Bank of America Corporation

Srikanth Vemula

Sasidhar Purushothaman

Pragyan Paramita Hembram

Srinivas Darga

Ramkumar Masilamani

Sravan Kumar Kommu

Sajid A Shah

Manohar Reddy Singamareddy

Ramaiah Muvvala

Namrata Kurmi

Samir Kiranbhai Desai

Rahul Uniyal

Sunil Reddy Gaddam

Mohit Dhingra

Austin J Fernandez

Praveen Kumar Kondabathini

Dileep Umakant Verma

Systems, methods, and apparatus are provided for generating dynamic metaverse accommodations using artificial intelligence. A user device may initiate a metaverse session. Deep learning networks may derive a user profile based on data from the user device and generate a set of samples from the user profile. The generated samples may include user characteristics and avatar features associated with the user characteristics. The generated samples may be used to train a multitask machine learning model. Deep learning networks may generate a metaverse accommodation targeted to the user profile. The accommodation may be rendered in the metaverse environment. The accommodation may be transmitted to an actuator at the user device. The accommodation may be stored in association with user characteristics in a distributed ledger.

Dynamic metaverse accommodations

Embodiments of the present invention provide a system for dynamically monitoring and filtering data packets associated with accessing one or more entity resources. The system is configured for identifying a data packet in a network comprising at least a first data unit and a second data unit, determining that the first data unit and the second data unit of the data packet are attempting to access an entity resource, determining that first data associated with the first data unit and second data associated with the second data unit cannot access the entity resource at a same instance based on a first signature bit associated with the first data unit and a second signature bit associated with the second data unit, and attenuating the first data unit or the second data unit from the data packet based on the first signature bit and the second signature bit.

System for performing dynamic monitoring and prioritization of data packets

Systems, computer program products, and methods are described herein for continuous cognitive code logic detection and prediction using machine learning techniques. The present invention is configured to receive, from a user input device, source code scripts and target code scripts for functional code logic components of a full stack, wherein the source code scripts and the target code scripts are associated with one or more tiers; generate a training dataset based on at least the source code scripts, the target code scripts, and the functional code logic components of the full stack; train, using a machine learning algorithm, a machine learning model using the training dataset; determine a prediction accuracy associated with the machine learning model; determine that the prediction accuracy is greater than a predetermined threshold; and deploy the machine learning model on unseen source code scripts.

Cognitive software application learner and enhancer

Aspects of the disclosure relate to a data wrapper engine. A computing platform may receive a query comprising a request for data stored as a CLOB. The computing platform may obtain, from a data storage system, the data stored as a CLOB. The computing platform may generate a file wrapper for the data, wherein generating the file wrapper comprises converting the CLOB to a VARCHAR object and storing the VARCHAR object in the file wrapper. The computing platform may generate, using the VARCHAR object stored in the file wrapper, a SQL response to the query. The computing platform may execute the dynamic SQL response to generate a response to the query. The computing platform may send, to a user device, the response to the query and commands directing the user device to display the response to the query, which may cause the user device to display the response.

Independent object generator and wrapper engine

Embodiments of the present invention provide a system for dynamically monitoring and filtering data packets associated with accessing one or more entity resources. The system is configured for identifying a data packet in a network comprising at least one data unit, determining that the data packet is attempting to access an entity resource, determining if the at least one data unit of the data packet comprises a signature bit, and allowing or denying the at least one data unit in the data packet to access the entity resource based on determining if the at least one unit comprises the signature bit.

System for performing dynamic monitoring and filtration of data packets

An executable application's architecture may be mapped by executing the executable application, inputting a series of request data sets into the executable application, receiving one or more responses from the executable application, and performing an evaluation based on the responses. One or more indications of an architectural component may be extracted from metadata associated with the one or more received responses and associated with a corresponding request data set of the series of request data sets. The one or more indications of an architectural component may be associated with processing by the executable application of the corresponding request data set of the series of request data sets. An architecture of the executable application may be determined based on the one or more indications of an architectural component.

Architecture mapping of applications

A system is configured for determining a technology stack in a software application to perform a work project. The system receives and evaluates the work based on its characteristics. A plurality of technology stacks is generated by implementing different combinations of technology stack components. The technology stack components include application servers and webservers. Each of the technology stacks is simulated performing the work project. Based on the simulation results of each technology stack, a performance of each technology stack is evaluated. The system identifies a first technology stack performing at a level higher than a performance threshold and at a highest performance level among the plurality of technology stacks. The system deploys the first technology stack in the software application to perform the work project.

System and method for optimizing technology stack architecture

A system is configured for determining a technology stack in a software application to perform a work project. The system receives the work project from a user. The work project is evaluated based on its characteristics. A plurality of technology stacks is generated by implementing different combinations of technology stack components. The technology stack components include application servers and webservers. Each of the technology stacks is simulated performing the work project. Based on the simulation results of each technology stack, a performance value corresponding to a success rate of performing the work project by the software application. The system identifies a first technology stack having a first performance value that is the highest performance value and above a performance threshold value. The system deploys the first technology stack in the software application to perform the work project.

Disclosed is systems, methods, and computer program products that provide for a technique for reducing computing resources, storage space needs, and network bandwidth associated with collaborative decision making. More particularly, this disclosure relates to a system for performing automatic predictive decision making using predictive fit models derived from previous user responses and the user characteristics of each responding user, and using the results to reduce the amount of computing and operational resources needed to operate a collaborative decision engine.

Growing community of inventors

Hyderabad Telangana, India

Srikanth Vemula