The knowledge database may be the multi-dimensional database 114 as
explained earlier in the description. The MTP expert engine 104 may
determine usual preferences for parking such as vehicular based
parking, parking prices and the like from the user behavior.
[0241] At step 1908, the MTP expert engine 104 may share the
processed parking list with the parking providers through the
enterprise service bus. At step 1910, a determination is made as to
whether the parking providers support the pre-booking. At step
1912, a parking ticket may be generated when parking provider
supports the pre-booking and the parking provider may transmit the
parking ticket to the MTP expert engine 104. At step 1914, the MTP
expert engine 104 may process the parking ticket with the fuzzy
logic or neural network so as to update the user behavior in the
knowledge database. Subsequently, the MTP expert engine 104 may
transmit the parking ticket to MTP 102 that may push the parking
ticket to the user.
[0242] Otherwise, at step 1918, the parking providers may generate
a reference number when the parking providers may not provide a
pre-booking. The reference number may be forwarded to the MTP
expert engine 104 that may save the reference to provide live feed
to the user on availability of parking and best available prices.
The MTP 102 may send the reference id to the enabled ecosystem when
the user has started journey and may not have the pre-booked
ticket. The live feed may be pushed to the user device and the user
may take action on these live feeds depending on the
requirement.
[0243] As described more fully below, a system comprises a mobile
transaction platform (MTP) in communication with a plurality of
service providers and one or more containers operating on a mobile
device. The MTP is configured to facilitate mobile transactions
between the one or more containers and the plurality of service
providers and to derive analytic data from the mobile transactions.
The one or more containers may include a lifestyle container as
described in further detail later herein. An expert engine is in
communication with a plurality of sources of third party user data
and the MTP and is configured to consolidate the analytic data and
the plurality of sources of third party user data to create a
multidimensional context for determining life occurrences and
resolution paths for resolving aspects of a life occurrence.
[0244] The resulting system 100 enables a user experience through
the mobile device by relying on a lifestyle container 408 that
facilitates presenting notifications of triggered life occurrences
derived from a robust multidimensional context with associated
consolidated resolution actions. Such resolution actions may
include, for example, at least one secure mobile transaction and
collectively serve to guide a user through a series of choices to
resolve the triggered occurrence. In this way, a triggered life
occurrence event may drive the transactions that determine the user
experience. As described more fully below, the MTP 102 aggregates
disparate domains and attends to the complexities of secure
transactions comprising components of the resolution actions. The
expert engine consolidates numerous sources of analytics to create
and maintain the multidimensional user specific context that drives
the derivation of the life occurrence-related triggers.
[0245] With reference to FIG. 20, there is illustrated a system
2000 according to an exemplary and non-limiting embodiment. The
Mobile Transaction Platform (MTP) 102 as discussed in conjunction
with various figures already operates, generally, to facilitate
communication between the external entities 122 and the lifestyle
container 408. Examples of the external entities 122 include, but
are not limited to networks and gateways, offers and value added
services (VAS), other host systems, Trust Service Managers (TSMs)
and Certificate Authorities (CAs), user/host databases, and the
like. In accordance with an exemplary and non-limiting embodiment,
the MTP 102 communicates with the external entities 104 via the
enterprise service bus (ESB) 124. While the MTP 102 operates to
facilitate mobile transactions between the external entities 122
and the lifestyle container 408, it facilitates passing data
between the external entities 122 and the lifestyle container 408
without substantively altering their content. However, the MTP 102
does acquire and collect for storage in, for example, the
transactional analytics database 128, information and metadata
related to various attributes of the transactions enabled by the
MTP 102. For example, the MTP 102 may store in the transactional
analytics database 128 information related to transaction times,
transaction amounts, service provider identifiers, life
occurrence-related trigger, user action(s) to effect the
transaction, and the like.
[0246] In communication with the MTP 102, the expert engine 104
operates to consolidate various transactional analytics received
from the MTP 102 with one or more of the third party sources 130 to
create a multidimensional context that is suitable for determining
life occurrences, developing and maintaining occurrence action
triggers, generating resolution paths that resolve an aspect of a
life occurrence via uses of the mobile device, and the like.
Examples of the third party sources 130 include, but are not
limited to, third party analytics, social networks and various
other context drivers examples of which are described more fully
below. In accordance with an exemplary and non-limiting embodiment,
the expert engine 108 communicates with the third party sources 130
via the ESB 124. As described more fully below, the expert engine
104 may employ a feedback system operating between, for example, a
fuzzy system 204 or rules engine and a neural network 208. In
operation, the expert engine 104 may employ one or more algorithms
to consolidate various transactional analytics from the MTP 102
with data from the third party sources 130 to produce a
multidimensional context from which triggers may be produced. Such
algorithms may further order and prioritize the display of life
occurrence-related alerts to a user of the mobile device.
[0247] As described more fully below, the expert engine 104 makes
use of various context drivers to create the multidimensional
context including past transactions, learning from preferences of a
user, the presence of a network or a particular account, the
presence of vouchers and promotions, loyalty points and the
like.
[0248] In some embodiments, the multidimensional context may
comprise at least one of user or life occurrence location
information. For example, the current location of the user or the
life occurrences may be determined using any location determination
technologies such as global positioning system (GPS) and the like.
The multidimensional context may comprise at least one of time of
life occurrence and current time.
[0249] The data derived from the transactional analytics database
128 may be incorporated into a static user profile and a dynamic
profile of the user for use by the MTP expert engine 104. In some
embodiments, the transaction data may be analyzed by the MTP 102 in
context of other users, similar or interested vendors, etc. to
establish some sort of weighting, importance, etc. This analysis
may result in determination of a new trigger, action, or
occurrence. The transactional analytics database 128 and the MTP
expert engine 104 may exchange resolution triggers, static user
profiles, and dynamic user profiles. The static user profiles may
be used in conjunction with current context data such as time,
space, and user input for the MTP expert engine 104 in order to
determine life occurrences.
[0250] With reference to FIG. 21, there is illustrated a system
2100 according to an exemplary and non-limiting embodiment. The
Mobile Transaction Platform (MTP) 102 operates, generally, to
facilitate communication between the external entities 122 and the
lifestyle container 408. In accordance with an exemplary and
non-limiting embodiment, the MTP 102 communicates with the external
entities 122 via the enterprise service bus (ESB) 124. Among other
elements and components, the system 2100 may include a switch 2102.
The switch 14A02 may facilitate access to the ecosystem resources
such as third party analytics, social networks, context drivers and
at least one of networks and gateways, offers and value added
services, host systems, trusted service managers (TSM), certificate
authorities (CA), and databases for the MTP expert engine 104 and
the transactional analytics facility 128. The switch 2102 may be
configured to access transactional components in the ecosystem for
facilitating financial transactions through for example prepaid
cards among users, service providers, billing agents, and financial
services agents and the like. For example, the switch 2102 may be
communicatively connected with the transactional analytics 128 for
facilitating such transactions among various entities to provide a
user-centric experience. The switch 2102 may be accessible through
the lifestyle container 408 deployable on a node such as a mobile
device and the like.
[0251] The system 2100 may further include a risk scoring module
2104 that generates a risk score that may be utilized as a context
and decision driver to determine if one or more resolution actions
are suitable for presenting to a user. The risk based decisioning
is an expert process and could be carried out at either the level
of expert engine 108 or at the level of enterprise service bus 124.
In accordance with the illustrated embodiment, the risk scoring
module 2104 may operate in association with the transactional
analytics facility 128 for analyzing user transactions associated
with the mobile transaction platform 102 and third-party sources of
user-related data to generate a risk profile of users,
trigger-events, third-parties, resolution actions, life
occurrences, potential transactions, and the like based on
multidimensional live occurrence context. In an example, the risk
profile may be used to determine if one or more resolution actions
are suitable for presenting to a user. In an example, the risk
profile is useful to rank resolution actions. Based on risk
calculations by the risk scoring module 2104, the expert engine 104
may determine life occurrences and suggest resolutions based on
multidimensional context derived from analysis of associated risks
in connection with third party sources and the like. In an example,
the risk scoring module 2104 may facilitate maximize checkout
conversion rates and decrease fraud on transactions through use of
a risk based authentication system and dynamic multi-factor
authentication methods. A user may be able to choose if he wishes
what type of Checkout he may want to use during Account Management.
The system may re-use existing functionality for merchants to opt
into advanced checkout (Direct and BMU) and onboard 3DS
information. As part of account management, merchants may be able
to opt in to different levels of authentication. In an example, the
system 2100 may assign a confidence interval to indicate if account
owner is likely a fraudster or a legitimate customer and normalize
it as a generic value for the RBD.
[0252] In accordance with exemplary and non-limiting embodiments,
the expert engine 104 may utilize risk as a context driver when
generating triggers and attendant resolution actions. For example,
as the MTP 102 executes one or more transactions in response to a
user's inputs in response to an alert of a trigger, the MTP 102 may
dynamically identify one or more attributes of the transactions as
amounting to an unacceptable risk. In response, the MTP 102 may
alert the user to, for example, chose a different mode of payment
or another vendor. For example, a user may be provided multiple
payment options for proceeding with the mobile transaction. The
user may further have defined a default credit card for mobile
transactions that may be selected in the event that no other form
of payment is selected and/or if a chosen form of payment is not
acceptable. In this way, the MTP, in cooperation with the lifestyle
container 408 and/or other MTP resources on the mobile device, may
automatically switch forms of payment and/or vendors in response to
detection of an unacceptable level of risk based on the risk score
generated by risk scoring module 2104. A non-limiting example of
risk management, the user may buy neckties and the expert engine
104 may identify matching cufflinks and suggest to the user to
purchase the matching cufflinks at an identified vendor with the
vendor's issued credit card. The user agrees and the lifestyle
container 408 is updated to facilitate presenting a consolidated
view of the transaction. However, the expert engine 104 determines
that an aspect of risk of this transaction is unacceptable (e.g.
payment terms of the vendor's credit card are onerous) and suggests
to the user the choice of using a different credit card that is
accessible in the user's mobile wallet via the mobile device
instead of the store card, even though the user will lose out on
some vendor-specific loyalty points.
[0253] With reference to FIG. 22, there is illustrated a schematic
diagram of the operation of the expert engine 104 according to an
exemplary and non-limiting embodiment. As illustrated, it is
evident the similarity in the manner in which the expert engine 104
handles temporal and spatial context drivers/data. With regard to
time based actions, illustrated in the block to the left of the
expert engine 104, actions including the display of notifications,
alerts, suggestions and the like are taken based, at least on part
on the current time, a temporal window and information defined by
the user and/or information from third party systems such as, for
example, social network sites. As illustrated in the bock to the
right of the expert engine 104, location based information is
treated in much the same manner. Alternatively, the expert engine
108 may handle temporal and spatial context drivers/data
dissimilarly.
[0254] In general, the mobile transaction platform 102 may receive
user data from a source, such as the external entity 122, and such
as via the ESB 124. The MTP 102 may then transmit the user data to
a user such as a user operating a mobile device executing the
lifestyle container 408. Next, the MTP 102 may derive a plurality
of transactional analytics data from the transmitted user data
which it may then transmit to the expert engine 104 which may be
configured to consolidate the plurality of transactional analytics
data with at least one third party source of user data. As
described more fully below, the MTP 102 may receive from the expert
engine 104 one or more triggers derived from the multidimensional
context wherein each trigger is triggered based on an occurrence
for which one or more resolution actions are provided to the user
of the lifestyle container 408 enabled mobile device.
[0255] An expert engine, such as the expert engine 104 depicted
herein may be based on a variety of known technologies including
several technologies co-owned by the applicants hereof. In U.S.
patent application Ser. No. 10/284,676 filed Oct. 31, 2002 that is
incorporated herein by reference in its entirety, an expert system
in context of a transaction environment is described. This expert
system performs data consolidation from a variety of sources
including direct client input, service institutions, merchants,
vendors, government agencies, client profile, transaction records
analysis, rules/flags, and the like for providing to a knowledge
based system that effects services to clients. Services include
among other things, matching services to connect users with
providers of services/products based on the user's
request/interests (implicit and explicit), personal data, account
data, and transaction data; suggesting a transaction based on user
personal, account, and transaction data and a database of
vendor/service provider information; and confidentially
"negotiating" an offer on behalf of the user by revealing some
confidential information related to the specific negotiation
objective (e.g. frequent flyer miles/points details, size of
household, etc.).
[0256] Another expert system embodiment is described in U.S. patent
application Ser. No. 11/539,024 filed Oct. 5, 2006, that is
incorporated herein by reference in its entirety, an expert system
in context to a transaction environment for secure mobile
transactions is described. This expert system facilitates
customizing the user interface experience and determining user
preferences by operating over time using the "user's behavior,
usage patterns, transaction history and qualified external inputs",
particularly as depicted in FIG. 47; includes a learning engine
that may learn which services a user tends to use and push data to
the mobile device to improve delivery of those services;
rules-based operation to handle prioritizing data flow and
transactions based on payment due date, payment "importance", etc.,
managing application throughput to improve user's access to data,
and the like.
[0257] Yet another expert system embodiment is described in U.S.
patent application Ser. No. 13/651,028 filed Oct. 12, 2012, that is
incorporated herein by reference in its entirety, an expert system
in context of a mobile transaction platform for secure personalized
transactions is described. This expert system facilitates
delivering a simplified user experience, customization of service
and personalization elements, analyzing user habits; automatically
adjusting the platform features to present itself in the manner
most suitable to individual users including regional preferences
("most French like it this way while the Americans like it that
way"), mobile device capabilities (screen size, keyboard available
or only on-screen, and the like), and differences in client base
(low-end versus luxury customers); carrying out analytics on the
transactions, usage patterns, and other parameters that will help
the `learning` process of an inference engine, and the like.
[0258] Any of these expert systems may be capable of performing at
least portions of the methods and systems of life occurrence
determination and resolution path generation as described herein.
An expert system of U.S. patent application Ser. No. 10/284,676
may, among other things, facilitate data consolidation from a
plurality of data sources including mobile transactions performed
in association with a transaction platform of the expert system. An
expert system of U.S. patent application Ser. No. 11/539,024 filed
Oct. 5, 2006 may, among other things, facilitate generating aspects
of a multidimensional context that is suitable for life occurrence
determination based at least in part on analysis over time of a
user's behavior, usage patterns, transaction history and the like.
An expert system of U.S. patent application Ser. No. 13/651,028
filed Oct. 12, 2012 may, among other things, provide context for a
simplified and improved user experience, such as by analyzing user
habits, carrying out analytics on transactions, and automatically
adjusting service delivery-related features of an MTP so that the
user perceives an output of an inference engine in a manner most
suitable for an individual user.
[0259] In an example, the multidimensional context may include at
least one of user or life occurrence location information. In an
example, the multidimensional context may include at least one of
time of life occurrence and current time. The multidimensional
context derived from user transactions may be used by the MTP 102
to determine life occurrences and for generating resolutions paths.
In an example, the MTP expert engine 104 may use the
multidimensional context derived from the user transactions handled
through the MTP 102 to generate action trigger-events for resolving
life occurrences by facilitating user directed mobile actions.
[0260] As depicted, the MTP 102 may utilize automated algorithms,
learning and knowledge systems, discovery systems, inference
engines for enabling intelligent solutions through the expert
engine 104 in determining life occurrences and determining
available resolution paths. Further, these systems may facilitate
in developing a customer-centric or user-centric experience by
utilizing user transactions data and recognizing user context
through such as rules based systems, fuzzy systems, neural network
and the like. These intelligent systems may facilitate an
interactive and collaborative communication between the temporal
window and the spatial window enabled through the expert engine 104
of the MTP 102.
[0261] The expert engine 104 may determine a type of life
occurrence of an individual among a set of possible life
occurrences based on a multidimensional data set, and may generate
a resolution path for resolving aspects of the occurrence via uses
of a life occurrence node (e.g. the individual's mobile device).
The resolution path may be based on an overall context of the
individual that includes the point in time at which the
determination is made, data from a mobile transaction platform
(MTP) through which the individual conducts mobile transactions,
data from a third party source relating to the individual, and
location data for the individual at the point in time. The location
data and the temporal data may be coordinated through the temporal
window and the spatial window by the expert engine 104 for
determination of the life occurrence and determination of the
available resolution paths.
[0262] With reference to FIG. 23 there is illustrated an
interconnection of the expert engine 104 with the MTP 102. The
expert engine 108 communicates a variety of types data and performs
a range of functions with the MTP 102. Data types include data,
notification, alerts, suggestions, time, location, and the like.
Functions include trigger bus exchange, synchronization,
reconciling temporal/spatial windows for contextual
consistency.
[0263] With reference to FIG. 23, there is illustrated the exchange
of information between the expert engine 108 and the MTP 102
according to an exemplary and non-limiting embodiment. As
illustrated, various forms of data are exchanged between the expert
engine 104 and the MTP 102 including, but not limited to, trigger
bus data, sync data, notifications, alerts, suggestions, temporal
data, and the like.
[0264] In accordance with the description above, various exemplary
and non-limiting embodiments enable an intuitive and seamless user
experience wherein applications drive potential transactions. While
the system 2300 is a general purpose architecture that may be
adapted to any scenario, domain, transaction category or the like,
various vertical application spaces are enabled including, not
limited to, finance, retail, health care and government services.
The system 2300 further enables the incorporation and seamless
integration of a plurality of payment channels including, but not
limited to, NFC, bar/QR codes, cloud, online and offline payment
channels. The expert engine 108 utilizes proactive intelligence in
the form of user inputs, host rules, behavioral analytics and the
like. As described above, the expert engine 104 incorporates
various context drivers including, but not limited to time and
location drivers to produce, for example, push notifications/alerts
in the form of triggered occurrences.
[0265] In accordance with exemplary and non-limiting embodiments,
the user experience as realized via, for example, a graphical user
interface (GUI) of the lifestyle container 408 might be customized.
For example default display of either an alert centric or a
notification centric perspective may be customized, whether or not
a panel in which an action is presented to the user is opened or
merely executed when the user selects the action, and the like.
[0266] In an example, the MTP 102 and the expert engine 104 may
synchronously communicate and exchange information such as data,
triggers, time-location information, notifications, alerts,
suggestions, temporal or spatial window-based information and the
like. As already discussed above in conjunction with various
figures, the expert engine 102 may implement intelligent learning
solutions through use of such as fuzzy logic, neural networks,
inference engines or systems and the like. The MTP 102 may also
associate information related to users such as by maintaining a
user database 2302 that may incorporate user transaction related
details also. The MTP 102 may be communicatively linked with an ID
management system 2304.
[0267] Referring to FIG. 24, an enhanced mobile transaction
platform (MTP) 102 is depicted that provides services and solutions
for a variety of environments using multiple client-side delivery
models 2402 over all payment and transaction channels and
environments. A client app 2406 provided on the phone seamlessly
interfaces with a server application 2408 to enable transactions
across a range of service providers and point of sale (POS)
instruments. The enhanced MTP 102 includes robust infrastructure
and interface 2404 to and through the mobile device resources while
facilitating an aggregation of disparate domains including retail,
finance, health, government, business, and other service providers.
The client app 1706 residing on the phone interacts with an MTP
enabling layer 2410 to interface with the service providers and
point of sale (POS) instruments. The enabling layer 2410 of the MTP
102 may comprise wallet management applications, NFC channels,
barcode systems and applications, widget management applications,
and secure communication and transaction engine.
[0268] Referring to FIG. 25, the methods and systems of mobile
lifestyle and life occurrence handling may be based on a set of
guiding principles that deliver minimal intrusions on the user
while maximizing usability of a mobile-enabled ecosystem for secure
personalized transactions. The principles ensure that a user
centric experience provides seamless interfacing of applications
that drive transactions across verticals, payment channels, and
input sources. The guiding principles also ensure that the user's
experience is balanced among key aspects such as tokens 2502 (e.g.
cards, receipts, coupons, etc.), alerts 2504 (for keeping the user
on track), and notifications 2506 that address what a user wants to
do rather than what the user has to do. In addition, a mobile
lifestyle and life occurrence handling environment based on such
guiding principles may include a context that is driven by time and
location; provides specific instructions and exceptions (e.g.
through push notifications and alerts), and frames the experience
in the form of suggestions and recommendations that are closely
coupled to life occurrences of or related to the user.
[0269] In accordance with the description above, various exemplary
and non-limiting embodiments enable an intuitive and seamless user
experience wherein applications drive potential transactions. While
the system 100 is a general purpose architecture that may be
adapted to any scenario, domain, transaction category or the like,
various vertical application spaces are enabled including, not
limited to, finance, retail, health care and government services.
The system 100 further enables the incorporation and seamless
integration of a plurality of payment channels including, but not
limited to, NFC, bar/QR codes, cloud, online and offline payment
channels. The expert engine utilizes proactive intelligence in the
form of user inputs, host rules, behavioral analytics and the like.
As described above, the expert engine 104 incorporates various
context drivers including, but not limited to time and location
drivers to produce, for example, push notifications/alerts in the
form of triggered occurrences.
[0270] The user experience as realized via, for example, a
graphical user interface (GUI) of the lifestyle container might be
customized. For example default display of either an alert centric
or a notification centric perspective may be customized, whether or
not a panel in which an action is presented to the user is opened
or merely executed when the user selects the action, and the like.
With reference to FIG. 26, there is illustrated an exemplary and
non-limiting embodiment of a token centric user interface 2600, an
alert centric user interface, and a notification centric user
interface. A user may shift between these three different modes to
view context-generated information in customizable fashion.
[0271] FIG. 27 depicts an embodiment of a lifestyle user interface
(UI) 2700 that is also referred to herein as an activity feed or
screen for facilitating interactions of a user with the life
occurrence handling methods and systems described herein. The
lifestyle user interface 2700 may be presented on a display of a
life occurrence node, such as a mobile phone.
[0272] The user interface 2700 comprises a plurality of moving
panels 2702 that utilize the portion of the life occurrence node
display that is allocated to the activity feed to provide timely,
contextual updates and life occurrence-related information to a
user. An activity feed may present life occurrences,
trigger-events, offers, resolution actions, alerts, and the like
related to life occurrences as may be determined by an MTP expert
engine as described herein. Any of the plurality of movable panels
2702 may dwell in a position for a while, move to another position,
move out of view of the user, and the like based on a range of
parameters associated with life occurrences, such as those
parameters found in a multi-dimensional database described herein.
Panels with content that is considered to be more urgent or
important may remain visible in the activity feed for a longer time
than other panels. Important or urgent panels may be moved toward
the top of the user interface to assist with emphasis for the user.
Important or urgent panels may appear more frequently or may
reappear sooner in the activity feed than panels with less
important content. Panels may also be actionable by a user, such as
by the user selecting the panel to reveal additional details, or
other content relevant to the life occurrence associated with the
panel.
[0273] In the illustrated example, the activity screen 2700
displays a moving panel 2702a related to a prepaid account/card for
Chicago Convention and Tourism. The moving panel 2702a further
shows the account balance amount and Chicago Convention and Tourism
bureau branding along with user actionable options to reload. This
panel may be presented to the user based on user preferences and/or
life occurrence-related multi-dimensional context that impacts when
such an account should be presented for reload. In this case, the
user may have opted to have the card reload action be presented for
user acceptance rather than the MTP automatically executing
transactions to effect a reload.
[0274] The activity screen 2700 further includes another moving
panel 2702b that displays Chicago transit related information such
as name of the transit authority, balance amount, a transit-related
alert, and the like. In this example of the activity feed user
interface 2700, the CTA panel is dynamically moving laterally off
of the display. This may occur for a wide range of reasons
including, without limitation, that a user has swiped away this
panel; the panel may have been presented to the user for longer
than a presentation threshold; the alert noted in the panel may
have expired; and other such reasons.
[0275] The activity screen 2700 further includes healthcare moving
panel 2702c related to a hospital or other healthcare service
provider. The healthcare moving panel 2702c displays information
about a life occurrence that includes an upcoming appointment of
the user with Dr. Sing who is associated with Hospital of Saint
Raphael. The healthcare moving panel 2702c may include interactive
features that allow the user to address aspects of this life
occurrence. Through this panel, the user may retrieve more
information about the appointment and associated hospital
facilities. The healthcare moving panel may include features for
accessing options such as appointment details, insurance
information, travel directions to the appointment, and the like.
The user may touch or click one of these options to present
respective information in the user interface. FIGS. 35 and 36
include examples of these options.
[0276] The moving panels 2702 can be moved relative to one another
such as shown in FIG. 28. As depicted in FIG. 28, the healthcare
moving panel 2702c has moved up at the top position unlike in the
previous where the healthcare moving panel 2702c was placed at the
bottom position. Likewise, a new moving panel 2702d related to a
prepaid MasterCard has also been presented. In addition,
corresponding activity screen 2800 now includes a new third moving
panel 2702e related to Loblaws. A comparison of the FIG. 27 user
interface 2700 and the FIG. 28 user interface 2800 depicts relative
movement of the moving panels 2702 and appearance of new moving
panels on a user interface and disappearing of some moving panels
from the user interface. Panel movement and dwell time may be based
on contextual or multidimensional information or other types of
information retrieved by the MTP-Expert Engine from a plurality of
data sources.
[0277] FIG. 29 depicts another user interface 2900 that shows a new
moving shopping panel 2702f positioned on top of the activity
screen or the 2900 causing other moving panels including the
prepaid card moving panel 2702d and the healthcare moving panel
2702c to move down the activity screen 2900. The Loblaws moving
panel 2702e no more exists on the user interface 2900 and is moved
out. The 2900 may further show alerts 2902 related to various
moving panels 2702. Alerts, that may be described elsewhere herein
might be associated with a trigger action event of a life
occurrence. The platform may configure one or more mobile
transactions for executing with the MTP in response to a user
taking some action in response to the alerts. The alerts may for
example result as a consequence of the MTP-Expert Engine generating
available resolution paths and configuring presentations to the
user through such alerts for implementing a plurality resolution
paths associated with a plurality of life occurrences. For example,
as depicted, the shopping moving panel 2702f includes three such
alerts that may be of interest to the user. If the user finds these
alerts as non-relevant, he may just decline and skip them. Of
course, the user is not required to take any action based on
presentation of an alert.
[0278] FIG. 30 depicts another user interface 3000 that shows how
screen space may be utilized by manipulating locations and sizes of
the various moving panels 2702 on the user interface 3000. A new
moving panel 2702g related to travel appears on the user interface
3000. Further, another moving panel 2702h that relates to a
birthday reminder appears on the 3000 upon the MTP-Expert Engine
identifying about an approaching birthday of Mehul from the
contextual and multidimensional information associated with the
user through previous mobile transactions and other data sources.
As the space on the 3000 is limited and no more space is available
for more moving panels, therefore, considering the importance and
urgency of the moving panel 2702h, it is presented as a banner over
the moving panel 2702g overlaying a portion of the moving panel
2702g. In other embodiments, not depicted, however, the user may
have an open to resize dimensions of the various moving panels 2702
so as to accommodate more or fewer simultaneously presented moving
panels.
[0279] FIG. 31 depicts another exemplary user interface 3100 that
comprises a few more examples of moving panels such as including a
flight moving panel 2702i positioned on top of the interface 3100.
The flight moving panel 2702i alerts the user about a delay in the
scheduled flight with options for more details. As discussed in
conjunction with various embodiments in this document, the
MTP-Expert Engine may be configured to determine an impact on
potential life occurrences related to the flight delay. The
information used to present such an urgent panel may be derived
from user past transactions for air travel, a user calendar of
events that are close in time to the originally scheduled arrival
time, flight information, and the like. The MTP-Expert Engine may
further determine a plurality of available resolution paths
associated with the life occurrence of flight delay and present
them to the user as depicted in, for example, FIG. 40. For example,
the options provided to the user in the flight moving panel 2702i
may link the user to information containing various available
resolution paths. This may for example include without limitations,
alternative flight schedules, arrangement in next earliest flights,
hotel stay nearby and the like without limitations. The MTP-Expert
Engine may use a plurality of intelligent solutions, capabilities,
or algorithms for generating the resolution paths and presenting
them on a new panel that is described in association with FIG. 40.
the flight moving panel 2702i. These may for example include
without limitations, fuzzy logic, neural networks, and defined
rules etc. and have been discussed in this document elsewhere.
[0280] Also, the MTP-Expert Engine may also generate resolution
paths for Mehul's birthday and may accordingly present gift
solutions when the user selects the moving panel banner 102h. The
resolution paths about suggesting a gift that Mehul may like may be
determined based on social network profile information of Mehul,
available gift solutions for his age group, nearby gift shops'
inventory, and the like. The user may click on the moving panel
2702h or activate the associated options in other manners to
execute one or more of various available paths associated with the
birthday alert. FIGS. 33 and 34 show exemplary detail panels that
may be presented in response to a user selecting panel 2702h.
[0281] FIG. 32 illustrates an enlarged interface portion or moving
panel 2702h depicting the banner for Mehul's birthday which may be
presented to the user on a portion of the user interface 3100 or on
any other screen based on user preferences. The banner may provide
an option to search for more details about what Mehul may like as a
birthday gift. For example, FIGS. 33 and 34 depict interfaces 3300
and 3400 that show more details after the user selects the option
of viewing more details from the banner or the moving panel
102h.
[0282] As shown in FIG. 33, the MTP-Expert Engine may suggest a
birthday gift. For example, the MTP-Expert Engine may utilize
contextual information and the multidimensional information to
recognize that Mehul had purchased a shirt in the recent past and
therefore a matching cuff link may be a good option for him as a
birthday gift. Therefore, among various other options, the
MTP-Expert Engine suggests Mont Blanc Platinum Cuff-Link. The 3300
may also present purchase options along with options for shipping.
The MTP-Expert Engine may also identify possible saving schemes
(e.g. offers) and report them to the user and update him about
total savings through the purchase. FIG. 34 depicts the user
interface 3400 showing checkout options to purchase the birthday
gift. The user can buy the gift and select a checkout option by
using any of his registered credit cards for which options may be
displayed to the user and presented.
[0283] FIG. 35 depicts an exemplary user interface 35 of details
for the user's Dr. Sing appointment shown in healthcare moving
panel 2702c. The user interface 35 may be presented to the user
when the user selects moving panel 2702c. When the user accesses
the option for more details, the user is presented his medical
checkup details for example cardiology records of the user in this
case. The presented details are determined by the MTP-Expert Engine
based on information derived from the multidimensional context,
user preferences, user past transactions, and the like. The
MTP-Expert Engine may also determine information about the
particular appointment with Dr. Sing by accessing a healthcare
portal of the user associated with the doctor, the hospital, or
both. For example, in the exemplary case depicted in FIG. 35, the
MTP-Expert Engine determines that the user needs a prostate checkup
and therefore, provides another option for the user to learn more
about the prostate checkup procedure as depicted in FIG. 36 that is
described below.
[0284] In an aspect of the present invention, the MTP-Expert Engine
may also show actions that the system has already taken care of
based on user preferences, managed on-device settings and the like
for automatic life occurrence resolution actions. For example, the
user interface 3500 may display that a financial obligation related
to the appointment (e.g. a co-pay) will be take care of
automatically with the user's prepaid MasterCard. In addition, the
MTP Expert engine has automatically arranged for insurance
information to be updated (e.g. the user's insurance card details
have been transmitted to the insurance carrier).
[0285] FIG. 3600 depicts a detailed user interface 3600 for the
particular procedure that the user is scheduled for with Dr. Sing
in the `Hospital of Saint Raphael`. This content may be displayed
in response to the user selecting the "Read about prostate checkup"
option in 3500. The content displayed in 3600 may be derived from
various sources including Internet sources. The MTP-expert engine
may determine the best sources for such information based on user
and other reviews of content presented on various websites, prior
user access to medical information, and/or user preferences for
such information. In this way, the user may accordingly prepare for
the procedure before actually visiting the doctor without having to
spend time researching various websites to determine which website
content to read.
[0286] FIG. 37 depicts another example of a detailed user interface
3700 that is presented when the user selects the prepaid card
moving panel 2702d. The 3700 shows various activities associated
with the user MasterCard, such as bill payments and the like. In
the illustrated example, the user is shown that current bill is
exceptionally high. The MTP-Expert Engine may compare the current
bill with those of the historical bills and accordingly interact
with the user through the interface 3700 so as to alert him about
the high bill and seek his approval for bill payment prior to
automatically paying the bill using the MasterCard of the user as
is generally done for normal bill payments. The user interface 3700
may also show options to view the bill in detail and also to
confirm for payment of the bill through the MasterCard associated
with the moving prepaid card panel 2702d. The user may also be
provided with an option to just ignore the bill and do nothing. The
user interface 3700 may also show the various recent payments that
were automatically taken care of by the lifestyle system or the
MTP-Expert Engine based on user preferences for automatically
taking action. The user is always in control of how bills are paid,
including thresholds that require manual authorization, and the
like.
[0287] FIG. 38 further depicts another example of a detailed user
interface 3800 that is presented when the user selects the shopping
moving panel 2702f. The detail shopping 3800 shows shopping
highlights including actions that the lifestyle system has taken
care of. For example, the lifestyle system determines a plurality
of offers that may be related to user life occurrences and
accordingly presents these offers to the user through 3800. The
user may also scroll the user interface 3800 to view details and
actions that the user can take regarding various shopping items
that the lifestyle system has performed automatically. For example,
such a scrolled user interface 3800 is depicted in 39. The user can
view shopping lists or options to make payments for ordered items
through this extended scrolled portion of the 39. FIG. 39 provides
an example of vertical scrolling of the user interface 39 to
accommodate presenting more details to the user.
[0288] FIG. 40 depicts another example of a detailed user interface
4000 that is presented when the user selects the flight moving
panel 2702h. Upon selection of the flight moving panel that shows
an air travel alert pertaining to a delay in the flight (see FIG.
29), details regarding flight delays are presented to the user
through the detailed screen 4000. The 4000 shows details related to
the flight delays and other life occurrences such as meetings and
the like that may be impacted by the delayed flight. The lifestyle
system may automatically take certain actions based on user
preferences such as proposing rescheduling of meetings in
accordance with revise flight timings, rescheduling pickup services
and the like. Accordingly, various items that can be automatically
handled may be presented on user interface 4000. In some cases, the
user approval of some aspect of the life occurrence may be needed
for the expert engine to provide available resolutions for the life
occurrences. For example, as shown in FIG. 40, the user interface
presents options for updating the meetings and rescheduling them
for a different time and possibly at a time different location. The
user can act on any of these options by clicking the respective
options presented in this user interface.
Use Cases
[0289] The transactions triggers by a life occurrence management
platform may be in the form of time-based trigger-events that may
be explicit or implicit for example based on user-defined
preferences (explicit) or from information derived from ecosystem
or from MTP, ES, FB, IM, Skype (implicit) and the like. The
trigger-events may in other cases be of the location type such as
for transit environments or spatial fences. For example, in case of
a transit environment, when a user goes to a station, a
trigger-event that says the station that the user normally uses is
out of service. A life occurrence management platform may point the
user to an alternative mode of transportation (which may be other
mode of transport such as station for a bus), to parking for the
other mode, to timetables, and the like based on available
resolutions assessed by the expert engine of a life occurrence
management platform.
[0290] In accordance with an exemplary and non-limiting embodiment,
the expert engine mines personal data of a mobile device user and
compares it to third party data (e.g. airline flight schedule data)
and discovers that the user's flight out of Chicago has been
delayed until the next day. The expert engine determines that the
last time a similar delay occurred at LaGuardia Airport, the user
stayed at a particular hotel at the airport. The expert engine
sends an alert message to the mobile device of the user indicating
the flight delay as well as suggested resolution actions (e.g.
making hotel reservations at the particular hotel) that may be
confirmed in response to the occurrence. The alert message is
displayed on the mobile device via the lifestyle container 106,
thereby showing the nature of the occurrence and the suggested
resolution including an option for confirming a hotel reservation
at the airport and a rental car. Such a display is illustrated at
FIG. 4.
[0291] By way of example, the expert engine mines personal data of
a mobile device user and discovers that the user's brother has a
birthday in 5 days. The expert engine determines that the
occurrence of the birthday requires a resolution action comprising,
at least, purchasing a gift for the brother. The expert engine
notices that the user purchased and sent a dress shirt last year in
response to that birthday occurrence. The expert engine determines
that a complimentary gift for this year is cuff links and locates a
pair of platinum Mont Blanc cuff links for sale to which may be
applied a 10% discount when purchased via a mobile transaction
using a Loblaws gift card. The expert engine sends an alert message
to the mobile device enabling the lifestyle container indicating
the impending birthday occurrence as well as suggested resolution
actions that may be confirmed in response to the occurrence. The
alert message is displayed on the mobile device showing the nature
of the occurrence, the suggested resolution, a suggested method of
shipment and a suggested use of the Loblaw card as illustrated in
FIG. 4A. With reference to FIG. 4A there is illustrated an
exemplary and non-limiting embodiment of a user interface of a
lifestyle container operating on a mobile device, such as a mobile
phone. Scrolling to the bottom of the screen, as illustrated in
FIG. 4B, there is displayed a suggested form of credit for the
mobile transaction. By scrolling horizontally, the user may choose
a preferred form of payment for the mobile transaction and complete
the resolution actions associated with the occurrence.
[0292] In accordance with another exemplary and non-limiting
embodiment, a user follows a similar route most weekday mornings
from the subway station to his office four blocks away. Most
mornings he purchases a Stardollar coffee from the first of four
such Stardollar he passes. As the Stardollar are individually
franchised, different Stardollar may offer different deals. Using a
GPS location signal and a time stamp as spatial and temporal
context drivers, the expert engine consolidates the location and
time user data with transactional analytic data acquired by the MTP
relating to past purchases by the user to create multidimensional
context data indicative of the options available to the user. For
example, the expert engine may generate and transmit to the MTP a
trigger with attendant context-based generated actions that are
triggered on the condition that the user has exited the subway and
appears to be heading on the usual route to the office. Once the
MTP and/or the expert engine and/or the container observe the
trigger condition to be met, the user may be informed of the
suggested actions.
[0293] For example, the user may receive an alert via the lifestyle
container that he can purchase a coffee at a Stardollar one block
away and on his prospective route at a 10% discount using his
Stardollar card. Upon the user accepting the offer, the MTP
seamlessly places the order for the coffee in the name of the user
and pays for it using the user's Stardollar credit card. As the
user passes or prepares to pass the Stardollar at which his coffee
is waiting, his location is used to trigger an alert to remind him
that his coffee is waiting.
[0294] Note that in this example, both location and time serve as
context drivers for the generation of the triggers. If, for
example, the user was at this same place on a Saturday, this
temporal context driver might cause the expert engine to discount
the probability that the user would be following his normal weekday
route. If, for example, the user was at the same subway stop in the
afternoon, the expert engine may use the temporal context driver to
determine that the user is heading to lunch. This context driven
conclusion may be reinforced by access to a user's Facebook posting
that he is looking forward to having lunch with his friend on
Saturday. As a result, the expert engine may generate one or more
triggers enabling making reservations or calling a taxi for the
user. In the above example, the location context driver of a GPS
location may likewise drive the context driven trigger creation.
For example, if the user exits a different subway stop than usual
in the morning, the expert engine may conclude that the user is
likely to still want coffee from a Stardollar, may search for a
Stardollar near the user and may generate a trigger to alert the
user.
[0295] In one exemplary and non-limiting embodiment, the expert
engine determines from the multidimensional context that a user
prefers JavaJeff coffee over Stardollar. In such an instance, the
expert engine generates a trigger to alert the user to the
possibility of obtaining coffee at JavaJeff that may, for example,
be a short distance from a Stardollar in front of which the user is
currently standing. The use of location based context drivers can
aid in creating the multidimensional context. In the present
example, the expert engine may gather that a Stardollar is directly
next to or in very close proximity to JavaJeff store when a user
chooses to purchase a coffee at JavaJeff. Such an action is a
strong indicator that the user prefers JavaJeff coffee over
Stardollar coffee. The strength of the indicator varies in inverse
proportion to the proximity of the two stores at the time of the
user's choice to purchase one brand over another.
[0296] Significant changes in purchasing patterns can serve as
context drivers. In an example, the expert engine may observe from
transaction analytics that a user has begun to purchase certain
products, such as diapers, or in certain stores, such as Home
Depot. Such changes may be indicative of the user becoming a parent
or buying a house, respectively.
[0297] The expert engine may generate triggers related to a level
of loyalty points. By analyzing transactions, the MTP can ascertain
if a user's level of loyalty points is high or low. Such
information is more than just knowledge of the mere membership of a
user in a loyalty program. Once such information is consolidated
into a multidimensional context, the expert engine may generate
triggers to propose offers which are especially attractive when the
user redeems some of his loyalty points, or where an extra amount
of loyalty points can be collected.
[0298] The status of a credit card or account may comprise a
context driver. For example, if the expert engine determines how
`strained` a certain credit card already is, then, depending on the
amount to be paid, it might propose using another card for a
transaction. Also, the user might have a preference to pay for
expensive goods (or travel-related things) with a specific credit
card, because it offers some additional insurance that are
beneficial in that situation. The expert engine may automatically
select this specific credit card based on the situation to help the
user gain the most or most important benefit available.
[0299] In addition to time/date being part of a life occurrence
descriptor or metadata, one might add an `urgency+importance`
attribute to the life occurrence descriptor. This
`urgency+importance` attribute is likely to be very personal for
each user (and its weight for determining its value to the expert
engine might change over time). The expert engine can learn these
variations in urgency and importance to the user and make
appropriate proposals. For example, a certain person just likes to
pay all bills and taxes absolutely on time, so the closer the due
date of this kind of transaction comes the more prominent a certain
element of the screen would become, such as being promoted to the
top of an occurrence list, increasing in size or changing in color,
or having a nagging UI dialogue. Another person is not so focused
on the bills, but more on the relationships, so for her a friend's
birthday will be more important as a reminder, because she needs to
find the perfect present. Therefore, her friend's birthday
occurrence may become more prominent until the event is resolved
(e.g. by sending her friend a gift, attending a birthday party, and
the like).
[0300] The expert engine might determine from the multidimensional
context that a user has an upcoming doctor's appointment. In
response, an alert may be displayed to a user on via a GUI
controlled by the lifestyle container 106. A visual indicia may
guide the user to a GPS application that is preprogrammed to direct
the user from his current location to a parking lot in proximity to
the doctor office for the appointment and, if desired, to the
doctor's office. If this is the first time the user has been to
this doctor's office, the user may select a visual indicia
corresponding to an option to have the user's medical records
securely transferred to the doctor's office. Upon exiting the
doctor's office, the expert engine may aggregate 3rd party content
from the doctor's office to create a trigger for acquiring
prescribed medication. The expert engine may identify one or more
pharmacies on the user's way home and offer a selection of
pharmacies from which the user may choose a desired destination.
Once chosen, the MTP may execute the back-end transactions required
to place the order for the user's prescription to be picked up at a
predetermined time.
[0301] While methods and systems of life occurrence management have
been disclosed in connection with the preferred embodiments shown
and described in detail, various modifications and improvements
thereon will become readily apparent to those skilled in the art.
Accordingly, the spirit and scope of any claims presented herein is
not to be limited by the foregoing examples, but is to be
understood in the broadest sense allowable by law.
[0302] While the methods and systems of life occurrence management
have been described in connection with certain preferred
embodiments, other embodiments may be understood by those of
ordinary skill in the art and are encompassed herein.
[0303] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software,
program codes, and/or instructions on a processor. The processor
may be part of a server, client, network infrastructure, mobile
computing platform, stationary computing platform, or other
computing platform. A processor may be any kind of computational or
processing device capable of executing program instructions, codes,
binary instructions and the like. The processor may be or include a
signal processor, digital processor, embedded processor,
microprocessor or any variant such as a co-processor (math
co-processor, graphic co-processor, communication co-processor and
the like) and the like that may directly or indirectly facilitate
execution of program code or program instructions stored thereon.
In addition, the processor may enable execution of multiple
programs, threads, and codes. The threads may be executed
simultaneously to enhance the performance of the processor and to
facilitate simultaneous operations of the application. By way of
implementation, methods, program codes, program instructions and
the like described herein may be implemented in one or more thread.
The thread may spawn other threads that may have assigned
priorities associated with them; the processor may execute these
threads based on priority or any other order based on instructions
provided in the program code. The processor may include memory that
stores methods, codes, instructions and programs as described
herein and elsewhere. The processor may access a storage medium
through an interface that may store methods, codes, and
instructions as described herein and elsewhere. The storage medium
associated with the processor for storing methods, programs, codes,
program instructions or other type of instructions capable of being
executed by the computing or processing device may include but may
not be limited to one or more of a CD-ROM, DVD, memory, hard disk,
flash drive, RAM, ROM, cache and the like.
[0304] A processor may include one or more cores that may enhance
speed and performance of a multiprocessor. In embodiments, the
process may be executed on a dual core processor, quad core
processors, other chip-level multiprocessor and the like that
combine two or more independent cores (called a die).
[0305] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software
on a server, client, firewall, gateway, hub, router, or other such
computer and/or networking hardware. The software program may be
associated with a server that may include a file server, print
server, domain server, Internet server, intranet server and other
variants such as secondary server, host server, distributed server
and the like. The server may include one or more of memories,
processors, computer readable media, storage media, ports (physical
and virtual), communication devices, and interfaces capable of
accessing other servers, clients, machines, and devices through a
wired or a wireless medium, and the like. The server may execute
the methods, programs or codes as described herein and elsewhere.
In addition, other devices required for execution of methods as
described in this application may be considered as a part of the
infrastructure associated with the server.
[0306] The server may provide an interface to other devices
including, without limitation, clients, other servers, printers,
database servers, print servers, file servers, communication
servers, distributed servers and the like. Additionally, this
coupling and/or connection may facilitate remote execution of
program across the network. The networking of some or all of these
devices may facilitate parallel processing of a program or method
at one or more location without deviating from the scope. In
addition, any of the devices attached to the server through an
interface may include at least one storage medium capable of
storing methods, programs, code and/or instructions. A central
repository may provide program instructions to be executed on
different devices. In this implementation, the remote repository
may act as a storage medium for program code, instructions, and
programs.
[0307] The software program may be associated with a client that
may include a file client, print client, domain client, Internet
client, intranet client and other variants such as secondary
client, host client, distributed client and the like. The client
may include one or more of memories, processors, computer readable
media, storage media, ports (physical and virtual), communication
devices, and interfaces capable of accessing other clients,
servers, machines, and devices through a wired or a wireless
medium, and the like. The methods, programs or codes as described
herein and elsewhere may be executed by the client. In addition,
other devices required for execution of methods as described in
this application may be considered as a part of the infrastructure
associated with the client.
[0308] The client may provide an interface to other devices
including, without limitation, servers, other clients, printers,
database servers, print servers, file servers, communication
servers, distributed servers and the like. Additionally, this
coupling and/or connection may facilitate remote execution of
program across the network. The networking of some or all of these
devices may facilitate parallel processing of a program or method
at one or more location without deviating from the scope. In
addition, any of the devices attached to the client through an
interface may include at least one storage medium capable of
storing methods, programs, applications, code and/or instructions.
A central repository may provide program instructions to be
executed on different devices. In this implementation, the remote
repository may act as a storage medium for program code,
instructions, and programs.
[0309] The methods and systems described herein may be deployed in
part or in whole through network infrastructures. The network
infrastructure may include elements such as computing devices,
servers, routers, hubs, firewalls, clients, personal computers,
communication devices, routing devices and other active and passive
devices, modules and/or components as known in the art. The
computing and/or non-computing device(s) associated with the
network infrastructure may include, apart from other components, a
storage medium such as flash memory, buffer, stack, RAM, ROM and
the like. The processes, methods, program codes, instructions
described herein and elsewhere may be executed by one or more of
the network infrastructural elements.
[0310] The methods, program codes, and instructions described
herein and elsewhere may be implemented on a cellular network
having multiple cells. The cellular network may either be a
frequency division multiple access (FDMA) network or a code
division multiple access (CDMA) network. The cellular network may
include mobile devices, cell sites, base stations, repeaters,
antennas, towers, and the like. The cell network may be a GSM,
GPRS, 3G, EVDO, mesh, or other type network.
[0311] The methods, programs codes, and instructions described
herein and elsewhere may be implemented on or through mobile
devices. The mobile devices may include navigation devices, cell
phones, mobile phones, mobile personal digital assistants, laptops,
palmtops, netbooks, pagers, electronic books readers, music players
and the like. These devices may include, apart from other
components, a storage medium such as a flash memory, buffer, RAM,
ROM and one or more computing devices. The computing devices
associated with mobile devices may be enabled to execute program
codes, methods, and instructions stored thereon. Alternatively, the
mobile devices may be configured to execute instructions in
collaboration with other devices. The mobile devices may
communicate with base stations interfaced with servers and
configured to execute program codes. The mobile devices may
communicate on a peer-to-peer network, mesh network, or other
communications network. The program code may be stored on the
storage medium associated with the server and executed by a
computing device embedded within the server. The base station may
include a computing device and a storage medium. The storage device
may store program codes and instructions executed by the computing
devices associated with the base station.
[0312] The computer software, program codes, and/or instructions
may be stored and/or accessed on machine readable media that may
include: computer components, devices, and recording media that
retain digital data used for computing for some interval of time;
semiconductor storage known as random access memory (RAM); mass
storage typically for more permanent storage, such as optical
discs, forms of magnetic storage like hard disks, tapes, drums,
cards and other types; processor registers, cache memory, volatile
memory, non-volatile memory; optical storage such as CD, DVD;
removable media such as flash memory (e.g. USB sticks or keys),
floppy disks, magnetic tape, paper tape, punch cards, standalone
RAM disks, Zip drives, removable mass storage, off-line, and the
like; other computer memory such as dynamic memory, static memory,
read/write storage, mutable storage, read only, random access,
sequential access, location addressable, file addressable, content
addressable, network attached storage, storage area network, bar
codes, magnetic ink, and the like.
[0313] The methods and systems described herein may transform
physical and/or or intangible items from one state to another. The
methods and systems described herein may also transform data
representing physical and/or intangible items from one state to
another.
[0314] The elements described and depicted herein, including in
flow charts and block diagrams throughout the figures, imply
logical boundaries between the elements. However, according to
software or hardware engineering practices, the depicted elements
and the functions thereof may be implemented on machines through
computer executable media having a processor capable of executing
program instructions stored thereon as a monolithic software
structure, as standalone software modules, or as modules that
employ external routines, code, services, and so forth, or any
combination of these, and all such implementations may be within
the scope of the present disclosure. Examples of such machines may
include, but may not be limited to, personal digital assistants,
laptops, personal computers, mobile phones, other handheld
computing devices, medical equipment, wired or wireless
communication devices, transducers, chips, calculators, satellites,
tablet PCs, electronic books, gadgets, electronic devices, devices
having artificial intelligence, computing devices, networking
equipment, servers, routers and the like. Furthermore, the elements
depicted in the flow chart and block diagrams or any other logical
component may be implemented on a machine capable of executing
program instructions. Thus, while the foregoing drawings and
descriptions set forth functional aspects of the disclosed systems,
no particular arrangement of software for implementing these
functional aspects should be inferred from these descriptions
unless explicitly stated or otherwise clear from the context.
Similarly, it may be appreciated that the various steps identified
and described above may be varied, and that the order of steps may
be adapted to particular applications of the techniques disclosed
herein. All such variations and modifications are intended to fall
within the scope of this disclosure. As such, the depiction and/or
description of an order for various steps should not be understood
to require a particular order of execution for those steps, unless
required by a particular application, or explicitly stated or
otherwise clear from the context.
[0315] The methods and/or processes described above, and steps
thereof, may be realized in hardware, software or any combination
of hardware and software suitable for a particular application. The
hardware may include a general-purpose computer and/or dedicated
computing device or specific computing device or particular aspect
or component of a specific computing device. The processes may be
realized in one or more microprocessors, microcontrollers, embedded
microcontrollers, programmable digital signal processors or other
programmable device, along with internal and/or external memory.
The processes may also, or instead, be embodied in an application
specific integrated circuit, a programmable gate array,
programmable array logic, or any other device or combination of
devices that may be configured to process electronic signals. It
may further be appreciated that one or more of the processes may be
realized as a computer executable code capable of being executed on
a machine-readable medium.
[0316] The computer executable code may be created using a
structured programming language such as C, an object oriented
programming language such as C++, or any other high-level or
low-level programming language (including assembly languages,
hardware description languages, and database programming languages
and technologies) that may be stored, compiled or interpreted to
run on one of the above devices, as well as heterogeneous
combinations of processors, processor architectures, or
combinations of different hardware and software, or any other
machine capable of executing program instructions.
[0317] Thus, in one aspect, each method described above and
combinations thereof may be embodied in computer executable code
that, when executing on one or more computing devices, performs the
steps thereof. In another aspect, the methods may be embodied in
systems that perform the steps thereof, and may be distributed
across devices in a number of ways, or all of the functionality may
be integrated into a dedicated, standalone device or other
hardware. In another aspect, the means for performing the steps
associated with the processes described above may include any of
the hardware and/or software described above. All such permutations
and combinations are intended to fall within the scope of the
present disclosure.
[0318] While the methods and systems described herein have been
disclosed in connection with certain preferred embodiments shown
and described in detail, various modifications and improvements
thereon may become readily apparent to those skilled in the art.
Accordingly, the spirit and scope of the methods and systems
described herein is not to be limited by the foregoing examples,
but is to be understood in the broadest sense allowable by law.
* * * * *