Bank of America Corporation

Nagar P Varadarajan

Madhusudhanan Krishnamoorthy

Shilpi Choudhari

Ravindran Padmanaban

Kumar Amit Dwary

Neeraj Kumar Choudhary

MadhuSudhanan Krishnamoorthy

Deepika Sehgal

Embodiments of the invention are directed to a system, method, or computer program product for providing an intelligent quality accelerator with root mapping system. The system provides a business language model and process automation that stratifies sampling of resource exchanges from products using a machine learning loop and provides an end to end simulation for root cause analysis. In this way, the system provides two layers, a robust sampling and root cause analysis.

Intelligent quality accelerator with root mapping overlay

Systems, computer program products, and methods are described herein for implementing auto didactic content generation using reinforcement learning. The present invention is configured to retrieve a user interaction portfolio of a user associated with a completion of a first task; determine requirements associated with the first task; determine an interaction score associated with the user; determine a target interaction score associated with the first task; determine that the interaction score associated with the user is less than the target interaction score; electronically receive, from a knowledge repository, a first video file demonstrating the one or more interaction requirements; generate a modified first video file; and transmit control signals configured to cause a computing device of the user to display the modified first video file to the user.

System for implementing auto didactic content generation using reinforcement learning

Systems, computer program products, and methods are described herein implementing content retrofitting using information vectorization. The present invention is configured to retrieve a user interaction portfolio of a user associated with a completion of a first task; determine requirements associated with the first task; determine an interaction score associated with the user; determine a target interaction score associated with the first task; determine that the interaction score associated with the user is greater than the target interaction score associated with the first task; generate one or more second image frames based on at least the one of the one or more user interactions that did not meet the one or more interaction requirements; generate at least one modified video file based on at least the one or more second image frames; and store the at least one modified video file in a knowledge repository.

System for implementing content retrofitting using information vectorization

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Hyderabad Telangana, India

Nagar P Varadarajan