Bank of America Corporation

Samson Paulraj

Suki Ramasamy

Vinay Jaisinghani

Kumaresan Karuppiah

Ravi Ranjan Shandilya

Obi Reddy Yanamala

Awadhesh Pratap Singh

Venkata Durga Rao Medisetti

Chandrashekhar Radhakrishnan

Raghavendra Veerupakshappa

Mudit Chawla

Nikhil Ram

Joshua C Wolfe

Scott B Desalvo

Gina L Tammelleo

Jyothi Lloyd Leslie

Santanu Sarkar

Kavitha Ganapathi Raman

Mahendran Vella Pandian

Garima Dhir

Balasubramanian Bagavathiappan

Phillip Matt Hancock

Kenneth William Schmidt, Jr

Cynthia D Adams

Timothy Krak

Karrie A Loatman

Christophe M Marin

Aspects of this disclosure relate to a blockchain system for management of scripts associated with software applications. The blockchain system may be supplemented by an artificial intelligence (AI)-based system for generation of test scripts. The blockchain system may employ smart contracts for submission and validation of scripts by different nodes of a peer-to-peer (P2P) network.

Blockchain system for source code testing and script generation with artificial intelligence

A system is provided for secure verification of authentication data using quantum computing and a distributed server network. In particular, the system may store a reference set of authentication data associated with a user within a distributed server database in an encrypted form. Subsequently, when the system receives live set of authentication data associated with the user, the system may, using a quantum authenticator, compare the encrypted live set of authentication data with the encrypted reference set of authentication data for authorization purposes. The system may further comprise a quantum machine learning authorization engine which may track historical data and/or settings associated with the user to model and predict behavior patterns of the user. In this way, the system provides a secure and efficient way to perform authentication and/or authorization of the user.

System for secure verification of authentication data using quantum computing and a distributed server network

Embodiments of the invention are directed to intelligently and dynamically controlling both changes made within EUC applications and the control rules associated with such changes. A similarity index is calculated/assigned for each data entry field (i.e., cell/intersection) and the controls implemented when a changes to data in the entry fields occurs is based on the similarity index. In other embodiments, a change to data entry fields dynamically prompts analysis of the change based on historical approval and/or denial patterns specific to the EUC application, the data entry field(s) and/or the user of the application. In response to the analysis the control rules may be dynamically updated, and applied to the current change. In other embodiments, inputs, such as reviewer's comments, are the basis for determining a need to update existing controls or add new controls associated with data entry field(s) and the conditions associated therewith are determined and applied.

Intelligent controls for end-user computing

Aspects of the disclosure relate to dynamic transformation code prediction and generation for an unavailable data element. In some embodiments, a computing platform may execute an extract, transform, and load process on input data received from a plurality of data sources and detect a missing data element in the received input data. Subsequently, the computing platform may generate a prediction model with respect to the missing data element, which may include executing a first, second, and third machine learning algorithm. Next, the computing platform may determine a confidence level of the prediction model. In response to determining that the confidence level is at or above the predetermined threshold, the computing platform may generate executable transformation code implementing the prediction model. Thereafter, the computing platform may monitor transformation code implementations and execute a fourth machine learning algorithm to adjust the prediction model based on the transformation code implementations.

Dynamic transformation code prediction and generation for unavailable data element

A software defect detection tool determines a modification in a software code at a first time and analyzes an execution of the software code to detect a performance issue at a second time. The software defect detection tool detects a defect in the software code by a comparison of the first time and a second time. A software defect analysis tool generates a cause/category combination for a software code defect. The software defect analysis tool determines whether the cause/category combination is an approved combination and whether the software code defect is a false positive. The software defect analysis tool generates a corrective action plan indicating measures to implement to reduce software defects.

Software defect analysis tool

Embodiments of the present invention provide a system for the utilization of a robotic process automation system for functional evaluation and improvement of back end instructional constructs. The system may scan a set of code to identify an exception comprising an exception tag and an exception string located at a first location within the set of code. The system may then transmit the set of code and the identified exception to a robotic process automation system, along with instructions for the robotic process automation system to compare the exception tag and exception string to a replacement code database to identify an exception string. The robotic process automation system can then replace the exception string in the set of code with the identified replacement string.

Robotic process automation system for functional evaluation and improvement of back end instructional constructs

Growing community of inventors

Telangana, India

Samson Paulraj