公开(公告)号：US20180107827A1

公开(公告)日：2018-04-19

申请号：US15293941

申请日：2016-10-14

Applicant: Bank of America Corporation

Inventor： Richard Scot ,  Kesha Hamilton ,  Jason Greeter ,  Terry G. McConnell

IPC: G06F21/60 ,  G06F17/30

CPC classification number: G06F21/604

Abstract: Systems and arrangements for integrating two or more overlapping requirements from different assessments are presented. In some examples, determining whether requirements are considered overlapping may include identifying a plurality of aspects of each requirement and comparing the aspects to aspects of other requirements to determine whether at least a threshold number of aspects are the same. Upon identifying two or more overlapping requirements, the system may integrate the two or more overlapping requirements into an integrated requirement. A unique identifier may be generated for the integrated requirement and associated with the integrated requirement. Data may be received responsive to a request for data for an integrated requirement and the system may associate the received data with the integrated requirement and may map the received data to the two or more requirements integrated into the integrated requirement.

公开(公告)号：US20240427862A1

公开(公告)日：2024-12-26

申请号：US18214368

申请日：2023-06-26

Applicant: Bank of America Corporation

Inventor： Dinesh Kumar Agrawal ,  Gilbert M. Gatchalian ,  Steven Greene ,  Richard Scot ,  Sanjay Lohar ,  Benjamin F. Tweel ,  James Siekman ,  Erik Dahl ,  Vijaya L. Vemireddy

IPC: G06F21/31

Abstract: Arrangements for detecting unauthorized activity based on input method analysis and monitoring are provided. In some aspects, identity information associated with a user may be received and be stored. An input may be received from a computing device of the user. An input pattern of the received input may be determined. Using a machine learning model, the input pattern of the received input may be compared to input patterns of humans and input patterns of machines. Based on the comparison, it may be determined whether the user is a human user or a non-human user. Responsive to determining that the user is a non-human user, a request may be transmitted to the user to provide increased authentication credentials. Responsive to determining that the user is a human user, an identity of the user may be verified by comparing the input pattern of the received input to the stored identity information.

公开(公告)号：US11811616B1

公开(公告)日：2023-11-07

申请号：US18173654

申请日：2023-02-23

Applicant: Bank of America Corporation

Inventor： Stephen Jack Williams ,  Richard Scot ,  Rebecca Lynn Pietro ,  John Shelton ,  Abelardo Espinoza ,  Nathan Alexander Dalpini ,  Vani Reddy Nareddy

IPC: H04L41/16 ,  H04L67/306 ,  H04L43/02 ,  H04L43/062 ,  H04L43/04

CPC classification number: H04L41/16 ,  H04L43/02 ,  H04L43/04 ,  H04L43/062 ,  H04L67/306

Abstract: A system for predicting an anomalous request comprises a processor associated with a server. The processor is configured to parse a user profile from a plurality of user profiles to generate a first set of data objects associated with the first user profile. The processor is configured to compare the first set of the data objects to approved data and audit data to generate a second set of data objects with a set of anomalous data indicators for the first user profile. The processor is further configured to process the second set of the data objects through an anomaly learning model to determine a predictive degree of approval associated with the user profile. The processor is further configured to determine to approve, flag or disapprove the user profile based on the predictive degree of approval. The processor is further configured to assign a profile indicator to the user profile.

公开(公告)号：US20240177069A1

公开(公告)日：2024-05-30

申请号：US18071169

申请日：2022-11-29

Applicant: BANK OF AMERICA CORPORATION

Inventor： Daniel Joseph Serna ,  Jeffrey Kyle Johnson ,  Jennifer Tiffany Renckert ,  Richard Scot ,  Benjamin Tweel ,  Frank J. Yanan ,  Jake Michael Yara

IPC: G06N20/20

CPC classification number: G06N20/20

Abstract: Systems, computer program products, and methods are described herein for processing data using an optimized machine learning architecture. The present disclosure is configured to monitor usage data for a plurality of network devices; analyze the usage data using a first machine learning engine; determine, based on an output of the first machine learning engine, at least one data trend; and instruct a second machine learning engine to analyze local data associated with the at least one data trend, wherein the second machine learning engine is hosted on a first network device of the plurality of network devices.

公开(公告)号：US20240031243A1

公开(公告)日：2024-01-25

申请号：US18469952

申请日：2023-09-19

Applicant: Bank of America Corporation

Inventor： Stephen Jack Williams ,  Richard Scot ,  Rebecca Lynn Pietro ,  John Shelton ,  Abelardo Espinoza ,  Nathan Alexander Dalpini ,  Vani Reddy Nareddy

IPC: H04L41/16 ,  H04L67/306 ,  H04L43/04 ,  H04L43/062 ,  H04L43/02

CPC classification number: H04L41/16 ,  H04L67/306 ,  H04L43/04 ,  H04L43/062 ,  H04L43/02

Abstract: A system for predicting an anomalous request comprises a processor associated with a server. The processor is configured to generate a first set of data objects associated with a first user profile. The processor is configured to compare the first set of the data objects to approved data and audit data to generate a second set of data objects with a set of anomalous data indicators for the first user profile. The processor is further configured to process the second set of the data objects through an anomaly learning model to determine a predictive degree of approval associated with the user profile. The processor is further configured to determine to how to process the user profile based on the predictive degree of approval. The processor is further configured to assign a profile indicator to the user profile.

公开(公告)号：US10157285B2

公开(公告)日：2018-12-18

申请号：US15293941

申请日：2016-10-14

Applicant: Bank of America Corporation

Inventor： Richard Scot ,  Kesha Hamilton ,  Jason Greeter ,  Terry G. McConnell

IPC: G06F17/30 ,  G06F21/60

Abstract: Systems and arrangements for integrating two or more overlapping requirements from different assessments are presented. In some examples, determining whether requirements are considered overlapping may include identifying a plurality of aspects of each requirement and comparing the aspects to aspects of other requirements to determine whether at least a threshold number of aspects are the same. Upon identifying two or more overlapping requirements, the system may integrate the two or more overlapping requirements into an integrated requirement. A unique identifier may be generated for the integrated requirement and associated with the integrated requirement. Data may be received responsive to a request for data for an integrated requirement and the system may associate the received data with the integrated requirement and may map the received data to the two or more requirements integrated into the integrated requirement.

公开(公告)号：US12088477B2

公开(公告)日：2024-09-10

申请号：US18469952

申请日：2023-09-19

Applicant: Bank of America Corporation

Inventor： Stephen Jack Williams ,  Richard Scot ,  Rebecca Lynn Pietro ,  John Shelton ,  Abelardo Espinoza ,  Nathan Alexander Dalpini ,  Vani Reddy Nareddy

IPC: H04L41/16 ,  H04L43/02 ,  H04L43/04 ,  H04L43/062 ,  H04L67/306

CPC classification number: H04L41/16 ,  H04L43/02 ,  H04L43/04 ,  H04L43/062 ,  H04L67/306

Abstract: A system for predicting an anomalous request comprises a processor associated with a server. The processor is configured to generate a first set of data objects associated with a first user profile. The processor is configured to compare the first set of the data objects to approved data and audit data to generate a second set of data objects with a set of anomalous data indicators for the first user profile. The processor is further configured to process the second set of the data objects through an anomaly learning model to determine a predictive degree of approval associated with the user profile. The processor is further configured to determine to how to process the user profile based on the predictive degree of approval. The processor is further configured to assign a profile indicator to the user profile.

公开(公告)号：US20240176804A1

公开(公告)日：2024-05-30

申请号：US18071227

申请日：2022-11-29

Applicant: BANK OF AMERICA CORPORATION

Inventor： Marcus Raphael Matos ,  Richard Scot ,  Daniel Joseph Serna ,  Matthew K. Bryant

IPC: G06F16/28

CPC classification number: G06F16/285

Abstract: Systems, computer program products, and methods are described herein for automatically classifying data based on data usage and accessing patterns in an electronic network. The present invention is configured to receive at least one query log comprising a plurality of data identifiers; generate a data identifier total based on each data identifier of the plurality of data identifiers; determine a data classification for each data identifier based on the data identifier total, wherein the data classification comprises at least one of an important classification or an unimportant classification; and generate a data catalogue comprising at least one data identifier associated with the important classification.

公开(公告)号：US11658881B1

公开(公告)日：2023-05-23

申请号：US17854365

申请日：2022-06-30

Applicant: Bank of America Corporation

Inventor： Stephen Jack Williams ,  Richard Scot ,  Rebecca Lynn Pietro ,  John Shelton ,  Abelardo Espinoza ,  Nathan Alexander Dalpini ,  Vani Reddy Nareddy

IPC: H04L41/16 ,  H04L67/306 ,  H04L43/04 ,  H04L43/062 ,  H04L43/02

CPC classification number: H04L41/16 ,  H04L43/02 ,  H04L43/04 ,  H04L43/062 ,  H04L67/306

Abstract: A system for predicting an anomalous request comprises a processor associated with a server. The processor is configured to parse a user profile from a plurality of user profiles to generate a first set of data objects associated with the first user profile. The processor is configured to compare the first set of the data objects to approved data, audit data, disapproved data to generate a second set of data objects with a set of anomalous data indicators for the first user profile. The processor is further configured to process the second set of the data objects through an anomaly learning model to determine a predictive degree of approval associated with the user profile. The processor is further configured to determine to approve, flag or disapprove the user profile based on the predictive degree of approval. The processor is further configured to assign a profile indicator to the user profile.