Bank of America Corporation

Jyothi Gaddam

Venugopala Rao Randhi

Pitti Venkateswarlu

Rama Venkata S Kavali

Rama Venkata Kavali

Mohan Varma Kalidindi

A quantum computing platform may train, using historical workload information, a container configuration generation model. The computing platform may receive, from a workload processing system, a data feed indicating current workload information. The computing platform may input, into the container configuration generation model, the current workload information, which may cause the container configuration generation model to produce a container configuration output, where the container configuration output may be an optimal batch configuration for processing the data feed, and where the optimal batch configuration may be a configuration that optimizes between computing resources and processing speed. The computing platform may send, to the workload processing system, the container configuration output and one or more commands directing the workload processing system to process the data feed using the optimal batch configuration, which may cause the workload processing system to process the data feed using the optimal batch configuration.

Real time optimization apparatus for dynamic code evolution using quantum machine learning with non-fungible tokens

Systems, computer program products, and methods are described herein for asynchronous data migration. The present invention is configured to identify a source file data based on an update trigger for the source file data; identify, using an artificial intelligence (AI) engine, a source file type for the source file data; determine a dataset range for the source file data to ingest to a target data file, wherein the dataset range comprises a portion of data of the source file data; and ingest the dataset range to the target data file.

Systems and methods for asynchronous data migration

A quantum computing platform may establish a smart contract approval and management model, including: rules for automated validation, and rules for smart contract approver validation. The computing platform may receive, from a workload processing system, a data feed indicating current workload information. The computing platform may generate, based on the data feed, a first container configuration output, defining a batch configuration for use in processing the data feed. The computing platform may validate, using the one or more rules for automated validation, the first container configuration output. The computing platform may send, to the workload processing system, the first container configuration output and one or more commands directing the workload processing system to process the data feed using the batch configuration defined by the first container configuration output, which may cause the workload processing system to process the data feed using the batch configuration.

Growing community of inventors

Telangana, India

Jyothi Gaddam