Bank of America Corporation

Venugopala Rao Randhi

Rama Venkata S Kavali

Damodarrao Thakkalapelli

Vijaya Kumar Vegulla

Praveen Kumar Trivedi

Anshuman Mohanty

Netla Hanumantha Reddy

Ravindra Dabbiru

Jyothi Gaddam

Ritesh Kumar Dash

Aditya Kulkarni

Rama Venkata Kavali

Sandeep D'Souza

Pitti Venkateswarlu

Rajasekhar Maramreddy

Nagendra B Grandhye

Lawrence Anthony D'Silva

Anil Kumar Venkata Kalyanam

Kumar Mahadeva Setty

Lawrence D′silva

Saritha Talluri

Lawrence D'silva

Manohar Aedma

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

In conventional networked computing systems, each system may have many atomic values shared across multiple application servers. Each atomic value may require different levels of computational resources to store, maintain, and access, and may provide a different level of usefulness within the computing system. As such, a need exists for a system of improving the efficacy of each data element with a neural network or networked computing system. The system provided herein solves with problem by identifying each data element within a computing system; crawling multiple application servers to collect usage values for each data element; calculating a computational weightage of each data elements, identifying critical values; and creating hash values for each critical value.

System and method for improving the efficacy of a data element within a neural network

Systems, computer program products, and methods are described herein for evaluating, validating, correcting, and loading data feeds based on artificial intelligence input. The present invention may be configured to receive a data feed from a source for loading to a target data structure, analyze, based on historical feed data, metadata of the data feed to determine a likelihood of the data feed failing to load, and determine whether the likelihood of the data feed failing to load satisfies a threshold. The present invention may be configured to load the data feed to the target data structure, determine, after loading the data feed to the target data structure, whether the data feed failed to load, and either correct errors in the data feed or add error-containing portions of the data feed to a failed data log.

Systems and methods for evaluating, validating, correcting, and loading data feeds based on artificial intelligence input

Methods and systems are provided for restoring a source database to a target environment. The methods and systems may include calculating an amount of data on each drive of the source database. The methods and systems may include calculating free space of each drive of the target environment. The methods and systems may include shrinking filegroups in the drives of the source database. The methods and systems may include fetching historical transactional queries run on tables in the filegroups. The methods and systems may include identifying interdependencies between tables using the queries. The methods and systems may include ingesting, by an AI engine, the calculations, the queries, the identified interdependent tables, historical information of table movement and a business use case. The methods and systems may include the AI engine determining how to restore the source database using the ingested information.

Intelligent file and filegroup reorganizer for backup and restore in multi environments

A device configured to identify a first link between a value of a first data element in a first plurality of data elements and values of a first set of data elements in a second plurality of data elements and to remove the first link between the first data element and the first set of data elements. The device is further configured to input the data elements into a machine learning model that is configured to output a second link between the first data element and a second set of data elements. The device is further configured to create an entry in a relationship table that identifies the first data element and the second set of data elements. The device is further configured to generate a data stream with the first data element and the second set of data elements and to output the data stream.

Data flow control and routing using machine learning

A system includes one or more source memory devices of a source computing environment that store a database comprising data files, wherein each of a plurality of data tables of the source computing environment includes data from one or more of the data files, one or more target memory devices of a target computing environment and at least one processor configured to receive a command to copy data files from the source memory devices to the target memory devices, detect that the target memory devices have insufficient memory, calculate a value coefficient for each data table, assign a priority index to each data table based on the value coefficient, order the data files in a copy queue based on the priority index of the data tables, and copy the ordered data files to the target memory devices.

Table prioritization for data copy in a multi- environment setup

A system accesses a set of devices transferring a plurality of data elements from a source device to a destination device. The system determines that a first subset of data elements from among the plurality of data elements is transformed in a first subset of devices. The system determines that a second subset of data elements from among the plurality of data elements is transformed in a second subset of devices. The system splits the plurality of data elements into the first subset of data elements and the second subset of data elements. The system communicates the first subset of data elements using a first transfer path through the first subset of devices. The system communicates the second subset of data elements using a second transfer path through the second subset of devices.

System and method for splitting data elements for data communication based on transformation types implemented on the data elements at different devices

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.

Real time optimization apparatus using smart contracts for dynamic code validation and approval

Systems for dynamic data reorganization and query optimization are provided. In some aspects, a database query may be received and evaluated to determine whether it meets one or more criteria. If the database query meets the one or more criteria, the query may be executed and the output of the executed database may be transmitted to the user device. If the database query does not meet the one or more criteria, data associated with the database query may be dynamically modified and/or reorganized (e.g., using machine learning). Modified data may be generated based on the dynamically modified and/or reorganized data. The modified data may be stored in an optimized query cache. An updated query may be generated based on the modified data and stored in the optimized query cache. The updated query may be executed and an output of the executed updated query may be transmitted to the user device.

Multicomputer system with machine learning engine for query optimization and dynamic data reorganization

A system includes one or more source memory devices of a source computing environment that store a database comprising data files grouped in a plurality of file groups, wherein each of a plurality of data tables of the source computing environment includes data from one or more of the data files grouped in to one or more of the file groups, one or more target memory devices of a target computing environment and at least one processor configured to receive a command to copy data files from the source memory devices to the target memory devices, detect that the target memory devices have insufficient memory, calculate a value coefficient for each data table, assign a priority index to each data table based on the value coefficient, re-arrange the file groups based on priority indices of the data tables, and copy the re-arranged file groups to the target memory devices.

Table prioritization for data copy in a multi-environment setup

Systems, computer program products, and methods are described herein for monitoring and automatically controlling batch processing. The present invention may be configured to receive a plurality of data processing requests and determine a processing plan for the plurality of data processing requests. The present invention may be configured to provide, to processing applications and based on the processing plan, actions for performance by the processing applications to complete the plurality of data processing requests. The present invention may be configured to predict, while the processing applications are performing the actions and using a completion time predicting machine learning model, completion times for the plurality of data processing requests.

Electronic system for monitoring and automatically controlling batch processing

An illustrative computing system for a dynamic user access control management system classifies users and data resources according to their risk and importance by a user management engine with artificial intelligence, machine learning characteristics. The dynamic user access control management system analyzes the log files of data resources to measure system performance characteristics and user access behavior. This system monitors the device and network by which a data access request to a data resource is made. The dynamic user access control management system validates the leave status of a user initiating a data access request. The dynamic user access control management system automatically determines a user access level for a data resource through intelligent analysis of collected information and defers to a user's manager for an access level determination when the determination to grant an access level is outside of the knowledge base of the user management engine.

Growing community of inventors

Hyderabad Telangana, India

Venugopala Rao Randhi