Bank of America Corporation

Charles Christopher Harbinson

Letishia Renee Hunt

Adam Nathaniel Desautels

Brendan P Murphy

Christopher David Greth

Verlon S Watson

John Barrett Hall

Eric Dryer

Michael Gerald Smith

Jeanne M Moulton

Shawn Cart Gunsolley

Jasher David Fowles

Jeffrey R Goertz

Timmy Leo Gauvin

Verlon Safold Watson, Iii

Clarence Edward Lee, Ii

Leslie LaRock

Kenneth R Cox

Timothy Alan Mincey

Georgeann Paschal Gregory

Coty Tyler Cherrix

Clarence E Lee

Tami Marie Shepard

Shashi R Warrier

Barry Hall

Brigitte Marie Doughty

Collin Jarel Cacho Lenon

Kyaw Htet Kyi

Clement Chellaraj

Haywood P Enloe

Christine Malabad

Raymond William Schall, Jr

Aspects of the disclosure relate to performing enhanced exception processing using cognitive automation tools. In some embodiments, a computing platform may receive interaction data identifying one or more actions performed by one or more users in resolving a plurality of exception items associated with an exception queue. Subsequently, the computing platform may train, using a learning engine, a machine learning model to resolve a first exception and a second exception of one or more exceptions based on the interaction data. Based on training the machine learning model, the computing platform may generate one or more configuration commands directing a processing module to implement the machine learning model to process additional exception items associated with the exception queue. The computing platform then may send the one or more configuration commands to the processing module.

Performing enhanced exception processing using cognitive automation tools

A system is provided for file capture and in-line error correction. The system comprises: a controller configured to capture and process a file during a file capture sequence comprising one or more capture events, the controller being configured to: capture a file using a file capture device associated with a user device, wherein capturing the file comprises receiving a file image and a user input data field associated with the file; identify, using an in-line file analysis module, an error with a capture event during the file capture sequence, wherein the error is a discrepancy identified between the user input data field and an image-derived data field; correct the error in the file before the file capture sequence is completed and the file is exported; and export the file, wherein the file capture sequence is completed.

File capture and processing system with in-line error detection

Aspects of the disclosure relate to performing enhanced deposit item processing using cognitive automation tools. In some embodiments, a computing platform may receive, from a deposit item input support server, image data associated with a deposit item. Subsequently, the computing platform may apply a machine learning classification model to the image data associated with the deposit item. In doing so, the computing platform may produce one or more predicted values for one or more fields of the deposit item and one or more confidence scores for the one or more predicted values. Based on these predicted values and confidence scores, the computing platform may generate and send one or more commands directing the deposit item input support server to accept or reject the deposit item, which in turn may cause the deposit item input support server to accept or reject the deposit item.

Performing enhanced deposit item processing using cognitive automation tools

Systems for controlling scanning devices and capturing image data are provided. In some examples, image data may be received by a computing platform. The image data may be received via a first network and from another computing platform in wired communication with a scanning device. In some arrangements, image quality analysis may be performed and data may be extracted from the image data. The results of the data analysis, as well as the image data and extracted data may be transmitted, via a second network different from the first network, to an associate computing device. In some examples, input received by the associate computing device may be transmitted to the computing platform indicating whether the image is approved or rejected for further processing. If the image is approved, the image data and extracted data may be transmitted, via the second network, to another device for matching and further processing functions.

Dynamic image capture device control system

An image item processing engine for image item exchange may receive an image item data packet including two image items, each including a digital image documenting an action for a recipient institution and associated metadata. The metadata may identify the image item sender and a country associated with an image item, the packet originator, or the recipient institution, at least one country associated with the second image item being different than a country associated with the first image item. The engine may apply sorting rules to determine item types for the first and second image items based on their metadata, determine a recipient institution for the packet based on the item types, assemble a data transfer packet including the digital images and metadata included in the first and second image items, and provide the data transfer packet to a transfer packet routing service for routing to the selected recipient institution.

System and method for transferring image systems of different types between computers in a single data packet

An article of manufacture is described. The article of manufacture may include a computer usable medium having computer readable program code embodied therein. The code when executed by a processor may cause a computer to generate version performance digital image data delta information. The code may cause the computer to receive a plurality of digital images, a plurality of legacy rejections associated with the digital images and a plurality of updated rejections associated with the digital images. The code may cause the computer to identify one or more digital images that trigger a first rejection with the legacy version and that trigger a second rejection with the updated version wherein the first rejection and the second rejection are the same rejection and to identify one or more occurrences wherein the first rejection and the second rejection are different rejections.

Version performance data delta tester and methods associated with using same

Growing community of inventors

Mooresville, NC, United States of America

Charles Christopher Harbinson