Bank of America Corporation

George Albero

Maharaj Mukherjee

Elijah Clark

Edward Lee Traywick

Jinna Zevulun Kim

Scot Lincoln Daniels

Dustin Paul Stocks

Jake Michael Yara

Rick Wayne Sumrall

Konata Stinson

Emanuel David Guller

Manu Jacob Kurian

Olga Kocharyan

Cody Dean Searl

Deborah Mayers

Benjamin D Moores

William August Stahlhut

Elizabeth R Liuzzo

Kevin A Delson

Kyle A Mayers

Michael Beveridge

Youshika C Scott

Charlene L Ramsue

Gulsen Saffel

Elizabeth Jordan Knight

Lauren Thanh Nguyen-Lo

Robert Jon Armstrong

Nathalie Elizabeth Conley

Keaton Drake

Michael Robert Young

Michael Ogrinz

Siten Sanghvi

Heather Roseann Dolan

Naga Vamsi Krishna Akkapeddi

Morgan Stewart Allen

Prashant Thakur

Madhavi Mendu

Caleb G Mann

Elena Kvochko

Marci Landy

Brian H Corr

Daniel Horne

Karen E Davis

Elizabeth Swanzy-Parker

Robert A Lang

Scott Nielsen

Thomas G Frost

David Joseph Walsh

Amit Pandey

Ariel Fontaine Hill

Daniel Martin

Paul Swiencicki

Guisen Saffel

Stephen C Brown

Pratap Dande

Monika Kapur

Christopher Daniel Birch

Daniel Joseph Serna

Richard Scot

James J Siekman

Carrie Elaine Gates

Sanjay Arjun Lohar

Robert Nyeland Huggins

Jeffrey Kyle Johnson

Ron Papka

Jon Codispoti

Timothy Scott Murphy

Lisa Snow Pugh

Hannah Kaplan

Kristoffer Matthew Bertsch

Elizabeth Rhodes Kral

Anna Maria Mirarchi

Andrew Sides

Anna Xu

Samantha Ilse Quales

John Scott Alston

Michael B Jones, Jr

A system is provided for remediating computing system breaches by reversibly distributing data to a plurality of edge points in a distributed network. In particular, the system may intelligently track network data to predict breach vector pathways and may track various types of network traffic data for computing systems within the network. Based on the network traffic data, the system may generate a network topology, mapping the various types of network connections across the computing systems in the network environment as well as the datasets that were transferred across such connections. In the event of a system breach, the system may divide each dataset into a plurality of partial data packets. The system may transmit each partial data packet to a different user device or computing device in the network. After the breach is remediated, the system may recall the individual data packets and reassemble the datasets for future use.

Establishing dynamic edge points in a distributed network for agnostic data distribution and recovery

Systems, computer program products, and methods are described herein for implicit item embedding within a simulated electronic environment. In various embodiments, the invention includes utilizing a hybrid recommendation engine, the invention suggests product placements based on user data and preferences of a specific user. During the initial warm-up period, either user-based or product-based collaborative filtering is applied to assign the user to a collaborative user group until more information about the user becomes available. The hybrid recommendation engine is enhanced through a collaborative clustering component, which involves assigning the user to the collaborative user group via user-based collaborative filtering based on their similar user characteristics, product interests, or product preferences. The invention dynamically alters digital content streamed to the user's device. An implicit product placement module integrates recommended products within the entertainment content, providing a seamless and personalized experience for the user.

System and method for implicit item embedding within a simulated electronic environment

Aspects of the disclosure relate to predicting the spread of malicious software. The computing platform may identify malicious software at a computing device and may input characteristics of the malicious software into a machine learning model to produce time horizons for the malicious software. The computing platform may identify, using a knowledge graph and based on the time horizons, subsets of computing devices, each corresponding to a particular time horizon. The computing platform may perform, at a time within a first time horizon, a first security action for a first subset of computing devices within the first time horizon and a second security action for a second subset of computing devices located within a second time horizon, where the first time horizon and the second time horizon indicate that the first subset will be affected by the malicious software prior to the second subset.

Hybrid machine learning and knowledge graph approach for estimating and mitigating the spread of malicious software

Systems, computer program products, and methods are described herein for automated translation of digital multimedia. The present disclosure is configured to receive, from a user input device, a search request; determine the response for the search query in a first natural language, wherein the response comprises digital multimedia; reconfigure the response for display on the user input device in the preferred natural language, wherein reconfiguring further comprises: determining an intermediate natural language based on a lexical similarity with the preferred natural language; translating, using a first natural language translation subsystem, the response from the first natural language to the intermediate language; and translating, using a second natural language translation subsystem, the response from the intermediate language to the preferred natural language; and display the reconfigured response on the user input device in the preferred natural language.

Natural language generation system for automated translation of digital multimedia

A system is provided for implementing time-restricted access control to electronic digital resources. In some embodiments, the custom set of executable code used to generate the digital resource may provide granular control over access restrictions for accessing, viewing, and/or transferring the digital resource. Such access restrictions may include time duration restrictions, access frequency restrictions, data quality restrictions, and/or the like. In this way, the system may limit access to the digital resource in a secure manner.

System and method for implementing time-restricted access control to electronic digital resources

A computing platform may receive, from a plurality of IoT information sources, historical information associated with a user. The computing platform may generate, based on the historical information, a user specific policy, defining information distribution rules for the user. The computing platform may receive, from an information collection system, a request for information. The computing platform may identify whether or not the request for information requests personal information of the user. Based on identifying that the request for information requests the personal information of the user, the computing platform may identify whether or not the requested personal information violates the user specific policy. Based on identifying that the requested personal information does not violate the user specific policy, the computing platform may obscure the requested personal information, and send, to the information collection system, the obscured personal information.

Permission based information transfer based on internet of things (IoT) insights

Aspects of the disclosure relate to message validation. A computing platform may receive a message, sent by a first device and directed to a second device, from an electronic messaging server. The computing platform may verify, based on message sender details, an identity of a sender of the message. The computing platform may generate, after verifying the identity of the sender of the message, an authentication token for the message, and may inject the authentication token into the message. The computing platform may route, to the electronic messaging server and after injecting the authentication token into the message, the message. The computing platform may receive a request to validate the message, which may include the authentication token and details of the message. The computing platform may identify, using the authentication token and the details of the message, that the message is authenticated.

Email verification using injected tokens for message authentication

User authentication/login occurs prior to a user accessing a network, application, web site/service or coming in contact with a computing device. Pre-authenticating/logging-in of users in advance of the user visiting or accessing the computing technology. A single device passcode that unlocks the device and a passcode vault storing the user's passcodes. Unlocking the passcode vault provides for the passcodes stored therein to be automatically applied to the corresponding entities requiring authentication to grant users access without requiring any user input/actions. Machine-Learning (ML) models that are trained to learn user login patterns are implemented to identify login pattern(s) and subsequently passcodes are retrieved for the entities making up the login pattern(s) from the passcode vault. Prior to the user accessing the entities that make up the login pattern, the present invention accesses the entities and applies the retrieved passcodes to pre-authenticate the user.

Pre-access automated login to computing devices, applications and web sites/services based on machine-learning determined user access patterns

A single device-level passcode serves to unlock a passcode vault, such that, automated login/access to other computing devices, applications, networks and/or websites/web services, which have the user's passcodes stored in the vault, occurs. Specifically, in the case of other computing devices, short-range wireless communication between the initial logged-in computing device and another computing device triggers retrieval of the other computing devices passcode from the passcode vault and automated user log-in at other computing device. The passcode may be mapped to previously-captured user characteristics, such that multi-factored authentication including user characteristic verification may occur at the time the passcode vault is unlocked (i.e., when the single passcode is entered), continuously throughout the device session and/or when the passcode vault is accessed to retrieve a passcode for another device, network, application or web site/service.

Single device-level passcode that provides automated access to multiple computing devices and passcode-protected applications and web sites/services

Arrangements for providing dynamic user authentication are provided. In some aspects, a user may initiate a transaction at a merchant point-of-sale (POS) system, via a merchant website, or the like. In response, user data from one or more pre-registered user devices may be retrieved. The data may be analyzed to identify one or more data points for use as a correct answer to an authentication question. An authentication question may be dynamically generated and transmitted to the merchant system for display and the user may provide authentication response data. The authentication response data may be received and compared to the data points providing the basis for the authentication question. If the data matches, the user may be authenticated and the transaction may be processed. If not, additional user authentication data may be requested. The system may then delete the received user data.

Secure user authentication based on dynamically generated user prompts

Arrangements for secure user authentication through hardware analysis and monitoring are provided. In some aspects, a computing platform may receive, from one or more hardware devices, user interaction data. The computing platform may establish a baseline indicating an expected usage pattern of the user. The computing platform may receive, from the one or more hardware devices, subsequent user interaction data and determine whether the subsequent data is unauthorized. Based on the subsequent data not being unauthorized, the computing platform may calculate an updated baseline in real-time as the subsequent data is received. The computing platform may monitor for additional user interaction data from the one or more hardware devices and compare the additional user interaction data with the updated baseline. In response to the additional user interaction data deviating from the updated baseline by a predetermined threshold, the computing platform may identify an anomaly and transmit a security notification.

Secure user authentication through hardware analysis and monitoring

Alternative distribution/communication of and access to One-Time Passwords/Passcodes (OTPs). Triggering events are detected that result in the OTP being communicated to a communication device different from the device designated by the user for receiving OTPs. The triggering event may be determination that the user's designated device (i) is not currently in possession of the user, (ii) is located in a geo-location designated for No-OTP receipt, and/or (iii) is currently in an inoperable state. The different communication device may be a device other than one in possession/control of the user and may be a device designated by the user or the device may be one selected, based on proximity of location to the user, from a network of trusted devices/individuals.

Alternative distribution and accessing of one-time passwords (OTPs)

A computing platform may train, using historical call information, a prompt generation model to identify, for an initiated call between a first individual and a second individual, one or more security prompts to validate an identity of the first individual. The computing platform may detect and temporarily pause a call. The computing platform may input, into the prompt generation model, information of the call, which may cause the prompt generation model to output the security prompts, which may be customized CAPTCHA tests based on the information. The computing platform may send, while the call is paused and to a user device of the first individual, the security prompts. The computing platform may receive, while the call is paused and from the user device, responses to the one or more security prompts. The computing platform may validate, while the call is paused, the responses, and resume the call.

Automated vishing detection to prevent deepfake and chatbot attacks

Aspects of the disclosure relate to energy optimization. A computing platform may receive a cryptocurrency request indicating a gas price and KPIs of the cryptocurrency request. The computing platform may input the gas price and the KPIs into a mining optimization model, which may cause the mining optimization model to output an energy mix for use in processing the cryptocurrency request. The energy mix may indicate energy sources, capable of providing an amount of energy needed to process the cryptocurrency request and used to support operation of a data center configured to process cryptocurrency requests, and a cost of the amount of energy from the energy mix may be less than a cost of the amount of energy from other available energy sources. The computing platform may send commands directing the data center to process the cryptocurrency request using the energy mix, which may cause it to do so.

Energy optimization platform for cryptocurrency mining

A device for performing a secured operation is disclosed. The device is embedded with a predefined data item and verification information. The verification information is uniquely associated with the user of the device. The verification information is linked to the identity of the user. The verification information is configured to be extracted in conjunction with the secured operation being performed. The secured operation is performed in response to the verification information being verified. A system and method for performing the secured operation are also disclosed.

System, device, and method for performing secured operations

Growing community of inventors

Charlotte, NC, United States of America

George Albero