# US 12,003,285 B2 · System and method for guiding card positioning using phone sensors

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- Number: US 12,003,285 B2
- Document ID: US12003285B2
- Status: Granted
- Kind: B2
- Application number: 17/751,751
- Filed: 2022-05-24
- Published: 2022-11-10
- Granted: 2024-06-04
- Earliest claimed: 2019-07-15
- Assignees: Capital One Services, LLC
- Applicants: Capital One Services, LLC
- Inventors: Jeffrey Rule; Rajko Ilincic; Colin Hart; Daniel Herrington; Kevin Osborn
- Web page: https://patentgrep.com/patent/US12003285B2

## CPC

- H04B 5/77: for interrogation
- G06F 3/14: Digital output to display device {; Cooperation and interconnection of the display device with other functional units}
- G06F 3/147: using display panels
- G06F 16/583: using metadata automatically derived from the content
- G06F 16/5854: {using shape and object relationship}
- G06N 20/00: Machine learning
- G06Q 20/32: using wireless devices
- G06Q 20/3276: {using a pictured code, e.g. barcode or QR-code, being read by the M-device}
- G06Q 20/3278: {RFID or NFC payments by means of M-devices}
- G06Q 20/341: {Active cards, i.e. cards including their own processing means, e.g. including an IC or chip}
- G06Q 20/352: {Contactless payments by cards}
- G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments
- G06T 7/521: from laser ranging, e.g. using interferometry; from the projection of structured light
- G06T 7/73: using feature-based methods
- G06T 7/74: {involving reference images or patches}
- H04M 1/0264: {for a camera module assembly}
- H04M 1/72454: according to context-related or environment-related conditions
- H04N 23/60: Control of cameras or camera modules
- G06T 2207/10028: Range image; Depth image; 3D point clouds
- G06T 2207/10048: Infrared image
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30241: Trajectory
- G09G 2354/00: Aspects of interface with display user
- H04M 2250/04: including near field communication means, e.g. RFID
- H04M 2250/52: including functional features of a camera
- H04N 23/90: Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
- H04B: TRANSMISSION
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08)
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION
- G09G: ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION (arrangements for transferring data between digital computers and displays G06F3/14; static indicating arrangements comprising an association of a number of separate sources or light control cells G09F9/00; static indicating arrangements comprising integral associations of a number of light sources H01J, H01K, H10H, H05B33/12; scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission, details thereof H04N1/00)

## Abstract

A position alignment system facilitates positioning of a contactless card in a ‘sweet spot’ 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 is 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.

## Claims

### Claim 1

A computer method, comprising:
capturing, by a camera of the device, a series of images of a contactless card;
identifying a position and a trajectory of the contactless card from the series of images of the contactless card;
predicting a projected position of the card relative to the device based on the position of the card and the trajectory of the contactless card;
determining whether the card is in a target position relative to the device;
in response to determining the card is in the target position, triggering, by the device, an operation to receive data from the card; and
in response to determining the card is not in the target position:
identifying one or more variances between the projected position and the target position including identifying at least one trajectory adjustment predicted to reduce the one or more variances;
determining one or more prompts to achieve the trajectory adjustments; and
displaying the one or more prompts on a display of the device.

### Claim 2 (depends on 1)

The method of claim 1 wherein determining the position and the trajectory of the contactless card uses at least one of a machine learning model or a simultaneous localization and mapping (SLAM) process.

### Claim 3 (depends on 1)

The method of claim 1 wherein capturing by the camera the series occurs automatically based on a detection made by a proximity sensor.

### Claim 4 (depends on 2)

The method of claim 2, wherein the data comprises a counter value, a shared secret, a key or a combination thereof.

### Claim 5 (depends on 1)

The method of claim 1, wherein the data is received in a cryptogram, and the method comprises:
forwarding the cryptogram to an authentication server, the authentication server maintaining a copy of the data stored by the contactless card; and
receiving an authorization when the cryptogram is validated by the authentication server using the copy of the data stored by the contactless card.

### Claim 6 (depends on 1)

The method of claim 1, wherein the series of images comprises one or both of two-dimensional image information and three-dimensional image information related to one or more of an infrared energy and a visible light energy measured at the device.

### Claim 7 (depends on 6)

The method of claim 6, comprising generating a volume map of a three-dimensional volume proximate to the device using the series of images, the volume map comprising a pixel data for a plurality of pixel locations within the three-dimensional volume proximate to the device.

### Claim 8 (depends on 1)

The method of claim 1 wherein identifying the position and the trajectory of the contactless card includes forwarding the series of images to a feature extraction machine learning model trained to process series of images to detect one or more features of the card and to identify the position and the trajectory of the card in response to the detected one or more features.

### Claim 9 (depends on 8)

The method of claim 8, comprising forwarding the series of images to a second machine learning model trained to predict the position and trajectory based on a historic attempt to position the card.

### Claim 10 (depends on 9)

The method of claim 9, wherein the historic attempt used to train the second machine learning model is customized to a user of the device.

### Claim 11

An apparatus comprising:
a memory configured to store instructions; and
a processor coupled with the memory, the processor configured to execute the instructions, that when executed, cause the processor to:
process a series of images of a contactless card;
determine a position and a trajectory of the contactless card from the series of images of the contactless card;
predict a projected position of the card relative to the device based on the position of the card and the trajectory of the contactless card;
determine whether the card is in a target position relative to the device;
in response to determining the card is in the target position, trigger, by the device, an operation to receive data from the card; and
in response to determining the card is not in the target position:
identify one or more variances between the projected position and the target position including identifying at least one trajectory adjustment predicted to reduce the one or more variances;
determine one or more prompts to achieve the trajectory adjustments; and
display the one or more prompts on a display of the device.

### Claim 12 (depends on 11)

The apparatus of claim 11 wherein the processor to determine the position and the trajectory of the contactless card using at least one of a machine learning model or a simultaneous localization and mapping (SLAM) process.

### Claim 13 (depends on 11)

The apparatus of claim 11 comprising capturing, by a camera, the series occurs automatically based on a detection made by a proximity sensor.

### Claim 14 (depends on 11)

The apparatus of claim 11, wherein the data comprises a counter value, a shared secret, a key or a combination thereof.

### Claim 15 (depends on 11)

The apparatus of claim 11, wherein the data is received in a cryptogram, and the processor to:
forward the cryptogram to an authentication server, the authentication server maintaining a copy of the data stored by the contactless card; and
receive authorization when the cryptogram is validated by the authentication server using the copy of the data stored by the contactless card.

### Claim 16 (depends on 11)

The apparatus of claim 11, wherein the series of images comprises one or both of two-dimensional image information and three-dimensional image information related to one or more of an infrared energy and a visible light energy measured at the device.

### Claim 17 (depends on 16)

The apparatus of claim 16, the processor to generate a volume map of a three-dimensional volume proximate to the device using the series of images, the volume map comprising a pixel data for a plurality of pixel locations within the three-dimensional volume proximate to the device.

### Claim 18 (depends on 11)

The apparatus of claim 11 wherein identifying the position and the trajectory of the contactless card includes forwarding the series of images to a feature extraction machine learning model trained to process series of images to detect one or more features of the card and to identify the position and the trajectory of the card in response to the detected one or more features.

### Claim 19 (depends on 18)

The apparatus of claim 18 the processor to forward the series of images to a second machine learning model trained to predict the position and trajectory based on a historic attempt to position the card.

### Claim 20 (depends on 19)

The apparatus of claim 19, wherein the historic attempt used to train the second machine learning model is customized to a user of the device.