Bank of America Corporation

Avinash Basavant Nigudkar

Lingaraj Gopalakrishnan

Anmol Puri

Ashok Kumar

Vijay Kumar Battiprolu

Vikas Kumar Sahu

Yadava Krishnan Srinivasan

Srikanth Gonda

Nusrath Fathima

Sanjay Pillay

Piedad L Burnside

Anton Sumin

Narsing Raj

Muthuraj Kumaresan

Karthikeyan Krishnan

Silvano Valle

Mukesh Kumar Jain

Kommu John Bilton

Shalu Wadhwa

Kajuluri Venkata Ashok

John Bilton Kommu

Param Jabbal

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

Various aspects of the disclosure relate to automated conversion of time and/or date information from a first format to a second format and initiating computing functionality based on a successful conversion event. Mainframe computer hardware and software are provided for automatically converting differing time formats used across an enterprise computing network, automatically presenting times in multiple formats and/or translations of times across time zones for different regional computing systems of the enterprise computing network, and translating time formats based on an input and automatically pulling data records based on time zones of one or more of the involved computing systems and/or a location associated with the input source.

Time transformation engine for a mainframe system

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

A quantum computing system for determining diagnostic solutions for detected faults in computing devices using at least quantum annealing is described. The quantum computing system takes advantage of superposition and entanglement properties of qubits. A plurality of qubits is initialized into states representing historical data associated with historical faults and historical diagnostic solutions. Couplers entangle the plurality of qubits together based on the detected fault, generating a set of states in uniform superposition. Biases influence the energy levels of the set of states based on the detected fault. The quantum computing system measures a state with the lowest energy level within the set of states to determine diagnostic solutions to the detected fault. The system may further leverage a blockchain ledger for securely storing and accessing historical data associated with historical faults and historical diagnostic solutions.

TechWiki—computer diagnostics using quantum annealing and blockchain framework

Systems, computer program products, and methods are described herein for determining application degradation using advanced computational models for data analysis and automated decision-making. The present disclosure is configured to receive one or more applications, wherein the one or more applications comprise one or more access paths; update the one or more access paths, wherein updating the one or more access paths comprises reconfiguring details associated with the access paths; create, in response to updating the one or more access paths, updated access paths; determine that the one or more updated access paths experience one or more application degradations; and implement one or more degradation solutions for the one or more application degradations to an artificial intelligence model.

System and method for determining application degradation using advanced computational models for data analysis and automated decision-making

A system is provided for monitoring computing server performance using an artificial intelligence-based plugin. In particular, the system may be configured to continuously aggregate computing performance metric data of a computing device in the network, such as a server. The system may analyze the performance metric data using an artificial intelligence-based process to identify the hardware and/or software layers affected by computing workloads. Based on identifying the affected layers, the system may predict the impact of future workloads on the server and, based on the predictions, generate one or more recommendations for remediating server outages or downtime which may be caused by unanticipated volumes of network traffic, system calls, and the like. In this way, the system may provide an efficient way to optimize the use of computing resources within the network environment.

System for monitoring computing server performance using an artificial intelligence-based plugin

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

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Maharashtra, India

Avinash Basavant Nigudkar