Bank of America Corporation

Stephen Thomas Shannon

James Alexander

Brian Joseph Smith

Siten Sanghvi

David J Smiddy

Morgan Stewart Allen

Elizabeth Votaw

Pratap Dande

Monika Kapur

Fnu Sidharth

Matthew E Carroll

Richard Scot

Tamara S Kingston

Naga Vamsi Krishna Akkapeddi

William August Stahlhut

Sean Denton

Penelope York

Gilbert M Gatchalian

Christopher Lamont Rice

Susan J Moss

Kamesh R Gottumukkala

Elvin Crabbe, Jr

F N U Sidharth

Manu Jacob Kurian

Michael Ogrinz

Jinna Zevulun Kim

Elena Kvochko

Christine D Black

Katherine Kei-Zen Dintenfass

Adithya Gadwale

Richard Martin Seymour Scot

Harold Joseph Kennedy

Richard M Korzenko

Shola L Oni

Anthony Pang Fang

Jacquelin Smith

Kimberly Anne Stittsworth

Malini Petersen

Robert J Graulich

Courtney Lee Prior

Lea Marie Souther

Spencer A Jones

Peter M Berbee

Scott Thomas Bonomo

Joseph Justin Chuba

Aspects of the disclosure relate to a decision model to open and close enterprise facilities. A computing platform may receive one or more attributes associated with an operation of an enterprise facility. Subsequently, the computing platform may identify, based on the one or more attributes, a business operation rule for the enterprise facility. Then, the computing platform may determine, based on the one or more attributes and the business operation rule, an operational parameter for the enterprise facility. Then, the computing platform may compare the operational parameter to a threshold parameter, where the threshold parameter may be indicative of whether to keep the enterprise facility open for business. Then, the computing platform may, upon a determination that the operational parameter fails to exceed the threshold parameter, determine to temporarily close the enterprise facility. Then, the computing platform may redistribute user traffic to one or more open customer resource facilities.

Machine learning based decision model to open and close enterprise facilities

Aspects of the disclosure relate to detecting usage issues on enterprise systems and dynamically providing user assistance. In some embodiments, a computing platform may receive, from an automated teller system, user presence information indicating that a user has been detected at an automated teller machine associated with the automated teller system. Subsequently, the computing platform may identify, based on the user presence information received from the automated teller system, that the user has a need for assistance with the automated teller machine associated with the automated teller system. In response to identifying that the user has the need for assistance, the computing platform may generate and send one or more commands directing the automated teller machine associated with the automated teller system to execute one or more automated assistance actions corresponding to the need for assistance.

Detecting usage issues on enterprise systems and dynamically providing user assistance

Aspects of the disclosure relate to machine learning based automated management of a user account. A computing platform may determine, via a computing device and based on historical user activity of a user, a pattern of the user activity. Subsequently, the computing platform may detect a deviation from the pattern of the user activity. Then, the computing platform may identify an anticipated transaction of the user. Then, the computing platform may retrieve, from a repository of user data, one or more user-defined preference rules associated with the anticipated transaction. Then, the computing platform may determine whether the one or more preference rules apply to one or more attributes of the anticipated transaction. Subsequently, the computing platform may trigger, based on a determination that the one or more preference rules apply to the one or more attributes of the anticipated transaction, an action associated with the anticipated transaction.

Machine learning based automated management of customer accounts

Aspects of the disclosure relate to automated pairing of customers and businesses. A computing platform may determine, based on historical user activity of a user, a pattern of the user activity, and may identify, based on the pattern of the user activity, an anticipated purchase activity of the user. Then, the computing platform may determine a sales offering by a vendor. Then, the computing platform may match the anticipated purchase activity with the sales offering. Then, the computing platform may retrieve user-defined preference rules associated with the anticipated purchase activity. Then, the computing platform may determine whether the preference rules apply to one or more attributes of the anticipated purchase activity. Subsequently, the computing platform may trigger, based on a determination that the preference rules apply to the one or more attributes of the anticipated purchase activity, an action associated with the anticipated purchase activity.

Machine learning based automated pairing of individual customers and small businesses

Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.

Recommending and initiating contactless events based on machine learning

With the advent of augmented reality devices becoming increasingly prevalent, accessible, and cross-compatible, there is an opportunity to leverage the capabilities of such devices in order to streamline user interactions related to complex resource activities which require elevated security and typically are only conducted on an in-person basis. The present invention provides systems and methods for securely verifying a user during an augmented reality device session for the purposes of conducting complex resource activities.

Systems and methods for multi-point validation in communication network with associated virtual reality application layer

Aspects of the disclosure relate to monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources. In some embodiments, a computing platform may receive, from one or more data source computer systems, passive monitoring data. Based on applying a machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, the computing platform may determine to trigger a data capture process at an enterprise center. In response to determining to trigger the data capture process, the computing platform may initiate an active monitoring process to capture event data at the enterprise center. Thereafter, the computing platform may generate one or more alert messages based on the event data captured at the enterprise center. Then, the computing platform may send the one or more alert messages to one or more enterprise computer systems.

Monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources

A system for conducting multi-session user interactions accesses a first interaction session between the user and a first entity, where the first interaction session is via a first communication channel. The system determines first context information associated with the first interaction session, where the first context information associated with the first interaction session comprises a summary associated with the first interaction session. The system generates a token identifier (ID) uniquely identifying the user and the first context information. The system associates the token ID to the user interaction information. The system accesses a second interaction session between the user and a second entity, wherein the second interaction session is via a second communication channel. The system authenticates the user based at least in part upon the token ID. The system communicates the first context information associated with the first interaction session to the second entity.

System and method for conducting multi-session user interactions

This application describes apparatus and methods for using edge-computing to control resource distribution among access channels, such as a retail banking center. Edge-nodes may be configured to move a product display in response to detected or expected customer traffic flow in or near a retail location. Edge-nodes may be configured to redirect resources provided by a cloud computing environment to or away from the retail location. Based on customer traffic flow, edge-nodes may direct customers/resources to a retail location and ensure the retail location provides a predetermined quality of service.

Edge-node controlled resource distribution

Growing community of inventors

Charlotte, NC, United States of America

Stephen Thomas Shannon