US 12,229,738 B1
Mobile check deposit system and method
Jeffrey A. Fietsam, Bloomington, IL (US); Robert Thurwanger, Bloomington, IL (US); Michael Pelaccio, Normal, IL (US); and Pinky Desai, Bloomington, IL (US)
Assigned to State Farm Mutual Automobile Insurance Company, Bloomington, IL (US)
Filed by STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY, Bloomington, IL (US)
Filed on Oct. 20, 2020, as Appl. No. 17/075,529.
Claims priority of provisional application 62/930,895, filed on Nov. 5, 2019.
Claims priority of provisional application 62/923,706, filed on Oct. 21, 2019.
Int. Cl. G06N 20/00 (2019.01); G06Q 20/04 (2012.01); G06Q 20/10 (2012.01); G06Q 20/40 (2012.01); G06Q 30/018 (2023.01); G06Q 40/02 (2023.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06V 10/40 (2022.01); G06V 30/182 (2022.01); G06V 20/20 (2022.01); G06V 20/30 (2022.01); G06V 30/14 (2022.01); G06V 30/146 (2022.01); H04W 88/02 (2009.01)
CPC G06Q 20/108 (2013.01) [G06N 20/00 (2019.01); G06Q 20/0425 (2013.01); G06Q 20/4012 (2013.01); G06Q 20/40145 (2013.01); G06Q 30/0185 (2013.01); G06Q 40/02 (2013.01); G06T 7/0002 (2013.01); G06V 30/182 (2022.01); G06Q 2220/00 (2013.01); G06T 2207/30168 (2013.01); G06V 20/20 (2022.01); G06V 20/30 (2022.01); G06V 30/1444 (2022.01); G06V 30/1463 (2022.01); H04W 88/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method implemented by a mobile device for remote deposit, the computer-implemented method comprising:
detecting, by a camera of a mobile device registered to a first user, a digital image of a plurality of objects;
determining, via object detection and object localization, and using a machine learning model trained on a training dataset of check images by a processing unit of the mobile device prior to a photograph of the plurality of objects being taken using the mobile device, (1) that the plurality of objects in the digital image comprise a plurality of checks and (2) respective locations of individual checks of the plurality of checks in the digital image;
determining, by the processing unit, that the digital image of the plurality of checks as detected is of sufficient quality;
in response to determining that the digital image of the plurality of checks is of the sufficient quality, generating, by the processing unit, an instruction for a photograph of the plurality of checks to be taken;
causing, by the processing unit, the instruction to be presented on a display of the mobile device;
cropping, using the machine learning model, by the processing unit, and based upon the respective locations of the individual checks as determined, the photograph of the plurality of checks into a plurality of cropped images, wherein each of the plurality of cropped images contains one of the plurality of checks;
determining, by the processing unit and based on biometric data collected via a sensor of the mobile device, that a current user of the mobile device is a second user different from the first user;
based on determining that the current user is different from the first user, creating, by the processing unit, an association between the plurality of cropped images and a financial account of the second user; and
transmitting, by a transmitter of the mobile device, the plurality of cropped images, and an identifier uniquely identifying the financial account of the second user, to a server via a network.