Capital One Services, LLC

Joseph Ford, Iii

Reza Farivar

Rittika Adhikari

Austin Grant Walters

Kenneth Taylor

Cody Stancil

Thomas Oliver Cantrell

Marc Meckler

Matthew S Edwards

Xiaowen Zhang

Methods and systems are described herein for identifying the location and nature of any blur within one or more images received as a user communication and generating an appropriate correction. The system utilizes a first machine learning model, which is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information. The system may apply a corrective action selected by the first machine learning model, which may comprise stitching blurred images together to a sharp product image and/or some other method appropriate for rectifying images received.

Systems and methods for identifying and correcting blurred components within images

Techniques are disclosed for customizing the feedback provided to a user at a point-of-sale (POS) terminal upon completion of an electronic payment transaction. The feedback may include the content of one or more media files that may be customized by a user in response to different conditions being met, and which may be specified as part of a user profile. Additional or alternative feedback may be specified by an entity identified with the user's payment account, such as a bank or creditor, which may comprise additional or alternative media file(s) that may be identified in response to specific predetermined conditions being met upon the electronic payment transaction being initiated. The feedback may be sent to the POS terminal and/or to a different device based upon the type of electronic payment transaction and/or the capabilities of the POS terminal.

Customizable media content for point of sale (POS) transactions

In some implementations, a training platform may receive data for generating synthetic models of a body part, such as a hand. The data may include information relating to a plurality of potential poses of the hand. The training platform may generate a set of synthetic models of the hand based on the information, where each synthetic model, in the set of synthetic models, representing a respective pose of the plurality of potential poses. The training platform may derive an additional set of synthetic models based on the set of synthetic models by performing one or more processing operations with respect to at least one synthetic model in the set of synthetic models, and causing the set of synthetic models and the additional set of synthetic models to be provided to a deep learning network to train the deep learning network to perform image segmentation, object recognition, or motion recognition.

Generating synthetic models or virtual objects for training a deep learning network

A computer implemented method, system, and non-transitory computer-readable device that may be used in a remote deposit environment. A plurality of differing neural networks, such as customizable neural networks, are selected, based on an architecture and features of the data field, to extract data from specific data fields on a document. The selected customizable neural networks are trained by historical or synthetic data corresponding to the data fields. Upon receiving, from a neural network Optical Character Recognition (OCR) system, a selected first trained customizable neural network model and at least a second selected trained customizable neural network model, the data fields are extracted, based on a series or parallel configuration of the customizable neural networks, and the extracted data communicated to a remote deposit process.

System for automatically processing documents

Disclosed embodiments may include a method that may include receiving a corpus of unlabeled text documents, generating, using the first machine learning model, a first classification of each unlabeled text document in the corpus of unlabeled text documents as positive or negative, defining, using the first machine learning model and based on the first classification, a first subset of the unlabeled text documents and a second subset of the unlabeled text documents, generating, using the second machine learning model, a second classification of each unlabeled text document in the first subset of the unlabeled text documents as positive or negative, generating, using the third machine learning model, a third classification of each unlabeled text document in the second subset of the unlabeled text documents as positive or negative, and modifying the first classification, based on the second classification and the third classification, to create a fourth classification.

Systems and methods for natural language processing

An ATM device may receive a request to process an ATM transaction; dispense, via an instrument dispenser, a plurality of instruments based on the request; perform image segmentation of one or more images of an area surrounding the instrument dispenser, wherein the image segmentation is performed using a deep learning network trained using synthetic models of hands; detect, based on performing the image segmentation, that a user's hand approaches the instrument dispenser after dispensing the plurality of instruments; determine, after dispensing the plurality of instruments and after detecting that the user's hand approaches the instrument dispenser, that a portion of the plurality of instruments is present at the instrument dispenser; and perform one or more actions based on determining that the portion of the plurality of instruments is present at the instrument dispenser.

Counter-fraud measures for an ATM device

A system for guiding image sensor angle settings in different environments. The system may include a memory storing executable instructions, and at least one processor configured to execute the instructions to perform operations. The operations may include obtaining a plurality of synthetic images, the synthetic images representing a plurality of scenes; training a classification model to classify, based on the synthetic images, a plurality of images captured from an environment of a user by an image sensor; determining, based on the classification, whether the image sensor is positioned at a predetermined angle; and adjusting, based on the determination, a position of the image sensor.

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Manakin Sabot, VA, United States of America

Joseph Ford, Iii