Google Inc.

Glenn Berntson

Xiaohang Wang

Justin Max Scheiner

Harry Lee Butler, Iv

Glenn Thrope

Alessandro Bissacco

Marria Nazif

Sam Shih

Daniel Talavera

Mark Leslie Snyder

Justin Scheiner

Identifying the geolocation of POS terminals using non-payment events to predict when the geolocation of a computing device at a time when the device detects events corresponds to the geolocation of the terminal. The device monitors for pre-selected events and transmit data to the account system. The account system determines a frequency of the events and it reaches a pre-defined threshold, the account system identifies the location of the terminal by identifying the common geolocation of the events. The identified geolocation is saved so that when a user then enters the location and transmits event data to the account system, the system can compare the geolocation of the event data to the saved geolocation to determine whether the computing device is located at the terminal. If the computing device is located at the terminal, the account system transmits offers or other content for display and use at the identified terminal.

Point of sale terminal geolocation

Extracting card information comprises a server at an optical character recognition ('OCR') system that interprets data from a card. The OCR system performs an optical character recognition algorithm an image of a card and performs a data recognition algorithm on a machine-readable code on the image of the card. The OCR system compares a series of extracted alphanumeric characters obtained via the optical character recognition process to data extracted from the machine-readable code via the data recognition process and matches the alphanumeric series of characters to a particular series of characters extracted from the machine-readable code. The OCR system determines if the alphanumeric series and the matching series of characters extracted from the machine-readable code comprise any discrepancies and corrects the alphanumeric series of characters based on the particular series of characters extracted from the machine-readable code upon a determination that a discrepancy exists.

Extracting card identification data

Inferring purchase intent using non-payment transaction signals predicts whether a payment transaction has been completed based on non-payment information. An account system that operates outside of the payment path does not take part in and the approval of a financial transaction between the user and the merchant system, distributes an offer to the user. The user completes a financial payment transaction with the merchant and the account system determines whether a trigger event has occurred. The user performs an action or enters information using the user computing device, and the user computing device transmits an indication of the action to the account system. In another example, the account system receives notification from another system or device. The account system determines whether the action is a trigger event and the predictive model determines whether the user completed a financial transaction and/or redeemed the distributed offer.

Inferring purchase intent using non-payment transaction events

A user captures an image of a payment card via a user computing device camera. An optical character recognition system receives the payment card image from the user computing device. The system performs optical character recognition and visual object recognition algorithms on the payment card image to extract text and visual objects from the payment card image, which are used by the system to identify a payment card type. The system may categorize the payment card as a credit card or a non-credit card. In an example embodiment, the system determines that the payment card type is a credit card and transmits fee structure to the user. The user selects a second payment card for use in the transaction and the transaction is processed using financial account information associated with the second payment card.

Identifying payment card categories based on optical character recognition of images of the payment cards

The technology of the present disclosure includes computer-implemented methods, computer program products, and systems to filter images before transmitting to a system for optical character recognition ('OCR'). A user computing device obtains a first image of the card from the digital scan of a physical card and analyzes features of the first image, the analysis being sufficient to determine if the first image is likely to be usable by an OCR algorithm. If the user computing device determines that the first image is likely to be usable, then the first image is transmitted to an OCR system associated with the OCR algorithm. Upon a determination that the first image is unlikely to be usable, a second image of the card from the digital scan of the physical card is analyzed. The optical character recognition system performs an optical character recognition algorithm on the filtered card.

Client side filtering of card OCR images

A user captures an image of a payment card via a user computing device camera. An optical character recognition system receives the payment card image from the user computing device. The system performs optical character recognition and visual object recognition algorithms on the payment card image to extract text and visual objects from the payment card image, which are used by the system to identify a payment card type. The system may categorize the payment card as an open-loop card or a closed-loop card, or as a credit card or a non-credit card. In an example embodiment, the system allows or prohibits extracted financial account information from the payment card to be saved in the digital wallet account based on the determined payment card category. In another example embodiment, the system transmits an advisement to the user based on the determined payment card category.

Growing community of inventors

Jersey City, NJ, United States of America

Glenn Berntson