Bank of America Corporation

Daniel Joseph Serna

Marcus Raphael Matos

Christopher Lee Danielson

Patrick Nicholas Lawrence

Jack Lawson Bishop, Iii

Jake Michael Yara

Benjamin F Tweel

Robert Cain Durbin, Jr

Brandon Sloane

Gilbert M Gatchalian

Marci Landy

Harvey Summers

Tina Berumen Pachorek

Jessica Hope Thompson

Joseph Henry Pindell, Jr

Mrunal Mody

George Albero

Michael Ogrinz

Elena Kvochko

James J Siekman

Matthew K Bryant

Richard Scot

Jennifer T Renckert

Jeffrey Kyle Johnson

Frank J Yanan

Sheng Tang Hsiang

Ryan Hanley

Systems, computer program products, and methods are provided for code revision impact analysis. The method includes generating a system map based on data received from a plurality of network devices; receiving a data transmission including a text file; processing the text file via a natural language processing engine, where an output of the natural language processing engine comprises a plurality of expected updates; determining, based on the system map, at least one downstream effect of the plurality of expected updates; and performing a remedial action.

Natural language processing engine for component level data mapping and code revision impact analysis

Systems, computer program products, and methods are provided for automated detection of source code discrepancies. The method includes receiving a data transmission including a text file and a source code file; processing the source code file via a machine learning engine, where an output of the machine learning engine includes a plurality of identified updates; processing the text file via a natural language processing engine, where an output of the natural language processing engine includes a plurality of expected updates; identifying a difference between the plurality of identified updates and the plurality of expected updates; and performing a remedial action.

Natural language processing engine for automated detection of source code discrepancies

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.

Systems, methods, and apparatuses for automatically classifying data based on data usage and accessing patterns in an electronic network

Systems, computer program products, and methods are described herein for verifying authentication credentials in an electronic network. The present invention is configured to receive a first IP address associated with a first access attempt; determine a first geolocation data based on the first IP address; receive a second IP address associated with a second access attempt; determine a second geolocation data based on the second IP address; determine a geolocation variance between the first and the second geolocation data; determine, based on the geolocation variance, an indication of potential movement between the access attempts; apply, based on the indication of potential movement, a verification machine learning model to the account identifier; generate, by the verification machine learning model, a privacy score; and generate, based on the privacy score, an alert user interface component to configure a GUI of a device associated with the user of the account.

Systems, methods, and apparatuses for verifying authentication credentials in an electronic network

Systems, computer program products, and methods are described herein for identifying unauthorized use of a user's authentication credentials to an electronic network based on non-public data access. The present invention is configured to receive a verified access attempt at a first time for a user account; receive an unverified access attempt at a second time for the user account; determine the unverified access attempt is a credential sharing event for the user account; determine the user account is an internal account; determine an unverified user associated with the unverified access attempt is an external user; receive unverified account access logs associated with the unverified access attempt, the unverified account access logs comprising access to non-public data; and generate an unverified data access interface component to configure a graphical user interface of a device associated with a manager of the system.

Systems, methods, and apparatuses for identifying unauthorized use of a user's authentication credentials to an electronic network based on non-public data access

Systems, computer program products, and methods are described herein for analyzing micro-anomalies in anonymized electronic data. The present disclosure is configured to import or retrieve a first data set, process the first data set to develop at least one event-outcome projection, define an outcome projection data set, import or receive a monitored user data set, anonymize the monitored user data set, define an avatar data set process the avatar data set, wherein the steps of import or receive a monitored user data set, anonymize the monitored user data set, and define an avatar data set are repeated one or more times.

System and method for analyzing micro-anomalies in anonymized electronic data

A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to input the user activity information into a first machine learning model that is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a second machine learning model that is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Threat detection and prevention for information systems

A system is configured for associating a CVE with a particular device profile is disclosed. The system receives a request from a user to associate a CVE with a particular device profile. For each device profile from a plurality of device profiles stored in a memory, the system determines feature importance values for features of each device profile. The features of each device profile include at least an operating system and a CPU architecture. The feature importance value of a corresponding feature of a device profile associated with a CVE indicates a probability of the CVE to affect the device profile with respect to that feature. The system identifies a device profile that has features with a total feature importance value above a feature importance threshold value. The system identifies a particular CVE associated with the identified device profile. The system associates the particular CVE with the particular device profile.

System and method for associating a common vulnerability and exposures (CVE) with a computing device and applying a security patch

A device that is configured to receive user activity information from a network device. The user activity information includes information about user interactions with the network device for a plurality of users. The device is further configured to input the user activity information into a machine learning model. The machine learning model is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The set of bad actor candidates identifies one or more users from among the plurality of users. The device is further configured to receive the set of bad actor candidates from the machine learning model and to output the set of bad actor candidates.

Detecting bad actors within information systems

Apparatus and methods for dynamically controlling application of a graphic processing unit ('GPU') based on the current processing needs is described. A number of objects in each image may be detected. A machine learning model may be utilized to detect and classify objects in each image. Based on the number of detected objects, the GPU could be switched on to provide increased image processing performance. Dynamically turning the GPU on/off may reduce power consumption. When the GPU is turned off, a central processing unit (“CPU”) may be used to perform image processing.

Object recognition subject to a throttling effect

A system for determining a likelihood that a pre-determined customer will enter a selected customer support center (CSC) is provided. The system includes a customer-tracking system for determining whether the customer is within a predetermined distance of the CSC. The tracking system is set to a tracking state in response to receiving a customer tracking opt-in selection. The system also includes a biometric monitoring system that monitors, using an electronic device in close proximity to the customer, when the customer is determined to be within the pre-determined distance of the CSC. The biometric monitoring system monitors the customer's biometric functions to determine the likelihood of the customer entering the CSC. When a determination of the likelihood of the customer physically entering the CSC is higher than a threshold level, the system may provide the likelihood, as further influenced by the customer sentiment condition, of the customer entering the CSC.

Customer sentiment-driven, multi-level, proximity responder

A system for leveraging a tokening system to authenticate a traveler while maintaining anonymity of the traveler is provided. The system may include a secure central server configured to store identification data associated with a plurality of users. The system may also include a mobile token application for providing a token identifier representing a user's identification data. The mobile token application may be activated by the central server on a mobile device of the user registered with the central server and stored in a secure storage on the mobile device of the user. The system may also include a travel service provider. The travel service provider may be configured to enable reserving a travel reservation file. The travel service provider may be enabled to retrieve a token identifier from the user's mobile device as representing the identification of the user and verify the token identifier with the central server.

Travel identity tokening

A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to receive a set of bad actor candidates that identifies one or more users from among the plurality of users. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a machine learning model. The machine learning model is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Growing community of inventors

The Colony, TX, United States of America

Daniel Joseph Serna