Capital One Services, LLC

Sun Microsystems, Inc.

Oracle America, Inc.

Kevin Grant Osborn

Jeffrey Rule

Srinivasa Chigurupati

David Kelly Wurmfeld

Wayne David Lutz

James M Ashfield

Tyler Maiman

Colin Hart

Kaitlin J Newman

Daniel Herrington

William Duane

Joshua S Edwards

Paul Y Moreton

Bryant Alan Yee

Saleem Ahmed Sangi

Rajko Ilincic

Samuel Rapowitz

Daniel John Marsch

Lara Mossler

James Zarakas

Xiaoguang Zhu

Timur Sherif

Lin Ni Lisa Cheng

Latika Gulati

Melissa Yoemans Heng

Michael Bakr Mossoba

George Bergeron

Eric Loucks

Brian Lee

David Gabriele

Kyle Rankin Johnson

William Carroll

Sophie Bermudez

Sarah Jane Cunningham

Kevan Emmott

Charles Nathan Crank

Andrew Cogswell

Kimberly Haynes

Austin Erickson

Ian Prince

Francis Ogbennah

Anita Eradla

Jason Ji

Christopher Jay Wu

Kyle T Grucci

Ryan S O'Connell

Matthew Kloster

Tao Lin

Tyler Sylvain Locke

Kevin Patrick Kelly

Steve Blanchet

Dennis S Brennan

Samuel Edwards

Imren Johar

Jeff Pharr

Cara Weikel

Michael Wolf

Elisa-Michelle Rodriguez

Matt J Oliver

Lance J Andersen

Asher Smith-Rose

Andrea Montealegre

Vladimir Matena

Mark W Hapner

Kathryn Tikoian

Mia Rodriguez

Jackson Macomber

Brian McClanahan

Hazar Alsahnawi

Vincent Ngo

Andrew Marr

Mary Sweeney

Peter Tanski

Bob Uni Koshy

Shelton Smith

Wendy Wu

Ryan K Lubke

Jaikishan Prasad

Jose Catala Castellar

Mark C Dupuis

Hans Hrasna

Benjamin Eng

Kyle Johnson

Christopher Wu

A method and related system of operations that include retrieving a set of on-chain transactions of an on-chain address from a scan of on-chain transactions based on a request identifying the on-chain address and generating a sequence of embedding vectors by providing the set of on-chain transactions to a first machine learning model. The method further includes predicting a first use pattern of the on-chain address by providing the sequence of embedding vectors to a second machine learning model and determining whether the first use pattern matches with a second use pattern. The method further includes updating a record of the on-chain address in an off-chain database to indicate anomalous activity in response to a determination that the first use pattern does not match with the second use pattern.

Machine learning to detect misclassified on-chain addresses

Example embodiments provide systems and methods for validating an action using a physical token, such as a near-field-communications (NFC)-capable chip. A server may receive a request to perform the action, and may require validation from the holder of the physical token. The holder of the physical token may log into an application using their login credentials, providing a first tier of authentication. The holder may then scan the physical token with a reader on their mobile device, which provides a second tier of authentication. The scan may reveal a value for a counter on the physical token, which may be compared to a counter at the server in order to validate that the physical token has been used as expected. If the server deems it appropriate, a third (or further) tier may be required, such as scanning a photographic identification of the holder.

Systems and methods for cross coupling risk analytics and one-time-passcodes

Various embodiments are directed to securely generating and managing passwords using a near-field communication (NFC) enabled contactless smart card. For example, a secure password may be generated by generating a random number via a random number generator of the contactless smart card and converting the random number to one or more human-readable characters. In another example, a secure cryptographic hash function of the contactless smart card may generate a hash output value, which may be converted to one or more human-readable characters. The human-readable characters may be used as the secure password or it may be transformed to add more layers of security and complexity.

Secure password generation and management using NFC and contactless smart cards

Example embodiments of systems and methods for data transmission between contactless card and receiving devices are provided. In an embodiment, the contactless card may be configured to create a cryptogram based on a plurality of keys and a counter. The cryptogram may be transmitted to the receiving device. The contactless card may be configured to transmit a one-time password to the client device. The counter value may be adjusted each time the one-time password is generated, and the counter may be configured to increment in a non-monotonic sequence, the sequence associated with one or more cryptographic algorithms.

Systems and methods for cryptographic authentication of contactless cards

Example embodiments of systems and methods for data transmission between a contactless card and a server are provided. The card may include an applet, a counter, and a plurality of encryption keys. The applet may rotate the plurality of keys based on a predetermined key rotation, select one or more keys for a cryptographic operation, perform the operation using the selected keys and the counter to generate a cryptographic result, and transmit the result and the counter to the server. Upon receipt of the result, a server, in communication with the card and containing the plurality of encryption keys, may rotate the plurality of keys based on the predetermined key rotation, select one or more keys for decryption, wherein the keys selected by the server matches the keys selected by the applet, and perform the decryption on the result.

Systems and methods of key selection for cryptographic authentication of contactless cards

Techniques generally directed to performing verification techniques with one or more wireless interfaces based on a provided pattern.

Systems and techniques to perform verification operations with wireless communication

Methods, systems, and apparatuses are described herein for protecting the privacy of parties conducting Non-Fungible Token (NFT) transfers by conducting separate NFT transactions on a private blockchain separate from a public blockchain. An issuer and recipient may generate a token secret, and the issuer may send a create token transaction request comprising a unique token identifier, the token secret, and zero-knowledge proof data. Based on that request, an NFT may be minted on a private blockchain. A recipient may request the token by providing the unique token identifier and a zero-knowledge proof generated, by the recipient, based on the token secret. Based on comparing the zero-knowledge proof and the zero-knowledge proof data, the NFT may be sent to the recipient. A hash corresponding to a recipient address and the unique token identifier may be stored in a public blockchain.

Anonymous non-fungible token transfer using private blockchain

Techniques for improved management of data may include the aggregation of connections with data sources, the aggregation of data, and/or the dynamic caching of data using data caching models. The caching model(s) may be adaptable based on data characteristics, user information, and/or usage information.

Account aggregation and caching systems and methods

Transaction card

Systems and methods for recovery access to lost blockchain wallet seed phrases are disclosed. The systems and methods can store seed phrases for users for future retrieval. In some examples, a decentralized oracle creates an oracle private key that can be used to authenticate a user who is requesting the seed phrase. In some examples, the oracle creates a private/public key pair that can be used to transmit and store an encrypted version of the seed phrase. The authentication steps described herein also include knowledge-based authentication questions about the wallet, e.g., prior transactions using the wallet, value of the wallet, and the like.

Knowledge-based authentication for asset wallets

A secure smart card is described. The smart card can include a processor, a memory and a transceiver. The smart card can communicate with various terminals and store a digital signature and other information on the card. Another terminal can validate the information stored on the smart card using the digital signature. In certain embodiments, the terminal can also validate the information by using a blockchain. The advanced design of the smart card obviates the need for a network connection.

System and apparatus for encrypted data collection using RFID cards

Systems and methods of providing multimodal ATM accessibility are disclosed. In one embodiment, an exemplary computer-implemented method may comprise detecting a presence of a wireless device of a user in proximity to the ATM; establishing a communication session between the ATM and the wireless device based on a session key; instructing an app to provide a first graphical user interface (GUI) to the user; authenticating the user for access to the user account at the wireless device based at least on the entered account login information; and commencing, in response to successful authentication of the user, an ATM transaction session for the user by instructing the app to provide a second GUI to the user at the wireless device, the second GUI comprising one or more GUI elements operable by the user to perform one or more ATM transactions with the user account at the wireless device.

Computer-based systems configured to provide multimodal ATM access via mobile devices and methods of use thereof

Systems, methods, and apparatuses are described herein for performing sentiment analysis on electronic communications relating to one or more image-based communications methods, such as emoji. Message data may be received. The message data may correspond to a message that is intended to be sent but has not yet been sent to an application. Using a first machine learning model, one or more subsets of the plurality of emoji may be determined. The one or more subsets of the plurality of emoji may comprise one or more different types and quantities of emoji, and may each correspond to the same or a different sentiment. Using a second machine learning model, one or more emojis may be selected from the one or more subsets. The one or more emojis selected may correspond to responses to the message.

Response prediction for electronic communications

A computer-implemented method includes detecting a communication session established between a first computing device of a first user and a second computing device of a second user; generating session identification information for the communication session; detecting a triggering condition during the communication session to verify an identity of the first user; causing the second computing device to instruct the first user to interact a smart transaction card with the first computing device such that a one-time data item is transmitted from the smart transaction card to an application executing on the first computing device, the one-time data item dynamically generated by the smart transaction card, where the first user is authenticated via the application based at least in part on the level of authentication and the one-time data item; generating a verification token for the communication session, the authentication result stored in association with the session information.

Computer-based systems and/or computing devices programmed for role-based authentication during customer service sessions; and methods of use thereof

Methods and systems are described here for porting accounts to multiple computing platforms. Blockchain technology may be used and in particular cryptographic tokens (e.g., non-fungible tokens) may be used. A cryptographic token may represent a user account and may enable usage of the cryptographic token to provide portability of the user account. An access control system may be used to perform operations described herein. The access control system may receive a request for using, at a computing platform, an account of the user. The request may include an identifier of a cryptographic token. The access control system may determine, based on the cryptographic token, that access to the user account may be permitted and the request may be fulfilled.

Managing user data for different platforms

Growing community of inventors

Newton Highlands, MA, United States of America

Kevin Grant Osborn