Bank of America Corporation

William August Stahlhut

Joseph Benjamin Castinado

Cameron Darnell Wadley

Patrick Nicholas Lawrence

Katherine Dintenfass

Suki Ramasamy

Jisoo Lee

John Christopher Checco

Brad Romano

Minh N Vuong

Susan Varghese

Lekha Ananthakrishnan

Mohamed A Gahnoog

George Albero

Stephen Thomas Shannon

Siten Sanghvi

Morgan Stewart Allen

Kamesh R Gottumukkala

Jeffery B Schroeder

Manu Jacob Kurian

Maharaj Mukherjee

Victoria Lynn Dravneek

Gilbert M Gatchalian

Christopher Lamont Rice

Anna Hollifield

Eric Eugene Sifford

Gail R Davis

Elizabeth Votaw

Jinna Zevulun Kim

Naga Vamsi Krishna Akkapeddi

Elena Kvochko

Kevin A Delson

Todd Michael Goodyear

Christine D Black

Jesse Strickland Newsom, Iii

Mardochee Macxis

Prabhat Ranjan

John Ryan Bowling

Shola L Oni

Jeffrey B Schroeder

Apparatus and methods for determining transaction transmission fidelity are provided. A sender may receive a transaction log including multiple transactions in two or more rows and two or more columns. The transaction log may be encrypted. Each of the transactions may be tagged. Each row and column of the transaction log may be hashed. The encrypted transaction log and the hash values may be transmitted to a recipient. The recipient may decrypt the transaction log and hash each row and column of the decrypted transaction log. The hash values may be compared and if there is a difference, a report or alert may be generated.

Hash-based transaction tagging

A computer system for optimizing an application for monitoring operation of a network is provided. The application may utilize Artificial Intelligence (AI). The computer system may be configured to detect a network transaction volume level that exceeds a first threshold level. In response to a detection of the network transaction volume level that exceeds a first threshold level, the system may utilize the AI to determine whether to implement one or more over-threshold-level mitigation techniques. When the AI instructs the processor to implement the one or more over-threshold-level mitigation techniques, the system may query the AI for an instruction directing an implementation of the over-threshold-level mitigation techniques.

Pro-active digital watch center

Systems and methods for lossless restoration of a digital system are provided. A method may include creating a digital twin of the digital system. Creating the digital twin may include constructing a digital model that replicates hardware and software components and performance metrics of the digital system. The components and the performance metrics may be detected via a plurality of edge devices. The digital model may be configured to be run on a processor to simulate performance of the digital system. The method may include receiving an indication that the digital system is disconnected from a central server, syncing the digital twin with the digital system while the digital system is disconnected from the central server, and, in response to an indication that the digital system has reconnected with the central server, syncing the central server with the digital twin.

Systems and methods for lossless network restoration and syncing

Systems and methods for maximizing resource utilization in a digital communication system are provided. A method may include receiving a direction for a first one of a plurality of edge devices to execute a task, wherein the edge devices communicate with each other and with a central server via a communication network. The central server may be operated by an entity that is independent of the communication network. The plurality of edge devices may each include an authenticated software application that is provided by the entity. The method may also include calculating, via the ML engine for each of the plurality of edge devices, a predicted resource availability score, and distributing, via the central server, the task among the plurality of edge devices based on the predicted resource availability scores.

Machine-learning (ML)-based systems and methods for maximizing resource utilization

Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Automated event processing computing platform for handling and enriching blockchain data

Systems for using cognitive automation techniques to detect unauthorized events are provided. In some examples, a request for a product or service (e.g., event processing) may be received. The request may be received electronically, via a telephone communication, in person, or the like. The request may be analyzed (e.g., using a cognitive automation model) to determine whether it is potentially unauthorized. If so, one or more requests for additional information may be generated or identified. For instance, requests for authentication information, responses to questions or a series of questions, or the like, may be generated. In some examples, the additional information requested may be identified using the cognitive automation model. Responses to the request for additional information may be analyzed (e.g., using the cognitive automation model) to determine whether the request for the product or service is unauthorized. If so, processing the request may be prevented.

Cognitive automation platform for dynamic unauthorized event detection and processing

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

Aspects of the disclosure relate to cognitive automation-based engine processing on audio files and streams received from meetings and/or telephone calls. A noise mask can be applied to enhance the audio. Real-time speech analytics separate speech for different speakers into time-stamped streams, which are transcribed and merged into a combined output. The output is parsed by analyzing the combined output for correct syntax, normalized by breaking the parsed data into record groups for efficient processing, validated to ensure that the data satisfies defined formats and input criteria, and enriched to correct for any errors and to augment the audio information. Notifications based on the enriched data may be provided to call or meeting participants. Cognitive automation functions may also identify callers or meeting attendees, identify action items, assign tasks, calendar appointments for future meetings, create email distribution lists, route transcriptions, monitor for legal compliance, and correct for regionalization issues.

Cognitive automation-based engine BOT for processing audio and taking actions in response thereto

A system for capturing communication variables associated with a communication between two or more parties and implementing a quantum optimizer to analyze the communication variables to determine the actual context of a communication held by one or more parties to the communication. Once the actual context is determined, a further determination is made as to whether the actual context poses or potentially poses a security threat to one or more parties to the communication or a third-party and, if so, notifies the party of the security threat. In this regard, parties to a communication that are unaware of the actual context of the communication held by another party are made aware of that actual context poses a security threat and, in some embodiments, notifies the parties while the communication is still occurring.

Distractional variable identification for authentication of resource distribution

Growing community of inventors

The Colony, TX, United States of America

William August Stahlhut