公开(公告)号：US11895133B2

公开(公告)日：2024-02-06

申请号：US17222440

申请日：2021-04-05

Applicant: BANK OF AMERICA CORPORATION

Inventor： Scott Anderson Sims ,  Jeffrey Brian Bashore ,  Michael Joseph Carroll ,  Christopher J. Cooley ,  Andrew DongHo Kim ,  Pavan Kumar Reddy Kotlo ,  Randy J. Nelson ,  Jennifer Quillen ,  Lizabeth Rosenberg ,  Dharmender Kumar Satija ,  James F. Stevens ,  Craig Douglas Widmann

IPC: H04L29/06 ,  H04L9/40

CPC classification number: H04L63/1425

Abstract: Embodiments of the present invention provide an innovative system, method, and computer program product for automated device activity analysis in both a forward and reverse fashion. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns associated with particular user devices is provided. The system is also designed to generate a historical query of user device touch points or interaction points with entity systems across multiple data vectors, and generate system alerts as patterns or potential issues are identified. Common characteristics of data may be used to detect patterns that are broadened in scope and used in a generative neural network approach.

公开(公告)号：US20220321586A1

公开(公告)日：2022-10-06

申请号：US17222440

申请日：2021-04-05

Applicant: BANK OF AMERICA CORPORATION

Inventor： Scott Anderson Sims ,  Jeffrey Brian Bashore ,  Michael Joseph Carroll ,  Christopher J. Cooley ,  Andrew DongHo Kim ,  Pavan Kumar Reddy Kotlo ,  Randy J. Nelson ,  Jennifer Quillen ,  Lizabeth Rosenberg ,  Dharmender Kumar Satija ,  James F. Stevens ,  Craig Douglas Widmann

IPC: H04L29/06

Abstract: Embodiments of the present invention provide an innovative system, method, and computer program product for automated device activity analysis in both a forward and reverse fashion. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns associated with particular user devices is provided. The system is also designed to generate a historical query of user device touch points or interaction points with entity systems across multiple data vectors, and generate system alerts as patterns or potential issues are identified. Common characteristics of data may be used to detect patterns that are broadened in scope and used in a generative neural network approach.