Wells Fargo Bank, N.a.

Eduardo DeLa Torre

Darren M Goetz

Miranda C Hill

Paul Vittimberga

Robert Glenn Hamchuk

Pey-Ning Huang

Charles Cowell

Eric David Greene

Orsolya Oldroyd

Jennifer Stacey Borchardt

Adam Clifford Christensen

Dennis Emmanuel Montenegro

Andrew J Garner, Iv

Prasanth Nandanuru

Dinakar Channakal Krishnappa

Yevanna M Yejjala

Chinababu Kona

Shiju Thukalankuzhy John

Sai Krishna Verma Sundaragiri

Priyanka Amara

Kory Bunya

Kourtney Eidam

Dominik Vltavsky

Erik Pilz

Harlen Bayha

Richard Harrison Luttrell, Jr

Clyde E Cutting

Wei Su

Keith W Harris

A system for monitoring financial threats includes: at least one central processing unit and system memory that causes the system to: receive information about a threat associated with a financial services device; identify a type of the threat; identify a location associated with the threat; and notify one or more customers associated with the financial services device based upon the type or the location of the threat.

Threat monitoring and notifications

Systems, methods, and apparatuses are described for providing trusted data. A digital trusted data (DTD) computing system of a service provider can accept requests for trusted data from internal computing systems and/or external computing systems of third parties. Based on the data requested, a retrieval protocol is determined, the retrieval protocol identifying trusted data sources and a sequence (order and/or timing) for accessing the data sources. The trusted data sources may, for example, be internal computing systems, external computing systems, and/or documents provided by a client. The requested data may be used for populating a form being completed by the client as part of an application for services from the service provider or from a third party. Data can be retrieved from data sources that are less cumbersome for the client before sources that are more cumbersome are accessed.

Systems and methods for providing digital trusted data

A method implemented on an augmented reality electronic device includes establishing a wireless connection with an automated teller machine (ATM). The AR electronic device is used to authenticate a user of the AR electronic device at the ATM. An initiation is permitted of a financial transaction through user interface functionality for the ATM that is displayed on the AR electronic device. Financial information for the financial transaction is displayed on a display screen of the AR electronic device, instead of displaying the financial information at the ATM.

Augmented reality security applications

An application can be converted to new programming language or framework. A source application written for a legacy framework is analyzed and divided into smaller segments of code. The smaller segments are evaluated for quality in view of best practices design for writing applications. A rubric is determined for each segment and compared to a best practice threshold. Segments can be evaluated for features that can be passed through a model. The model converts the features into new code adapted for target framework. Machine learning and rules databases are updated with details of the conversion.

Programming language conversion

An example method for displaying renovation options for a structure includes: receiving homeowner financial information; receiving a selection of a renovation option; receiving pricing information of the renovation option; generating a financial impact analysis based on the homeowner financial information and the pricing information; displaying a renovation view on an augmented reality device, the renovation view displaying the structure with a representation of the renovation option depicted thereon; and displaying a financial impact analysis view showing the financial impact analysis, including a renovation budget amount.

Augmented or virtual reality to scenario plan property purchase or renovation

Systems and methods for converting an application to new programming language or framework is provided. A source application written in a legacy framework is analyzed and divided into smaller segments of code. The smaller segments are evaluated for quality in view of best practices design for writing applications. A rubric is determined for each segment and compared to a best practice threshold. Segments can be evaluated for features that can be passed through a model. The model converts the features into code in the new programming language. Machine learning and rules databases are updated with details of the conversion.

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Oakland, CA, United States of America

Eduardo DeLa Torre