Bank of America Corporation

Shilpi Choudhari

Jeannette M Ross

Tomy Victor Pathrose

Priyanka Kapoor

Antonio J Kinney

Madhusudhanan Krishnamoorthy

Ravindran Padmanaban

Nagar P Varadarajan

Kumar Amit Dwary

Neeraj Kumar Choudhary

Durga Prasad Kutthumolu

Sakshi Bakshi

Thenamudhan Arumugasamy

Saurabh Rajpal

Giddaiah Kummari

Hwee Leng Toh

Rajasekhar Reddy Patlolla

Ankit Kumar

Aspects of the disclosure relate to a distributed evaluation platform. The distributed evaluation platform may train a machine learning model based on historical nonfungible tokens. The distributed evaluation platform may receive client information from a client device. The distributed evaluation platform may generate NFTs corresponding to the client information. The distributed evaluation platform may generate soft tokens corresponding to each NFT. The distributed evaluation platform may apply test cases to the soft tokens. The distributed evaluation platform may generate remedial tokens based on the soft tokens and remediation actions. The distributed evaluation platform may apply the test cases to the remedial tokens. The distributed evaluation platform may overwrite the NFTs using the remedial tokens. The distributed evaluation platform may send an event processing request to an event processing system. The distributed evaluation platform may refine the machine learning model based on the NFTs.

Distributed evaluation platform for nonfungible tokens using virtual token cloning

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

A guided sampling tool guides the sampling of datapoints in large datasets. Generally, the guided sampling tool applies a machine learning algorithm to a database of historical issues encountered by an organization to guide the sampling of a large dataset. The guided sampling tool can evaluate and change provided variables and weights for performing a sampling. After the datapoints are sampled, the guided sampling tool compares the historic transactions represented by those datapoints to baseline images to determine if the historic transactions encountered a problem or issue, which would affect the overall quality assessment.

Guided sampling for improved quality testing

An apparatus for assessing and controlling the quality of a project in a production environment is provided. The apparatus is configured to: receive a first score, wherein the first score comprises a first numerical value associated with a first level of quality, wherein the first level of quality is associated with a first deliverable; receive a second score, wherein the second score comprises a second numerical value associated with a second level of quality, wherein the second level of quality is associated with the first deliverable; and process the first score and the second score to generate a third score.

Determining a quality score for internal quality analysis

An apparatus for assessing and controlling the quality of a project in a production environment is provided. The apparatus comprises a memory, a processor, and a module stored in memory, executable by the processor, and configured to: receive a deliverable; process the deliverable, wherein processing the deliverable comprises assigning a quality score to the deliverable; compare the quality score value to a threshold value; and determine if the deliverable requires an action in response to comparing the quality score to the threshold value.

Framework for internal quality analysis

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Hyderabad Telangana, India

Shilpi Choudhari