Bank of America Corporation

Robert Nyeland Huggins

Pratap Dande

Jinna Zevulun Kim

Brandon Sloane

Vijaya L Vemireddy

Katherine Kei-Zen Dintenfass

Joseph Benjamin Castinado

Lalit Dhawan

Naoll Addisu Merdassa

Karen Lea MacQueen

Michael Joseph Carroll

Graham Wyllie

George Albero

Cameron Darnell Wadley

Richard Scot

Dustin Paul Stocks

Kelly Renee-Drop Keiter

Michael Young

Kapil Chhibber

Kelly Keiter

Sandra Ann Breece

Melissa Christine Derville Hart

Aspects of the disclosure relate an IoT authentication system to strengthen and streamline authentication for transaction processing for users. A transaction computing platform using artificial intelligence may receive from a plurality of IoT devices information regarding interactions with a user. In some arrangements, transaction computing platform may generate constellations based on the received information. A trust status may be determined based on the generated constellations. Transaction authentication may be based at least in part on the determined trust status. In some embodiments, real-time updates of trust status along with transaction alerts may be provided to IoT devices.

IoT based authentication

Resource exchange event authentication predicated on user location that is determined from short-range wireless signals transmitted from a card or wearable device in possession of the user. Artificial Intelligence (AI), including Machine Learning (ML) techniques are relied on to determine an authentication rule and/or parameters for an authentication rule as part of the authentication process. Thus, at least a portion of the authentication rules and/or parameters, which may be based not only on the aforementioned determined location of the user, but also on other factors such as time, amount of resource exchange event, type of objects/services being exchanged in the event and the like, are determined intelligently and dynamically at the time that the resource exchange event occurs.

Artificial intelligence-based system for authenticating a resource exchange event based on geolocation of a carded or wearable device

Arrangements for providing frictionless unauthorized activity detection and user authentication are provided. In some aspects, user data, such as transaction data may be received and used to train a machine learning model. In some examples, the machine learning model may be executed to generate one or more expected user patterns. In some arrangements, a request for transaction may be received. The request for transaction may include transaction details. In response, the system may request current geo-location data of a user. In some examples, the transaction details and geo-location data may be analyzed (e.g., compared to the expected user patterns) to generate an authentication output. The authentication output may then be transmitted to one or more systems to process the requested transaction, prevent transaction processing, or the like.

Secure user authentication using machine learning and geo-location data

Embodiments of the present invention provide a secure edge device for contactless resource distribution and resource crediting from an automobile. In this way, the edge device may be affixed to or embedded within the side or the mirror of an automobile and be able to communicate with a third party device using near field communication. Upon authentication of a user, the edge device may communicate with the third party to transmit and present data about the resource distribution to the automobile display. The system allows for the user to select the resource distribution via the automobile display and provide a completion of resource distributions.

Secure edge device for contactless vehicle processing

According to certain embodiments, a routing system comprises a memory operable to store historical data associated with a plurality of previous data processing transactions and a processor operably coupled to a network interface and the memory. The processor is configured to obtain the historical data associated with the plurality of previous data processing transactions transacted using a first routing between a first computing system and a second computing system and determine a pattern based on the historical data, the pattern determined based on machine learning. The processor is further configured to associate the pattern with a second routing between the first computing system and the second computing system, determine an upcoming data processing transaction corresponding to the pattern, and use the second routing to transact the upcoming data processing transaction.

Routing a data processing transaction based on machine learning

A system is provided for controlling resources using parallel computing devices. In particular, the system may comprise one or more applications installed across one or more computing devices, where the one or more applications may be configured to control resource transfers. Certain applications may be configured such that one application, such as a primary application, may implement one or more controls or restrictions on resource transfers that may be executed by another application, or a secondary application. The primary application may further be configured to monitor the secondary application to retrieve various types of data from the secondary application, such as resource transfer metrics data. The system may be configured to, based on the data associated with the secondary application, generate one or more resource-related projections with respect to the secondary application. In this way, the system provides an efficient way to control resource transfers across parallel computing devices.

System and method for controlling resources using parallel computing devices

Systems, computer program products, and methods are described herein for intelligent assessment models for non-fungible electronic resources. The present invention is configured to receive, from a user input device, a request to value a non-fungible token (NFT) of a resource, wherein the NFT is recorded in a distributed ledger; retrieve, using a valuation engine, one or more attributes associated with the NFT from one or more metadata layers of the NFT; initiate a machine learning model on the one or more attributes associated with the NFT; predict, using the machine learning model, a value of the NFT; and transmit control signals configured to cause the user input device to display the value of the NFT.

System for intelligent assessment models for non-fungible electronic resources

A system is provided including a host processor, a plurality of service processors, a universal processor associated with the host processor and capable of exchanging data with each service processor, a first user device of the first user and a second user device of a second user. The universal processor receives a request from the first user device for a data transaction between the host processor and a service processor, and in response generates a code that can be interpreted by the service processor. Universal processor transmits the code to the first user device, which transmits the code to the second user device. The second user device transmits the code to the service processor. The service processor decodes the code and requests the universal processor to perform the data transaction with the host processor. In response, the universal processor sets up the data transaction.

Data processing transactions between disparate systems using a universal processor

Systems, computer program products, and methods are described herein for implementing resource access protocols within a networked medium. The present invention is configured to receive, from a second user input device of a second user, a request to access a resource in the metaverse; retrieve a non-fungible token (NFT) of the resource in response to the request; retrieve, from a licensing layer associated with the NFT of the resource, licensing rules associated with access to the resource; determine, using a valuation engine, a value associated with access to the resource based on at least the one or more licensing rules; receive, from the second user input device, an indication acknowledging the licensing rules and a digital transfer of an amount of funds corresponding to the value associated with the access to the resource; and authorize the second user input device to access the resource in the metaverse.

Growing community of inventors

Charlotte, NC, United States of America

Robert Nyeland Huggins