Bank of America Corporation

Ashok Kumar

Narsing Raj

Avinash Basavant Nigudkar

Lingaraj Gopalakrishnan

Anmol Puri

Venkata Lakshmi Sai Siva Kumar Devulapalli

Srikanth Gonda

Nusrath Fathima

Carl Parziale

Vishal Wadhwa

Mangesh Narendra Chore

Sunil Reddy Gaddam

Lakshmipriya Varada

Prabhakar Rao Bolleddu

Kenneth M Fischer

Jessica Boettner

Shalu Wadhwa

Gaddam Sunil Reddy

John Bilton Kommu

Kommu John Bilton

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 early detection of operational failure in component-level functions within a computing environment. The present invention is configured to receive, from one or more source devices, log data; determine, using a trained machine learning model, a likelihood that a first subset of the log data is associated with an operational failure of one or more component-level functions; determine that the likelihood that the first subset of the log data is associated with the operational failure of one or more component-level functions is greater than a predetermined threshold; determine that the first subset of the log data reflects a current state of a first subset of source devices; generate a notification indicating that the first subset of source devices is likely to experience the operational failure of one or more component-level functions; and display the notification on an administrator device associated with the first subset of source devices.

System for early detection of operational failure in component-level functions within a computing environment

Aspects of the disclosure relate to monitoring an automatic teller machine (ATM) network and determining anomalous fault behavior in the ATM network. A system may determine historical fault volumes in the ATM network and generate a time-series model of the fault volumes. The system may predict future fault volumes based on the generated time-series model. Based on the predicted future fault volumes and actual future fault volumes, the system may determine anomalous fault behavior and perform a remedial action associated with the ATM fault code determined from anomalous observed fault volumes.

Identification of anomalies in an automatic teller machine (ATM) 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

Aspects of the disclosure relate to an intelligent failsafe engine. A computing platform may determine that a backup should be initiated that corresponds to a determined time window. The computing platform may select either a full backup method or a differential backup method. The computing platform may initiate the selected backup method, which may include backing up only blocks that have been modified since a previous backup. The computing platform may identify that the backup has paused at a particular data block. The computing platform may identify that the backup may be resumed, and may resume the backup at the particular data block. The computing platform may send data health information and commands directing a user device to display the data health information, which may cause the user device to display the data health information.

Intelligent failsafe engine

The invention provides a server administration tool for migration of a relational database to a cloud server environment. The server administration tool includes a database migration tool a server patching tool to monitor and maintain a server system. The database migration tool allows for the migration and upload of a database stored on a source server to a target cloud server. The migrated database may be directly uploaded to the target server or hosted on a virtual machine. The server patching tool allows for patching of an initially incompatible source server enabling migration of a stored database to a new target server. The tool analyzes a database stored on a source server for compatibility with a new, target cloud server to determine whether an implemented patch would succeed or fail. In this way, errors reported by the tool, may be rectified without requiring costly, additional server downtime.

Automated server analysis and patching for enabling relational database migration to a cloud network

Growing community of inventors

Hyderabad Telangana, India

Ashok Kumar