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

Benjamin D Moores

Deborah Mayers

William August Stahlhut

Elizabeth R Liuzzo

Kevin A Delson

Kyle A Mayers

Michael Beveridge

Youshika C Scott

Charlene L Ramsue

Keaton Drake

Lauren Thanh Nguyen-Lo

Nathalie Elizabeth Conley

Robert Jon Armstrong

Gulsen Saffel

Elizabeth Jordan Knight

Michael Ogrinz

Siten Sanghvi

Heather Roseann Dolan

Naga Vamsi Krishna Akkapeddi

Morgan Stewart Allen

Caleb G Mann

Madhavi Mendu

Michael Robert Young

Elena Kvochko

Marci Landy

Brian H Corr

Daniel Horne

Karen E Davis

Robert A Lang

Scott Nielsen

Thomas G Frost

Elizabeth Swanzy-Parker

David Joseph Walsh

Amit Pandey

Daniel Martin

Guisen Saffel

Paul Swiencicki

Stephen C Brown

Ariel Fontaine Hill

Pratap Dande

Monika Kapur

Christopher Daniel Birch

Daniel Joseph Serna

James J Siekman

Richard Scot

Carrie Elaine Gates

Sanjay Arjun Lohar

Robert Nyeland Huggins

Prashant Thakur

Jeffrey Kyle Johnson

Jon Codispoti

Ron Papka

Timothy Scott Murphy

Lisa Snow Pugh

Anna Xu

Elizabeth Rhodes Kral

Anna Maria Mirarchi

Samantha Ilse Quales

Kristoffer Matthew Bertsch

Hannah Kaplan

Michael B Jones, Jr

John Scott Alston

Andrew Sides

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

Various aspects of the disclosure relate to monitoring a physical location to determine and/or predict anomalous activities. One or more machine learning algorithms may be used to analyze inputs from one or more sensors, cameras, audio recording equipment, and/or any other types of sensors to detect anomalous measurements/patterns. Notifications may be sent one or more devices in a network based on the detection.

Artificial intelligence (AI)-based security systems for monitoring and securing physical locations

A computing device may configure a plurality of device partitions. The computing device may send, to a one time password (OTP) server and via an interface of a first partition of the plurality of device partitions, a login request. The computing device may receive, at a second partition of the plurality of device partitions, an OTP. The computing device may access, by validating authentication credentials corresponding to the second partition of the plurality of device partitions, the OTP. The computing device may send, to the OTP server, the OTP. The computing device may access, upon receiving confirmation of validation of the OTP, services hosted by the OTP server.

Enhanced security with multiple factor authentication at devices using partitions

A security feature within an apparatus that includes a fingerprint scanner used for purposes of authentication. Specifically, a first indicator is detected that is a user input to a fingerprint scanner and indicates the occurrence of a duress-inducing event. In response to detecting the first indicator, (i) access to the apparatus is controlled, and (ii) access to functionality provided by the apparatus is controlled. As such, if the user is being forced to input their fingerprint by a nefarious entity desiring access to the apparatus, the present invention provides a means by which the user can covertly activate certain features on the apparatus that prevent the nefarious entity from access to the apparatus and/or functionality provided by the apparatus.

Fingerprint authentication-related indicators for controlling device access and/or functionality

Arrangements for continuous authentication and secure access control are provided. In some aspects, a computing platform may receive user data from a plurality of user data sources. The user data may include a plurality of different data types. The computing platform may use the user data to train a machine learning model, which may then be used to generate user specific baseline data. Subsequent user data may be received and analyzed, using the machine learning model, to determine whether an anomaly exists between the subsequent user data and the baseline data. If not, the user may be considered authenticated and second user data may be received and analyzed to continuously authenticate the user. If an anomaly is detected, the anomalous data and other data may be further analyzed to determine whether to authenticate the user or execute a response action.

Multi-computer system for providing continuous authentication and secure access control

Aspects of the disclosure relate to energy optimization. A computing platform may receive an event processing request. The computing platform may identify parameters of the event processing request. The computing platform may input the parameters into a global energy optimization model, to identify an edge computing system at which to process the event processing request. The computing platform may route the event processing request to the edge computing system along with commands to process the event processing request using an energy mix identified by a local energy optimization model, corresponding to the edge computing system, which may cause the edge computing system to: input the parameters of the event processing request into the local energy optimization model to identify the energy mix, and process, using the energy mix, the event processing request.

Energy optimization platform using artificial intelligence and edge computing

Growing community of inventors

Charlotte, NC, United States of America

George Albero