Truist Bank

Lenovo (singapore) Pte. Ltd.

David Stone

Amy L Rose

Joseph Aguayo

Scott Travis Leith

William Mark Connolly

Christopher Aaron Whitesock

William Laurence Jaeger, Jr

Randall Scott Springfield

Steven Dale Goodman

Mikio Hagiwara

Eitaroh Kasamatsu

Kohsuke Ohtani

Doyoung Park

Yasmin Martinez

Account permissions and data accessibility can be modified based on level of confidence for a login attempt to the account. User activity observations corresponding to one or more login attempts to access a user account can be stored. A confidence score associated with a successful login attempt of the user account can be determined. The confidence score is based on the user activity observations. A level of access to an application with functions and data for the user account can be determined. The level of access is based on the confidence score. The level of access is associated with the functions and the data that are executable and accessible subsequent to the successful login attempt.

Modifying application function based on login attempt confidence score

A method can include intercepting, by an interceptor positioned between a transfer system and a transfer settlement system, a transaction prior to the transaction being provided to the transfer settlement system. The method can include confirming that data from the transaction matches information in an immutable ledger to which parameters of the transaction have been provided by a notary system that confirms the parameters of the transaction and applies a signature using a private key to the transaction in response to confirming the parameters. The method can include, in response to confirming that the data matches the information, providing the transaction for settlement by the transfer settlement system.

In-line verification of transactions

A source of a login attempt to a user account can be classified using machine learning. For example, a computing system can input user activity observations associated with one or more login attempts to one or more user accounts into a trained machine learning model. One or more distinguishing factors for the one or more login attempts can be received from the trained machine learning model. The computing system can determine a source of a current login attempt by applying a clustering algorithm to current values of the one or more distinguishing factors. The current values may be derived from current user activity observations associated with the current login attempt. The computing system can determine an authentication level for the current login attempt to the user account based on the source of the current login attempt.

Classifying a source of a login attempt to a user account using machine learning

A transaction system can include a computing device with a communication port, a processor device, and a non-transitory computer-readable storage memory. The communication port can communicate data with other computing devices. The non-transitory computer-readable storage memory includes code that is executable by the processor device to perform operations. The operations include receiving parameters of a transfer, verifying the transfer using a multi-factor authentication process, in response to verifying the transfer, using a private key to countersign the transfer and return, via the communications port, a signed transfer package for execution of the transfer, and providing, via the communications port, the parameters of the transfer and countersign data to an immutable ledger.

A transaction system can include a notary system and an interceptor for providing security in connection with processing a transaction. The notary system is configured to confirm parameters of the transaction and to apply a signature using a private key to the transaction in response to confirming the parameters. The notary system is also configured to output a signed transaction for execution, and to provide the parameters of the transaction to an immutable ledger. The interceptor can be positioned between a transfer system and a transfer settlement system. The interceptor is configured to intercept the transaction before the transfer settlement system, to confirm that data from the transaction matches information in the immutable ledger, and, in response to confirming that the data matches the information, to provide the transaction for settlement by the transfer settlement system.

For confining data to particular set of data servers based on a location restriction of the data, systems, apparatus, methods, and program products are disclosed. The apparatus may include a storage device for storing data, a processor, and a memory that stores code executable by the processor. In one embodiment, the processor identifies a location restriction of the data, encrypt. In another embodiment, the processor encrypts the data. In a further embodiment, the processor confines the data to particular set of data servers based on the location restriction.

Confining data based on location

For validating computing device firmware, systems, apparatus, and methods are disclosed. The apparatus includes a processor, and a memory that stores code executable by the processor, the code including code that acquires a firmware manifest for locally stored firmware, authenticates a digital signature of the firmware manifest, and validates contents of the locally stored firmware using the firmware manifest. The apparatus may also include code that generates a local firmware manifest from the firmware, wherein validating the contents of the firmware includes comparing the local firmware manifest to the firmware manifest. In certain embodiments, the apparatus may indicate, to a user, one of success and failure of the firmware validation. The firmware manifest may be a location-specific firmware manifest corresponding to a geographical location of the apparatus and/or computing device including the locally stored firmware.

Growing community of inventors

Wake Forest, NC, United States of America

David Stone