Bank of America Corporation

Lingaraj Gopalakrishnan

Anmol Puri

Ashok Kumar

Avinash Basavant Nigudkar

Srikanth Gonda

Nusrath Fathima

Narsing Raj

Vijay Kumar Battiprolu

Kommu John Bilton

Shalu Wadhwa

John Bilton Kommu

Aspects of the disclosure relate to s computing system that is configured to use heuristic and/or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and/or adaptable solution to detecting and preventing defects in software applications.

Intuitive defect prevention with swarm learning intelligence over blockchain network

Systems, computer program products, and methods are described herein for determining causes of network anomalies across a distributed network. The method includes determining one or more known indicator patterns from one or more error logs for one or more applications on a network. The method also includes receiving one or more performance indicators during an execution of an application. The method further includes comparing the one or more performance indicators to one or more known indicator patterns. The method also includes determining at least one of the one or more performance indicators that is malfeasant. The method also includes determining a malfeasant component of the network based on the one or more performance indicators that is malfeasant. The method also includes determining a remediation action to be executed in response to the comparison of the one or more performance indicators to one or more known indicator patterns.

System and method for determining causes of network anomalies across a distributed network

Systems, computer program products, and methods are described herein for end-to-end automation of network infrastructure development changes. The present disclosure is configured to collect and archive data comprising feature logs, resource logs, and events in a central historical data storage. This data is subsequently normalized and events are correlated to specific features. This correlation involves locating differences in pre and post-deployment feature performance, which includes resource utilization, and maintaining a history of the analyzed data in the central historical data storage.

Systems and methods for end-to-end automation of network infrastructure development changes

Growing community of inventors

Tamilnadu, India

Lingaraj Gopalakrishnan