U.S. patent application number 16/511731 was filed with the patent office on 2021-01-21 for system and method for using image data to trigger contactless card transactions.
This patent application is currently assigned to Capital One Services, LLC. The applicant listed for this patent is Capital One Services, LLC. Invention is credited to Colin HART, Jason JI, Jose VAZQUEZ.
Application Number | 20210019735 16/511731 |
Document ID | / |
Family ID | 1000005313100 |
Filed Date | 2021-01-21 |
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United States Patent
Application |
20210019735 |
Kind Code |
A1 |
HART; Colin ; et
al. |
January 21, 2021 |
SYSTEM AND METHOD FOR USING IMAGE DATA TO TRIGGER CONTACTLESS CARD
TRANSACTIONS
Abstract
A method for controlling a near field communication between a
device and a transaction card is disclosed. The method includes the
steps of capturing, by a front-facing camera of the device, a
series of images of the transaction card and processing each image
of the series of images to identify a darkness level associated
with a distance of the transaction card from the front of the
device. The method includes comparing each identified darkness
level to a predetermined darkness level associated with a preferred
distance for a near field communication read operation and
automatically triggering a near field communication read operation
between the device and the transaction card for the communication
of a cryptogram from an applet of the transaction card to the
device in response to the identified darkness level corresponding
to the predetermined darkness level associated with the preferred
distance for the near field communication read operation
Inventors: |
HART; Colin; (Arlington,
VA) ; VAZQUEZ; Jose; (Vienna, VA) ; JI;
Jason; (Reston, VA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Capital One Services, LLC |
McLean |
VA |
US |
|
|
Assignee: |
Capital One Services, LLC
McLean
VA
|
Family ID: |
1000005313100 |
Appl. No.: |
16/511731 |
Filed: |
July 15, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04W 4/80 20180201; G06Q
20/353 20130101; G06T 2207/20021 20130101; H04M 1/0202 20130101;
G06Q 20/3278 20130101; G06T 7/97 20170101 |
International
Class: |
G06Q 20/34 20060101
G06Q020/34; G06Q 20/32 20060101 G06Q020/32; G06T 7/00 20060101
G06T007/00; H04W 4/80 20060101 H04W004/80 |
Claims
1. A method, including: initiating a near field communication
between a device and a transaction card to communicate a cryptogram
from an applet of the transaction card to the device; capturing, by
a front-facing camera of the device, a series of images of the
transaction card; processing each image of the series of images to
identify a darkness level associated with a distance of the
transaction card from the front of the device; comparing each
identified darkness level to a predetermined darkness level,
wherein the predetermined darkness level is calculated based on a
near field communication distance to successfully perform the near
field communication read operation between the device and the
transaction card; and automatically triggering the near field
communication read operation between the device and the transaction
card to communicate the cryptogram from the applet of the
transaction card to the device in response to an identified
darkness level corresponding to the predetermined darkness level
based on the distance for the near field communication read
operation.
2. The method of claim 1, wherein the step of automatically
triggering includes the of step automatically executing a function
associated with a user interface input element provided on a
graphic user interface of a display of the device, wherein the
function includes the near field communication read operation.
3. The method of claim 1 including the step of identifying the
darkness level for each image of the series of images to provide a
series of darkness levels and analyzing the series of darkness
levels to identify a pattern of darkness levels, wherein
automatically triggering of the near field communication read
operation between the device and the transaction card occurs in
response to the pattern of darkness levels.
4. The method of claim 3 wherein the pattern of darkness levels
resulting in automatic triggering of the near field communication
read operation includes one of a predetermined number of successive
darkness levels that exceed the predetermined darkness level
associated with the near field communication distance for the near
field communication read operation.
5. The method of claim 3 wherein the pattern of darkness levels
resulting in automatic triggering of the near field communication
includes a predetermined total number of darkness levels that
exceed the predetermined darkness level associated with the near
field communication distance for the near field communication read
operation.
6. The method of claim 3 wherein the pattern of darkness levels
resulting in automatic triggering of the near field communication
indicates one or more of a darkness trend or a darkness spike or a
darkness plateau in the series of darkness levels.
7. The method of claim 1 wherein each image of the series of images
is comprised of a plurality of pixels and wherein, for each image,
a subset of the plurality of pixels are used to identify the
darkness level for the image.
8. The method of claim 6 further including the step of processing
the series of images to identify a feature of the transaction card,
and wherein a subset of a plurality of pixels that is used to
identify the darkness level of each image includes the feature.
9. The method of claim 7 wherein the darkness level for at least
one image comprises an average of pixel values of the subset of the
plurality of pixels of the at least one image.
10. The method of claim 8 wherein the darkness level for the at
least one image comprises a weighted average of pixel values of the
at least one image, where a weight of a pixel is determined based
upon a proximity of the pixel to the feature.
11. The method of claim 6 wherein the plurality of pixels of the
image is apportioned into a plurality of subsets of pixels, each
subset of pixels comprises a subset darkness level, and the
darkness level of an image is determined using the subset darkness
levels of the plurality of subsets of pixels.
12. The method of claim 6 further including the steps of:
processing each image of the series of images to identify a
complexity level of the image; comparing each identified complexity
level to a predetermined complexity level associated with the
distance for the near field communication read operation between
the device and the transaction card; and automatically triggering a
near field communication read operation between the device and the
transaction card to communicate the cryptogram from the applet of
the transaction card to the device in response to the identified
complexity level corresponding to the predetermined complexity
level associated with the preferred distance for the near field
communication read operation.
13. The method of claim 12, wherein the step of processing each
image of the series of images to identify the complexity level of
the image includes the steps of, for each image: identifying a
complexity level for each pixel, the complexity level for each
pixel corresponding to a difference between one or more of a
darkness level or a color of the pixel and darkness levels or
colors of neighboring pixels; and determining the complexity level
of the image using the complexity levels of the plurality of pixels
of the image.
14. The method of claim 1 wherein the step of initiating the near
field communication between the device and the transaction card for
communication of the cryptogram from the applet of the transaction
card to the device occurs in response to receipt of a READ input
command at a user interface of the device, and wherein the near
field communication read operation includes: sending an applet
select message with an applet identifier (ID) for the applet to the
transaction card, and receiving, by the device, the cryptogram from
the applet from the transaction card.
15. A device comprising: a front-facing camera configured to
capture a series of images of a transaction card in response to
initiation of a near field communication between the device and the
transaction card for communication of a cryptogram from an applet
of the transaction card to the device; a near field communication
interface; a processor coupled to the front-facing camera and the
near field communication interface; a non-volatile storage device
comprising program code stored thereon operable when executed upon
by the processor to: process each image of the series of images to
identify a darkness level associated with a distance of the
transaction card from a front of the device; compare each
identified darkness level to a predetermined darkness level,
wherein the predetermined darkness level is calculated based on a
near field communication distance for a near field communication
read operation between the device and the transaction card; and
automatically trigger the near field communication read operation
between the device and the transaction card to communicate the
cryptogram from the applet of the transaction card to the device in
response to the identified darkness level corresponding to the
predetermined darkness level associated with the distance for the
near field communication read operation.
16. The device of claim 15, wherein the program code is further
operable when executed upon by the processor to: process each image
of the series of images to identify a complexity level of the
image; compare each identified complexity level to a predetermined
complexity level associated with the distance for a near field
communication read operation between the device and the transaction
card; and automatically trigger the near field communication read
operation between the device and the transaction card for the
communication of the cryptogram from an applet of the transaction
card to the device in response to the identified complexity level
corresponding to the predetermined complexity level associated with
the preferred distance for the near field communication read
operation.
17. The device of claim 16, wherein the program code is further
operable when executed upon by the processor to process the series
of images to detect a feature, each image is comprised of a
plurality of pixels and a contribution of each pixel to one or more
of the darkness level or the complexity level of the image is
weighted in accordance with a proximity of each pixel to the
feature.
18. The device of claim 16 further comprising a user interface
element configured to perform a function when selected by a user,
and wherein the program code is further operable when executed upon
by the processor to automatically perform the function of the user
interface element in response to one or more of the darkness level
or complexity level, and wherein the function is the near field
communication read operation.
19. A system comprising: a transaction card configured for near
field communication, the transaction card comprising a memory
storing an applet comprising a cryptogram; a device configured for
near field communication with the transaction card, the device
comprising: a near field communication interface; a camera
configured to capture a series of images of the transaction card in
response to initiation of a near field communication exchange
between the device and the transaction card, the series of images
used to control the near field communication interface to retrieve
the cryptogram from an applet of the transaction card; a processor
coupled to the camera; a non-volatile storage device comprising
program code stored thereon operable when executed upon by the
processor to: process each image of the series of images to
identify a darkness level associated with a distance of the
transaction card from the device; compare each identified darkness
level to a predetermined darkness level wherein the predetermined
darkness level is calculated based on a preidentified distance for
a near field communication read operation between the device and
the transaction card; and automatically trigger a near field
communication read operation between the device and the transaction
card to receive the cryptogram from the applet of the transaction
card at the near field communication interface in response to the
identified darkness level corresponding to the predetermined
darkness level associated with the preidentified distance for a
near field communication read operation.
20. The system of claim 19 wherein the program code is further
operable when executed upon by the processor to: process each image
of the series of images to identify a complexity level of the
image; automatically trigger a near field communication read
operation between the device and the transaction card for
communication of the cryptogram from the applet of the transaction
card to the device in response to the identified complexity level
corresponding to a predetermined complexity level associated with
the preidentified distance for the near field communication read
operation.
Description
BACKGROUND
[0001] Near-field communication (NFC) includes a set of
communication protocols that enable electronic devices, such as a
mobile device and a contactless card, to wirelessly communicate
information. NFC devices may be used in contactless payment
systems, similar to those used by contactless credit cards and
electronic ticket smartcards. In addition to payment systems,
NFC-enabled devices may act as electronic identity documents and
keycards, for example.
[0002] A contactless device (e.g., card, tag, transaction card or
the like) may use NFC technology for bi-directional or
uni-directional contactless short-range communications based on,
for example, radio frequency identification (RFID) standards, an
EMV standard, or using NFC Data Exchange Format (NDEF) tags, for
example. The communication may use magnetic field induction to
enable communication between powered electronic devices, including
mobile wireless communications devices and unpowered, or passively
powered, devices such as a transaction card. In some applications,
high-frequency wireless communications technology enables the
exchange of data between devices over a short distance, such as
only a few centimeters, and two devices may operate most
efficiently in certain placement configurations.
[0003] While the advantages of using an NFC communication channel
for contactless card transactions are many, including simple set up
and low complexity, one difficulty faced by NFC data exchanges may
be difficulty transmitting a signal between devices with small
antennas, including contactless cards. Movement of the contactless
card relative to the device during an NFC exchange may undesirably
impact the received NFC signal strength at the device and interrupt
the exchange. In additions, features of the card, for example metal
cards, may cause noise, dampen signal reception, or other
reflections that erroneously trigger NFC read transactions. For
systems that use contactless cards for authentication and
transaction purposes, delays and interruption may result in lost
transactions and customer frustration.
SUMMARY
[0004] A system of one or more computers can be configured to
perform particular operations or actions by virtue of having
software, firmware, hardware, or a combination of them installed on
the system that in operation causes or cause the system to perform
the actions.
[0005] According to one general aspect, a method for controlling a
wireless communication between a device and a transaction card,
includes: initiating a near field communication between the device
and the transaction card to communicate a cryptogram from an applet
of the transaction card to the device; capturing, by a front-facing
camera of the device, a series of images of the transaction card;
processing each image of the series of images to identify a
darkness level associated with a distance of the transaction card
from the front of the device; comparing each identified darkness
level to a predetermined darkness level associated with a preferred
distance for a near field communication read operation between the
device and the transaction card; and automatically triggering the
near field communication read operation between the device and the
transaction card to communicate the cryptogram from the applet of
the transaction card to the device in response to the identified
darkness level corresponding to the predetermined darkness level
associated with the preferred distance for the near field
communication read operation. Other embodiments of this aspect
include corresponding computer systems, apparatus, and computer
programs recorded on one or more computer storage devices, each
configured to perform the actions of the methods.
[0006] Implementations may include one or more of the following
features. The method where the step of automatically triggering
includes the of step automatically executing a function associated
with a user interface input element provided on a graphic user
interface of a display of the device, where the function includes
the near field communication read operation. The method including
the step of identifying the darkness level for each image of the
series of images to provide a series of darkness levels and
analyzing the series of darkness levels to identify a pattern of
darkness levels, where automatically triggering of the near field
communication read operation between the device and the transaction
card occurs in response to the pattern of darkness levels. The
method where the pattern of darkness levels resulting in automatic
triggering of the near field communication read operation includes
one of a predetermined number of successive darkness levels that
exceed the predetermined darkness level associated with the
preferred distance for the near field communication read operation.
The method where the pattern of darkness levels resulting in
automatic triggering of the near field communication includes a
predetermined total number of darkness levels that exceed the
predetermined darkness level associated with the preferred distance
for the near field communication read operation. The method where
the pattern of darkness levels resulting in automatic triggering of
the near field communication indicates one or more of a darkness
trend or a darkness spike or a darkness plateau in the series of
darkness levels. The method further including the step of
processing the series of images to identify a feature of the
transaction card, and where the subset of the plurality of pixels
that is used to identify the darkness level of each image includes
the feature. The method where the darkness level for the at least
one image includes a weighted average of pixel values of the at
least one image, where a weight of a pixel is determined based upon
a proximity of the pixel to the feature. The method where the
plurality of pixels of the image is apportioned into a plurality of
subsets of pixels, each subset of pixels includes a subset darkness
level, and the darkness level of the image is determined using the
subset darkness levels of the plurality of subsets of pixels The
method where the step of processing each image of the series of
images to identify the complexity level of the image includes the
steps of, for each image: identifying a complexity level for each
pixel, the complexity level for each pixel corresponding to a
difference between one or more of a darkness level or a color of
the pixel and darkness levels or colors of neighboring pixels; and
determining the complexity level of the image using the complexity
levels of the plurality of pixels of the image. The method further
including the steps of: processing each image of the series of
images to identify a complexity level of the image, comparing each
identified complexity level to a predetermined complexity level
associated with the preferred distance for a near field
communication read operation between the device and the transaction
card, and automatically triggering a near field communication read
operation between the device and the transaction card to
communicate the cryptogram from the applet of the transaction card
to the device in response to the identified complexity level
corresponding to the predetermined complexity level associated with
the preferred distance for the near field communication read
operation. The method where each image of the series of images is
included of a plurality of pixels and where, for each image, a
subset of the plurality of pixels are used to identify the darkness
level for the image. The method where the darkness level for at
least one image includes an average of pixel values of the subset
of the plurality of pixels of the at least one image. The method
where the step of initiating the near field communication between
the device and the transaction card for communication of the
cryptogram from the applet of the transaction card to the device
occurs in response to receipt of a read input command at a user
interface of the device. Implementations of the described
techniques may include hardware, a method or process, or computer
software on a computer-accessible medium.
[0007] According to one general aspect, a device includes: a
front-facing camera configured to capture a series of images of a
transaction card in response to initiation of a near field
communication between the device and the transaction card for
communication of a cryptogram from an applet of the transaction
card to the device, a near field communication interface. The
device also includes a processor coupled to the front-facing camera
and the near field communication interface; a non-volatile storage
device including program code stored thereon operable when executed
upon by the processor to: process each image of the series of
images to identify a darkness level associated with a distance of
the transaction card from a front of the device, compare each
identified darkness level to a predetermined darkness level
associated with a preferred distance for a near field communication
read operation between the device and the transaction card, and
automatically trigger the near field communication read operation
between the device and the transaction card to communicate the
cryptogram from the applet of the transaction card to the device in
response to the identified darkness level corresponding to the
predetermined darkness level associated with the preferred distance
for the near field communication read operation.
[0008] Implementations may include one or more of the following
features. The device where the program code is further operable
when executed upon by the processor to: process each image of the
series of images to identify a complexity level of the image. The
device may also include compare each identified complexity level to
a predetermined complexity level associated with the preferred
distance for a near field communication read operation between the
device and the transaction card. The device may also include
automatically trigger the near field communication read operation
between the device and the transaction card for the communication
of the cryptogram from an applet of the transaction card to the
device in response to the identified complexity level corresponding
to the predetermined complexity level associated with the preferred
distance for the near field communication read operation. The
device where the program code is further operable when executed
upon by the processor to process the series of images to detect a
feature, each image is included of a plurality of pixels and a
contribution of each pixel to one or more of the darkness level or
the complexity level of the image is weighted in accordance with a
proximity of each pixel to the feature. The device further
including a user interface element configured to perform a function
when selected by a user, and where the program code is further
operable when executed upon by the processor to automatically
perform the function of the user interface element in response to
one or more of the darkness level or complexity level, and where
the function is the near field communication read operation
Implementations of the described techniques may include hardware, a
method or process, or computer software on a computer-accessible
medium.
[0009] According to one general aspect, a system includes: a
transaction card configured for near field communication, the
transaction card including a memory storing an applet including a
cryptogram; a device configured for near field communication with
the transaction card, the device including: a near field
communication interface; a camera configured to capture a series of
images of the transaction card in response to initiation of a near
field communication exchange between the device and the transaction
card, the series of images used to control the near field
communication interface to retrieve the cryptogram from the applet
of the transaction card; a processor coupled to the camera; a
non-volatile storage device including program code stored thereon
operable when executed upon by the processor to:. The system also
includes process each image of the series of images to identify a
darkness level associated with a distance of the transaction card
from the device. The system also includes compare each identified
darkness level to a predetermined darkness level associated with a
preidentified distance for a near field communication read
operation between the device and the transaction card. The system
also includes automatically trigger a near field communication read
operation between the device and the transaction card to receive a
cryptogram from an applet of the transaction card at the near field
communication interface in response to the identified darkness
level corresponding to the predetermined darkness level associated
with the preidentified distance for a near field communication read
operation. Other embodiments of this aspect include corresponding
computer systems, apparatus, and computer programs recorded on one
or more computer storage devices, each configured to perform the
actions of the methods.
[0010] Implementations may include one or more of the following
features. The system where the program code is further operable
when executed upon by the processor to: process each image of the
series of images to identify a complexity level of the image. The
system may also include automatically trigger a near field
communication read operation between the device and the transaction
card for communication of the cryptogram from the applet of the
transaction card to the device in response to the identified
complexity level corresponding to a predetermined complexity level
associated with the preidentified distance for the near field
communication read operation. Implementations of the described
techniques may include hardware, a method or process, or computer
software on a computer-accessible medium.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] FIGS. 1A and 1B are diagrams provided to illustrate an
interaction between a contactless card and a contactless card
reading device;
[0012] FIG. 2 is an illustration of an exemplary operating volume
of a Near Field Communication device;
[0013] FIG. 3 is a view of a sensor bar of a mobile phone that may
be configured to perform position alignment as disclosed
herein;
[0014] FIG. 4 is a block diagram illustrating exemplary components
of one embodiment of a device configured as disclosed herein;
[0015] FIG. 5 is a flow diagram of exemplary steps of a position
alignment system and method that may be performed by the NFC
transaction device of FIG. 4;
[0016] FIG. 6 is a detailed flow diagram illustrating exemplary
steps that may be performed to align a position of the contactless
card relative to the device;
[0017] FIG. 7 is a flow diagram illustrating exemplary steps that
may be performed to train a machine leaning model as disclosed
herein;
[0018] FIG. 8 is a flow diagram illustrating exemplary steps that
may be performed in a Simultaneous Localization and Mapping (SLAM)
process that may be used as disclosed herein;
[0019] FIG. 9 is a flow diagram illustrating exemplary steps that
may be performed to position a contactless card for NFC
communication using a combination of proximity sensors and image
capture devices of a mobile phone device;
[0020] FIG. 10 illustrates an exemplary phone/card interaction and
display during proximity sensing;
[0021] FIG. 11 illustrates an exemplary phone/card interaction and
display during position alignment;
[0022] FIGS. 12A-12C illustrate exemplary mobile phone displays
that may be provided following successful alignment for NFC
communication, including prompts for adjusting contactless card
positioning to maximize received signal strength by the mobile
device;
[0023] FIGS. 13A, 13B and 13C illustrate an exemplary phone/card
interaction as disclosed herein; and
[0024] FIG. 14 is a flow diagram of one embodiment of an exemplary
process for controlling an interface of a card reader of a device
using captured image data as disclosed herein.
DETAILED DESCRIPTION
[0025] A position alignment system and method disclosed herein
facilitates positioning of a contactless card relative to the
device, for example positioning the contactless card proximate to a
target position within a three-dimensional target volume. In one
embodiment, the position alignment system uses a proximity sensor
of the device to detect a contactless card's approach. Upon
detection of the approach, a series of images may be captured by
one or more imaging elements of the device, for example including
by a camera of the device and/or by an infrared sensor/dot
projector of the device. The series of images may be processed to
determine a position and trajectory of the card relative to the
device. The position and trajectory information may be processed by
a predictive model to identify a trajectory adjustment to reach the
target position and one or more prompts to achieve the trajectory
adjustment. Such an arrangement provides real-time positioning
assist feedback to a user using existing imaging capabilities of
mobile devices, thereby improving the speed and accuracy of
contactless card alignment and maximizing received NFC signal
strength.
[0026] According to one aspect, a triggering system may
automatically initiate a near field communication between the
device and the card to communicate a cryptogram from an applet of
the card to the device. The triggering system may operate in
response to a darkness level or change in darkness levels in the
series of images captured by the device. The triggering system may
operate in response to a complexity level or change in complexity
level in the series of images. The triggering system may
automatically trigger an operation controlled by a user interface
of the device, for example automatically triggering a read of the
card. The triggering system may be used alone or with assist of one
or more aspects of the position alignment system disclosed
herein.
[0027] These and other features of the invention will now be
described with reference to the figures, wherein like reference
numerals are used to refer to like elements throughout. With
general reference to notations and nomenclature used herein, the
detailed descriptions which follow may be presented in terms of
program processes executed on a computer or network of computers.
These process descriptions and representations are used by those
skilled in the art to most effectively convey the substance of
their work to others skilled in the art.
[0028] A process is here, and generally, conceived to be a
self-consistent sequence of operations leading to a desired result.
Processes may be implemented in hardware, software, or a
combination thereof. These operations are those requiring physical
manipulations of physical quantities. Usually, though not
necessarily, these quantities take the form of electrical, magnetic
or optical signals capable of being stored, transferred, combined,
compared, and otherwise manipulated. It proves convenient at times,
principally for reasons of common usage, to refer to these signals
as bits, values, elements, symbols, characters, terms, numbers, or
the like. It should be noted, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to those
quantities.
[0029] Further, the manipulations performed are often referred to
in terms, such as adding or comparing, which are commonly
associated with mental operations performed by a human operator. No
such capability of a human operator is necessary, or desirable in
most cases, in any of the operations described herein which form
part of one or more embodiments. Rather, the operations are machine
operations. Useful machines for performing operations of various
embodiments include general purpose digital computers or similar
devices.
[0030] Various embodiments also relate to apparatus or systems for
performing these operations. This apparatus may be specially
constructed for the required purpose, or it may comprise a
general-purpose computer as selectively activated or reconfigured
by a computer program stored in the computer. The processes
presented herein are not inherently related to a particular
computer or other apparatus. Various general-purpose machines may
be used with programs written in accordance with the teachings
herein, or it may prove convenient to construct more specialized
apparatus to perform the required method steps. The required
structure for a variety of these machines will appear from the
description given.
[0031] In the following description, for purposes of explanation,
numerous specific details are set forth in order to provide a
thorough understanding thereof. It may be evident, however, that
the novel embodiments may be practiced without these specific
details. In other instances, well-known structures and devices are
shown in block diagram form to facilitate a description thereof.
The intention is to cover all modifications, equivalents, and
alternatives consistent with the claimed subject matter.
[0032] FIGS. 1A and 1B each illustrate a mobile phone device 100
and a contactless card 150. A contactless card 150 may comprise a
payment or transaction card (hereinafter a transaction card), such
as a credit card, debit card, or gift card, issued by a service
provider. In some examples, the contactless card 150 is not related
to a transaction card, and may comprise, without limitation, an
identification card or passport. In some examples, the transaction
card may comprise a dual interface contactless transaction card.
The contactless card 150 may comprise a substrate including a
single layer, or one or more laminated layers composed of plastics,
metals, and other materials.
[0033] In some examples, the contactless card 150 may have physical
characteristics compliant with the ID-1 format of the ISO/IEC 7810
standard, and the contactless card may otherwise be compliant with
the ISO/IEC 14443 standard. However, it is understood that the
contactless card 150 according to the present disclosure may have
different characteristics, and the present disclosure does not
require a contactless card to be implemented in a transaction
card.
[0034] In some embodiments, contactless cards may include an
embedded integrated circuit device that can store, process, and
communicate data with another device, such as a terminal or mobile
device, via NFC. Commonplace uses of contactless cards include
transit tickets, bank cards, and passports. Contactless card
standards cover a variety of types as embodied in ISO/IEC 10536
(close-coupled cards), ISO/IEC 14443 (proximity cards) and ISO/IEC
15693 (vicinity cards), each of the standards incorporated by
reference herein. Such contactless cards are intended for operation
when very near, nearby and at a longer distance from associated
coupling devices, respectively.
[0035] An exemplary proximity contactless card and communication
protocol that may benefit from the positioning assist system and
method disclosed herein includes that described in U.S. patent
application(s) Ser. No. 16/205,119 filed Nov. 29, 2018, by Osborn,
et. al, entitled "Systems and Methods for Cryptographic
Authentication of Contactless Cards" and incorporated herein by
reference (hereinafter the '119 Application).
[0036] In one embodiment, the contactless card comprises NFC
interface comprised of hardware and/or software configured for
bi-directional or uni-directional contactless short-range
communications based on, for example, radio frequency
identification (RFID) standards, an EMV standard, or using NDEF
tags. The communication may use magnetic field induction to enable
communication between electronic devices, including mobile wireless
communications devices. Short-range high-frequency wireless
communications technology enables the exchange of data between
devices over a short distance, such as only a few centimeters.
[0037] NFC employs electromagnetic induction between two loop
antennas when NFC-enabled devices exchange information. ISO/IEC
14443-2:2016 (incorporated herein by reference) specifies the
characteristics for power and bi-directional communication between
proximity coupling devices (PCDs) and proximity cards or objects
(PICCs). The PCD produces a high frequency alternating magnetic
field. This field inductively couples to the PICC to transfer power
and is modulated for communication, operating within the radio
frequency ISM band of 13.56 MHz on ISO/IEC 18000-3 air interface at
rates ranging from 106 to 424 kbit/s. As specified by the ISO
standard, a PCD transmission generates a homogeneous field strength
("H") varying from at least Hmin of 1.5 A/m (rms) to Hmax of 7.5
A/m (rms) to support Class 1, Class 2 and/or Class 3 antenna
designs of PICC devices.
[0038] In FIGS. 1A and 1B, mobile phone 100 is a PCD device, and
contactless card 150 is a PICC device. During a typical contactless
card communication exchange, as shown in FIG. 1A a user may be
prompted by the mobile phone 100 to engage the card with the mobile
device, for example by including a prompt 125 indicating a card
placement location on display 130. For the purposes of this
application, `engaging` the card with the device includes, but is
not limited to, bringing the card into a spatial operating volume
of the NFC reading device (i.e., mobile phone 100), wherein the
operating volume of the NFC reading device includes the spatial
volume proximate to, adjacent to and/or around the NFC reading
device wherein the homogeneous field strength of signals
transmitted by and between the mobile device 100 and card 150 are
sufficient to support data exchange. In other words, a user may
engage a contactless card with a mobile device by tapping the card
to the front of the device or holding the card within a distance
from the front of the device that allows for NFC communication. In
FIG. 1A, the prompt 125 provided on display 130 is provided to
achieve this result. FIG. 1B illustrates the card disposed within
the operating volume for a transaction. Reminder prompts, such as
prompt 135, may be displayed to the user during a transaction as
shown in FIG. 1B.
[0039] An exemplary exchange between the phone 100 and the card 150
may include activation of the card 150 by an RF operating field of
the phone 100, transmission of a command by the phone 100 to the
card 150 and transmission of a response by the card 150 to the
phone 100. Some transactions may use several such exchanges and
some transactions may be performed using a single read operation of
a transaction card by a mobile device.
[0040] In an example, it may be appreciated that successful data
transmission may be best achieved by maintaining magnetic field
coupling throughout the transaction to a degree at least equal to
the minimum (1.5 A/m (rms)) magnetic field strength, and that
magnetic field coupling is a function of signal strength and
distance between the card 150 and the mobile phone 100. When
testing compliance of NFC enabled devices, for example, to
determine whether the power requirements (determining operating
volume), transmission requirements, receiver requirements, and
signal forms (time/frequency/modulation characteristics) of the
devices meet the ISO standards, a series of test transmissions are
made at test points within an operating volume defined by the NFC
forum analog specification.
[0041] FIG. 2 illustrates an exemplary operating volume 200
identified by the NFC analog forum for use in testing NFC enabled
devices. The operating volume 200 defines a three-dimensional
volume disposed about the contactless card reader device (e.g. a
mobile phone device) and may represent a preferred distance for a
near field communication exchange, for example for an NFC read of
the card by the device. To test NFC devices, received signals may
be measured at various test points, such as point 210, to validate
that the homogeneous field strength is within the minimum and
maximum range for the NFC antenna class.
[0042] Although the NFC standard dictates particular operating
volumes and testing methods, it will be readily appreciated that
the principles described herein are not limited to operating
volumes having particular dimensions, and the method does not
require that operating volumes be determined based upon signal
strengths of any particular protocol. Design considerations,
including but not limited to the power of a PCD device, the type of
PICC device, the intended communication between the PCD and PICC
device, the duration of communication between the PCD and PICC
device, the imaging capabilities of the PCD device, the anticipated
operating environment of the devices, historical behavior of the
user of the devices, etc., may be used to determine the operating
volume used herein. As such, any discussions below refer to a
`target volume` that may comprise, in various embodiments, the
operating volume or a subset of the operating volume.
[0043] While in FIGS. 1A and 1B the placement of the card 150 on
the phone 100 may appear straightforward, typically the sole
feedback provided to a user when card alignment is suboptimal is a
transaction failure. Contactless card EMV transactions may comprise
a series of data exchanges requiring connectivity for up to two
seconds. During such a transaction, a user juggling the card, the
NFC reading device, and any merchandise may have difficulty
locating and maintaining the target position of the card relative
to the phone to maintain the preferred distance for a successful
NFC exchange.
[0044] According to one aspect, to overcome these issues a card
alignment system and method activates imaging components of a
mobile device to capture a series of images. The series of images
may be used to locate the position and trajectory of the card in
real-time to guide the card to the preferred distance and/or target
location for an NFC exchange. The series of images may also be used
to automatically trigger an NFC exchange or operation, for example
by measuring a darkness level and/or complexity level, or patterns
thereof, in the series of captured images.
[0045] For example, using this information, the alignment method
may determine trajectory adjustments and identify prompts
associated with the trajectory adjustments for directing the card
to the target volume. The trajectory adjustment prompts may be
presented to the user using audio and/or display components of the
phone to guide the card to a target location within the target
volume and/or to initiate an NFC read. In various embodiments, a
`target location` (or `target position`) may be defined at various
granularities. For example, a target location may comprise the
entire target volume or a subset of the target volume.
Alternatively, a target location may be associated with a specific
position of the contactless card within the target volume, and/or a
space surrounding and including the specific position.
[0046] FIG. 3 is a front facing top portion 300 of one embodiment
of a mobile phone that may be configured to support the alignment
system and method disclosed herein. The phone is shown to include a
sensor panel 320 disposed along the top edge of portion 300,
although it is appreciated that many devices may include fewer or
more sensors that may be positioned differently on their devices,
and the invention is not limited to any particular type, number,
arrangement, position, or design of sensors. For example, most
phones have front facing and rear facing cameras and/or other
sensors, any of which may be used for purposes described herein for
position alignment guidance.
[0047] Sensor panel 320 is shown to include an infrared camera 302,
a flood illuminator 304, a proximity sensor 306, an ambient light
sensor 308, a speaker 310, a microphone 312, a front camera 314 and
a dot projector 316.
[0048] Infrared camera 302 may be used together with the dot
projector 316 for depth imaging. An infrared emitter of the dot
projector 316 may project up to 30,000 dots in a known pattern onto
an object, such as a user's face. The dots are photographed by
dedicated infrared camera 302 for depth analysis. Flood illuminator
304 is a light source. Proximity sensor 306 is a sensor able to
detect the presence of nearby objects without any physical
contact.
[0049] Proximity sensors are commonly used on mobile devices and
operate to lock UI input, for example, to detect (and skip)
accidental touchscreen taps when mobile phones are held to the ear.
An exemplary proximity sensor operates by emitting an
electromagnetic field or a beam of electromagnetic radiation
(infrared, for instance) at a target, and measuring the reflected
signal received from the target. The design of a proximity sensor
may vary depending upon a target's composition; capacitive
proximity sensors or photoelectric sensors may be used to detect a
plastic target, and inductive proximity sensor may be used to
detect a metal target. It is appreciated that other methods of
determining proximity are within the scope of this disclosure, and
the present disclosure is not limited to a proximity sensor that
operates by emitting an electromagnetic field.
[0050] The top portion 300 of the phone also is shown to include an
ambient light sensor 308 used, for example, to control the
brightness of a display of the phone. Speaker 310 and microphone
312 enable basic phone functionality. Front camera 314 may be used
for two dimensional and/or three-dimensional image capture as
described in more detail below.
[0051] FIG. 4 is a block diagram of representative components of a
mobile phone or other NFC capable device incorporating elements
facilitating card position alignment as disclosed herein. The
components include interface logic 440, one or more processors 410,
a memory 430, display control 435, network interface logic 440 and
sensor control 450 coupled via system bus 420.
[0052] Each of the components performs particular functions using
hardware, software or a combination thereof. Processor(s) 410 may
comprise various hardware elements, software elements, or a
combination of both. Examples of hardware elements may include
devices, logic devices, components, processors, microprocessors,
circuits, processor circuits, circuit elements (e.g., transistors,
resistors, capacitors, inductors, and so forth), integrated
circuits, application specific integrated circuits (ASIC),
programmable logic devices (PLD), digital signal processors (DSP),
field programmable gate array (FPGA), Application-specific Standard
Products (ASSPs), System-on-a-chip systems (SOCs), Complex
Programmable Logic Devices (CPLDs), memory units, logic gates,
registers, semiconductor device, chips, microchips, chip sets, and
so forth. Examples of software elements may include software
components, programs, applications, computer programs, application
programs, system programs, software development programs, machine
programs, operating system software, middleware, firmware, software
modules, routines, subroutines, functions, methods, procedures,
processes, software interfaces, application program interfaces
(API), instruction sets, computing code, computer code, code
segments, computer code segments, words, values, symbols, or any
combination thereof. Determining whether an embodiment is
implemented using hardware elements and/or software elements may
vary in accordance with any number of factors, such as desired
computational rate, power levels, heat tolerances, processing cycle
budget, input data rates, output data rates, memory resources, data
bus speeds and other design or performance constraints, as desired
for a given implementation.
[0053] Image processor 415 may be a any processor or alternatively
may be a specialized digital signal processor (DSP) used for image
processing of data received from the camera(s) 452, infrared sensor
controller 455, proximity sensor controller 457 and dot projector
controller 459. The image processor 415 may employ parallel
computing even with SIMD (Single Instruction Multiple Data) or MIMD
(Multiple Instruction Multiple Data) technologies to increase speed
and efficiency. In some embodiments, the image processor may
comprise a system on a chip with multi-core processor architecture
enabling high speed, real-time image processing capabilities.
[0054] Memory 430 may comprise a computer-readable storage medium
to store program code (such as alignment unit program code 432 and
payment processing program code 433) and data 434. Memory 430 may
also store user interface program code 436. The user interface
program code 436 may be configured to interpret user input received
at user interface elements including physical elements such as
keyboards and touchscreens 460. The user interface program code 436
may also interpret user input received from graphical user
interface elements such as buttons, menus, icons, tabs, windows,
widgets etc. that may be displayed on a user display under control
of display control 435. According to one aspect, and as described
in more detail below, memory 430 may also store triggering program
code 431. Triggering program code 431 may be used to automatically
trigger NFC communications between the device and a card, for
example in response to determined darkness levels and/or complexity
levels of a series of images captured by cameras 452 or other
sensor devices. In some embodiments, operations that are
automatically triggered may be those generally performed as a
response to user input, for example automatically triggering a read
operation that is generally initiated by activation of a user
interface element such as a read button provided on a graphic user
interface. Automatic triggering reduces delays and inaccuracies
associated with using user interface elements to control NFC
communications.
[0055] Examples of a computer-readable storage medium may include
any tangible media capable of storing electronic data, including
volatile memory or non-volatile memory, removable or non-removable
memory, erasable or non-erasable memory, writeable or re-writeable
memory, and so forth. Program code may include executable computer
program instructions implemented using any suitable type of code,
such as source code, compiled code, interpreted code, executable
code, static code, dynamic code, object-oriented code, visual code,
and the like. Embodiments may also be at least partly implemented
as instructions contained in or on a non-transitory
computer-readable medium, which may be read and executed by one or
more processors to enable performance of the operations described
herein.
[0056] Alignment unit program code 432 comprises program code as
disclosed herein for positioning assist for contactless card/phone
communications. The alignment unit program code 432 may be used by
any service provided by the phone that uses contactless card
exchanges for authentication or other purposes. For example,
services such as payment processing services, embodied in payment
processing program code 433 may use contactless card exchanges for
authentication during initial stages of a financial
transaction.
[0057] The system bus 420 provides an interface for system
components including, but not limited to, the memory 430 and to the
processors 410. The system bus 420 may be any of several types of
bus structure that may further interconnect to a memory bus (with
or without a memory controller), a peripheral bus, and a local bus
using any of a variety of commercially available bus
architectures.
[0058] Network Interface logic includes transmitters, receivers,
and controllers configured to support various known protocols
associated with different forms of network communications. Example
network interfaces that may be included in a mobile phone
implementing the methods disclosed herein include, but are not
limited to a WIFI interface 442, an NFC interface 444, a Bluetooth
Interface 446 and a Cellular Interface 448.
[0059] Sensor control 450 comprises a subset of sensors that may
support the position alignment methods disclosed herein, including
camera(s) 452 (which may include camera technology for capturing
two dimensional and three dimensional light based or infrared
images) an infrared sensor 454 and associated infrared sensor
controller 455, a proximity sensor 456 and associated proximity
sensor controller 457 and a dot projector 458 and associated dot
projector controller 459.
[0060] Referring now to FIG. 5, a flow diagram is shown of an
exemplary process 500 for contactless card positioning using image
information obtained in real-time from sensors of the NFC reading
device. The process includes detecting contactless card proximity
at step 510 and, upon detection, triggering image capture at step
515 using imaging capabilities of the device and processing the
captured series of images at step 520. Processing the images may be
performed at least in part by alignment unit program code and may
include locating the contactless card within a target volume
proximate to the device and determining the trajectory of the card
at step 525. Processing the images may also include, at step 535,
predicting a trajectory adjustment for aligning the card with a
target position within the target volume, identifying a prompt for
achieving the trajectory adjustment and displaying the prompt on
the device. The prompt may include one or more of instructions (in
text or symbol form), images, including one or more of the captured
images, colors, color patterns, sounds and other mechanisms.
[0061] The process of capturing images at 515 and processing images
at 520 continues until it is determined that the contactless card
is in its target position (and/or a preferred distance from the
device) at step 540. The alignment process may then initiate, or
cause to be initiated, the data exchange transaction/communication
between the card and the device at step 545. For example, the
alignment process may perform one or more of providing a display
prompt to a user to cause the user to initiate the transaction.
Alternatively, the alignment process may automatically initiate the
data exchange process when alignment is detected at step 540. In
embodiments which use NFC interface technology, the alignment
process may turn on the NFC interface to enable the NFC
communication, and at step 550 the NFC communication is
executed.
[0062] FIG. 6 is a flow diagram of a first exemplary embodiment of
a position alignment process 600 that processes captured images
using machine-learning predictive models to extract features,
locate the card in a three-dimensional target volume, and to
determine a card trajectory. The system may also use
machine-learning predictive models to identify trajectory
adjustments to move the card to a target position within the target
volume and to identify prompts to achieve the trajectory
adjustment.
[0063] At step 605, a phone monitors reflected energy emitted by
and reflected back to the device, including detecting that the card
is proximate to the device when the reflected energy exceeds a
threshold by a proximity sensor. In some phones, the proximity
sensor may be implemented using a light sensor chip. Common light
sensor chips include the ISL29003/23 & GP2A by Intersil &
Sharp respectively. Both these sensor-chips are primarily active
light sensors, which provide the ambient light intensity in LUX
units. Such sensors are implemented as Boolean sensors. Boolean
sensors return two values, "NEAR" & "FAR." Thresholding is
based on the LUX value, i.e. the LUX value of the light sensor is
compared with a threshold. A LUX-value more than threshold means
the proximity sensor returns "FAR." Anything less than the
threshold value and the sensor returns "NEAR." The actual value of
the threshold is custom-defined depending on the sensor-chip in use
and its light-response, the location & orientation of the chip
on the smart-phone body, the composition and reflective response of
the target contactless card, etc.
[0064] At step 610, responsive to the card being proximate to the
device, the device initiates image capture. Image capture may
include capturing two-dimensional images using one or more of the
cameras accessible on the device. The two-dimensional images may be
captured by one or both of visible light and infrared cameras. For
example, some mobile devices may include a rear-facing camera
capable of shooting high-dynamic range (HDR) photos.
[0065] Certain mobile devices may include dual cameras which
capture images along different imaging planes to create a
depth-of-field effect. Some may further include a "selfie" infrared
camera or may include an infrared emitter technology, for example
for projecting a dots matrix of infrared light in a known pattern
onto a target. Those dots may then be photographed by the infrared
camera for analysis.
[0066] The captured images from any one or more of the above
sources, and/or subsets of or various combinations of the captured
images, may then be forwarded to steps 615 and 620 for image
processing and contactless card localization, including determining
a position and trajectory of the contactless card.
[0067] According to one aspect, image processing includes building
a volume map of a target volume proximate to the phone, including
an area proximate to and/or including at least a portion of an
operating volume of an NFC interface of the phone, wherein a volume
map is represented as a three-dimensional array of voxels storing
values related to color and/or intensity of the voxel within a
visible or infrared spectrum. In some embodiments, a voxel is a
discrete element in an array of elements of volume that constitute
a notional three-dimensional space, for example each of an array of
discrete elements into which a representation of a
three-dimensional object is divided.
[0068] According to one aspect, position alignment includes
processing the voxels of the target volume to extract features of
the contactless card to determine a position of the card within the
target volume and comparing voxels of target volumes constructed at
different points in time to track the movement of the card over
time to determine a card trajectory. Various processes may be used
to track position and trajectory, including using machine learning
models and alternatively using SLAM techniques, each now described
in more detail below.
[0069] Machine learning is a branch of artificial intelligence that
relates to mathematical models that can learn from, categorize, and
make predictions about data. Such mathematical models, which may be
referred to as machine-learning models, can classify input data
among two or more classes; cluster input data among two or more
groups; predict a result based on input data; identify patterns or
trends in input data; identify a distribution of input data in a
space; or any combination of these. Examples of machine-learning
models can include (i) neural networks; (ii) decision trees, such
as classification trees and regression trees; (iii) classifiers,
such as Naive bias classifiers, logistic regression classifiers,
ridge regression classifiers, random forest classifiers, least
absolute shrinkage and selector (LASSO) classifiers, and support
vector machines; (iv) clusterers, such as k-means clusterers,
mean-shift clusterers, and spectral clusterers; (v) factorizers,
such as factorization machines, principal component analyzers and
kernel principal component analyzers; and (vi) ensembles or other
combinations of machine-learning models. In some examples, neural
networks can include deep neural networks, feed-forward neural
networks, recurrent neural networks, convolutional neural networks,
radial basis function (RBF) neural networks, echo state neural
networks, long short-term memory neural networks, bi-directional
recurrent neural networks, gated neural networks, hierarchical
recurrent neural networks, stochastic neural networks, modular
neural networks, spiking neural networks, dynamic neural networks,
cascading neural networks, neuro-fuzzy neural networks, or any
combination of these.
[0070] Different machine-learning models may be used
interchangeably to perform a task. Examples of tasks that may be
performed at least partially using machine-learning models include
various types of scoring; bioinformatics; cheminformatics; software
engineering; fraud detection; customer segmentation; generating
online recommendations; adaptive websites; determining customer
lifetime value; search engines; placing advertisements in real time
or near real time; classifying DNA sequences; affective computing;
performing natural language processing and understanding; object
recognition and computer vision; robotic locomotion; playing games;
optimization and metaheuristics; detecting network intrusions;
medical diagnosis and monitoring; or predicting when an asset, such
as a machine, will need maintenance.
[0071] Machine-learning models may be constructed through an at
least partially automated (e.g., with little or no human
involvement) process called training. During training, input data
may be iteratively supplied to a machine-learning model to enable
the machine-learning model to identify patterns related to the
input data or to identify relationships between the input data and
output data. With training, the machine-learning model may be
transformed from an untrained state to a trained state. Input data
may be split into one or more training sets and one or more
validation sets, and the training process may be repeated multiple
times. The splitting may follow a k-fold cross-validation rule, a
leave-one-out-rule, a leave-p-out rule, or a holdout rule.
[0072] According to one embodiment, a machine learning model may be
trained to identify features of a contactless card as it approaches
an NFC reading device using image information captured by one or
more imaging elements of the device, and the feature information
may be used to identify a position and trajectory of the card
within the target volume.
[0073] An overview of training and use method 700 of a
machine-learning model for position and trajectory identification
will now be described below with respect to the flow chart of FIG.
7. In block 704, training data may be received. In some examples,
the training data may be received from a remote database or a local
database, constructed from various subsets of data, or input by a
user. The training data may be used in its raw form for training a
machine-learning model or pre-processed into another form, which
can then be used for training the machine-learning model. For
example, the raw form of the training data may be smoothed,
truncated, aggregated, clustered, or otherwise manipulated into
another form, which can then be used for training the
machine-learning model. In embodiments, the training data may
include communication exchange information, historical
communication exchange information, and/or information relating to
the communication exchange. The communication exchange information
may be for a general population and/or specific to a user and user
account in a financial institutional database system. For example,
for position alignment, training data may include processing image
data comprising contactless cards in different orientations and
from different perspectives to learn the voxel values of features
of the card at those orientations and perspectives. For trajectory
adjustment and prompt identification, such training data may
include data relating to the impact of trajectory adjustments to
the card when at different locations. The machine learning model
may be trained to identify prompts by measuring the effectiveness
of prompts at achieving the trajectory adjustment, wherein the
effectiveness may be measured in one embodiment by time to card
alignment.
[0074] In block 706, a machine-learning model may be trained using
the training data. The machine-learning model may be trained in a
supervised, unsupervised, or semi-supervised manner. In supervised
training, each input in the training data may be correlated to a
desired output. The desired output may be a scalar, a vector, or a
different type of data structure such as text or an image. This may
enable the machine-learning model to learn a mapping between the
inputs and desired outputs. In unsupervised training, the training
data includes inputs, but not desired outputs, so that the
machine-learning model must find structure in the inputs on its
own. In semi-supervised training, only some of the inputs in the
training data are correlated to desired outputs.
[0075] In block 708, the machine-learning model may be evaluated.
For example, an evaluation dataset may be obtained, for example,
via user input or from a database. The evaluation dataset can
include inputs correlated to desired outputs. The inputs may be
provided to the machine-learning model and the outputs from the
machine-learning model may be compared to the desired outputs. If
the outputs from the machine-learning model closely correspond with
the desired outputs, the machine-learning model may have a high
degree of accuracy. For example, if 90% or more of the outputs from
the machine-learning model are the same as the desired outputs in
the evaluation dataset, e.g., the current communication exchange
information, the machine-learning model may have a high degree of
accuracy. Otherwise, the machine-learning model may have a low
degree of accuracy. The 90% number may be an example only. A
realistic and desirable accuracy percentage may be dependent on the
problem and the data.
[0076] In some examples, if the machine-learning model has an
inadequate degree of accuracy for a particular task, the process
can return to block 706, where the machine-learning model may be
further trained using additional training data or otherwise
modified to improve accuracy. If the machine-learning model has an
adequate degree of accuracy for the particular task, the process
can continue to block 710.
[0077] At this point in time, the machine learning model(s) have
been trained using a training data set to: process the captured
images to determine a position and trajectory, predict a projected
position of the card relative to the device based on the current
position and trajectory, identify at least one trajectory
adjustment and one or more prompts to achieve the trajectory
adjustment.
[0078] In block 710, new data is received. For example, new data
may be received during position alignment for each contactless card
communication exchange. In block 712, the trained machine-learning
model may be used to analyze the new data and provide a result. For
example, the new data may be provided as input to the trained
machine-learning model. As new data is received, the results of
feature extraction prediction, position and trajectory prediction
may be continually tuned to minimize a duration of the alignment
process.
[0079] In block 714, the result may be post-processed. For example,
the result may be added to, multiplied with, or otherwise combined
with other data as part of a job. As another example, the result
may be transformed from a first format, such as a time series
format, into another format, such as a count series format. Any
number and combination of operations may be performed on the result
during post-processing.
[0080] Simultaneous Localization and Mapping (SLAM) has become
well-defined in the robotics community for on the fly
reconstruction of 3D image space. For example, "MonoSLAM: Real-Time
Single Camera SLAM" by Davidson et. al, IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 29, No. 6, 2007
(incorporated herein by reference), focusses on localization and
presents a real-time algorithm which can recover the 3D trajectory
of a monocular camera, moving rapidly through a previously unknown
scene. According to one aspect it is realized that the techniques
described by Davidson for camera tracking may be leveraged for use
in the position alignment system and method disclosed herein.
Rather than track the advancement of the card to the phone, as
described above, SLAM techniques may be used to track the
advancement of the camera of the phone to the detected features of
the card to achieve a similar result of positioning the card
relative to the phone.
[0081] Referring now to FIG. 8, a flow diagram illustrating
exemplary steps of a MonoSLAM method 800 for contactless card
localization, that may be used to perform the functions of steps
615 and 620 of FIG. 6 will now be described. The technique
disclosed by Davidson, constructs a persistent map of scene
landmarks to be referenced indefinitely in a state-based framework.
Forming a persistent map may be advantageous when camera motion is
restricted, and thus SLAM techniques may be beneficial to position
alignment processes focused on a particular object such as a
contactless card. Use of the persistent map enables the processing
requirement of the algorithm to be bounded and continuous real-time
operation may be maintained.
[0082] SLAM allows for on the-fly probabilistic estimation of the
state of the moving camera and its map to limit predictive searches
using the running estimates to guide efficient processing.
[0083] At step 810, an initial probabilistic feature-based map may
be generated, representing at any instant a snapshot of the current
estimates of the state of the camera and all features of interest
and, the uncertainty in these estimates. The map may be initialized
at system start-up and persists until operation ends but may evolve
continuously and dynamically as it is updated over time with new
image information. The estimates of the probabilistic state of the
camera and features are updated during relative camera/card motion
and feature observation. When new features are observed the map may
be enlarged with new states and, if necessary, features can also be
deleted. However, it is appreciated that, once the features of the
contactless card may be identified with a high probabilistic
certainty, further image processing can limit subsequent searches
to the located feature.
[0084] The probabilistic character of the map lies in the
propagation over time not only of the mean "best" estimates of the
states of the camera/card but a first order uncertainty
distribution describing the size of possible deviations from these
values. Mathematically, the map may be represented by a state
vector and covariance matrix P. State vector x{circumflex over ( )}
may be composed of the stacked state estimates of the camera and
features and P may be a square matrix of equal dimension which may
be partitioned into submatrix elements as shown in Equation I
below:
x ^ = ( x ^ u y ^ 1 y ^ 2 ) , P = [ P xx P xy 1 P xy 2 P y 1 x P y
1 y 1 P y 1 y 2 P y 2 x P y 2 y 1 P y 2 y 2 ] . Equation I
##EQU00001##
[0085] The resulting probability distribution over all map
parameters may be approximated as a single multivariate Gaussian
distribution in a space of dimension equal to the total state
vector size. Explicitly, the camera's state vector xv comprises a
metric 3D position vector r.sup.W, orientation quaternion q.sup.RW,
velocity vector v.sup.W, and angular velocity vector .omega..sup.R
relative to a fixed world frame W and "robot" frame R carried by
the camera (13 parameters) as shown in Equation II below:
x v = ( r W q WR v W .omega. R ) . Equation II ##EQU00002##
[0086] Where feature states y.sub.i are the 3D position vectors of
the locations of point features; according to one aspect, the point
features may include features of the contactless card. The role of
the map 825 permits real-time localization capturing a sparse set
of high-quality landmarks. Specifically, each landmark may be
assumed to correspond to a well-localized point feature in 3D
space. The camera may be modeled as a rigid body needing
translation and rotation parameters to describe its position, and
we also maintain estimates of its linear and angular velocity.
According to one aspect, the camera modeling herein may be
translated relative to the extracted feature (i.e., the contactless
card) to define the translational and rotational parameters of card
movement to maintain linear and angular card velocity relative to
the phone.
[0087] In one embodiment, Davison employs relative larger
(11.times.11 pixel) image patches to serve as long-term landmark
features at step 830. Camera localization information may be used
to improve matching over camera displacements and rotations.
Salient image regions may be originally detected automatically
(i.e., based on card attributes) using, for example, techniques
described in J. Shi and C. Tomasi, "Good Features to Track," Proc.
IEEE Conf. Computer Vision and Pattern Recognition, pp. 593-600,
1994 (incorporated herein by reference) which provides for
repeatable visual landmark detection. Once the 3D location,
including depth, of a feature, has been fully initialized, each
feature may be stored as an oriented planar texture. When making
measurements of a feature from new (relative) camera positions, its
patch may be projected from 3D to the image plane to produce a
template for matching with the real image. Saved feature templates
are preserved over time to enable remeasurement of the locations of
features over arbitrarily long time periods to determine feature
trajectory.
[0088] According to one embodiment, a constant velocity, constant
angular velocity model may be used that assumes that the camera
moves at a constant velocity over all time with undetermined
accelerations occurring within a Gaussian profile. Although this
model imparts a certain smoothness to the relative card/camera
motion, it imparts robustness to systems using sparse visual
measurements. In one embodiment, a predicted position of an image
feature (i.e., a predicted card location) may be determined before
searching for the feature within the SLAM map.
[0089] One aspect of Davison's approach involves predicting feature
position at 850 and limiting image review to the predicted feature
position. Feature matching between image frames itself may be
carried out using a straightforward normalized cross-correlation
search for the template patch projected into the current camera
estimate; the template may be scanned over the image and tested for
a match, starting at a predicted location, until a peak is found.
Sensible confidence bound assumptions focus image processing
efforts, enabling image processing to be performed in real-time, at
high frame-rates by limiting searching to tiny search regions of
incoming images using the sparse map.
[0090] In one embodiment, predicting position may be performed as
follows. First, using the estimates x.sub.v of camera position and
y.sub.i of feature position, the position of a point feature
relative to the camera is expected to be as shown in Equation III
below:
h.sub.L.sup.R=R.sup.RW(y.sub.i.sup.W-r.sup.W). Equation III:
[0091] With a perspective camera, the position (u,v) at which the
feature would be expected to be found in the image is found using
the standard pinhole model shown in Equation IV below:
h i = ( u v ) = ( v 0 - fk u h kv R h kv R v 0 - fk v h kv R h kv R
) , ##EQU00003##
[0092] Where fk.sub.u, fk.sub.v, u.sub.0 and v.sub.0 comprise
standard camera calibration parameters. This method enables active
control of the viewing direction toward profitable measurements
having high innovation covariance, enabling limitation the maximum
number of feature searches per frame to the 10 or 12 most
informative.
[0093] According to one aspect, it is thus appreciated that
performance benefits associated with SLAM, including the ability to
perform real-time localization of the contactless card while
limiting extraneous image processing, would be advantageous to a
position alignment system disclosed herein.
[0094] Referring back to FIG. 6, once position and trajectory
information may be obtained via either a machine learning model,
SLAM technique or other method, according to one aspect the
position alignment system and method include a process 625 for
predicting a trajectory adjustment and associated prompt to guide
the card to a target position within the target volume. According
to one aspect, the prediction may be performed using a predictive
model, such as a machine learning model trained and maintained
using machine learning principles described above, to identify
trajectory adjustments and prompts based on the effectiveness of
previous trajectory adjustments and prompts, and thereby be
customized by user behavior. The trajectory adjustments may be
determined, for example, by identifying a variance between a target
position and a predicted position and selecting the adjustment to
the current trajectory to minimize the variance. Effectiveness may
be measured in a variety of manners, including but not limited to
the duration of the position alignment process. For example, in
some embodiments, artificial intelligence, neural networks or other
aspects of a machine-learning model may self-select those prompts
most effective for assisting the user to achieve the end result of
card alignment.
[0095] In some embodiments, it is envisioned that trajectory
adjustments may be linked to a set of one or more prompts
configured to achieve the associated trajectory adjustment. The set
of one or more prompts may include audible and visual prompts and
may be in the form of one or more of instructions (in text or
symbol form), images, including one or more of the captured images,
colors, color patterns, sounds and other mechanisms that are
displayed by the device. In some embodiments, an effectiveness
value may be stored for each prompt, where the effectiveness value
relates to the historic reaction and effect of display of such
prompt to achieve the trajectory adjustment. The effectiveness
value may be used by the machine-learning model to select one or
more of a trajectory adjustment and/or prompt to guide the card to
the target location.
[0096] At step 630, the prompts may be displayed on the display of
the phone. At step 635, the process continues capturing image
information, determining positions and trajectories, identifying
trajectory adjustments and displaying prompts until at step 635 it
may be determined that the variances between the target position
and the predicted position are within a predetermined threshold.
The predetermined threshold is a matter of design choice and may
vary in accordance with one or more of the target volume, the NFC
antennas, etc.
[0097] Once it is determined at step 635 that the variance is
within a threshold, the card may be considered aligned, and at step
630 the NFC mobile device may be triggered at step 640 to initiate
a communication exchange with the card.
[0098] According to one aspect, the data exchange may be a
cryptogram data exchange as described in the '119 Application.
During a cryptogram exchange, after communication has been
established between the phone and the contactless card, the
contactless card may generate a message authentication code (MAC)
cryptogram in accordance with the NFC Data Exchange Format. In
particular, this may occur upon a read, such as an NFC read, of a
near field data exchange (NDEF) tag, which may be created in
accordance with the NFC Data Exchange Format. For example an
application being executed by the device 100 (FIG. 1A) may transmit
a message to the contactless card 150 (FIG. 1A), such as an applet
select message, with the applet ID of an NDEF producing applet,
where the applet may be an applet stored in a memory of the
contactless card and operable when executed upon by processing
components of the contactless card to produce the NDEF tag. Upon
confirmation of the selection, a sequence of select file messages
followed by read file messages may be transmitted. For example, the
sequence may include "Select Capabilities file", "Read Capabilities
file", and "Select NDEF file". At this point, a counter value
maintained by the contactless card may be updated or incremented,
which may be followed by "Read NDEF file."
[0099] At this point, the message may be generated which may
include a header and a shared secret. Session keys may then be
generated. The MAC cryptogram may be created from the message,
which may include the header and the shared secret. The MAC
cryptogram may then be concatenated with one or more blocks of
random data, and the MAC cryptogram and a random number (RND) may
be encrypted with the session key. Thereafter, the cryptogram and
the header may be concatenated, and encoded as ASCII hex and
returned in NDEF message format (responsive to the "Read NDEF file"
message).
[0100] In some examples, the MAC cryptogram may be transmitted as
an NDEF tag, and in other examples the MAC cryptogram may be
included with a uniform resource indicator (e.g., as a formatted
string).
[0101] In some examples, application may be configured to transmit
a request to contactless card, the request comprising an
instruction to generate a MAC cryptogram, and the contactless card
sends the MAC cryptogram to the application.
[0102] In some examples, the transmission of the MAC cryptogram
occurs via NFC, however, the present disclosure is not limited
thereto. In other examples, this communication may occur via
Bluetooth, Wi-Fi, or other means of wireless data
communication.
[0103] In some examples, the MAC cryptogram may function as a
digital signature for purposes of verification. For example, in one
embodiment the MAC cryptogram may be generated by devices
configured to implement key diversification using counter values.
In such systems, a transmitting device and receiving device may be
provisioned with the same master symmetric key. In some examples,
the symmetric key may comprise the shared secret symmetric key
which may be kept secret from all parties other than the
transmitting device and the receiving device involved in exchanging
the secure data. It is further understood that both the
transmitting device and receiving device may be provided with the
same master symmetric key, and further that part of the data
exchanged between the transmitting device and receiving device
comprises at least a portion of data which may be referred to as
the counter value. The counter value may comprise a number that
changes each time data is exchanged between the transmitting device
and the receiving device. In addition, the transmitting device and
receiving device may use an appropriate symmetric cryptographic
algorithm, which may include at least one of a symmetric encryption
algorithm, HMAC algorithm, and a CMAC algorithm. In some examples,
the symmetric algorithm used to process the diversification value
may comprise any symmetric cryptographic algorithm used as needed
to generate the desired length diversified symmetric key.
Non-limiting examples of the symmetric algorithm may include a
symmetric encryption algorithm such as 3DES or AES128; a symmetric
HMAC algorithm, such as HMAC-SHA-256; and a symmetric CMAC
algorithm such as AES-CMAC.
[0104] In some embodiments, the transmitting device may take the
selected cryptographic algorithm, and using the master symmetric
key, process the counter value. For example, the sender may select
a symmetric encryption algorithm, and use a counter which updates
with every conversation between the transmitting device and the
receiving device. The transmitting device may then encrypt the
counter value with the selected symmetric encryption algorithm
using the master symmetric key, creating a diversified symmetric
key. The diversified symmetric key may be used to process the
sensitive data before transmitting the result to the receiving
device. The transmitting device may then transmit the protected
encrypted data, along with the counter value, to the receiving
device for processing.
[0105] The receiving device may first take the counter value and
then perform the same symmetric encryption using the counter value
as input to the encryption, and the master symmetric key as the key
for the encryption. The output of the encryption may be the same
diversified symmetric key value that was created by the sender. The
receiving device may then take the protected encrypted data and
using a symmetric decryption algorithm along with the diversified
symmetric key, decrypt the protected encrypted data to reveal the
original sensitive data. The next time sensitive data needs to be
sent from the sender to the recipient via respective transmitting
device and receiving device, a different counter value may be
selected producing a different diversified symmetric key. By
processing the counter value with the master symmetric key and same
symmetric cryptographic algorithm, both the transmitting device and
receiving device may independently produce the same diversified
symmetric key. This diversified symmetric key, not the master
symmetric key, may be used to protect the sensitive data.
[0106] In some examples, the key diversification value may comprise
the counter value. Other non-limiting examples of the key
diversification value include: a random nonce generated each time a
new diversified key is needed, the random nonce sent from the
transmitting device to the receiving device; the full value of a
counter value sent from the transmitting device and the receiving
device; a portion of a counter value sent from the transmitting
device and the receiving device; a counter independently maintained
by the transmitting device and the receiving device but not sent
between the two devices; a one-time-passcode exchanged between the
transmitting device and the receiving device; and a cryptographic
hash of the sensitive data. In some examples, one or more portions
of the key diversification value may be used by the parties to
create multiple diversified keys. For example, a counter may be
used as the key diversification value. Further, a combination of
one or more of the exemplary key diversification values described
above may be used.
[0107] FIG. 9 is a flow diagram 900 that illustrates the use of the
position alignment system disclosed herein to align a contactless
card with an NFC mobile device equipped with a proximity sensor and
imaging hardware and software. At step 905 the position alignment
logic detects a request by the device to perform a communication
exchange. At step 910 the position alignment logic measures, they
are using a proximity sensor of the device, a reflected energy
emitted by and reflected to the device including determining when
the reflected energy exceeds a predetermined threshold indicative
of a proximity of the card to the device.
[0108] FIG. 10 illustrates a contactless card 1030 approaching an
operating volume 1020 of a proximity sensor 1015 of a phone 1010.
As the phone enters the operating volume 1020, in one embodiment an
infrared beam emitted by the proximity sensor 1015 reflects back to
the proximity sensor 1015 as signal R 1035. As the card moves
closer to the operating volume of the phone, the reflected signal
strength increases until a triggering threshold is reached, at
which point the proximity sensor indicates that the card is `NEAR`.
In some embodiments, during the proximity search a display 1050 of
the phone may prompt the user, for example by providing notice that
it is searching for the card as shown in FIG. 10, by providing
visual or audible instruction, or the like.
[0109] At step 915 (FIG. 9), when the proximity sensor is
triggered, the position alignment logic controls at least one of a
camera and an infrared depth sensor of the device to capture a
series of images of a three-dimensional volume proximate to the
device when the reflected energy exceeds a predetermined threshold.
Depending upon the location of the NFC reader and the location of
the cameras on the phone, it may be appreciated that cameras may be
selected for image capture which comprise an operating volume that
overlaps at least a portion of an operating volume of the NFC
interface of the phone.
[0110] At step 920 the position alignment logic processes the
captured plurality of images to determine a position and trajectory
of the card in the three-dimensional volume proximate to the
device. As described previously, the processing may be performed by
one or both of a machine learning model trained using historic
attempts to guide the card to the goal position and a Simultaneous
Localization and Mapping (SLAM) process. At step 925 the position
alignment process predicts a projected position of the card
relative to the device based on the position and the trajectory of
the card and at step 930 identifies one or more variances between
the projected position and the target position including
identifying at least one trajectory adjustment selected to reduce
the one or more variances and identifying one or more prompts to
achieve the trajectory adjustments and, at step 935 the position
alignment process displays the one or more prompts on a display of
the device.
[0111] FIG. 11 illustrates an exemplary display 1105 of a phone
1110 that captures image information related to a card 1150 within
a target volume 1120. The display 1105 may include a number of
prompts, such as position prompt 1115 associated with a target
position, image prompt 1130 and arrow prompts 1140 that may be
displayed to a user to assist guidance of the card 1150 to the
target position. The image prompt 1130 may include, for example, a
portion of the images captured by the imaging components of the
phone 1110 during position alignment and may be beneficial to a
user to assist the user's understanding of their movements relative
to the target. The arrows 1140 may provide directional assistance,
for example as shown in FIG. 11 motioning the user to adjust the
card upward for proper alignment. Other types of prompts may also
be used, including but not limited to textual instructions, symbols
and/or emojis, audible instructions, color based guidance (i.e.,
displaying a first color (such as red) to the user when the card is
relatively far from the target, and transitioning the screen to
green as the card becomes aligned).
[0112] At step 940 (FIG. 9) the position alignment process may
repeat the steps of capturing image information, determining the
position and trajectory of the card, predicting the projected
position of the card, identifying the one or more variances, the at
least one trajectory adjustment and the one or more prompts and
displaying the one or more prompts until the one or more variances
are within a predetermined threshold. At step 945, the position
alignment process may trigger a read of the card by a card reader
of the device when the variances are less than the predetermined
threshold. In some embodiments, the position alignment process may
continue to operate during the data exchange between the card and
the mobile device, for example to provide prompts that adjust the
position of the card should it move during the read.
[0113] FIGS. 12A, 12B and 12C are examples of display prompts that
may be provided by the position alignment process once alignment is
detected. In FIG. 12A, prompt 1220 may be provided to notify a user
when the card is aligned with the target position. In some
embodiments, the interface may provide a link such as link 1225 to
enable a user to initiate a card read by the phone. In other
embodiments, alignment may automatically trigger the card read.
[0114] In FIG. 12B, during the card read process, a prompt may be
provided to the user, for example a countdown prompt 1230. In
addition, additional prompts, for example such as arrow 1240, may
be provided to enable a user to correct any movement that may have
occurred to the card during the read, to ensure that connectivity
is not lost and to improve the rate of success of the NFC
communication. Following the read, as shown in FIG. 12C, the
display provides a notification 1250 to the user regarding the
success or failure of the communication exchange
[0115] Accordingly, a position alignment system and method has been
shown and described that facilitates positioning of a contactless
card in a preferred location in a target volume relative to a
contactless card reading device. Alignment logic uses information
captured from available imaging devices such as infrared proximity
detectors, cameras, infrared sensors, dot projectors, and the like
to guide the card to a target location. The captured image
information may be processed to identify a card position,
trajectory and predicted location using one or both of a machine
learning model and/or a Simultaneous Localization and Mapping
logic. Trajectory adjustment and prompt identification may be
intelligently controlled and customized using machine-learning
techniques to customize guidance based on the preference and/or
historical behavior of the user. As a result, the speed and
accuracy of contactless card alignment is improved and received NFC
signal strength is maximized, thereby reducing the occurrence of
dropped transactions.
[0116] The above techniques have discussed various methods for
guiding placement of the contactless card to a desired position
relative to a card reader interface of the device, once proximity
of the card is initially detected using a proximity sensor.
However, it is appreciated that the principles disclosed herein may
be expanded to augment, or replace altogether, proximity sensor
information using captured image data to detect card proximity. The
captured image information may further be processed to determine
when the card is in a particular position relative to the card
reader interface, and to automatically perform an operation
associated with a user interface element, e.g., automatically
triggering an NFC read operation or other function by the mobile
device without waiting for user input. Such an arrangement enables
automatic triggering of capabilities without requiring user input,
to control the operations, for example bypassing the need for human
interaction with user interface elements of the device.
[0117] According to one aspect, the image processing logic 415
(FIG. 4) may be augmented to include program code for determining
an image parameter that may be suggestive of a proximity of a card
to the card reader. For example, the image parameter may relate to
a proximity feature of the image, i.e., a feature that indicates
that an object may be proximate to the camera. In some embodiments,
the card reader may be positioned on the same surface as the camera
of the device that is used to capture the image, and thus the image
information may be further indicative of a proximity of the card to
the card reader. In various embodiments, the card reader/camera may
be positioned on a front face, or rear face of the device.
[0118] In some embodiments, the image parameter comprises one or
more of a darkness level and/or a complexity level of the image.
For example, referring now briefly to FIGS. 13A and 13B, a device
1310 may be a device having a contactless card reading interface
configured as described above to retrieve a MAC cryptogram from the
contactless card 1320, for example when the card 1320 is brought
proximate to device 1310. For example, the device may send an
applet select message, with the applet ID of an NDEF producing
applet, where the applet may be an applet stored in a memory of the
contactless card and operable when executed upon by processing
components of the contactless card to produce the NDEF tag.
According to one aspect, a series of images may be captured using a
camera of the device, and the darkness levels and/or complexity
levels may be analyzed to determine when the card may be a
preferred distance from the device to automatically trigger the
forwarding of the NFC read operation from the NDEF producing applet
of the contactless card.
[0119] In FIGS. 13A and 13B, for purposes of explanation only, an
image 1320 is shown on the display 1340 of the device 1310,
although it is not necessary that captured images that are used as
disclosed herein to determine card proximity be displayed on device
1310.
[0120] According to one embodiment, when the device initiates an
NFC communication, (for example, by a user selecting an NFC read
operation (such as button 1225) on a user interface on the device,
or by the device receiving a request for the device to initiate an
NFC communication with the card, for example from a third party
(such as a merchant application or mobile communication device),
etc.) the device may capture a series images of the spatial volume
proximate to the device. The series of images may be processed to
identify one or more image parameters of one or more of the images
in the series, including but not limited to a darkness level or a
complexity level of the image. The complexity level and/or darkness
level may be used to trigger the NFC read. Alternatively, or in
conjunction, image processing may include identifying trends and/or
patterns in the darkness and/or complexity levels of series of
images or portions of the series of images that suggest advancement
of the card. The identification of the trend and/or the pattern
within the series of images that indicate that the card may be
preferred distance relative to the device may be used to
automatically trigger the NFC read.
[0121] For example, as shown in FIGS. 13A-13C, when the card is
further away from the device, the captured image (here represented
as image 1330A) may be relatively lighter than the image 1330B,
captured relatively later in time as the card 1320 approaches the
device. As shown in FIG. 13B, as the card moves closer, the image
becomes darker until, as shown in FIG. 13C, the captured image (not
visible in FIG. 13C) includes is dark, light is blocked from
appearing in the images by the card 1320. This may be because as
the card approaches the device, the card (or a hand) may block the
ambient light received by the camera.
[0122] As mentioned, card presence at a preferred distance from the
device may be determined in response to the darkness level,
darkness level trend, complexity level and/or complexity level
trend in the captured series of images. In particular, card
presence may be determined by processing pixel values of the series
of images to identify a darkness level of each processed pixel. For
example, assigning a gray scale value to the pixel. The darkness
level for the image may be determined by averaging the darkness
levels of the image pixels. In some embodiments, the darkness
levels may be compared against a threshold corresponding to a
darkness level when a card is a preferred distance from the device,
for example such distance supports a successful NFC read operation.
In some embodiments, the threshold may be an absolute threshold;
for example, in a system where `0` indicates white, and `1`
indicates dark, the card may be considered `present`, and the card
reader may be enabled, when the darkness level is equal to 0.8 or
more. In other embodiments, the threshold may be a relative
threshold that takes into consideration the ambient light of the
environment in which the communication exchange is to occur. In
such embodiments, the first image captured may provide a baseline
darkness level, and the threshold may relate to an amount over the
threshold to trigger the NFC communication; e.g. the threshold may
be a relative threshold. For example, in a darkened room with an
initial darkness level of 0.8 it may be desirable to delay
triggering NFC communication until the darkness level is equal to
0.95 or more.
[0123] In addition to triggering the NFC communication based on an
individually calculated darkness level, the system further
contemplates recognizing trends or patterns in image darkness
levels to trigger the NFC read. Recognizing trends may include, for
example, determining an average value across a set of images and
triggering read when the average value across the set of images
satisfies the threshold. For example, while an individual image may
exceed a threshold, the position of the card may not be stable
enough to perform an NFC read, and thus it may be desirable to
dictate that a predetermined number of successively captured images
exceed the darkness threshold prior to triggering a read. In
addition, or alternatively, successively processed images may be
monitored to identify spikes and/or plateaus, i.e., sudden shifts
in darkness levels that are maintained between successive images
that indicate activity at the card reader.
[0124] In some embodiments, the darkness level for the entire image
may be determined by averaging at least a subset of the calculated
pixel darkness values. In some embodiments, certain darkness values
may be weighted to increase their relevancy to the darkness level
calculation; for example, those portions of the image that are
known to be proximate to the card reader or which are closer to a
recognized feature may be more highly weighted than those that are
farther away from the card reader.
[0125] As described above, a complexity level may be calculated for
each captured image, where the complexity level relates generally
to the frequency distribution of pixel values within the captured
image. In one embodiment, the complexity value may be determined on
a pixel by pixel basis, by comparing a pixel value of each pixel to
the pixel value of one or more adjacent pixels. As a card gets
closer to the device, as shown in FIG. 13B, if the card is properly
positioned the background image may be obscured by the card. The
image by default becomes more uniform as the card covers the image,
and neighboring pixels generally comprise the same pixel value. In
various embodiments complexity may be determined for each pixel in
the image, or for a subset of pixels at previously identified
locations within the image. Complexity for each pixel may be
determined by examination of neighboring pixel values. A complexity
level for the entire image may be determined by averaging at least
a subset of the calculated pixel complexity values. In some
embodiments, certain complexity levels may be weighted to increase
their relevancy to the complexity calculation; for example, those
portions of the image that are known to be proximate to the card
reader or to an identified feature may be more highly weighted than
those that are farther away from the card reader or the identified
feature.
[0126] In other embodiments, machine learning methods such as those
disclosed herein may augment the image processing, for example by
recognizing patterns in pixel darkness/pixel complexity values in
successive images indicative of a known card activity proximate to
the card reader. Such patterns may include, for example, pixel
darkness/complexity levels that change in a known way, (i.e.,
getting darker from the top down or bottom up). The patterns may
also include image elements (such as stripes, icons, printing,
etc.) that assist in card recognition, and may be used as described
above to provide prompts for proper placement for the particularly
recognized card. Over time, information related to successful and
unsuccessful card reads may be used to determine the appropriate
image pattern that establishes a card presence for a successful NFC
card communication exchange.
[0127] FIG. 14 is a flow diagram of exemplary steps that may be
performed to trigger an NFC card read using one or both of the
darkness and/or complexity image attributes described above. At
step 1410, a near field communication may be initiated by the
device. Initiation of the near field communication may occur due to
selection of a user interface element on the device, such as a READ
button 1225 in FIG. 12A. Alternatively, or in conjunction,
initiation of the near field communication may occur as a result of
an action by an application executing on the device, for example an
application that leverages use of a cryptogram from the card for
authentication or other purposes.
[0128] During the initiation of the NFC communication, at step 1420
a camera of the device, such as a front facing camera, may capture
a series of images of the spatial volume in front of the device
camera. In some embodiments, 60, 120, 240 or more images may be
captured each second, although the present disclosure is not
limited to the capture of any particular number of images in the
series. At step 1430, the images may be processed to identify one
or more image parameters, such as a darkness level representing a
distance between the card and the device. At step 1440, the
processed darkness levels of the images are compared to a
predetermined darkness level, for example a darkness level
associated with a preferred distance for near field communication
operations. At step 1450, an NFC read operation may be
automatically triggered, for example to communicate a cryptogram
from an applet of the card, when it is determined that the darkness
level corresponds to the preferred darkness level for an NFC read
operation.
[0129] In some embodiments, the automatic triggering of the NFC
read operation may bypass or replaces a trigger historically
provided by a user interface element. For example, in some
embodiments, a graphical user interface element such as a read
button (1225) may be provided on a device to enable the user to
activate an NFC communication when the user determines that the
card may be appropriately located relative to the device. The user
interface elements may be associated with a function, such as a
read operation, in some embodiments. It may be appreciated that
other user interface elements may be triggered using the techniques
describes herein and various corresponding associated functions may
be automatically triggered. Automatic triggering as disclosed
herein may reduce delays and inaccuracies associated with
historically controlled user interface elements, improving NFC
communication flows and success rates.
[0130] Accordingly, a system and method for detecting card presence
to trigger an NFC read using captured image information has been
shown and described. Such a system may utilize machine learning
methods and/or SLAM methods as described in more detail above to
provide additional guidance, prior to the triggering the card read.
With such an arrangement, the placement of cards is improved and
the rate of success of NFC communication exchanges may be
improved.
[0131] As used in this application, the terms "system", "component"
and "unit" are intended to refer to a computer-related entity,
either hardware, a combination of hardware and software, software,
or software in execution, examples of which are described herein.
For example, a component may be, but is not limited to being, a
process running on a processor, a processor, a hard disk drive,
multiple storage drives, a non-transitory computer readable medium
(of either optical and/or magnetic storage medium), an object, an
executable, a thread of execution, a program, and/or a computer. By
way of illustration, both an application miming on a server and the
server may be a component. One or more components can reside within
a process and/or thread of execution, and a component may be
localized on one computer and/or distributed between two or more
computers.
[0132] Further, components may be communicatively coupled to each
other by various types of communications media to coordinate
operations. The coordination may involve the uni-directional or
bi-directional exchange of information. For instance, the
components may communicate information in the form of signals
communicated over the communications media. The information may be
implemented as signals allocated to various signal lines. In such
allocations, each message is a signal. Further embodiments,
however, may alternatively employ data messages. Such data messages
may be sent across various connections. Exemplary connections
include parallel interfaces, serial interfaces, and bus
interfaces.
[0133] Some embodiments may be described using the expression "one
embodiment" or "an embodiment" along with their derivatives. These
terms mean that a particular 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. Moreover, unless otherwise noted
the features described above are recognized to be usable together
in any combination. Thus, any features discussed separately may be
employed in combination with each other unless it is noted that the
features are incompatible with each other.
[0134] With general reference to notations and nomenclature used
herein, the detailed descriptions herein may be presented in terms
of functional blocks or units that might be implemented as program
procedures executed on a computer or network of computers. These
procedural descriptions and representations are used by those
skilled in the art to most effectively convey the substance of
their work to others skilled in the art.
[0135] A procedure is here, and generally, conceived to be a
self-consistent sequence of operations leading to a desired result.
These operations are those requiring physical manipulations of
physical quantities. Usually, though not necessarily, these
quantities take the form of electrical, magnetic or optical signals
capable of being stored, transferred, combined, compared, and
otherwise manipulated. It proves convenient at times, principally
for reasons of common usage, to refer to these signals as bits,
values, elements, symbols, characters, terms, numbers, or the like.
It should be noted, however, that all of these and similar terms
are to be associated with the appropriate physical quantities and
are merely convenient labels applied to those quantities.
[0136] Further, the manipulations performed are often referred to
in terms, such as adding or comparing, which are commonly
associated with mental operations performed by a human operator. No
such capability of a human operator is necessary, or desirable in
most cases, in any of the operations described herein, which form
part of one or more embodiments. Rather, the operations are machine
operations. Useful machines for performing operations of various
embodiments include general purpose digital computers or similar
devices.
[0137] Some embodiments may be described using the expression
"coupled" and "connected" along with their derivatives. These terms
are not necessarily intended as synonyms for each other. For
example, some embodiments may be described using the terms
"connected" and/or "coupled" to indicate that two or more elements
are in direct physical or electrical contact with each other. The
term "coupled," however, may also mean that two or more elements
are not in direct contact with each other, but still co-operate or
interact with each other.
[0138] It is emphasized that the Abstract of the Disclosure is
provided to allow a reader to quickly ascertain the nature of the
technical disclosure. It is submitted with the understanding that
it will not be used to interpret or limit the scope or meaning of
the claims. In addition, in the foregoing Detailed Description,
various features are grouped together in a single embodiment to
streamline the disclosure. This method of disclosure is not to be
interpreted as reflecting an intention that the claimed embodiments
require more features than are expressly recited in each claim.
Rather, as the following claims reflect, inventive subject matter
lies in less than all features of a single disclosed embodiment.
Thus, the following claims are hereby incorporated into the
Detailed Description, with each claim standing on its own as a
separate embodiment. In the appended claims, the terms "including"
and "in which" are used as the plain-English equivalents of the
respective terms "comprising" and "wherein," respectively.
Moreover, the terms "first," "second," "third," and so forth, are
used merely as labels, and are not intended to impose numerical
requirements on their objects.
[0139] What has been described above includes examples of the
disclosed architecture. It is, of course, not possible to describe
every conceivable combination of components and/or methodology, but
one of ordinary skill in the art may recognize that many further
combinations and permutations are possible. Accordingly, the novel
architecture is intended to embrace all such alterations,
modifications and variations that fall within the spirit and scope
of the appended claims.
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