Bank of America Corporation

Jayachandra Varma

Manu Jacob Kurian

Gopikrishna Nemalikanti

Jinna Zevulun Kim

Saritha Vrittamani

Ganesh Bonda

Michael Ogrinz

Gilbert M Gatchalian

Christine D Black

Jason T Findley

Harinath Meedinti Bhaskara Reddy

Erica Perkins

Sorin N Cismas

Siten Sanghvi

Kevin A Delson

Lalit Dhawan

Albena Noveva Fairchild

Crystal M Sundaramoorthy

Jesse Strickland Newsom, Iii

Abhishek Nagpal

Floyd Franklin Rock, Jr

Dimitra J Tsagris

Lixian Huang

Nupur Gupta

Hemantkumar D Gajjar

Sachin Lad

Petar Evan Puskarich

Balaji Subramanian

Michael George Horstman

Aeric John Solow

Anuradha Girish Sethuram

Keesara Himabindu

Angela Cain

Anshika Choudhary

Systems, computer program products, and methods are described herein for an intelligent processing engine for acquisition, storage, and subsequent refresh of data from a distributed network. The present invention is configured to receive, from one or more distributed computing systems, an enterprise dataset; convert the enterprise dataset to a standardized format; store the enterprise dataset in one or more storage locations based on a characteristic of the enterprise dataset; provide the one or more distributed computing systems with remote access to the one or more storage locations; monitor, via an artificial intelligence engine, usage data from the one or more distributed computing systems, wherein the usage data is associated with remote access of the one or more storage locations; generate, via the artificial intelligence engine, a use score associated with the one or more storage locations; and automatically refresh the one or more storage locations based on the use score.

Intelligent processing engine for acquisition, storage, and subsequent refresh of data from a distributed network

A system for administering regionally-based data storage and control is provided. The system may include a computer processor and a distributed memory. The distributed memory may be distributed across a plurality of regions. The distributed memory may provide data storage. The system may build a plurality of artificial intelligence (AI) models. Each of the models may correspond to a set of data rules. Each of the data rules governs the storage of data in each of the plurality of regions. The system may further create, or otherwise access, a plurality of data keys. Each of the data keys preferably corresponds to one of the regions. The system preferably centralizes the location of the plurality of data keys in a central location such that control of the data in the plurality of regions can be administered by the processor, via the plurality of data keys, from the central location.

Regionally-based data storage and control

A processor is configured to access a virtual environment comprising a plurality of virtual locations. The processor is configured to generate a primary avatar in a first virtual location based on a superset of the user data associated with a first set of the interaction criteria objects. The processor is configured to receive an interaction request from a user device for an interaction with the entity in a second virtual location. The processor is configured to generate a secondary set of user data based on the superset of the user data and the second set of the interaction criteria objects. The processor is configured to generate a secondary avatar associated with the user device in the second virtual location. The processor is further configured to conduct the interaction between the secondary avatar and the entity in the second virtual location based on the second set of the interaction criteria objects.

System and method for performing interactions across geographical regions within a metaverse

Provided herein is a method of storing an incoming dataset in a data mesh. The method may include a plurality of steps. The steps may include associating a metadata tag with a classifying feature and a storage instruction in a (first) relational database. The steps may include scanning incoming datasets to identify datasets characterized by the classifying feature. The steps may include tagging an incoming dataset to generate a tagged dataset. The steps may include storing the tagged dataset in the data mesh, according to the storage instruction. The steps may include associating, in a second relational database, the metadata tag with the initial storage location. The steps may include modifying the storage instruction. The steps may include storing the incoming dataset an additional time in the data mesh, according to the modified storage instruction.

Self-organization of data storage

A method for facilitating transfer of a new dataset across a network may include the following steps: (a) querying the global dataset via a computer processor to identify an existing dataset having data points corresponding to the new dataset; (b) identifying historical data rules previously used for a set of data transfers relating to the existing dataset; and (c) using the historical data rules to assist a machine learning engine in generating data rules for use with a data transfer relating to the new dataset.

Methods and systems for data leveraging

An apparatus for sorting and ordering data based on filtering parameters comprises a memory operable to store a digital folder and a processor operably coupled to the memory. The processor is configured to receive a transmission comprising verification of an interaction session established between a first user and an entity and to receive file information corresponding to one or more files stored in the digital folder from an external exchange. The processor is further configured to generate a file vector comprising the one or more files of the digital folder that is sorted based on applying a selection factor to the received file information for each of the one or more files. The processor is further configured to send a transmission to a user device comprising the file vector and an indication to utilize a first file of the file vector during the interaction session.

System and method for determining a volatile file based on a selection factor

An apparatus that matches one or more affiliates to a user includes a memory, a geofencing engine, an artificial intelligence engine, and processing circuitry communicatively coupled to each. The geofencing engine determines a current geofenced area of a mobile device of the user. The artificial intelligence engine determines anticipated needs of the user. The processing circuitry determines, using the affiliate database, a list of candidate affiliates for the current geofenced area, and transmits a query to an affiliate feed for the list of candidate affiliates, where the query is based at least in part on the anticipated need of the user. The processing circuitry determines a first affiliate and a second affiliate based on the query and receives query results for the first affiliate and the second affiliate from the affiliate feed. The results are transmitted to the mobile device for presentation in a user interface.

Provisioning services based on geolocation data and activity recognition

Aspects of the disclosure relate to providing cloud computing resources from one or more cloud service providers for a client computing device through a computing platform. The client computing device may benefit from an economy of scale while being able to obtain different types of cloud services over a plurality of cloud providers. The client computing device may request an initial amount of cloud services and subsequently may request cloud services that utilize a requested amount of cloud resources. The requested amount of cloud resources may be apportioned among the plurality of cloud service providers, to provide the requested cloud service. The computing platform may also support a cloud abstraction layer interacting between client computing device and one or more cloud providers so that the client computing device can obtain cloud service in a transparent manner.

Abstraction layer to cloud services

Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

Engine to propagate data across systems

A method for extracting, correlating, consolidating and presenting metadata from transmissions is provided. The method may include receiving a TCP/IP transmission. The transmission may include a header and a body. The method may include extracting an originating IP address from a location of the transmission. The location may be in the header or in the body. The IP address may be extracted in binary form. The method may include determining an accuracy and validity metric of the transmission using an artificial intelligence module. The method may include converting the extracted IP address from binary form into hexadecimal form. The method may include embedding the hexadecimal form of the IP address into one or more unused options of the header. The method may include processing the transmission. The processing may be completed upon determination that the transmission is a valid transmission.

Network forensic system for performing transmission metadata tracking and analysis

Systems and methods for batch multiplexing a plurality of transactions are provided. A system may include a machine-learning ('ML') based engine configured to receive instructions to execute a first transaction, derive a plurality of sets of delay parameters for the first transaction, and display the sets of delay parameters on a graphical user interface ('GUI') associated with a sender of the instructions. The engine may be configured to receive a selection of a set of delay parameters, and, based on the selection, append the first transaction to one of a plurality of pending transaction queues. The engine may be further configured to, within a maximum time window, combine the transactions in the pending transaction queue to create a single multiplexed transaction, and execute the multiplexed transaction. The engine may also be configured to demultiplex the multiplexed transaction after it is executed.

Batch transaction multiplexing engine

A file transfer system that includes a data source, a destination device, and a transfer server. The transfer server is configured to receive a file from the data source and determine a file size of the file. The transfer server is further configured to determine an available disk space for the destination device, to compare the available disk space to the file size of the file, and to determine that the available disk space is less than the file size of the file. In response to the determination, the transfer server is further configured to determine a file type for the file based on content within the file and to identify metadata linked with the determined file type. The transfer server is further configured to extract data from the file corresponding with the identified metadata and to send the data to the destination device.

Growing community of inventors

Irving, TX, United States of America

Jayachandra Varma