Bank of America Corporation

Sri Lakshmi Priya Doraiswamy

Siva Kumar Paini

Sathya Thamilarasan

Nagalaxmi Sama

Sakshi Bakshi

Srinivasa Jitendra Dhanwada

Shyamala Manoharan

Sudhakar Balu

Aspects of the disclosure relate to upgrading an application within a simulated version of an enterprise system to detect and correct potential errors as a result of the upgrade. A computing platform may create a simulated version of the enterprise system by receiving metadata associated with the enterprise system, and converting the metadata into system parameters. Virtual parameters may be created by the computing system based on upgrading an application within the simulated version of the enterprise system. The computing system may determine errors caused by the application upgrade within the simulated version of the enterprise system based on differences between the system parameters and the virtual parameters. The computing platform may determine actions to correct the errors and input the results and feedback into an AI engine to further refine the accuracy and reliability of the computing platform over time.

Artificial intelligence (AI) supported graph enabled method to manage upgrades for applications

Aspects of the disclosure relate to an intelligent resource evaluation engine. A computing platform may monitor the plurality of RPA machines to detect parameter information. The computing platform may store the parameter information along with corresponding RPA machines as a key value pairs in a database. The computing platform may identify first current parameter information for a first RPA machine using the key value pairs. The computing platform may input the first current parameter information into an intelligent resource evaluation model, which may output first machine selection information for the first RPA machine. Based on identifying that the first RPA machine is sufficient to execute the first robotic automation process, the computing platform may send direct the first RPA machine to execute the first robotic automation process.

Intelligent resource evaluator system for robotic process automations

Aspects of the disclosure relate to upgrading an application within a simulated version of an enterprise system to detect and correct potential errors as a result of the upgrade. A computing platform may create a simulated version of the enterprise system by receiving metadata associated with the enterprise system, and converting the metadata into system parameters. Virtual parameters may be created by the computing system based on upgrading an application within the simulated version of the enterprise system. The computing system may create system nodes and virtual nodes. The system nodes and virtual nodes may be dynamically linked in order to determine errors caused by the application upgrade within the simulated version of the enterprise system. The computing platform may determine actions to correct the errors and input the results and feedback into an AI engine to further refine the accuracy and reliability of the computing platform over time.

Graphical neural network for error identification

Growing community of inventors

Charlotte, NC, United States of America

Sri Lakshmi Priya Doraiswamy