U.S. patent application number 17/161471 was filed with the patent office on 2021-05-20 for customizing loan specifics on a per-user basis.
The applicant listed for this patent is State Farm Mutual Automobile Insurance Company. Invention is credited to Melissa Attig, Reena Batra, Puneit Dua, Elizabeth A. Flowers, Adam Mattingly, Alan Zwilling.
Application Number | 20210150625 17/161471 |
Document ID | / |
Family ID | 1000005369022 |
Filed Date | 2021-05-20 |
United States Patent
Application |
20210150625 |
Kind Code |
A1 |
Flowers; Elizabeth A. ; et
al. |
May 20, 2021 |
CUSTOMIZING LOAN SPECIFICS ON A PER-USER BASIS
Abstract
Techniques are disclosed to provide customized loans on a
per-user basis. With user permission or affirmative consent, user
data may be monitored for several users, which may be used to
calculate initial loan specifics such as a loan rate and term based
upon a portion of this user input data. The user data may include
demographic data, behavioral data, or other data indicative of a
user's future potential earnings or other relevant information that
may be analyzed to determine, for that specific user, the current
likelihood that the user will default on the loan and a future
likelihood of default. When this future statistical likelihood is
determined, the initial loan specific may be further modified
and/or a targeted notification may be sent indicating these
customized loan specifics.
Inventors: |
Flowers; Elizabeth A.;
(Bloomington, IL) ; Dua; Puneit; (Bloomington,
IL) ; Zwilling; Alan; (Downs, IL) ; Mattingly;
Adam; (Normal, IL) ; Attig; Melissa;
(Bloomington, IL) ; Batra; Reena; (Alpharetta,
GA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
State Farm Mutual Automobile Insurance Company |
Bloomington |
IL |
US |
|
|
Family ID: |
1000005369022 |
Appl. No.: |
17/161471 |
Filed: |
January 28, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15499203 |
Apr 27, 2017 |
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17161471 |
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62338749 |
May 19, 2016 |
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62332226 |
May 5, 2016 |
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62338752 |
May 19, 2016 |
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62341677 |
May 26, 2016 |
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62436899 |
Dec 20, 2016 |
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62436883 |
Dec 20, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 17/18 20130101;
G06Q 40/025 20130101; G06Q 50/20 20130101 |
International
Class: |
G06Q 40/02 20060101
G06Q040/02; G06F 17/18 20060101 G06F017/18 |
Claims
1. A computer-implemented method for determining a customized loan
for a user, comprising: receiving, by one or more processors and/or
transceivers, a request for a loan amount for a loan term;
calculating, by one or more processors, a statistical risk of
default on the requested loan; calculating, by one or more
processors, a loan rate for the requested loan amount and loan term
based upon the statistical risk of default; receiving, by one or
more processors, user input data including (i) demographic data for
the user, and (ii) user behavioral data associated with the user;
calculating, by one or more processors, an adjusted statistical
risk of default on the requested loan based upon the user input
data; adjusting, by one or more processors, the calculated loan
rate for the requested loan to an adjusted loan rate based upon the
adjusted statistical risk of default; and presenting, by one or
more processors and/or transceivers, the adjusted loan rate to the
user.
2. The computer-implemented method of claim 1, further comprising:
calculating, by one or more processors, the user's potential future
earnings over the loan term based upon the user behavioral data,
and wherein the act of calculating the adjusted statistical risk of
default includes calculating the adjusted statistical risk of
default based upon the user's current earnings from the user input
data and the user's potential future earnings.
3. The computer-implemented method of claim 2, wherein the act of
calculating the user's potential future earnings comprises:
determining whether the user is currently participating in one or
more training programs or college courses that will increase the
user's current earnings within the loan term.
4. The computer-implemented method of claim 2, wherein the act of
calculating the user's potential future earnings comprises:
identifying other users having a demographic profile that matches
that of the user, the other users having completed the same one or
more training programs or college courses in which the user is
currently participating; and calculating, as the user's potential
future earnings, an average income of each of the other users at a
future time within the loan term.
5. The computer-implemented method of claim 2, wherein the act of
adjusting the calculated loan rate to the adjusted loan rate
comprises: when the user's potential future earnings are greater
than the user's current earnings in excess of a threshold amount,
decreasing the calculated loan rate to a lower loan rate.
6. The computer-implemented method of claim 5, wherein the act of
adjusting the calculated loan rate to the lower loan rate
comprises: decreasing the calculated loan rate to the lower loan
rate by an amount that is proportional to an amount in which the
user's potential future earnings exceed the user's current
earnings.
7. The computer-implemented method of claim 1, further comprising:
transmitting, by one or more processors and/or transceivers, a
notification to a computing device associated with the user via
wireless communication or data transmission over one or more radio
links or wireless communication channels, and wherein the act of
presenting the adjusted loan rate to the user comprises: displaying
the adjusted loan rate on a display associated with the computing
device in response to the user responding to the notification.
8. A computer system, comprising: a client device configured to:
transmit a request for a loan amount for a loan term via wireless
communication or data transmission over one or more radio links or
wireless communication channels; and one or more back-end
components configured to: receive the request for the loan amount
for the loan term; calculate a statistical risk of default on the
requested loan; calculate a loan rate for the requested loan amount
and loan term based upon the statistical risk of default; receive
user input data including (i) demographic data for the user, and
(ii) user behavioral data; calculate an adjusted statistical risk
of default on the requested loan based upon the user input data;
adjust the calculated loan rate for the requested loan to an
adjusted loan rate based upon the adjusted statistical risk of
default; and transmit a notification including the adjusted loan
rate to the client device via wireless communication or data
transmission over one or more radio links or wireless communication
channels.
9. The computer system of claim 8, wherein the one or more back-end
components are further configured to calculate the user's potential
future earnings over the loan term based upon the user behavioral
data, and to calculate the adjusted statistical risk of default
based upon the user's current earnings from the user input data and
the user's potential future earnings.
10. The computer system of 9, wherein the one or more back-end
components are further configured to calculate the user's potential
future earnings by determining whether the user is currently
participating in one or more training programs or college courses
that will increase the user's current earnings within the loan
term.
11. The computer system of claim 9, wherein the one or more
back-end components are further configured to calculate the user's
potential future earnings by identifying other users having a
demographic profile that matches that of the user, the other users
having completed the same one or more training programs or college
courses in which the user is currently participating, and
calculating, as the user's potential future earnings, an average
income of each of the other users at a future time within the loan
term.
12. The computer-implemented method of claim 9, wherein the one or
more back-end components are further configured to adjust the
calculated loan rate to a lower loan rate when the user's potential
future earnings are greater than the user's current earnings in
excess of a threshold amount.
13. The computer-implemented method of claim 12, wherein the one or
more back-end components are further configured to decrease the
calculated loan rate to the lower loan rate by an amount that is
proportional to an amount in which the user's potential future
earnings exceed the user's current earnings.
14. The computer-implemented method of claim 1, wherein the one or
more back-end components are further configured to transmit a
notification to the client device via wireless communication or
data transmission over one or more radio links or wireless
communication channels, and wherein the client device is further
configured to display the adjusted loan rate in response to the
user responding to the notification.
15. A computer-implemented method, comprising: receiving, by one or
more processors and/or transceivers, a request for a loan amount
for a loan term from a user mobile device via wireless
communication or data transmission over one or more radio links or
wireless communication channels; calculating, by one or more
processors, a statistical risk of default on the requested loan;
calculating, by one or more processors, a loan rate for the
requested loan amount and loan term based upon the statistical risk
of default; receiving, by one or more processors, user input data
including (i) demographic data for the user, and (ii) user
behavioral data; calculating, by one or more processors, an
adjusted statistical risk of default on the requested loan based
upon the user input data; adjusting, by one or more processors, the
calculated loan rate for the requested loan to an adjusted loan
rate based upon the adjusted statistical risk of default; and
presenting, by one or more processors and/or transceivers, the
adjusted loan rate to the user by transmitting the adjusted loan
rate to the user mobile device via wireless communication or data
transmission over one or more radio links or wireless communication
channels.
16. The computer-implemented method of claim 15, further
comprising: calculating, by one or more processors, the user's
potential future earnings over the loan term based upon the user
behavioral data, and wherein the act of calculating the adjusted
statistical risk of default includes calculating the adjusted
statistical risk of default based upon the user's current earnings
from the user input data and the user's potential future
earnings.
17. The computer-implemented method of claim 16, wherein the act of
calculating the user's potential future earnings comprises:
determining whether the user is currently participating in one or
more training programs or college courses that will increase the
user's current earnings within the loan term.
18. The computer-implemented method of claim 16, wherein the act of
calculating the user's potential future earnings comprises:
identifying other users having a demographic profile that matches
that of the user, the other users having completed the same one or
more training programs or college courses in which the user is
currently participating; and calculating, as the user's potential
future earnings, an average income of each of the other users at a
future time within the loan term.
19. The computer-implemented method of claim 16, wherein the act of
adjusting the calculated loan rate to the adjusted loan rate
comprises: when the user's potential future earnings are greater
than the user's current earnings in excess of a threshold amount,
decreasing the calculated loan rate to a lower loan rate.
20. The computer-implemented method of claim 19, wherein the act of
adjusting the calculated loan rate to the lower loan rate
comprises: decreasing the calculated loan rate to the lower loan
rate by an amount that is proportional to an amount in which the
user's potential future earnings exceed the user's current
earnings.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of, and claims priority
to, U.S. patent application Ser. No. 15/499,203, entitled
"CUSTOMIZING LOAN SPECIFICS ON A PER-USER BASIS", filed on Apr. 27,
2017, which claims priority to (1) Provisional Application No.
62/338,749, entitled "Using Cognitive Computing To Customize
Loans," filed on May 19, 2016; (2) Provisional Application No.
62/332,226, entitled "Using Cognitive Computing To Provide a
Personalized Banking Experience," filed on May 5, 2016; (3)
Provisional Application No. 62/338,752, entitled "Using Cognitive
Computing To Provide a Personalized Banking Experience," filed on
May 19, 2016; (4) Provisional Application No. 62/341,677, entitled
"Using Cognitive Computing To Improve Relationship Pricing," filed
on May 26, 2016; (5) Provisional Application No. 62/436,899,
entitled "Using Cognitive Computing To Improve Relationship
Pricing," filed on December 20, 2016; and (6) Provisional
Application No. 62/436,883, entitled "Preventing Account Overdrafts
and Excessive Credit Spending," filed on Dec. 20, 2016, each of
which is incorporated herein by reference in its entirety.
TECHNICAL FIELD
[0002] The present disclosure relates to providing an improved
banking experience and, more particularly, to customizing the
details and specifics of loans on a per-user basis.
BACKGROUND
[0003] Traditionally, to apply for various types of loans, people
would visit a brick-and-mortar bank or other financial institution
to meet with a loan officer or other representative, submit the
necessary documentation and, if approved, sign a loan agreement
defining the loan specifics, such as the loan term and rate. More
recently, the loan application and approval process has been
simplified by providing users with online options to apply, and
potentially be approved for, various types of loans. Therefore,
because of the ease in which people may apply for loans and compare
interest rates among different lenders, the competition amongst
lenders has dramatically increased.
[0004] This competition has increased the importance of advertising
to users and identifying potential clients that may require loans,
but the personalization of these loans leaves much to be desired.
For instance, although the details of a loan such as the term and
interest rate may be calculated using a user's credit score, this
calculation does not take into account any potential future impact
on this credit score or finer details that could otherwise impact
these loan specifics. Therefore, traditional techniques directed to
calculating loan specifics for a user do not take into account the
level of detail regarding current or future events, and therefore
fail to adequately address user's needs.
BRIEF SUMMARY
[0005] In one aspect, a computer-implemented method for determining
a customized loan for a user may be provided. The method may
include (1) receiving a request for a loan amount for a loan term;
(2) calculating a statistical risk of default on the requested
loan; (3) calculating a loan rate for the requested loan amount and
loan term based upon the statistical risk of default; (4) receiving
user input data including (i) demographic data for the user, and
(ii) user behavioral data associated with the user; (5) calculating
an adjusted statistical risk of default on the requested loan based
upon the user input data; (6) adjusting the calculated loan rate
for the requested loan to an adjusted loan rate based upon the
adjusted statistical risk of default; and/or (7) presenting the
adjusted loan rate to the user. The method may include additional,
less, or alternate components, including those discussed elsewhere
herein.
[0006] In yet another aspect, a computer system for predicting when
a user requires a loan and presenting the user with a loan offer
may be provided. The system may include (1) a client device (or
mobile device) associated with a user, which may be configured to
transmit a request for a loan amount for a loan term via wireless
communication or data transmission over one or more radio links or
wireless communication channels; and (2) one or more back-end
components configured to (a) receive the request for the loan
amount for the loan term; (b) calculate a statistical risk of
default on the requested loan; (c) calculate a loan rate for the
requested loan amount and loan term based upon the statistical risk
of default; (d) receive user input data including (i) demographic
data for the user, and (ii) user behavioral data; (e) calculate an
adjusted statistical risk of default on the requested loan based
upon the user input data; (f) adjust the calculated loan rate for
the requested loan to an adjusted loan rate based upon the adjusted
statistical risk of default; and/or (g) transmit a notification
including the adjusted loan rate to the client device via wireless
communication or data transmission over one or more radio links or
wireless communication channels. The system may include additional,
less, or alternate components, including those discussed elsewhere
herein.
[0007] In still another aspect, a computer-implemented method for
determining a customized loan for a user may be provided. The
method may include (1) receiving a requested loan amount for a loan
term, such as from user mobile or computing device via wireless
communication or data transmission over one or more radio links or
wireless communication channels; (2) calculating a statistical risk
of default on the requested loan; (3) calculating a loan rate for
the requested loan amount and loan term based upon the statistical
risk of default; (4) receiving user input data including (i)
demographic data for the user, and (ii) user behavioral data; (5)
calculating an adjusted statistical risk of default on the
requested loan based upon the user input data; (6) adjusting the
calculated loan rate for the requested loan to an adjusted loan
rate based upon the adjusted statistical risk of default; and/or
(7) presenting the adjusted loan rate to the user, such as by
transmitting the adjusted loan rate in an electronic message to the
user's mobile or computing device via wireless communication or
data transmission over one or more radio links or wireless
communication channels. The method may include additional, less, or
alternate components, including those discussed elsewhere
herein.
[0008] Advantages will become more apparent to those of ordinary
skill in the art from the following description of the preferred
aspects which have been shown and described by way of illustration.
As will be realized, the present aspects may be capable of other
and different aspects, and their details are capable of
modification in various respects. Accordingly, the drawings and
description are to be regarded as illustrative in nature and not as
restrictive.
BRIEF DESCRIPTION OF DRAWINGS
[0009] FIG. 1 is a block diagram of an exemplary loan customization
system 100 in accordance with one aspect of the present
disclosure;
[0010] FIG. 2 illustrates exemplary user profiles 200 in accordance
with one aspect of the present disclosure;
[0011] FIG. 3 illustrates exemplary logic diagrams 300 indicating
several different example scenarios that may impact the initial
calculation of loan specifics for a certain type of loan requested
by a user in accordance with one aspect of the present disclosure;
and
[0012] FIG. 4 illustrates an exemplary computer-implemented method
flow 400 in accordance with one aspect of the present
disclosure.
[0013] The Figures depict aspects of the present invention for
purposes of illustration only. One skilled in the art will readily
recognize from the following discussion that alternate aspects of
the structures and methods illustrated herein may be employed
without departing from the principles of the invention described
herein.
DETAILED DESCRIPTION
[0014] The present aspects discussed herein may further relate to,
inter alia, using cognitive computing and/or predictive modeling
(and/or machine learning techniques) to provide a customized loan
on a per-user basis. This customized loan may be applicable to the
various loan offers upon the determination that a user is actively
seeking a loan, as discussed immediately above. Additionally or
alternatively, this customized loan may apply to separate loans
solicited by the user in various ways (e.g., online, in person,
over the phone, etc.). To facilitate the calculation of customized
loan specifics, one or more back-end components may collect user
input data, which may include the aforementioned information and
additionally or alternatively include data such as credit score
data, credit report data, and income, from which a statistical risk
of default on a particular loan may be calculated.
[0015] In the context of determining statistical risk of default,
collected behavioral data may include data that is leveraged to
predict changes in the user's statistical risk of default over the
life of a proposed loan term. For example, the behavioral data may
indicate that the user is currently attending classes for a
particular field of study. This data may then be used to adjust the
statistical risk of default and the calculated loan rate
accordingly to present the user with a tailored loan rate or other
loan specifics based upon that user's profile.
System Overview
[0016] FIG. 1 is a block diagram of an exemplary loan customization
system 100 in accordance with one aspect of the present disclosure.
In the present aspect, loan customization system 100 may include
one or more client devices 102, a personalized loan engine 120, one
or more financial institutions 150, and/or a communication network
116. Loan customization system 100 may include additional, less, or
alternate components, including those discussed elsewhere
herein.
[0017] For the sake of brevity, loan customization system 100 is
illustrated as including a single client device 102, a single
personalized loan engine 120, two financial institutions 150, and a
single communication network 116. However, the aspects described
herein may include any suitable number of such components. For
example, personalized loan engine 120 may communicate with several
client devices 102, each of which may be operated by a separate
user, to receive data from each separate client device 102 and/or
to transmit notifications to each separate client device 102, as
further discussed herein. To provide another example, personalized
loan engine 120 may receive data from one or more client devices
102 such that a user profile for each user may include data
received from each user's respective client device. To provide yet
another example, client device 102 may represent one client device
from several different client devices for the same user or for
different users. For example, client device 102 may represent a
user's smartphone as well as a user's desktop computer, each of
which may collect and transmit data to one or more financial
institutions 150 and/or personalized loan engine 120, as further
discussed below.
[0018] Communication network 116 may be configured to facilitate
communications between one or more client devices 102, one or more
financial institutions 150, and/or personalized loan engine 120
using any suitable number of wired and/or wireless links, such as
links 117.1-117.3, for example. For example, communication network
116 may include any suitable number of nodes, radio frequency
links, wireless or digital communication channels, additional wired
and/or wireless networks that may facilitate one or more landline
connections, internet service provider (ISP) backbone connections,
satellite links, public switched telephone network (PSTN), etc.
[0019] To facilitate communications between the various components
of loan customization system 100, the present aspects include
communication network 116 being implemented, for example, as a
local area network (LAN), a metropolitan area network (MAN), a wide
area network (WAN), or any suitable combination of local and/or
external network connections. To provide further examples,
communications network 116 may include wired telephone and cable
hardware, satellite, cellular phone communication networks, base
stations, macrocells, femtocells, etc. In the present aspects,
communication network 116 may provide one or more client devices
102 with connectivity to network services, such as Internet
services, for example, and/or support application programming
interface (API) calls between one or more client devices 102, one
or more financial institutions 150, and/or personalized loan engine
120.
[0020] Client device 102 may be configured to communicate using any
suitable number and/or type of communication protocols, such as
Wi-Fi, cellular, BLUETOOTH, NFC, RFID, etc. For example, client
device 102 may be configured to communicate with communication
network 116 using a cellular communication protocol to send data to
and/or receive data from one or more financial institutions 150
and/or personalized loan engine 120 via communication network 116
using one or more of radio links or radio frequency links
117.1-117.3 or wireless communication channels.
[0021] In various aspects, client device 102 may be implemented as
any suitable communication device. For example, client device 102
may be implemented as a user equipment (UE) and/or client device,
such as a smartphone, for example. To provide additional examples,
client device 102 may be implemented as a personal digital
assistant (PDA), a desktop computer, a tablet computer, a laptop
computer, a wearable electronic device, etc.
[0022] As further discussed below, data collected and/or
transmitted by client device 102 to one or more financial
institutions 150 and/or personalized loan engine 120 may include,
for example, any suitable or relevant information used by
personalized loan engine 120 to track a location of client device
102 and/or to track other types of information about users
associated with client device 102 to anticipate whether the user
associated with client device 102 may require a loan. Additionally
or alternatively, data collected and/or transmitted by client
device 102 to one or more financial institutions 150 and/or
personalized loan engine 120 may include data used by personalized
loan engine 120 to preapprove a user for a loan and/or to calculate
an adjusted loan rate or other loan specifics for a particular
user.
[0023] For example, with user permission, such as user opt-in to a
rewards or other program that provides financial benefits or cost
savings to the user, client device 102 may collect, monitor, store,
and/or transmit demographic information, data indicative of the
user's behavior such as spending habits, where the user has shopped
physically and/or online, data indicative of certain life events,
financial information such as account balances of one or more users
associated with client device 102, online web browsing history,
life event data, etc. This data is discussed in more detail below
with reference to FIG. 2.
[0024] Furthermore, data received by client device 102 from
personalized loan engine 120 may include any suitable information
used to notify the user of relevant loan offers, the result of a
loan preapproval, whether the user qualifies for a particular loan
product and, if so, the particular loan products the user may
qualify for. Additionally or alternatively, data received by client
device 102 from personalized loan engine 120 may facilitate
providing user notifications regarding specific loan terms and/or
loan rates, whether a particular loan includes personalized rates
or other terms, and the specific details about how the customized
loan details were adjusted for that user.
[0025] For example, if it is determined by personalized loan engine
120 that a user associated with client device 102 is likely to
require a loan, then client device 102 may display a notification
transmitted via personalized loan engine 120 regarding loan offers
of a specific loan type and amount before the user has obtained the
loan. To provide another example, if a user applies for a loan via
client device 102 or in another manner, personalized loan engine
120 may calculate user-specific loan specifics that take into
account changes in the user's statistical risk during the loan
term, which is further discussed below. Assuming the loan is
approved, personalized loan engine 120 may adjust the initial loan
specifics based upon this additional information and transmit this
information to client device 102, which may in turn display a
suitable notification including the details of the personalized
loan specifics.
Detailed Operation of Loan Customization System
[0026] In the present aspects, client device 102 may include one or
more processors 104, a communication unit 106, a user interface
108, a display 110, a location acquisition unit 112, and a memory
unit 114.
[0027] Communication unit 106 may be configured to facilitate data
communications between client device 102 and one or more of
communication network 116, one or more financial institutions 150,
and/or personalized loan engine 120 in accordance with any suitable
number and/or type of communication protocols. In the present
aspects, communication unit 106 may be configured to facilitate
data communications based upon the particular component and/or
network with which client device 102 is communicating.
[0028] Such communications may facilitate the transmission of
collected data from client device 102 that is utilized by
personalized loan engine 120 to provide loan customization and/or
to predict when a user associated with client device 102 is likely
in the market for a new loan, as further discussed herein. In the
present aspects, communication unit 106 may be implemented with any
suitable combination of hardware and/or software to facilitate this
functionality. For example, communication unit 106 may be
implemented with any suitable number of wired and/or wireless
transceivers, network interfaces, physical layers (PHY), ports,
antennas, etc.
[0029] User interface 108 may be configured to facilitate user
interaction with client device 102. For example, user interface 108
may include a user-input device such as an interactive portion of
display 110 (e.g., a "soft" keyboard displayed on display 110), an
external hardware keyboard configured to communicate with client
device 102 via a wired or a wireless connection (e.g., a BLUETOOTH
keyboard), an external mouse, or any other suitable user-input
device.
[0030] Display 110 may be implemented as any suitable type of
display that may facilitate user interaction, such as a capacitive
touch screen display, a resistive touch screen display, etc. In
various aspects, display 110 may be configured to work in
conjunction with user-interface 108 and/or one or more processors
104 to detect user inputs upon a user selecting a displayed
interactive icon or other graphic, to identify user selections of
objects displayed via display 110, to display notifications
regarding available loan offers, preapproval results, the terms of
a particular customized loan, which may be received, for example,
via personalized loan engine 120, etc.
[0031] Location acquisition unit 112 may be implemented as any
suitable device configured to generate location data indicative of
a current geographic location of client device 102. In one aspect,
location acquisition unit 102 may be implemented as a satellite
navigation receiver that works with a global navigation satellite
system (GNSS) such as the global positioning system (GPS) primarily
used in the United States, the GLONASS system primarily used in the
Soviet Union, the BeiDou system primarily used in China, and/or the
Galileo system primarily used in Europe.
[0032] Location acquisition unit 112 and/or one or more processors
104 may be configured to receive navigational signals from one or
more satellites and to calculate a geographic location of client
device 102 using these signals. Location acquisition unit 112 may
include one or more processors, controllers, or other computing
devices and memory to calculate the geographic location of client
device 102 without one or more processors 104. Alternatively,
location acquisition unit 112 may utilize components of one or more
processors 104. Thus, one or more processors 104 and location
acquisition unit 112 may be combined or be separate or otherwise
discrete elements.
[0033] One or more processors 104 may be implemented as any
suitable type and/or number of processors, such as a host processor
for the relevant device in which client device 102 is implemented,
for example. One or more processors 104 may be configured to
communicate with one or more of communication unit 106, user
interface 108, display 110, location acquisition unit 112, and/or
memory unit 114 to send data to and/or to receive data from one or
more of these components.
[0034] For example, one or more processors 104 may be configured to
communicate with memory unit 114 to store data to and/or to read
data from memory unit 114. In accordance with various embodiments,
memory unit 114 may be a computer-readable non-transitory storage
device, and may include any combination of volatile (e.g., a random
access memory (RAM)), or a non-volatile memory (e.g.,
battery-backed RAM, FLASH, etc.). In the present aspects, memory
unit 114 may be configured to store instructions executable by one
or more processors 104. These instructions may include machine
readable instructions that, when executed by one or more processors
104, cause one or more processors 104 to perform various acts.
[0035] In the present aspects, personalized loan application 115 is
a portion of memory unit 114 configured to store instructions, that
when executed by one or more processors 104, cause one or more
processors 104 to perform various acts in accordance with
applicable aspects as described herein. For example, instructions
stored in personalized loan application 115 may facilitate one or
more processors 104 performing functions such as periodically
reporting and/or transmitting the location of client device 102 as
part of a running background process (or causing location
acquisition unit 112 to do so), collecting various types of data,
sending various types of data to one or more financial institutions
150 and/or personalized loan engine 120, receiving data and/or
notifications from one or more financial institutions 150 and/or
personalized banking engine 120, displaying notifications and/or
other information using data received via one or more financial
institutions 150 and/or personalized banking engine 120, etc.
[0036] In some aspects, personalized loan application 115 may
reside in memory unit 114 as a default application bundle that may
be included as part of the operating system (OS) utilized by client
device 102. But in other aspects, personalized loan application 115
may be installed on client device 102 as one or more downloads,
such as an executable package installation file downloaded from a
suitable application source via a connection to the Internet or
other suitable device, network, external memory storage device,
etc.
[0037] For example, personalized loan application 115 may be stored
in any suitable portions of memory unit 114 upon installation of a
package file downloaded in such a manner. Examples of package
download files may include downloads via the iTunes store, the
Google Play Store, the Windows Phone Store, a package installation
file downloaded from another computing device, etc. Once
downloaded, personalized loan application 115 may be installed on
client device 102 as part of an installation package such that,
upon installation of personalized loan application 115, memory unit
114 may store executable instructions such that, when executed by
one or more processors 104, cause client device 102 to implement
the various functions of the aspects as described herein.
[0038] The user may initially create a user profile upon first
launching personalized loan application 115, through a registration
process via a website, over the phone, etc. This user profile may
include, for example, the customer's demographic information or any
suitable information that may be useful in facilitating various
portions of the aspects as described herein. For example, upon
installing and launching personalized loan application 115 on
client device 102, a user may be prompted to enter login
information and/or complete an initial registration process to
create a user profile with a lender or other relevant party
associated with or otherwise affiliated with personalized loan
engine 120.
[0039] Additionally or alternatively, personalized loan application
115 may periodically request data directly from the user as opposed
to collecting data in a passive manner. For example, personalized
loan application 115 may request certain types of information from
the user as part of one or more surveys and/or questionnaires. This
information may be requested, for example, upon an initial
registration and/or at any suitable time after the initial
registration. In this way, a user may be asked for certain types of
information that may be difficult to obtain through third party
data providers and/or public records. For example, a user may be
asked about the ages of his family members, his current income
level (or where his earnings fit within a range of earnings), his
hobbies and interests, whether he is planning to attend a
university or other training program in the near future and, if so,
in what field of study, whether he intends to make a large purchase
in the near future, etc.
[0040] An initial registration process may additionally or
alternatively include, for example, obtaining the user's consent to
track her location or to otherwise collect and utilize other types
of data to provide the various loan customization and/or prediction
services as discussed herein. For example, a user may opt in to
allow personalized loan engine 120 to track and/or receive the
user's financial account data including financial transactions,
spending history, credit card balances, web browsing history,
and/or account balances associated with the user's financial
accounts at one or more financial institutions 150, to opt in to a
system whereby the user's spending habits are tracked and/or
spending data at various retailers is collected, to provide
authorization (e.g., online login credentials) to access the user's
accounts held at one or more financial institutions 150, etc. In
return, the user may be presented with various offers or customized
financial products or loan offers, or other products discussed
herein.
[0041] In the present aspects, personalized loan application 115
may provide different levels of functionality based upon options
selected by a user and/or different implementations of personalized
loan application 115. For example, in some aspects, personalized
loan application 115 may facilitate client device 102 working in
conjunction with one or more financial institutions 150 and/or
personalized banking engine 120 to facilitate receiving
notifications for offers regarding loans anticipated by
personalized loan engine 120. To provide another example, other
aspects include personalized loan application 115 facilitating
client device 102 working in conjunction with one or more financial
institutions 150 and/or personalized banking engine 120 to collect
and transmit data to one or more financial institutions and/or
personalized loan engine 120. This may, for example, facilitate the
customization of loan specifics for loans anticipated by
personalized loan engine 120 and/or for a separate loan that may
be, for example, be solicited by the user via client device 102 or
in another suitable manner.
[0042] One or more financial institutions 150 may include any
suitable number and/or type of financial institutions that hold
and/or are associated with various financial accounts. For example,
one or more financial institutions 150 may include banks,
creditors, lenders, and/or brokers. One or more users (e.g., a user
associated with client device 102) may hold one or more accounts
with one or more financial institutions 150 such as checking
accounts, savings accounts, credit accounts, lines of credit, loan
accounts, charge accounts, money market accounts, brokerage
accounts, etc. These accounts may be held at a single institution
or spread out across several different financial institutions.
[0043] In one aspect, financial accounts held at one or more
financial institutions 150 may be accessible via a secure
connection to communication network 116, for example, by client
device 102 and/or personalized loan engine 120. For example, one or
more financial institutions 150 may provide online services that
allow a user to access her accounts using client device 102 and/or
another suitable computing device. Upon receipt of a valid
authenticated request for financial data, one or more financial
institutions 150 may transmit financial data to client device 102
and/or personalized loan engine 120. Examples of the financial data
transmitted by one or more financial institutions 150 may include
financial transaction data indicating previous credits and debits
to a user's accounts, a current account balance, loan payoff
balances, credit report history, credit scores, credit card
utilization, derogatory credit marks, spending data such as the
time, amount, and specific merchant for which previous account
debits and/or charges were made, whether the user has previously
defaulted on a particular loan, etc.
[0044] Personalized loan engine 120 may be affiliated or otherwise
associated with one or more parties, which may be the same party or
a different party than those affiliated with one or more financial
institutions 150. Personalized loan engine 120 may include any
suitable number of components configured to receive data from
and/or send data to one or more of client devices 102 and/or one or
more financial institutions 150 via communication network 116 using
any suitable number of wired and/or wireless links. In various
embodiments, personalized loan engine 120 may constitute a portion
of (or the entirety of) one or more back-end components, and may be
configured (alone or in conjunction with other back-end components)
to execute one or more applications to perform one or more
functions associated with the various aspects as discussed
herein.
[0045] For example, as shown in FIG. 1, personalized loan engine
120 may communicate with one or more external computing devices
such as servers, databases, database servers, web servers, etc. The
present aspects include personalized loan engine 120 working in
conjunction with any suitable number and/or type of back-end
components to facilitate the appropriate functions of the aspects
as described herein.
[0046] In the present aspects, personalized loan engine 120 may be
implemented, for example, as any suitable number and/or type of
servers configured to access data from one or more additional data
sources and/or store data to one or more storage devices. For
example, as shown in FIG. 1, additional data sources 170 may
represent data that is made accessible by personalized loan engine
120 from any number N of data sources, such as third-party data
providers or other data sources in addition to and/or including one
or more financial institutions 150. This additional data may be
utilized by personalized loan engine 120 to build a personalized
user profile for each user, which may be stored in one or more
suitable storage units (e.g., storage unit 180) and include other
types of data, as further discussed below. Personalized loan engine
120 may access each user's profile as part of the execution of one
or more cognitive computing and/or predictive modeling algorithms
to calculate a statistical likelihood that a user will default on a
particular loan and/or to determine a specific loan type and
accompanying loan specifics for a particular user based upon that
user's profile.
[0047] To provide some illustrative examples, additional data
sources 170 may include, again with user permission, data mined
from social media and/or user's web browsing habits (e.g., search
terms and websites), data provided by various retailers,
demographic data associated with other users in a particular region
and/or associated with a particular retailer, income levels of
various users, where different users commonly shop, assets
associated with various users such as the cars each user owns,
mortgage loan information associated with various users, how much
users typically spend at various retailers, etc. To provide further
examples, additional data sources 170 may include specific
demographic information for a particular user and his family
members, such as the age and gender of each child, for example.
[0048] To provide even more examples, additional data sources 170
may include data indicative of various user life events such as
getting married, a child about to attend (or currently attending)
college, paying off a previous loan, receiving a settlement or
inheritance, etc. Still further, additional data sources 170 may
include user data such as spending habits or detailed information
such as a college or university the user may be attending, courses
being taken by the user, a college major or other focused field of
study, etc. Personalized loan engine 120 may utilize any portion of
such data (as well as data from other data sources) as input to one
or more cognitive computing and/or predictive modeling algorithms
to perform the predictions and statistical calculations as
discussed herein, some examples of which are further discussed
below.
[0049] Personalized loan engine 120 may be implemented as any
suitable number of servers that are configured to generate and/or
store various user profiles in storage unit 180. Each user's
profile may include, for example, an aggregation of aforementioned
data and/or any other suitable data that may be utilized to
calculate a statistical risk of a user defaulting on a loan and/or
data which may be used to calculate loan specifics based upon a
user's profile.
[0050] For example, a user's profile may include demographic data,
data submitted by a user as part of an initial registration
process, data submitted by a user in response to solicited surveys,
spending data, financial data (e.g., credit card utilization,
credit card balances, bank account balances, etc.), credit report
data, credit scores, family or individual income, etc. Each user's
profile may be stored in storage unit 180 in any suitable manner
such that personalized banking engine 120 may access each user's
profile and correlate each user to his or her profile.
[0051] To provide an illustrative example, storage unit 180 may
include a number of user profiles organized in accordance with
suitable type of information to uniquely identify each particular
user so that each user may later be matched to her profile stored
in storage unit 180. For example, each user's profile may be
identified by a username that is used by one or more users in
accordance with personalized loan application 115, a first and last
name of each user, etc. These user profiles are discussed in
further detail below with reference to FIG. 2.
[0052] In the present aspects, personalized loan engine 120 may
include one or more processors 122, a communication unit 124, and a
memory unit 126. One or more processors 122, communication unit
124, and memory unit 126 may perform substantially similar
functions as one or more processors 104, communication unit 106,
and memory unit 114, respectively, of client device 102. Therefore,
only differences between these components will be further discussed
herein.
[0053] Of course, differences between components of personalized
loan engine 120 and client device 102 may be owed to differences in
device implementation rather than the functions performed by each
individual component. For example, if personalized loan engine 120
is implemented as a server whereas client device 102 is implemented
as a personal computing device, one or more processors 122 may have
more processing power (e.g., a faster processor, more cores, etc.)
than one or more processors 104, although one or more processors
122 may perform similar functions as one or more processors 104
(e.g., executing instructions stored in memory to perform various
acts, processing data, etc.).
[0054] In the present aspects, personalized loan engine 120 may be
configured to send or otherwise transmit various types of
notifications and/or inquiries to client device 102. These
notifications and/or inquiries may be sent, for example, from
personalized loan engine 120 via communication unit 124, and may
include any suitable type of data transmissions. For example,
personalized loan engine 120 may transmit appropriate notifications
and/or inquiries via emails, text messages, push notifications,
etc., to client device 102
[0055] Again, in various aspects, personalized loan engine 120 may
acquire data from various sources to facilitate the various aspects
described herein. Some of these sources may include data from
secure connections or may otherwise require secured or authorized
access. For example, one or more financial institutions 150 may
provide access to one or more user's financial account data via any
suitable authentication techniques, such as via a secure
connection, password authentication, public and/or private key
exchanges, biometric identification, etc.
[0056] Therefore, in the present aspects, personalized loan engine
120 may, when appropriate, implement any suitable techniques to
obtain information in a legal and technically feasible manner. For
example, as discussed above, a user may setup an account and/or
profile with a third party associated with personalized loan engine
120. As discussed above, the user may then opt in to data
collection via the various data sources that are to be collected
via personalized loan engine 120. The user may additionally or
alternatively provide authentication information for each account
and/or data source for which data is to be accessed, collected,
tracked, monitored, etc., such that personalized loan engine 120
may obtain any suitable type of data to carry out the aspects
described herein.
Cognitive Computing
[0057] Cognitive computing may refer to systems that learn at
scale, reason with purpose, and/or interact with humans naturally.
Rather than being explicitly programmed, cognitive computing
systems may learn and reason from their interactions and from their
experiences with their environment. As opposed to traditional
computing systems, which are deterministic, cognitive computing
systems are probabilistic. In other words, cognitive computing
systems generate not just answers to numerical problems, but
hypotheses, reasoned arguments, and recommendations about more
complex and meaningful bodies of data. Cognitive computing systems
may advantageously interpret and utilize data that is typically
referred to as being unstructured in nature. This allows such
systems to keep pace with the volume, complexity, and
unpredictability of information and systems in the modern world. To
do so, cognitive computing systems attempt to augment the reasoning
and thought processes of the human brain.
[0058] Therefore, in various aspects, personalized loan engine 120
may be implemented as a computing device (or a constituent part of
one or more computing devices) that is/are configured to process
data in accordance with one or more cognitive computing techniques.
For example, personalized loan engine 120 may be implemented as one
or more nets or nodes of an artificial neural network system and/or
other suitable system that models and/or mimics the reasoning and
processing of the human brain. Thus, cognitive computing and
predictive modeling application 127 may include one or more machine
learning algorithms, code, logic, and/or instructions to facilitate
the behavior, functionality, and/or processing of a cognitive
computing system.
[0059] In the present aspects, cognitive computing and predictive
modeling application 127 may include any suitable combination of
functions as discussed herein. For example, as shown in FIG. 1,
cognitive computing and predictive modeling application 127 may
include a data aggregation module 129 and a personalized loan
module 133. These modules are for illustrative purposes and
represent examples of some of the functionality that may be
performed by personalized loan engine 120 in accordance with a
cognitive computing-based system. However, aspects include
cognitive computing and predictive modeling application 127
including additional, less, or alternate actions, including those
discussed elsewhere herein. Furthermore, aspects include cognitive
computing and predictive modeling application 127 implementing
traditional, non-cognitive computing processes.
[0060] In one aspect, data aggregation module 129 may be a portion
of memory unit 126 configured to store instructions, that when
executed by one or more processors 122, cause one or more
processors 122 to perform various acts in accordance with
applicable embodiments as described herein. In the present aspects,
instructions stored in data aggregation module 129 may facilitate
one or more processors 122 performing functions such as mining data
for any suitable number of users. For example, instructions stored
in data aggregation module 129 may facilitate personalized loan
engine 120 providing the requested authorization to one or more
financial institutions 150 and/or additional data sources 170 as
needed and to receive data from one or more of these sources.
[0061] In the present aspects, instructions stored in data
aggregation module 129 may facilitate personalized loan engine 120
aggregating and organizing received data into one or more user
profiles, which may then be stored in storage unit 180. Again,
these user profiles may be associated with each user and organized
such that data contained as part of each user's profile may be
associated with each user. For example, each user's username and/or
other suitable identifying information may be stored as part of a
user profile that includes an aggregation of all data received for
that particular user from one or more data sources, as previously
discussed.
[0062] In the present aspect, personalized loan module 133 is a
portion of memory unit 126 configured to store instructions, that
when executed by one or more processors 122, causes one or more
processors 122 to perform various acts in accordance with
applicable aspects as described herein. In the present aspects,
instructions stored in personalized loan module 133 may facilitate
one or more processors 122 performing functions such as adjusting a
loan rate, adjusting the length of a loan, and/or adjusting other
specifics associated with a particular loan based upon various
sources of data. For example, personalized loan module 133 may
facilitate the calculation of a customized loan terms based upon an
analysis of data that may determine not only a present statistical
risk of a user defaulting on a loan, but an adjusted statistical
risk that compensates for changes in user information or user
behavior over the term of the loan.
[0063] That is, a user may request a loan in person or, for
example, submitting a request from a suitable computing device
(e.g., client device 102). This request may include, for example, a
loan amount, a type of loan, a loan term, etc. In such a case, an
initial statistical risk of default on the requested loan may be
calculated in accordance with any suitable techniques, which may
include, for example, any suitable number and/or type of user
information contributing to the calculation of an initial
statistical risk of default. This may include, for example, the
utilization of information such as such as credit history, credit
scores, previous loan defaults, family income, debt-to-income
ratio, etc. In an aspect, this initial statistical risk of default
may be calculated partially or entirely using traditional
techniques of risk analysis. In other aspects, this initial
statistical risk of default may utilize additional sources of data
from the user's profile that would not ordinarily be used in
traditional risk analyses, which is further refined with even more
user input data, as discussed below.
[0064] In any event, continuing this example, once an initial
statistical risk of default is calculated, a loan rate (i.e.,
interest rate) may be calculated that provides users with higher
risks of default higher loan rates, and more favorable interest
loan rates for lower risk clients. In the present aspects,
personalized loan module 133 may facilitate the adjustment of the
initial statistical risk of default and, in turn, an adjustment of
the initially calculated loan rate.
[0065] To provide an additional illustrative example, a user may
request a 30-year mortgage loan for $200,000. The user may have a
relatively low risk of default based upon his credit history, but
may have a relatively high debt-to-income ratio. Continuing this
example, personalized loan engine 120 may determine from an
analysis of demographic data and/or behavioral data (e.g., received
via client device 102 and/or data sources 170) that the client is
currently attending a university majoring in electrical
engineering. Using this information, the client's potential future
earnings may be calculated such that, in the next 4 years, the
client's income will increase and his debt-to-income ratio will
likely decrease. Because this change will happen early within the
30-year life of the loan, this potential future income may more
accurately reflect the future risk of the client defaulting on the
requested mortgage loan. Thus, in the present aspects, the
initially calculated statistical risk of default and initially
calculated loan rate may be adjusted to consider both the client's
current earnings as well as the client's potential future earnings
during the life of the loan. In this way, the present aspects allow
for a customized loan to be presented to the client with a loan
rate lower than what would otherwise be available.
[0066] In the present aspects, the user's potential future earnings
may be calculated in any suitable manner to accurately determine
this information. For example, personalized loan engine 120 may
identify other users with a demographic profile that matches the
user's demographic profile. This may include, for example matching
key components of user demographic profiles that are particularly
well correlated to earnings (e.g., region and age). From these
matched other users, the present aspects include further
identifying users who have completed the same training programs or
college courses that the user is currently participating--in this
case electrical engineering. Personalized loan engine 120 may then
average the salaries associated with each of these other users and
project (e.g., using inflation prediction models) what this average
salary will be at a point in the future within the term of the loan
(e.g., four years from when the client is applying). The client's
potential future earnings may then be set as the calculated
projected average salary of the other users in four years.
[0067] Although the aforementioned examples focus on the adjustment
of an initially calculated loan rate, the present aspects encompass
the use of predicted potential earnings or any other future factors
that impact risk to adjust any suitable loan specifics to provide a
customizes loan on a per-user basis. For example, the loan term may
be adjusted if this results in a higher payment but a lower overall
cost for the user when the result of the adjustment yields an
acceptable level of adjusted statistical risk. To provide another
example, the type of loan may be adjusted to consider future
earnings. In the case of a mortgage loan, this could include, for
example, adjusting a mortgage loan from an initial 30 year loan to
a 5/1 arm when the user's potential future earnings indicate that
after 5 years the statistical risk of default based upon any future
rate changes is acceptable.
[0068] Again, the adjusted loan rate or other loan specifics may be
based upon predicted or future risk in addition to current
statistical risk. Of course, the loan interest rate may be adjusted
up or down based upon the adjusted statistical risk of default when
this additional information is considered, and the adjusted loan
interest rates may be calculated in any suitable manner. For
example, when a user's potential future earnings are greater than
the user's current earnings in excess of a threshold amount, the
calculated loan rate may be adjusted to a lower loan rate, and
vice-versa. Continuing this example, the calculated loan rate may
be adjusted to a lower loan rate by an amount that is proportional
to an amount in which the user's potential future earnings exceed
the user's current earnings. That is, for every ten percent the
user's future earnings exceed the user's current earnings, the
interest rate may be adjusted another tenth of a point downward. In
this way, the present aspects calculate statistical risks based
upon both present and future information to provide customized loan
terms for each user's specific present and future choices,
behaviors, and demographics.
[0069] Once the loan specifics are calculated, the present aspects
include personalized loan engine 120 transmitting one or more
notifications to a suitable computing device associated with the
user (e.g., client device 102) via communication unit 124, network
116, and links 117.1 and 117.3, for example. The computing device
may then display these notifications and/or receive user input from
the user in response to receiving these notifications. Furthermore,
aspects include the user's computing device displaying the adjusted
loan rate and/or other loan specifics that have been calculated by
personalized loan engine 120 on a display associated with the
computing device (e.g., display 110) in response to the user
responding to the notification. For example, client device may
transmit data indicating that the user would like to view loan
specifics that have been calculated for a specific loan. Upon
receiving this data, personalized loan engine 120 may transmit the
loan specifics to client device 102, where they are then displayed.
In this way, a user may request a specific type of loan and then
view the loan specifics associated with that loan using the same
computing device.
Exemplary Calculations for Predicting Loan Needs Using Cognitive
Computing
[0070] FIG. 2 illustrates exemplary user profiles 200 in accordance
with one aspect of the present disclosure. As shown in FIG. 2, user
profiles 200 are an example of the various types of data that may
be stored in any suitable number of storage units or databases
(e.g., storage unit 180, as shown in FIG. 1). User profiles 200 may
be generated, organized, modified, and/or accessed via a
personalized loan engine, such as personalized loan engine 120, for
example, as shown in FIG. 1.
[0071] As shown in FIG. 2, user profiles 200 may include a number
of different types of collected data associated with a number of
individual users A-D. FIG. 2 illustrates four exemplary user
profiles and five different types of data associated with each user
profile. However, the present aspects include user profiles 200
including any suitable number of user profiles for any suitable
number of different users, which may be associated with any
suitable number and/or type of data.
[0072] User profiles 200 illustrate a number of different types of
data associated with each user. The different types of data
aggregated as part of each user's financial profile may be
collectively referred to as "user input data," although the user
input data may include information about the user as well as other
people, such as the user's family members. Again, this user input
data may be used in accordance with the present aspects to perform
calculations and predictions regarding whether a particular user
will soon require a loan and/or the calculation of specific
customized loan specifics.
[0073] As shown in FIG. 2, each user's financial profile contains
different types of information, and portions of each respective
type of information may represent user inputs for cognitive
computing and predictive modeling application 127. Inputs (a1-a6)
may represent each user's demographic data, such as the user's
individual age and/or the ages of each member of the user's family
(e.g., their birthdates), gender, marital status, household size,
whether the user owns a home or rents, the user's ethnicity, the
ethnicity of members of the user's family, etc.
[0074] Inputs (b1-b3) may represent different types of financial
data such as, for example, the user's current individual and/or
family annual income, the user's potential individual and/or family
annual income (which may be calculated from other portions of the
user's financial profile, as previously discussed), the user's
credit information such as credit card utilization, debt-to-income
ratios, credit scores, credit reports, etc.
[0075] Inputs (c1-c3) may represent various types of behavioral
data that indicate the user's previous and likely future choices,
behaviors, and/or actions. For example, behavioral data may
represent online data, such as online search terms, the content of
email such as various identified key words and their frequency of
use, various websites visited by the user, etc.
[0076] To provide an illustrative example, behavioral data may
indicate that the user recently researched different makes and
models of cars by visiting several car review websites, by entering
online search terms for specific types of vehicles, and/or by
visiting automotive manufacturer websites. To provide additional
illustrative examples, the behavioral data may indicate that a user
has visited different lending websites seeking specific types of
loans. To provide yet another illustrative example, the behavioral
data may indicate that the user has visited several real-estate
based websites looking to purchase a home and the range of home
values searched. To provide even further illustrative examples,
behavioral data may indicate that a user has completed an online
application for a university, has researched options for student
loans, and/or has visited certain college or university
websites.
[0077] Inputs (d1-d6) may represent various types of life event
data that may indicate if and when the user is ready to acquire a
new loan. The life event data may include different types of data
from public records, data submitted and/or collected from the user,
and/or data collected via other third party sources. As shown in
FIG. 2, the life event data may include information such as birth
records (e.g., birth certificates recently recorded), marriage
certificates, an indication that a child has started college, a
determination of whether a child is approaching legal driving age,
whether the user has recently purchased a new home, an indication
that a user's current home is a particular age that may require
repairs (and thus the user may be potentially interested in a home
equity loan), etc.
[0078] Inputs (e1-e2) may represent different types of location
data that may indicate the user's current geographic location
and/or a history of the user's previous locations. In the present
aspects, this location data may be analyzed and/or referenced to
other locations known to be associated with a user actively seeking
or otherwise likely to require a monetary loan. To provide an
illustrative example, the location data may indicate that the user
recently visited physical geographic locations associated with car
dealerships, college campuses, physical banks or lender locations,
high-end retailers, etc.
[0079] Again, in the present aspects, personalized loan engine 120
may actively collect, aggregate, and/or monitor data representing
user inputs for each user's profile to calculate adjusted loan
terms, as discussed herein. To perform these tasks, the present
aspects include personalized loan engine 120 analyzing the user
input data in accordance with any suitable predictive modeling
and/or cognitive computing techniques.
[0080] For example, assume that it is identified that user A is
attending college majoring in electrical engineering, and will
graduate in 1 year. Further assume that users B-D have a
demographic profile matching that of user A. For example, this may
be determined when users B-D have similar demographic data such
that a threshold number of inputs a1-a6 match or match within a
certain threshold, those of user A. In such a scenario, the present
aspects include personalized loan engine 120 calculating an initial
loan rate, term, and amount for user A to purchase a new home using
user A's current income level (i.e., input b1). Personalized loan
engine 120 may then average the corresponding inputs b1 from each
of users B-C and use this averaged income value as user A's
potential future earnings, which may compensate for inflation or
market factors in the next year. Personalized loan engine 120 may
then adjust the loan term, amount, rate, etc., based upon both user
A's current income (b1) and user A's potential future income
(b2).
[0081] In the present aspects, this adjustment may be made in
accordance with any suitable manner. For example, personalized loan
engine 120 may calculate a confidence level of this prediction by
using a statistical probability or likelihood of the calculated
potential earning being correct. In such a case, more weight may be
given (e.g., the rate may be reduced by a larger amount) for
calculated potential earnings associated with a higher likelihood
of being correct, while less confident calculations may result in
less adjustments to the initial loan term calculations.
Exemplary Logic Diagram for Different Scenarios Impacting the
Adjustment of Loan Specifics
[0082] FIG. 3 illustrates exemplary logic diagrams 300 indicating
several different example scenarios that may impact the initial
calculation of loan specifics for a certain type of loan requested
by a user in accordance with one aspect of the present disclosure.
As shown in FIG. 3, each of examples 1-3 is based upon different
portions of user input data stored as part of user profiles
associated with three different users A-C. The user profiles shown
in FIG. 3 may be implementations of user profiles 200, for example,
as shown in FIG. 2.
[0083] As discussed above, aspects include portions of a user's
profile being analyzed to determine future events and/or a
statistical likelihood that a user will default on a particular
loan based upon changes in the user's behavior, as indicated by the
user's input data, over the life of the loan. For example, as shown
in Example 1 of FIG. 3, at the time a user A has requested a
mortgage loan, user A's profile may reveal that user A was recently
admitted to a particular state university and that user A will
major in electrical engineering. Using this information,
personalized loan engine 120 may determine an initial statistical
rate of default on the requested mortgage loan using user input
data that does not include the information shown in FIG. 3. For
example, the initial statistical rate of default on the requested
mortgage loan may be based upon user A's credit history, credit
rating, and/or income at the time the loan was requested.
[0084] Personalized loan engine 120 may then incorporate the user
input data shown in FIG. 3 to adjust the calculated statistical
rate of default on the mortgage loan and, in turn, adjust the loan
specifics accordingly. For example, because electrical engineering
degrees have historically provided users with a higher income upon
graduation, personalized loan engine 120 may utilize this
additional information to determine that in the near future (e.g.,
about 4-5 years from the request, which is early in the life of
most mortgage loans) that user A's income will be greater than it
is today. As a result, the statistical risk of default will
likewise be lower in 4 years, and the adjusted loan specifics may
reflect this decrease in risk.
[0085] To provide another example, Example 2 illustrates that at
the time a user B has requested a vehicle loan, user B's profile
indicates that user B has increased her income over the last 5
years by 7% each year, that user B was married on Aug. 6, 2014, and
a new baby was born on May 13, 2015. Again, in this scenario,
personalized loan engine 120 may calculate an initial statistical
rate of default on the requested automotive loan using user input
data that does not include the information shown in FIG. 3.
However, personalized loan engine 120 may then use the information
shown in FIG. 3 to forecast user B's potential future earnings
based upon user B's previous increases in income the last 5 years,
which have been consistent and substantial. Furthermore, because
personalized loan engine 120 may mimic the logical thought
processes of the human brain, other data contained in user B's
profile may also be leveraged in conjunction with user B'
forecasted earnings to adjust the statistical rate of default of
the loan, and therefore the loan specifics for that loan.
[0086] For example, as shown in FIG. 3, user B has been recently
married and has a an 18-month old child, both of which strongly
correlate to a person's desire to maintain a favorable and
responsible credit history in the interest of maintaining financial
support for a young family. In other words, not only do does user
B's profile indicate a statistical likelihood of her income
continuing to increase, but it also indicates behaviors that are
relevant to user B's likelihood to not default on a new car loan.
Therefore, aspects include personalized loan engine 120 further
decreasing the specifics associated with the loan rate (e.g., the
interest rates in this example) to better reflect this decrease in
default risk for user B.
[0087] To provide yet another example, Example 3 illustrates that
at the time user C has requested a mortgage loan, that user C's
profile indicates that user C has a son who has been recently
accepted to a state university and that user C also owns two aging
vehicles. Again, in this scenario, personalized loan engine 120 may
calculate an initial statistical rate of default on the requested
mortgage loan using user input data that does not include the
information shown in FIG. 3. However, personalized loan engine 120
may then use data stored in user C's profile to predict that user C
will likely need additional loans early in the life of the mortgage
loan. Specifically, personalized loan engine 120 may ascertain that
user C may soon require a student loan and/or two additional
automotive loans due to the age of his current vehicles. In other
words, although user C may have a certain debt-to-income ratio at
the time the mortgage loan is requested, this metric is likely to
increase in the near future. Thus, in contrast to the two previous
examples, in this scenario the additional data contained in user
C's profile is of a type that is likely to increase the statistical
risk of default on the mortgage loan. Therefore, aspects include
personalized loan engine 120 increasing the specifics associated
with the loan rate (e.g., the interest rates) to better reflect
this increase in default risk for user C.
[0088] For brevity, the Examples shown in FIG. 3 and previously
described illustrate information obtained via one to three user
inputs. However, the present aspects include any suitable number of
user inputs or other factors being considered as part of the
calculation of an initial and/or adjusted rate of default on a
particular requested loan, as well as the adjustment to loan
specifics based on the adjusted rate of default.
Exemplary Method for Customizing Loan Specifics on a Per-user
Basis
[0089] FIG. 4 illustrates an exemplary computer-implemented method
flow 400 in accordance with an aspect of the present disclosure. In
the present aspects, one or more portions of method 400 (or the
entire method 400) may be implemented by any suitable device, and
one or more portions of method 400 may be performed by more than
one suitable device in combination with one another. For example,
one or more portions of method 400 may be performed by client
device 102 and/or personalized loan engine 120, as shown in FIG. 1.
In one embodiment, method 400 may be performed by any suitable
combination of one or more processors, instructions, applications,
programs, algorithms, routines, etc. For example, method 400 may be
performed via or more processors 122 executing instructions stored
in cognitive computing and predictive modeling application 127 in
conjunction with data collected, received, and/or generated via
personalized loan engine 120.
[0090] Method 400 may start when one or more processors receive a
request for a loan (block 402). The requested loan may have a
corresponding loan amount and/or loan term (block 402). The request
may be submitted by a user from a client device (e.g., client
device 102, as shown in FIG. 1) and, in such a case, may be
transmitted or otherwise submitted from the client device to a
personalized loan engine (block 402). The requested loan may
additionally or alternatively be received in the form of an
identified potential loan, such as when a calculated statistical
likelihood of a user requiring a loan has been exceeded, as
discussed herein (block 402).
[0091] Method 400 may include one or more processors calculating a
statistical risk of default for the requested loan (block 404).
This may include, for example, the statistical risk of default
being calculated in accordance with any suitable techniques to
adequately assess the risk of the user defaulting on the requested
loan (block 404). For example, method 400 may include determining
the user's current credit score, debt-to-income ratio, earnings,
derogatory marks, payment history, etc. (block 404).
[0092] Method 400 may include one or more processors calculating a
loan rate for the loan amount (block 406). This may include, for
example, calculating a loan rate and/or other specifics of the loan
based upon the calculated statistical risk of default (block 404)
and/or other factors, as further discussed herein (block 406).
[0093] Method 400 may include one or more processors receiving
demographic and/or behavioral data as part of user input data
(block 408). For example, the user input data may be received from
any suitable number and/or type of data sources, such as data
collected via client device 102, data collected via one or more
financial institutions 150, and/or data collected via one or more
additional data sources 170, as shown and discussed with reference
to FIG. 1 (block 408). To provide another example, the demographic
and/or behavioral data may represent one or more portions of the
user input data used to generate a user's profile, as shown and
discussed with reference to FIG. 2 (block 408).
[0094] Method 400 may include one or more processors calculating an
adjusted statistical risk of default on the requested loan based
upon the demographic and/or behavioral data (block 410). This may
include, for example, adjusting the initially calculated
statistical risk of default (block 404) based upon additional
factors determined from the demographic and/or behavioral data
(block 410). For example, the adjusted statistical risk of default
may consider the user's potential future earnings in addition to
the user's current earnings, the likelihood that additional loans
may be required in the near future, recent additions to the user's
family, etc., as discussed herein (block 410).
[0095] Method 400 may include one or more processors calculating an
adjusted loan rate for the loan amount (block 412). This may
include, for example, calculating an adjusted loan rate and other
specifics of the loan based upon the adjusted calculated
statistical risk of default (block 410) and/or other factors, as
further discussed herein (block 412).
[0096] Method 400 may include one or more processors presenting the
adjusted loan rate and/or other loan specifics (block 412) to the
user (block 414). This may include, for example, transmitting a
notification to the user's client device (e.g., via text message,
email, push notification, etc.), displaying the loan term
information locally via a personalized loan engine and/or a
computing device in communication with a personalized loan engine,
printing out and/or mailing the loan rate and/or other loan
specifics, etc. (block 414).
Technical Advantages
[0097] The aspects described herein may be implemented as part of
one or more computer components such as a client device and/or one
or more back-end components, such as a personalized loan engine,
for example. Furthermore, the aspects described herein may be
implemented as part of a computer network architecture and/or a
cognitive computing architecture that facilitates communications
between various other devices and/or components. Thus, the aspects
described herein address and solve issues of a technical nature
that are necessarily rooted in computer technology.
[0098] For instance, aspects include analyzing various sources of
data to calculate the impact of likely future events on a user's
statistical rate of default on a loan. In doing so, the aspects
overcome issues associated with the inconvenience of manual and/or
unnecessary monitoring of user input data by replacing manual
procedures with a cognitive-based computing system. Without the
improvements suggested herein, additional processing and memory
usage would be required to perform such monitoring and/or to
calculate the likelihood of various users require a loan.
[0099] Furthermore, the embodiments described herein function to
personalize loans based upon an analysis of data that facilitates
the impact of likely future event. The process improves upon
existing technologies by more accurately forecasting such events
may concurrently analyzing user input data from a larger number of
sources than would be feasible or practical. As a result, the
customization of loan specifics (e.g., the loan type, specific loan
programs, loan terms, etc.) improves the speed, efficiency, and
accuracy in which such calculations could otherwise be performed.
Due to these improvements, the aspects address computer-related
issues regarding efficiency over the traditional amount of
processing power and models used to calculate loan specifics and/or
perform data forecasting. Thus, the aspects also improve upon
computer technology by requiring less calculations due to the
increased efficiency provided, for example, by cognitive computing
versus traditional computing techniques. For instance, because
cognitive computing may be leveraged to determine whether a user
requires a loan, this result reflects an improvement in accuracy
and efficiency versus traditional computers, even assuming that
non-cognitive computers could perform the tasks associated with the
aspects disclosed herein. Therefore, the application of cognitive
computers in particular allows for less calculations and less
resource and power consumption than would otherwise be
possible.
A First Exemplary Method of Providing a Custom Loan on a Per-user
Basis
[0100] In one aspect, a computer-implemented method for determining
a customized loan for a user may be provided. The method may
include (1) receiving a request for a loan amount for a loan term;
(2) calculating a statistical risk of default on the requested
loan; (3) calculating a loan rate for the requested loan amount and
loan term based upon the statistical risk of default; (4) receiving
user input data including (i) demographic data for the user, and
(ii) user behavioral data associated with the user; (5) calculating
an adjusted statistical risk of default on the requested loan based
upon the user input data; (6) adjusting the calculated loan rate
for the requested loan to an adjusted loan rate based upon the
adjusted statistical risk of default; and/or (7) presenting the
adjusted loan rate to the user. The method may include additional,
less, or alternate components, including those discussed elsewhere
herein.
[0101] For instance, in various aspects, the method may include
calculating the user's potential future earnings over the loan term
based upon the user behavioral data, and calculating the adjusted
statistical risk of default based upon the user's current earnings
from the user input data and the user's potential future
earnings.
[0102] Additionally or alternatively, the method may include
calculating the user's potential future earnings by determining
whether the user's is currently participating in one or more
training programs or college courses that will increase the user's
current earnings within the loan term. In such aspects, the user's
potential future earnings may be calculated by identifying other
user's having a demographic profile that matches that of the user,
the other users having completed the same one or more training
programs or college courses in which the user is currently
participating, and calculating, as the user's potential future
earnings, an average income of each of the other users at a future
point within the loan term.
[0103] Furthermore, when the user's potential future earnings are
used as part of this calculation, the method may include decreasing
the calculated loan rate to a lower loan rate when the user's
potential future earnings are greater than the user's current
earnings in excess of a threshold amount. This decrease may
include, for example, decreasing the calculated loan rate to the
lower loan rate by an amount that is proportional to an amount in
which the user's potential future earnings exceed the user's
current earnings
[0104] Still further, the specifics of the loan may be sent to the
user as a notification and/or viewed by the user when responding to
the notification, such as via the user's computing device, for
example. Thus, in various aspects, the method may include
transmitting a notification to a computing device associated with
the user via wireless communication or data transmission over one
or more radio links or wireless communication channels, and
presenting the adjusted loan rate to the user by displaying the
adjusted loan rate on a display associated with the computing
device in response to the user responding to the notification.
A Second Exemplary Method of Providing a Custom Loan on A Per-user
Basis
[0105] In another aspect, a computer-implemented method for
determining a customized loan for a user may be provided. The
method may include (1) receiving a requested loan amount for a loan
term, such as from user mobile or computing device via wireless
communication or data transmission over one or more radio links or
wireless communication channels; (2) calculating a statistical risk
of default on the requested loan; (3) calculating a loan rate for
the requested loan amount and loan term based upon the statistical
risk of default; (4) receiving user input data including (i)
demographic data for the user, and (ii) user behavioral data; (5)
calculating an adjusted statistical risk of default on the
requested loan based upon the user input data; (6) adjusting the
calculated loan rate for the requested loan to an adjusted loan
rate based upon the adjusted statistical risk of default; and/or
(7) presenting the adjusted loan rate to the user, such as by
transmitting the adjusted loan rate in an electronic message to the
user's mobile or computing device via wireless communication or
data transmission over one or more radio links or wireless
communication channels. The method may include additional, less, or
alternate components, including those discussed elsewhere
herein.
[0106] For instance, in various aspects, the method may include
calculating the user's potential future earnings over the loan term
based upon the user behavioral data, and calculating the adjusted
statistical risk of default based upon the user's current earnings
from the user input data and the user's potential future
earnings.
[0107] Additionally or alternatively, the method may include
calculating the user's potential future earnings by determining
whether the user's is currently participating in one or more
training programs or college courses that will increase the user's
current earnings within the loan term. In such aspects, the user's
potential future earnings may be calculated by identifying other
user's having a demographic profile that matches that of the user,
the other users having completed the same one or more training
programs or college courses in which the user is currently
participating, and calculating, as the user's potential future
earnings, an average income of each of the other users at a future
point within the loan term.
[0108] Furthermore, when the user's potential future earnings are
used as part of this calculation, the method may include decreasing
the calculated loan rate to a lower loan rate when the user's
potential future earnings are greater than the user's current
earnings in excess of a threshold amount. This decrease may
include, for example, decreasing the calculated loan rate to the
lower loan rate by an amount that is proportional to an amount in
which the user's potential future earnings exceed the user's
current earnings
[0109] Still further, the specifics of the loan may be sent to the
user as a notification and/or viewed by the user when responding to
the notification, such as via the user's computing device, for
example. Thus, in various aspects, the method may include
transmitting a notification to a computing device associated with
the user via wireless communication or data transmission over one
or more radio links or wireless communication channels, and
presenting the adjusted loan rate to the user by displaying the
adjusted loan rate on a display associated with the computing
device in response to the user responding to the notification.
Exemplary System for Providing a Custom Loan on a Per-user
Basis
[0110] In yet another aspect, a computer system for predicting when
a user requires a loan and presenting the user with a loan offer
may be provided. The system may include (1) a client device (or
mobile device) associated with a user, which may be configured to
transmit a request for a loan amount for a loan term via wireless
communication or data transmission over one or more radio links or
wireless communication channels; and (2) one or more back-end
components configured to (a) receive the request for the loan
amount for the loan term; (b) calculate a statistical risk of
default on the requested loan; (c) calculate a loan rate for the
requested loan amount and loan term based upon the statistical risk
of default; (d) receive user input data including (i) demographic
data for the user, and (ii) user behavioral data; (e) calculate an
adjusted statistical risk of default on the requested loan based
upon the user input data; (f) adjust the calculated loan rate for
the requested loan to an adjusted loan rate based upon the adjusted
statistical risk of default; and/or (g) transmit a notification
including the adjusted loan rate to the client device via wireless
communication or data transmission over one or more radio links or
wireless communication channels. The system may include additional,
less, or alternate components, including those discussed elsewhere
herein.
[0111] For instance, in various aspects, the one or more back-end
components may be further configured to calculate the user's
potential future earnings over the loan term based upon the user
behavioral data, and to calculate the adjusted statistical risk of
default based upon the user's current earnings from the user input
data and the user's potential future earnings.
[0112] Additionally or alternatively, the one or more back-end
components may be further configured to calculate the user's
potential future earnings by determining whether the user is
currently participating in one or more training programs or college
courses that will increase the user's current earnings within the
loan term. In such aspects, the user's potential future earnings
may be calculated by identifying other user's having a demographic
profile that matches that of the user, the other users having
completed the same one or more training programs or college courses
in which the user is currently participating, and calculating, as
the user's potential future earnings, an average income of each of
the other users at a future point within the loan term.
[0113] Furthermore, when the user's potential future earnings are
used as part of this calculation, the one or more back-end
components may be configured to decrease the calculated loan rate
to a lower loan rate when the user's potential future earnings are
greater than the user's current earnings in excess of a threshold
amount. This may include, for example, decreasing the calculated
loan rate to the lower loan rate by an amount that is proportional
to an amount in which the user's potential future earnings exceed
the user's current earnings.
[0114] Still further, the specifics of the loan may be sent to the
client device as a notification and/or viewed by the user when
responding to the notification via the client device, for example.
Thus, in various aspects, the one or more back-end components may
be configured to transmit a notification to the client device via
wireless communication or data transmission over one or more radio
links or wireless communication channels, and the client device may
present the adjusted loan rate to the user via a suitable display
in response to the user responding to the notification.
Cognitive Computing & Machine Learning
[0115] The cognitive computing and/or predictive modeling
techniques discussed herein may include machine learning techniques
or algorithms. For instance, customer data may be input into
machine learning programs may be trained to (i) determine a
statistical likelihood that a customer is looking for a loan, or
may default on a loan, (ii) customize a loan product, and/or (iii)
predict a life event, based upon the customer data, such as the
types of customer data discussed elsewhere herein.
[0116] In certain embodiments, the cognitive computing and/or
predictive modeling techniques discussed herein may heuristic
engine and algorithms, and/or machine learning, cognitive learning,
deep learning, combined learning, and/or pattern recognition
techniques. For instance, a processor or a processing element may
be trained using supervised or unsupervised machine learning, and
the machine learning program may employ a neural network, which may
be a convolutional neural network, a deep learning neural network,
or a combined learning module or program that learns in two or more
fields or areas of interest. Machine learning may involve
identifying and recognizing patterns in existing data in order to
facilitate making predictions for subsequent data. Models may be
created based upon example inputs in order to make valid and
reliable predictions for novel inputs.
[0117] Additionally or alternatively, the machine learning programs
may be trained by inputting sample data sets or certain data into
the programs, such as de-personalized customer data, image, mobile
device, insurer database, and/or third-party database data. The
machine learning programs may utilize deep learning algorithms that
may be primarily focused on pattern recognition, and may be trained
after processing multiple examples. The machine learning programs
may include Bayesian program learning (BPL), voice recognition and
synthesis, image or object recognition, optical character
recognition, and/or natural language processing--either
individually or in combination. The machine learning programs may
also include natural language processing, semantic analysis,
automatic reasoning, and/or machine learning.
[0118] In supervised machine learning, a processing element may be
provided with example inputs and their associated outputs, and may
seek to discover a general rule that maps inputs to outputs, so
that when subsequent novel inputs are provided the processing
element may, based upon the discovered rule, accurately predict the
correct output. In unsupervised machine learning, the processing
element may be required to find its own structure in unlabeled
example inputs. In one embodiment, machine learning techniques may
be used to identify potential loan applicants and customize loan
products for individual customers.
[0119] In one embodiment, a processing element (and/or heuristic
engine or algorithm discussed herein) may be trained by providing
it with a large sample of images and/or user data with known
characteristics or features. Based upon these analyses, the
processing element may learn how to identify characteristics and
patterns that may then be applied to analyzing user device details,
user request or login details, user device sensors, geolocation
information, image data, the insurer database, a third-party
database, and/or other data. For example, the processing element
may learn, with the user's permission or affirmative consent, to
identify the user and/or the asset that is to be the subject of a
transaction, such as generating an insurance quote or claim,
opening a financial account, handling a loan or credit application,
processing a financial (such as a credit card) transaction or the
like.
Additional Considerations
[0120] This detailed description is to be construed as exemplary
only and does not describe every possible embodiment, as describing
every possible embodiment would be impractical, if not impossible.
One may be implement numerous alternate embodiments, using either
current technology or technology developed after the filing date of
this application.
[0121] Further to this point, although the embodiments described
herein often utilize credit report information as an example of
sensitive information, the embodiments described herein are not
limited to such examples. Instead, the embodiments described herein
may be implemented in any suitable environment in which it is
desirable to identify and control specific type of information. For
example, the aforementioned embodiments may be implemented by a
financial institution to identify and contain bank account
statements, brokerage account statements, tax documents, etc. To
provide another example, the aforementioned embodiments may be
implemented by a lender to not only identify, re-route, and
quarantine credit report information, but to apply similar
techniques to prevent the dissemination of loan application
documents that are preferably delivered to a client for signature
in accordance with a more secure means (e.g., via a secure login to
a web server) than via email.
[0122] Furthermore, although the present disclosure sets forth a
detailed description of numerous different embodiments, it should
be understood that the legal scope of the description is defined by
the words of the claims set forth at the end of this patent and
equivalents. The detailed description is to be construed as
exemplary only and does not describe every possible embodiment
since describing every possible embodiment would be impractical.
Numerous alternative embodiments may be implemented, using either
current technology or technology developed after the filing date of
this patent, which would still fall within the scope of the claims.
Although the following text sets forth a detailed description of
numerous different embodiments, it should be understood that the
legal scope of the description is defined by the words of the
claims set forth at the end of this patent and equivalents. The
detailed description is to be construed as exemplary only and does
not describe every possible embodiment since describing every
possible embodiment would be impractical. Numerous alternative
embodiments may be implemented, using either current technology or
technology developed after the filing date of this patent, which
would still fall within the scope of the claims.
[0123] The following additional considerations apply to the
foregoing discussion. Throughout this specification, plural
instances may implement components, operations, or structures
described as a single instance. Although individual operations of
one or more methods are illustrated and described as separate
operations, one or more of the individual operations may be
performed concurrently, and nothing requires that the operations be
performed in the order illustrated. Structures and functionality
presented as separate components in example configurations may be
implemented as a combined structure or component. Similarly,
structures and functionality presented as a single component may be
implemented as separate components. These and other variations,
modifications, additions, and improvements fall within the scope of
the subject matter herein.
[0124] Additionally, certain embodiments are described herein as
including logic or a number of routines, subroutines, applications,
or instructions. These may constitute either software (e.g., code
embodied on a machine-readable medium or in a transmission signal)
or hardware. In hardware, the routines, etc., are tangible units
capable of performing certain operations and may be configured or
arranged in a certain manner. In example embodiments, one or more
computer systems (e.g., a standalone, client or server computer
system) or one or more hardware modules of a computer system (e.g.,
a processor or a group of processors) may be configured by software
(e.g., an application or application portion) as a hardware module
that operates to perform certain operations as described
herein.
[0125] In various embodiments, a hardware module may be implemented
mechanically or electronically. For example, a hardware module may
comprise dedicated circuitry or logic that is permanently
configured (e.g., as a special-purpose processor, such as a field
programmable gate array (FPGA) or an application-specific
integrated circuit (ASIC)) to perform certain operations. A
hardware module may also comprise programmable logic or circuitry
(e.g., as encompassed within a general-purpose processor or other
programmable processor) that is temporarily configured by software
to perform certain operations. It will be appreciated that the
decision to implement a hardware module mechanically, in dedicated
and permanently configured circuitry, or in temporarily configured
circuitry (e.g., configured by software) may be driven by cost and
time considerations.
[0126] Accordingly, the term "hardware module" should be understood
to encompass a tangible entity, be that an entity that is
physically constructed, permanently configured (e.g., hardwired),
or temporarily configured (e.g., programmed) to operate in a
certain manner or to perform certain operations described herein.
Considering embodiments in which hardware modules are temporarily
configured (e.g., programmed), each of the hardware modules need
not be configured or instantiated at any one instance in time. For
example, where the hardware modules comprise a general-purpose
processor configured using software, the general-purpose processor
may be configured as respective different hardware modules at
different times. Software may accordingly configure a processor,
for example, to constitute a particular hardware module at one
instance of time and to constitute a different hardware module at a
different instance of time.
[0127] Hardware modules may provide information to, and receive
information from, other hardware modules. Accordingly, the
described hardware modules may be regarded as being communicatively
coupled. Where multiple of such hardware modules exist
contemporaneously, communications may be achieved through signal
transmission (e.g., over appropriate circuits and buses) that
connect the hardware modules. In embodiments in which multiple
hardware modules are configured or instantiated at different times,
communications between such hardware modules may be achieved, for
example, through the storage and retrieval of information in memory
structures to which the multiple hardware modules have access. For
example, one hardware module may perform an operation and store the
output of that operation in a memory device to which it is
communicatively coupled. A further hardware module may then, at a
later time, access the memory device to retrieve and process the
stored output. Hardware modules may also initiate communications
with input or output devices, and may operate on a resource (e.g.,
a collection of information).
[0128] The various operations of example methods described herein
may be performed, at least partially, by one or more processors
that are temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Whether temporarily
or permanently configured, such processors may constitute
processor-implemented modules that operate to perform one or more
operations or functions. The modules referred to herein may, in
some example embodiments, comprise processor-implemented
modules.
[0129] Similarly, the methods or routines described herein may be
at least partially processor-implemented. For example, at least
some of the operations of a method may be performed by one or more
processors or processor-implemented hardware modules. The
performance of certain of the operations may be distributed among
the one or more processors, not only residing within a single
machine, but deployed across a number of machines. In some example
embodiments, the processor or processors may be located in a single
location (e.g., within a home environment, an office environment or
as a server farm), while in other embodiments the processors may be
distributed across a number of locations.
[0130] The performance of certain of the operations may be
distributed among the one or more processors, not only residing
within a single machine, but deployed across a number of machines.
In some example embodiments, the one or more processors or
processor-implemented modules may be located in a single geographic
location (e.g., within a home environment, an office environment,
or a server farm). In other example embodiments, the one or more
processors or processor-implemented modules may be distributed
across a number of geographic locations.
[0131] Unless specifically stated otherwise, discussions herein
using words such as "processing," "computing," "calculating,"
"determining," "presenting," "displaying," or the like may refer to
actions or processes of a machine (e.g., a computer) that
manipulates or transforms data represented as physical (e.g.,
electronic, magnetic, or optical) quantities within one or more
memories (e.g., volatile memory, non-volatile memory, or a
combination thereof), registers, or other machine components that
receive, store, transmit, or display information.
[0132] As used herein any reference to "one embodiment" or "an
embodiment" means that a particular element, feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment. The appearances of the phrase
"in one embodiment" in various places in the specification are not
necessarily all referring to the same embodiment.
[0133] Some embodiments may be described using the expression
"coupled" and "connected" along with their derivatives. For
example, some embodiments may be described using the term "coupled"
to indicate that two or more elements are in direct physical or
electrical contact. The term "coupled," however, may also mean that
two or more elements are not in direct contact with each other, but
yet still co-operate or interact with each other. The embodiments
are not limited in this context.
[0134] As used herein, the terms "comprises," "comprising,"
"includes," "including," "has," "having" or any other variation
thereof, are intended to cover a non-exclusive inclusion. For
example, a process, method, article, or apparatus that comprises a
list of elements is not necessarily limited to only those elements
but may include other elements not expressly listed or inherent to
such process, method, article, or apparatus. Further, unless
expressly stated to the contrary, "or" refers to an inclusive or
and not to an exclusive or. For example, a condition A or B is
satisfied by any one of the following: A is true (or present) and B
is false (or not present), A is false (or not present) and B is
true (or present), and both A and B are true (or present).
[0135] In addition, use of the "a" or "an" are employed to describe
elements and components of the embodiments herein. This is done
merely for convenience and to give a general sense of the
description. This description, and the claims that follow, should
be read to include one or at least one and the singular also
includes the plural unless it is obvious that it is meant
otherwise.
[0136] The patent claims at the end of this patent application are
not intended to be construed under 35 U.S.C. .sctn. 112(f) unless
traditional means-plus-function language is expressly recited, such
as "means for" or "step for" language being explicitly recited in
the claim(s).
[0137] The various systems and methods described herein are
directed to an improvement to computer functionality, and improve
the functioning of conventional computers, as described, for
example, in the "Technical Advantages" Section and elsewhere
herein.
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