Note: Descriptions are shown in the official language in which they were submitted.
CA 02910667 2015-10-30
Image Recognition-Based Payment Requests
TECHNICAL FIELD
[0001] The present disclosure relates to computer systems and computer-
implemented methods for sending payment requests to one or more persons or
entities
based on images (e.g., photos) in which the persons or entities appear.
[0002] The concept of mobile payments, including mobile wallets, refers to a
type
of commerce where, in lieu of cash, checks, or credit cards, consumers can use
a mobile
device to pay for a wide range of services and digital or hard goods. Only
recently has
device and payment technology advanced enough to support a widely available
system
for payment. The demand for mobile payments, both in developed and developing
countries, provides merchants and payment processors with significant
opportunities to
open markets to mobile users.
[0003] For example, PayPal, Apple, Google, Visa, MasterCard, and other
financial processing providers have assisted the growth of mobile payments at
traditional
points of sale.
Through Near Field Communication (NFC), Radio-Frequency
Identification (RFID), and barcode-based solutions, more and more consumers
use their
mobile devices to pay for goods and services where they previously used credit
cards or
other means of payment.
[0004] Mobile devices, including phones and tablets, have been able to improve
the payment process for varying transactions over the last few years,
including by
allowing users to directly use their phones to initiate and respond to payment
requests. In
many cases, however, the person or entity to whom payment is being requested
from or
sent to must be a known entity, in that they must be either a current contact
of the
initiating user or payment instructions must be provided (e.g., via NFC, RFID,
or Wi-Fi-
based communications, via direct entry of payment instructions, or others).
For example,
some prior systems allow users to "tap" their devices together in order to
initiate a funds
transfer. In others, the device can be moved nearby a payment terminal at a
certain time
to exchange payment details via NFC. In still others, a barcode presented on
the device
may be scanned by a payment terminal to process the payment.
CA 02910667 2015-10-30
SUMMARY
[0005] The present disclosure involves systems, software, and computer-
implemented methods for sending payment requests to one or more persons or
entities
based on images in which the persons or entities appear. In one example, the
process
may include identifying an image associated with a payment request, the
identified image
containing at least one recipient associated with the payment request, and
wherein the
payment request includes a value, analyzing the identified image to identify
the at least
one potential recipient of the payment request, identifying contact
information associated
with the at least one identified recipient of the payment request, and sending
the payment
request to the at least one identified recipient of the payment request via a
destination
associated with the identified contact information.
[0006] In some implementations, the identified image is received from a mobile
device. In some instances, the payment request may be a request to send a
payment or a
request for payment. Analyzing the identified image to identify at least one
potential
recipient of the payment request can include performing facial recognition on
the
identified image to identify at least one potential recipient of the payment
request.
Analyzing the identified image to identify at least one potential recipient of
the payment
request may further includes performing a verification operation on the at
least one
identified potential recipient of the payment request. The verification
operations may
include identifying location metadata associated with the identified image and
a time the
image was generated, identifying information describing the location of the at
least one
identified potential recipient at the time the image was generated, and
comparing the
identified location metadata with the identified information describing the
location of the
at least one identified potential recipient at the time the image was
generated. In some
instances, the information describing the location of the at least one
identified potential
recipient at the time the image was generated may be determined or identified
from a
social network posting. Alternatively or in combination, the information
describing the
location of the at least one identified potential recipient at the time the
image was
generated may be identified based on location data from the at least one
identified
potential recipient's mobile device. Alternatively or in combination, the
information
describing the location of the at least one identified potential recipient at
the time the
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=
image was generated may be identified based on locations associated with
credit
purchases made by the at least one identified potential recipient at the time
the image was
generated. In some instances, the verification operation can include
identifying
contextual information related to a relationship between a user initiating the
payment
request and the at least one identified potential recipient of the payment
request and
determining whether a degree of relationship between the user initiating the
payment
request and the at least one identified potential recipient of the payment
request exceeds a
predetermined threshold.
[0007] When the payment request is a request for payment, the image can
include
at least two persons. In those instances, after identifying the at least two
recipients, the
value included in the payment requests to each of the at least two identified
recipients is
split between the at least two identified recipients.
[0008] In some instances, the operations may include receiving a confirmation
of
the payment request from the at least one identified recipient and
facilitating the payment
between the user initiating the payment request and the at least one
identified recipient.
In those instances, and in response to facilitating the payment, the image may
be
modified by embedding information related to the completed payment request
within the
image.
[0009] While generally described as computer-implemented software embodied
on tangible media that processes and transforms the respective data, some or
all of the
aspects may be computer-implemented methods or further included in respective
systems
or other devices for performing this described functionality. The details of
these and
other aspects and embodiments of the present disclosure are set forth in the
accompanying drawings and the description below. Other features, objects, and
advantages of the disclosure will be apparent from the description and
drawings, and
from the claims.
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DESCRIPTION OF DRAWINGS
[0010] FIG. 1 is a block diagram illustrating an example system for sending
payment requests to one or more persons or entities based on images in which
the persons
or entities appear.
[0011] FIG. 2 is a flowchart of an example operation related to sending
payment
requests to one or more persons or entities based on images in which the
persons or
entities appear.
[0012] FIG. 3 is a flowchart of an example operation related to analyzing and
verifying potential identities of persons included in the images.
[0013] FIG. 4A and 4B are a combined swim-lane diagram illustrating example
operations related to actions for sending payment requests to one or more
persons or
entities based on images in which the persons or entities appear.
DETAILED DESCRIPTION
[0014] The present disclosure describes a system that can receive an image
associated with a payment request. The system processes the image, e.g., using
facial and
image recognition, to identify one or more persons included in the received
image. The
system can then identify contact information associated with the identified
persons and
send requests to either receive or submit payments in response to the payment
request.
[0015] From a user's perspective, the user can request payment from other
persons another or initiate a payment to be sent to others using images alone
to initiate
such payment requests. The initiating user may capture the person to whom the
payment
request is to be sent in an image using a camera integrated into their mobile
device or can
identify a pre-existing image. Using that image, along with details of the
monetary value
to be sent or requested to be received, the initiating user can request
payment. Instead of
providing details on the recipients included in the image, the system can use
advanced
image analysis to identify the persons included in the image and direct
payment
accordingly.
[0016] In general, the present disclosure describes a system capable of
providing
a simpler and easier solution to users initiating a payment request, by
providing such
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w ,
, 1
functionality without requiring a prior relationship to the recipients of the
request (i.e.,
the system works for strangers as well as known associates), without requiring
an
exchange of money between present devices, and allows for delayed payments or
repayments (i.e., after initial payment by a single person paying for multiple
people).
Further, verification techniques used during the facial recognition process
can reduce
errors in the process to help avoid false claims and requests for repayment.
In some
instances, scanned facial and other biometric information may be provided to
and/or
obtained by a financial or health institution as an authentication mechanism.
That
information can later be used to verify the identity and/or accounts of
persons included in
an image before facilitating transfers of funds.
[0017] Several helpful use cases explain the additional benefits of such a
system.
In a first case, a transfer of monetary funds (or other suitable value) can be
performed
between two or more people. For example, in a person-to-person transaction,
Person A
may wish to pay Person B using an implementation of the described system.
Person B
may not have a mobile phone, while Person A may not have cash or a check.
Person A
can take a picture of Person B using Person A's mobile device and, using a
suitable
mobile application on Person A's mobile device, indicate that a particular
amount should
be sent to Person B. Instead of Person B requiring a mobile device, or
generally
accepting mobile charges, the implemented system receives the photo/image and,
using
facial recognition techniques, identifies contact information associated with
Person B.
The system can send Person B a notification of a request to send funds to
Person B. In
some instances, Person B can accept the transfer prior to the value being
sent. In
response to identifying Person B and confirming the contact information, the
system can
use traditional or specifically implemented payment processing techniques to
process the
payment from Person A to Person B.
[0018] In a second case, a group of friends or acquaintances may attend a
dinner
or event, such as a friend's birthday. Instead of splitting the bill multiple
ways at the
time, the participants may elect to have fewer than all participants pay
there, while later
collecting money from the remaining group to cover each's fair share. During
the
festivities, a waitress, third party, or participant may take a picture of one
or more of the
group. Using the photo of the group, and identifying a portion of the bill to
be split, an
CA 02910667 2015-10-30
implementation of the system can identify the persons in the photo using
facial
recognition, connect those participants to their contact and account
information, and
request payment as needed. One or more of the participants, such as the person
whose
birthday that is being celebrated, or those who were able to pay at the time,
may be
excluded as directed by the requesting user. In some instances, specific faces
may be
selected at the initial phase to keep certain persons from being included in
the payment
request. Various verification techniques can be used to ensure that the
persons identified
in the photo. In some cases, the photo recognition may be based on one or more
systems,
services, or repositories outside of the payment processor or entity providing
the solution.
For example, photo recognition may be used via one or more social networks,
search
engines, public or private databases, as well as others. In some instances,
the photo
recognition may be based on a dedicated facial recognition database provided
by the
payment processor. In some instances, additional sources may be used based on
incomplete or unavailable results from a primary data source.
[0019] Turning to the illustrated embodiment, FIG. 1 is a block diagram
illustrating an example system 100 for sending payment requests to one or more
persons
or entities based on photos or images in which the persons or entities appear.
As
illustrated in FIG. 1, system 100 is a client-server system capable of sharing
images
across network boundaries for the purposes of making or requesting payments,
analyzing
those images to identify one or more persons included within the photos, and
communicating with those identified persons to coordinate payments between the
initiating user and the one or more persons. Specifically, system 100 includes
or is
communicably coupled with a payment system 102, a first client 150a and one or
more
additional clients 150b, a network 140, and one or more third-party systems
170.
Although components are shown individually, in some implementations,
functionality of
two or more components, systems, or servers may be provided by a single
component,
system, or server. Similarly, in some implementations, the functionality of
one illustrated
component, system, or server may be provided by multiple components, systems,
servers,
or combinations thereof Conversely, multiple components may be combined into a
single component, system, or server, where appropriate.
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t
[0020] As used in the present disclosure, the term "computer" is intended to
encompass any suitable processing device. For example, payment system 102 may
be
any computer or processing device such as, for example, a blade server,
general-purpose
personal computer (PC), Mac , workstation, UNIX-based workstation, or any
other
suitable device. Moreover, although FIG. 1 illustrates a payment system 102,
payment
system 102 can be implemented using two or more systems, as well as computers
other
than servers, including a server pool. In other words, the present disclosure
contemplates
computers other than general purpose computers, as well as computers without
conventional operating systems. Further, illustrated payment system 102,
clients 150a/b,
and the third-party systems 170 may each be adapted to execute any operating
system,
including Linux, UNIX, Windows, Mac OS , JavaTM, AndroidTM, or i0S. According
to
one implementation, the illustrated systems may also include or be
communicably
coupled with a communication server, an e-mail server, a web server, a caching
server, a
streaming data server, and/or other suitable server or computer.
[0021] In general, the payment system 102 is used to receive and process
payment
requests. The payment system 102 illustrated herein is described in terms of
the
operations related to photo-based payment requests. However, the payment
system 102
may, in some implementations, be a larger system providing payment operations
related
to credit cards, banking, and other types of payments in addition to photo-
based payment
requests. In other implementations, the payment system 102 may be separate
from
traditional payment systems, and may use processing power and functionality
associated
with those traditional systems to perform the actual payment processing,
wherein the
photo recognition and payment requests sent are managed by the illustrated
payment
system 102.
[0022] The illustrated payment system 102 can receive payment requests from
one or more clients 150, where payments requests, whether requesting payment
from or
to another, include one or more photos each including one or more persons or
entities
associated with the payment request, as well as an amount or value to be
associated with
the requested payment. The payment system 102 has, or is associated with,
functionality
for determining the identity of the persons or entities included in the one or
more photos,
identifying account and/or contact information associated with the identified
persons or
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CA 02910667 2015-10-30
entities, and delivering the payment request to those identified persons or
entities based
on the account or contact information. In some instances, the payment system
102 may
automatically debit or credit an account associated with the identified person
or entity
without the need for acceptance or confirmation from the recipient, or
additionally or
alternatively, the requesting person.
[0023] The payment system 102 may, in some implementations, manage the
operations to be performed while leveraging available technologies, web
services,
existing knowledge bases, and other outside capabilities to perform some or
all of the
operations. For example, external photo recognition databases may be accessed,
such as
those associated with social networks, e.g., Facebook, LinkedIn, etc.
Additionally,
information from third-party systems may be useful in verifying particular
photo
determinations, such as location data from a user's mobile device that can be
used to
verify the location of the recipient matches or coincides with the location of
the photo.
Alternatively, information from social networks, such as check-ins and other
location-
and timing-related information, can be used to verify the determinations of
the facial
recognition system.
[0024] As illustrated, the payment system 102 includes an interface 104, a
processor 106, a payment processing application 108, and memory 126. In
general, the
payment system 102 is a simplified representation of one or more systems
and/or servers
that provide the described functionality, and is not meant to be limiting, but
rather an
example of the systems possible.
[0025] The interface 104 is used by the payment system 102 for communicating
with other systems in a distributed environment ¨ including within the
environment 100 ¨
connected to the network 140, e.g., clients 150, one of the third-party
systems 170, and
other systems communicably coupled to the network 140. Generally, the
interface 104
comprises logic encoded in software and/or hardware in a suitable combination
and
operable to communicate with the network 140. More specifically, the interface
104 may
comprise software supporting one or more communication protocols associated
with
communications such that the network 140 or interface's hardware is operable
to
communicate physical signals within and outside of the illustrated environment
100.
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[0026] As illustrated in FIG. 1, the payment system 102 includes a processor
106.
Although illustrated as a single processor 106 in FIG. 1, two or more
processors may be
used according to particular needs, desires, or particular implementations of
the
environment 100. Each processor 106 may be a central processing unit (CPU), an
application specific integrated circuit (ASIC), a field-programmable gate
array (FPGA),
or another suitable component. Generally, the processor 106 executes
instructions and
manipulates data to perform the operations of the payment system 102.
Specifically, the
processor 106 executes the algorithms and operations described in the
illustrated figures,
including the operations performing the functionality associated with the
payment system
102 generally, as well as the various software modules (e.g., the payment
processing
application 108), including the functionality for sending communications to
and receiving
transmissions from clients 150 and third-party systems 170.
[0027] The illustrated payment system 102 also includes memory 126, or
multiple
memories 126. The memory 126 may include any memory or database module and may
take the form of volatile or non-volatile memory including, without
limitation, magnetic
media, optical media, random access memory (RAM), read-only memory (ROM),
removable media, or any other suitable local or remote memory component. The
memory 126 may store various objects or data, including financial data, user
information,
administrative settings, password information, caches, applications, backup
data,
repositories storing business and/or dynamic information, and any other
appropriate
information including any parameters, variables, algorithms, instructions,
rules,
constraints, or references thereto associated with the purposes of the life
insurance
platform 102. Additionally, the memory 126 may store any other appropriate
data, such
as VPN applications, firmware logs and policies, firewall policies, a security
or access
log, print or other reporting files, as well as others. For example, memory
126 can store
user profile information 128 associated with one or more users of the photo
payment
system. The user profile information 128 can be used to associate pictures and
identified
persons with one or more accounts and contact information.
[0028] As illustrated, the user profile information 128 may include
information on
a plurality of users and can include user payment details 130, user
images/photos 132,
user login information 134, and user contact information 135. The user payment
details
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1
130 may provide information related to one or more bank accounts, credit card
accounts,
or other payment information, including online payment accounts. The user
payment
details 130 can allow the system and the payment processing application 108 to
access
and update accounts with transactions performed using the system 100. For
example, in
response to a first user submitting a payment request to pay a second user,
the user
payment details 130 from the first user's profile can be used to fund the
transaction.
Similarly, the user payment details 130 from the second user's profile can be
used to
receive the transaction. In some instances, the user payment details 130 may
be
associated with an online account. In those instances, the user login
information 134 may
be used to access that account if such access is needed. The user login
information 134
may also be used to access one or more accounts related to a particular user,
such as a
social network, mobile carrier, photo site, or others. In instances where the
user is not
associated with any login information or payment details, the system 102 may
contact the
user to register a new account, where necessary.
[0029] The user images 132 may be a collection of images (e.g., photos)
associated with a particular user. The user images 132 may include images in
which the
particular user is present, as well as images submitted by the particular user
for use in a
prior payment request. New images associated with the user can be added to the
repository for later use. In some instances, the analysis of particular images
may require
the determination of a person's identity using external facial recognition
services. In
those instances, images from the external source may be imported into the user
images
132 repository, if allowed by the user. This may enhance and increase the
speed of the
payment system. The images used in the present disclosure may be in or more
file
formats, including but not limited to JPEGs (Joint Photographic Experts
Group), GIFs
(Graphic Interchange Format), TiFFs (Tagged Image File Format), RAW (raw image
formats), and PNG (Portable Network Graphics). In some instances, metadata
associated
with the images can be included within the file (e.g., via headers) or
associated with the
file.
[0030] The contact information 135 of a user can be used to determine how to
contact the user when a new payment request associated with the user is
received. In
some instances, the contact information 135 may be similar to the user payment
details
CA 02910667 2015-10-30
130, or may be determined based on the user payment details 130. The contact
information 135 may be retrieved or identified based on the location from
where the
image recognition was made. For example, LinkedIn, Facebook, or other online
profiles
may be searched as one of the external sources. In those instances, contact
information
from those profiles and accounts may be used to contact the particular match
and can be
imported into the payment system 102, as appropriate.
[0031] Some or all of the information associated with the user profile 128 can
be
provided by the user corresponding to the user profile 128. The user can
initially set up
the user profile 128 to include the relevant information, such as when a first
payment
request is received, or upon sending the first payment request.
[0032] In addition to the user profile 128, memory 126 includes a set of third-
party system links 136 and payment rules 138. The third-party system links 136
can
include links and information associated with the one or more third-party
systems 170
used to assist in the image recognition-based payment process. This
information can
include details on one or more application programming interfaces (APIs)
associated with
the third-party systems 170, login information to particular databases and/or
repositories,
and procedural information and instructions for using those systems. Payment
rules 138
include a set of rules and algorithms defining how the payment processing
application
108 performs, including rules related to particular orders in which the
internal and
external modules and systems are used or employed during the image recognition-
based
payment process. In some instances, at least a portion of these rules may be
configurable,
such that an authorized individual can modify the performance of the payment
system
102.
[0033] As noted, the payment system 102 includes the payment processing
application 108. The payment processing application 108 represents an
application, set
of applications, software, software modules, or combination of software and
hardware
used to manage the image recognition-based payment request process of the
payment
system 102. In the present solution, the payment processing application 108
can perform
operations including receiving images from clients 150 associated with a
payment
request, analyze the photo to identify at least one person or entity included
in the image,
verify the person based on additional information to ensure the correct
recipient of the
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a ,
. ,
payment request, and interface with one or more external sources when
additional
information is necessary. Further, upon identifying and determining
information
associated with persons or entities within the image, the payment processing
application
108 can cause the payment request to be processed and completed, as needed.
The
payment processing application 108 can include and provide various
functionality to
assist in the management and execution of the image recognition-based payment
process.
As illustrated in FIG. 1, the payment processing application 108 includes a
payment
processing module 110, an identity module 112, an identity verification module
118, and
a third-party system interface 124. Additional modules and functionality may
be
included in alternative implementations.
[0034] Regardless of the particular implementation, "software" includes
computer-readable instructions, firmware, wired and/or programmed hardware, or
any
combination thereof on a tangible medium (transitory or non-transitory, as
appropriate)
operable when executed to perform at least the processes and operations
described herein.
In fact, each software component may be fully or partially written or
described in any
appropriate computer language including C, C++, JavaScript, JavaTM, Visual
Basic,
assembler, Perla, any suitable version of 4GL, as well as others.
[0035] The payment processing module 110 performs operations associated with
processing payments after identities are verified and payment requests are
received and
acknowledged. The payment processing module 110 can use the user payment
details
130 of each user in a particular transaction to debit and credit accounts, as
needed. The
payment processing module 110 may be part of a pre-existing payment processing
system, such as those managed by Visa, MasterCard, PayPal, or any other
transactional
provider. Alternatively, the payment processing module 110 may be associated
with one
or more financial institutions and can provide operations for the
transactions. The
operations performed in actually performing the payment processing may be
similar to
those in traditional payment processing, or they may be specifically designed
for the
image recognition-based payment process. Still further, the solution could be
implemented and used with person-to-person (P2P) transfers and wires as part
of the
payment processing operations.
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t ,
,
[0036] The identity module 112 performs operations for identifying persons
included in the one or more images associated with a payment request. The
identity
module 112 can determine, using the received image, a number of persons for
whom to
perform an identification on from the image. In some instances, the submission
of the
image from the client 150 may include an indication of one or more persons or
entities
within the image to be omitted from the facial recognition (e.g., persons not
associated
with the payment request). For the remaining persons, concurrent,
simultaneous, or
serialized facial recognitions can be performed. As illustrated, the identity
module 112
includes a facial recognition engine 116. The facial recognition engine 116
can perform
facial recognition on the one or more persons associated with the payment
request. The
facial recognition engine 116 can perform any suitable type of facial
recognition (e.g.,
traditional, 3-dimensional, or skin texture analysis, among others), using one
or more
databases, for example, the user images 132. Additionally, the identity module
112
includes an identification interface 114 for interacting with one or more
external facial
recognition systems, one or more facial or image databases, and one or more
other
external sources as appropriate. Using the identification interface 114, as
well as the
third-party system interface 124, one or more external services and knowledge
bases can
be used to perform or assist in the facial recognition. Such external systems
and sources
may be publicly or privately available in different implementations. After
operating, the
identity module 112 can return one or more potential matches from the one or
more
images. The operations may be able to handle and interpret both compressed and
uncompressed images. In situations where an error arises or the image cannot
be
processed and person(s) identified (e.g., based on image size, image quality,
image
orientation, person making a manipulated or distorted face), then the system
can prompt
the user to re-take the photo, provide additional information, or provide
alternative
methods to identify the person(s) in the image (e.g., email address, relevant
information,
etc.)
[0037] The identity verification module 118 performs verification operations
to
reduce misidentifications of persons or entities by the identity module 112.
In some
instances, the identity verification module 118 may be optional and may
include various
alternative methods of verifying or reducing misidentifications. The
illustrated identity
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verification module 118 includes a geo-location verification module 120 and a
contextual
verification module 122. The geo-location verification module 120 can be used
to verify
that the persons identified by the identity module 112 were physically located
in a
position at the time the one or more images were taken. Each digital image may
include
or be associated with a set of metadata providing information identifying the
owner of the
image, copyright and contact information, the camera or device that created
the file,
exposure information, and other descriptive information related to the digital
image.
Included in the descriptive information may be geo-location data from where
the image
was taken or created, such as a longitude and latitude received from a global
positioning
satellite. In some instances, the geo-location verification module 120 may use
such
location metadata from the digital file to verify that the one or more persons
identified by
the identity module 112 were located at or near the location at which the
image was
taken. Such verifications can be performed by accessing location information
on a device
associated with the recipient and confirming that the recipient was near the
location.
Alternatively, information may be retrieved or confirmed by a mobile carrier
providing
service to the recipient's device (with permission from the user), location
information
reported back by one or more apps associated with the recipient's mobile
device, or by
other suitable information. If the geo-location verification module 120
confirms the
person was in a similar location to that of where the digital image was taken
as
determined by the digital image's metadata, the payment request may be
verified.
[0038] The contextual verification module 122 may perform other verifications
of
the payment request. The contextual verification module 122 may also use the
metadata
associated with the digital image file. Instead of comparing the geo-location
of the
recipient, the contextual verification module 122 may use contextual
information to
compare with the digital file's location of origin. For example, the
contextual verification
module 122 may be capable of accessing one or more social networks. Using
check-ins,
statuses, and other information provided by the recipient, the contextual
verification
module 122 can determine whether the recipient was located near the location
of origin at
the time of the image. If the payment processor and the identity verification
module 118
are closely aligned, the location in the digital file may be compared to one
or more recent
purchases at establishments near the time of image to confirm the
verification.
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Additional contextual verifications may be based on relative friendships,
relationships,
and social circles of the persons involved in the transaction. A relative
relationship, even
if not direct, between the persons may indicate a higher probability of
verification. In
some instances, a threshold level of relationship (i.e., a second-degree
relationship, where
friends are held in common) may be required for verification. If the
identified person has
no relation to the person initiating the payment request, the verification may
indicate that
the identification is less likely, and other verifications may be needed.
In some
implementations, time-based verifications may be placed on received images.
For
example, some systems may implement a time verification identifying an age of
an image
(from creation of the file, the date the image was created, etc.) and
determining whether
the image is older than a threshold time limit. This may be used to prevent
potentially
fraudulent requests, untimely requests, and requests that may not be correct.
For
example, a time limit of three (3) days may be imposed. If an image file is
determined to
be four (4) days old, the system may reject or end the payment request.
Different time
limits may be imposed, such as a week, 24 hours, or any other suitable time.
Such
restrictions and verifications can be used together or separately, as
implemented.
[0039] Once the identity of the persons or entities in the image is verified,
the
information can be provided back to the payment processing module 110 to
continue and
facilitate the payment request.
[0040] The third-party system interface 124 provides functionality and
connectivity for the payment processing application 108 to access the one or
more third-
party systems 170. The third-party system interface 124 can provide defined or
dynamic
connections to the external systems via network 140, using those connections
to provide
additional information and processing power to the payment system 102 to
perform its
prescribed operations.
[0041] Network 140 facilitates wireless or wireline communications between the
components of the environment 100 (i.e., between the payment system 102 and
clients
150 and/or the third-party systems 170, between clients 150, and among
others), as well
as with any other local or remote computer, such as additional clients,
servers, or other
devices communicably coupled to network 140, including those not illustrated
in FIG. 1.
In the illustrated environment, the network 140 is depicted as a single
network, but may
CA 02910667 2015-10-30
be comprised of more than one network without departing from the scope of this
disclosure, so long as at least a portion of the network 140 may facilitate
communications
between senders and recipients. In some instances, one or more of the
illustrated
components may be included within network 140 as one or more cloud-based
services or
operations. The network 140 may be all or a portion of an enterprise or
secured network,
while in another instance, at least a portion of the network 140 may represent
a
connection to the Internet. In some instances, a portion of the network 140
may be a
virtual private network (VPN). Further, all or a portion of the network 140
can comprise
either a wireline or wireless link. Example wireless links may include
802.11a/b/g/n,
802.20, WiMax, LTE, and/or any other appropriate wireless link. In other
words, the
network 140 encompasses any internal or external network, networks, sub-
network, or
combination thereof operable to facilitate communications between various
computing
components inside and outside the illustrated environment 100. The network 140
may
communicate, for example, Internet Protocol (IP) packets, Frame Relay frames,
Asynchronous Transfer Mode (ATM) cells, voice, video, data, and other suitable
information between network addresses. The network 140 may also include one or
more
local area networks (LANs), radio access networks (RANs), metropolitan area
networks
(MANs), wide area networks (WANs), all or a portion of the Internet, and/or
any other
communication system or systems at one or more locations.
[0042] The third-party systems 170 can include any suitable external systems,
functionality, or operations that assist the payment processing application
108 in the
image recognition-based payment process. As illustrated, the third-party
systems 170
include one or more social networks 172, third-party payment systems 174,
public
databases 176, and private databases 178. The one or more social networks 172
can be
used for facial recognition and image identification as well as via location
and contextual
verification. The third-party payment systems 174 can be any existing systems
used by
the payment processing application 108 and its payment processing module 110
to
effectuate processing of the payment. The public and private databases 176,
178 may
include image and facial recognition databases used by public and private
entities. For
example, the public databases 176 may include governmental databases or other
openly
available sources of facial recognition-related data. The private databases
178 may be
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. .
. .
those managed by private sources, such that membership or access is provided
to the
payment system 102 in exchange for payment or other consideration. In some
instances,
external facial recognition engines may be used.
[0043] Clients 150 (150a and 150b) may be any computing devices operable to
connect to or communicate with payment system 102, other clients 150, or other
components via network 140, as well as the with the network 140 itself, using
a wireline
or wireless connection, and can include a desktop computer, a mobile device, a
tablet, a
server, or any other suitable computer device. In general, client 150
comprises an
electronic computer device operable to receive, transmit, process, and store
any
appropriate data associated with the environment 100 of FIG. 1.
[0044] As illustrated, clients 150 include an interface 152, a processor 154,
a
graphical user interface (GUI) 156, a camera 158, a client application 160,
and memory
162. The interface 152 and processor 154 may be similar to or different than
the interface
104 and processor 106 described for payment system 102. In general, processor
154
executes instructions and manipulates data to perform the operations of the
client 150.
Specifically, the processor 150 executes the algorithms and operations
described in the
illustrated figures, including the operations performing the functionality
associated with
the client application 160 and camera 158.
[0045] Client 150 executes a client application 160 operable to prepare and
submit payment requests, where the payment request includes submitting one or
more
images 164 to the payment system 102. The images 164 may be taken by an
integrated
camera 158 on the client 150, or the image may be taken from another camera
apart from
the client 150 or downloaded or obtained from an external location. Client
application
160 may be a web site, portal page, a dedicated mobile application, or other
software.
The client application 160 may include integrated camera software for taking
images with
camera 158 and/or importing images.
[0046] Client 150 can also include a graphical user interface (GUI) 156. The
GUI
156 interfaces with at least a portion of the environment 100 for any suitable
purpose,
including generating a visual representation of a web browser and/or the
client
application 160. In particular, the GUI 156 may be used to view and navigate
various
web pages located both internally and externally to environment 100, as well
as to view
17
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and navigate through information accessed by the client application 160, such
as
information stored at or associated with the payment system 102. Generally,
the GUI 156
provides the particular user with an efficient and user-friendly presentation
of data
provided by or communicated within the system. The GUI 156 may comprise a
plurality
of customizable frames or views having interactive fields, pull-down lists,
and buttons
operated by the user. For example, the GUI 156 may provide interactive
elements that
allow a user to view or interact with images to identify images for requesting
payment
and for sending said requested payment to the payment system 102. The GUI 156
may
present information associated with the client application 160 for viewing and
interaction.
In general, the GUI 156 is often configurable, supports a combination of
tables and
graphs (bar, line, pie, status dials, etc.), and is able to build payment
requests. Therefore,
the GUI 156 contemplates any suitable graphical user interface, such as a
combination of
a generic web browser, intelligent engine, and command line interface (CLI)
that
processes information in the platform and efficiently presents the results to
the user
visually.
[0047] The illustrated client 150 is intended to encompass any computing
device
such as a desktop computer, laptop/notebook computer, mobile device,
smartphone,
personal data assistant (PDA), tablet computing device, one or more processors
within
these devices, or any other suitable processing device. For example, the
client 150 may
comprise a computer that includes an input device, such as a keypad, touch
screen, or
other device that can accept user information, and an output device that
conveys
information associated with the operation of the client application 160 or the
client 150
itself, including digital data, visual information, or a GUI 156, as shown
with respect to
the client 150.
[0048] Client 150 also includes memory 162, which may be similar or different
to
memory 126. Memory 162 includes at least one image, either as captured by
camera 158
or that is imported or otherwise stored on the client 150.
[0049] As illustrated, client 150a may be associated with a first user
initiating the
payment request, where image 164a is submitted, using client application 160a,
to the
payment system 102. In this example, the client application 160a may be a
dedicated
mobile application associated with the payment system 102 and meant to provide
image -
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CA 02910667 2015-10-30
,
based payment requests. Client 150b may be associated with one or more
additional
users who are receiving the payment request (i.e., recipients) after the
payment system
102 performs the facial recognition and association with the corresponding
users. The
additional clients 150b may, e.g., via client application 160b, receive a
notification of the
payment request. If payment is requested from the client 150b, the client
application
160b may allow the corresponding users to accept and pay the requested amount.
If
payment is to be sent to the users associated with clients 150b, then a
notification
message may be received. In some instances, no action may need to be taken to
perform
the transaction when payment is being sent to the client 150b.
[0050] While portions of the software elements illustrated in FIG. 1 are shown
as
individual modules that implement the various features and functionality
through various
objects, methods, or other processes, the software may instead include a
number of sub-
modules, third-party services, components, libraries, and such, as
appropriate.
Conversely, the features and functionality of various components can be
combined into
single components as appropriate.
[0051] Further, while the facial recognition and analysis is managed at the
payment system 102 in FIG. 1, alternative implementations may perform at least
some of
the recognition and analysis at the client 150, or alternatively, at one or
more systems
and/or services external to the payment system 102, such as third-party facial
recognition
and verification services (e.g., governmental services, private services,
etc.).
[0052] FIG. 2 is a flowchart of an example operation 200 related to sending
payment requests to one or more persons or entities based on images or images
in which
the persons or entities appear. For clarity of presentation, the description
that follows
generally describes method 200 in the context of the system 100 illustrated in
FIG. 1.
However, it will be understood that method 200 may be performed, for example,
by any
other suitable system, environment, software, and hardware, or a combination
of systems,
environments, software, and hardware as appropriate.
[0053] At 205, an image associated with a payment request is identified,
wherein
the identified image contains at least one payment-related person or entity in
the image.
The image may be a photo in some instances. The payment request and image may
be
received from a mobile device. In some instances, the mobile device may have
taken the
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CA 02910667 2015-10-30
image with a camera integrated with the mobile device, while in other
instances, the
mobile device may identify a pre-existing or a prior-taken image to be
included with the
payment request. In some instances, the payment request may be received from a
non-
mobile device, such as a desktop computer. The identified image may be an
image
downloaded to or saved on the non-mobile device. In some instances, one or
both of the
mobile and non-mobile devices may have one or more applications capable of
identifying
the image and/or sending the payment request. The operations of method 200 may
be
performed, at least in part, at the mobile or non-mobile device, as well as at
a system
physically remote from the mobile or non-mobile devices.
[0054] Each payment request includes a value for which payment is requested.
For example, the value in a first payment request may be an amount identified
using
monetary funds, such as dollars, euros, or another denomination. In other
instances, the
value requested may be a non-monetary or non-traditional monetary amount, such
as
credits, goods, or services. The credits may include minutes or data from a
cellular or
wireless provider plan, Bitcoins or another crypto-currency, as well as any
other suitable
credit. In some instances, the value included in the payment request may be a
good or
service that cannot be easily transferred electronically, such that the actual
transaction
and transfer to complete the payment request may be performed manually via
face-to-
face interaction or over a communications medium, either providing the goods
or services
directly, by shipping goods, by performing services remotely, or by shipping
goods from
one to another.
[0055] At 210, the identified image is analyzed to identify the at least one
payment-related person. In some instances, one or more persons in the
identified image
may be omitted from a payment request facial recognition determination. For
example,
an initiating user can manually identify particular faces or persons within
the image who
should not be included in the analysis. During the analysis, those persons and
their faces
will not be considered by the analysis. For those remaining persons to be
analyzed, facial
or other recognition processes can be performed to identify those included in
the payment
request. In some instances, the persons may not be known to the initiating
user, either
within their personal contacts or based on prior relationships (e.g., via
social network). In
those instances, the system can still perform operations capable of
determining the
CA 02910667 2015-10-30
recipient based only on the identified image. The details of the analysis may
vary,
although one example set of operations is illustrated in FIG. 3.
[0056] Specifically, FIG. 3 is a flowchart of an example operation 210 related
to
analyzing and verifying potential identities of persons included in the
images. While
example operations are shown, the actual operations may vary based on
particular
implementations of the system and operations.
[0057] At 305, a facial recognition system for analyzing the identified image
is
identified. In some instances, the facial recognition system may be local to a
payment
processor managing the analysis, or the facial recognition system may be local
to a
mobile device initiating the payment request. In other instances, the facial
recognition
system may be an external service or source, such as a social network or third-
party
service. In some instances, more than one facial recognition system may be
used
concurrently or sequentially. For example, a local system may be used, where
an external
system is then used if no reliable matches are returned by the local system.
[0058] At 310, the identified image is provided to the identified facial
recognition
system. At 315, at least one possible payment-related person from the
identified image is
identified. In instances where the identified image includes multiple (and non-
omitted)
persons, the process may be performed multiple times before completing.
[0059] At 320 and 325, the at least one identified possible payment-related
person
may be, optionally, verified. The operations of 320 and 325 describe several
possible
verification techniques, although numerous other techniques may also be used,
where
appropriate and available. Some, all, alternative, or no verification
techniques may be
implemented in different situations.
[0060] At 320, a verification of at least of the identified possible persons
can be
performed based on information associated with the identified image. For
example, the
identified image may be a data file having a set of metadata associated with
it, including
a location where the image was created or where a photo in the image file was
taken, as
well as a time the image was taken. In some instances, this, or other
information
associated with the image (e.g., image metadata), can be used to verify, or
support, the
identity of the identified possible person. In one example, the location of
the identified
person at the time of the image may be determined and compared to the location
of the
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,
. ,
image. In some instances, information from or associated with a mobile device
of the
identified person can be used to determine the person's location at the image
time. Social
network check-ins, status updates, and messaging information may also be used
to
determine location. Still further, and in addition to other suitable options,
purchase
history from a credit card, automated teller machine (ATM), or other external
service may
also be used to verify location. Alternatively, location information embedded
in or
associated with the identified image, such as GPS-based metatags or other
metadata, may
also be used to verify location.
[0061] At 325, a verification of the at least one possible persons can be
performed
based on the person initiating the payment request and providing the image.
Specifically,
the initiating user's current and/or prior locations may be used to cross-
reference the
current and/or prior locations of the identified potential person to determine
whether such
locations overlapped. Similarly, even if the image does not include location
metadata,
information from a social network tagging both the initiating person and the
identified
potential person at a common location or event may provide sufficient
verification. In
some instances, such persons may be separately tagged or checked-in at a
particular
location within relatively close times. In those instances, such information
may act as a
verification that the identified person was present and a potential payment
request
recipient.
[0062] The verifications required to confirm the identified persons may vary
in
each implementation, and per image and/or payment request. For example, if the
initiating user and the identified possible person are related via social
networking,
particularly to a threshold degree, such verifications may not be used.
Conversely, where
no known relationship between the initiating user and the potential person are
identified,
significant verifications may be necessary to confirm the payment request.
[0063] Returning to FIG. 2, at (optional) 215, a list of the potential payment-
related persons identified from the image may be provided to the initiating
user. In one
instance, the image of the person from a facial database as determined during
the analysis
can be compared to the person from the identified image. One or more potential
persons
may be included in the list. At (optional) 220, confirmation of the at least
one identified
possible person from the provided list may be received.
22
CA 02910667 2015-10-30
[0064] At 225, contact information associated with the at least one identified
person is identified. In some instances, the information may be known and/or
obtained
via the same facial recognition technique used (e.g., via the social network).
Contact
information may be used to send the payment request to the identified
recipient as
needed. In some instances, the contact information used for a particular
person would be
based on the online location from which the person was identified. If the
person was
identified using a local image or facial database, local contact information
may be used.
Alternatively, if the facial recognition identified the person based on a
social network, the
contact information provided there may be used. Still further, if the analysis
process
identified the person based on a professional profile page, contact
information extracted
from that location may be used.
[0065] At 230, the payment request can be transmitted to the at least one
identified person via the identified contact information. At (optional) 235, a
confirmation
of the payment request from the at least one identified person may be
received. In some
instances, no confirmation may be necessary, such as where the person is
known, along
with bank or other account information, and the payment request relates to a
request to
pay the identified person. In those instances, no confirmation may be
necessary before
depositing the value into the identified person's account.
[0066] At optional 240, the processing of the payment request may be
facilitated
in response to a confirmation to perform the payment processing, or where no
confirmation is needed, upon transmitting the payment request. Method 200 can
use any
suitable payment processing functionality or service, including those of Visa,
MasterCard, PayPal, and others.
[0067] FIG. 4A and 4B are a combined swim-lane diagram 400 illustrating
example operations related to actions for sending payment requests to one or
more
persons or entities based on images in which the persons or entities appear.
The payment
request sender 405, the first recipient of the payment request 415, and the
second
recipient of the payment request 420 may be associated with one or more mobile
devices
in this example, where the persons can perform the operations at the mobile
devices.
Centralized systems 410 can represent the payment system 102 and any third-
party
systems 170, as well as other or alternative systems to perform the described
operations.
23
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[0068] At 430, the payment request sender can identify (e.g., capture or
select) an
image to use when performing a image-based payment process. At 435, a mobile
application associated with the image-based payment process can initiate the
recognition
process. In general, the mobile application may only need to identify a single
picture and
a value to be requested (whether to pay another or requesting payment from
another).
The sender may also identify one or more faces/persons included in the image
to omit
from the analysis via the mobile application.
[0069] At 440, the image is analyzed using image and/or facial recognition,
and at
445, determines one or more potential persons included in the analyzed image.
At 450,
one or more persons are matched to the image, and their corresponding contact
information/profile and/or payment information is determined. At 455, identity
verification of those determined in 445 and 450 may be optionally performed.
In some
instances, there may be multiple levels of verification based on initial
determinations and
analysis. For example, verifications may be based on a known relationship
between the
persons (e.g., via a social network). Depending on the level of relationship,
the location
and timing of or associated with the image may be compared to information
known about
the possible persons identified from the image. Additional, and varied, levels
of
verification can be performed.
[0070] At 460, the centralized systems 410 can send a request for payment to
the
payment request recipients as determined by the analysis operations and,
optionally, as
verified by the verification operations. The payment requests may be sent via
any
suitable channel, including as a push notification to a mobile device of the
one or more
recipients, to email addresses of the one or more recipients, to text
addresses of the one or
more recipients, to a social network inbox or messaging application of the one
or more
recipients, among others.
[0071] As illustrated, the first (415) and second (420) recipients receive the
payment requests at 465 and 475, respectively. At 470 and 480, respectively,
the first and
second recipients can reply to and/or confirm the payment requests. Such
replies may be
unnecessary in some instances, such that upon sending the payment notification
(or
before sending the notification), the payment request can be processed at 485.
Upon
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, L ,
processing the payment, the centralized systems 410 can send (at 490) a
confirmation that
the payment request has been processed to the sender 405.
[0072] In some implementations, additional operations may be performed upon
the image being used to initiate the payment request. For example, once
payment is
successfully used to facilitate payment, the image can be modifying by
embedding one or
a series of pixel modifications to the original image, thereby providing and
indicated a
record of the image being used as payment initiation. Pixel modification can
include
generic data such as payment amount and/or the date completed. Such
information can
be provided back to the initiating user by returning the image after
completion.
Additionally, if the image is stored for use in one of the source databases
for facial
recognition, the information stored within the image may be used to verify
and/or
maintain a record of the identified user's prior activities within the payment
systems.
Still further, by storing a modified version of the original image, future
submissions of
the original image may be denied outright or result in a notification that the
image has
previously been used and a transaction completed.
[0073] The preceding figures and accompanying description illustrate example
systems, processes, and computer-implementable techniques. While the
illustrated
systems and processes contemplate using, implementing, or executing any
suitable
technique for performing these and other tasks, it will be understood that
these systems
and processes are for illustration purposes only and that the described or
similar
techniques may be performed at any appropriate time, including concurrently,
individually, or in combination, or performed by alternative components or
systems. In
addition, many of the operations in these processes may take place
simultaneously,
concurrently, and/or in different orders than as shown. Moreover, the
illustrated systems
may use processes with additional operations, fewer operations, and/or
different
operations, so long as the methods remain appropriate.
[0074] In other words, although this disclosure has been described in terms of
certain embodiments and generally associated methods, alterations and
permutations of
these embodiments and methods will be apparent to those skilled in the art.
Accordingly,
the above description of example embodiments does not define or constrain this
disclosure. The scope of the claims should not be limited by the embodiments
set forth in
CA 02910667 2015-10-30
the examples, but should be given the broadest interpretation consistent with
the
description as a whole.
26
