Wells Fargo Bank, N.a.

Julio Jiron

Beth S Moss

Chris Theodore Kalaboukis

Raziq Yaqub

Kevin King

James Lyle Schafer

Kalyan Subramanian

Peter Bordow

Jeff J Stapleton

Ramesh Yarlagadda

Richard Orlando Toohey

Volkmar Scharf-Katz

Akhlaq M Khan

Thomas E Bell

Traci H Nguyen

Michael H Chang

Dennis Emmanuel Montenegro

Darren M Goetz

Darrell Lee Suen

Paul Vittimberga

Laura Marie Fontana

Wayne Barakat

Jeffrey A Cornman

Bryan D Hall

John Andrew Chuprevich

Christopher Phillip Clausen

Kevin R Cieslak

Lisa Schur

Joseph Ng

Sotirios K Barkas

JoAnn Mar

Bryan Kroll

Damodar Raval

Samuel B Martin

Priyamvada Singh

Kenneth L Wright

Samuel Downing

Virginia Randle

Disclosed are example methods, systems, and devices that allow for secure data processing using data packages generated by edge devices. The techniques include generating a biometric signature using information captured by a computing device, and encrypting user data obtained via the computing device using the biometric signature and a device identifier of the computing device. A security token can be generated and utilized by the computing device to generate a data package, which is configured such that any change to the data package would cause a validation process of the data package using the security token to fail. The data package can be encrypted using various digital keys and provided to secondary computing systems.

Secure data processing using data packages generated by edge devices

Disclosed are example methods, systems, and devices that allow for secure digital authorization via generated datasets. The techniques include receiving a first dataset of a first user and a second dataset of a second user and generating a first set of identity elements and a second set of identity elements based on the first dataset and the second dataset, respectively. A linkage definition can be generated based on the first and second datasets, which can be associated with a set of activation elements. The techniques include determining that a set of inputs satisfy one or more of the set of activation elements and, in response, generating a set of security access tokens based on the linkage condition. The security access tokens can be transmitted to a computing device upon analyzing and verifying biometric data received from that computing device.

Secure digital authorization based on identity elements of users and/or linkage definitions identifying shared digital assets

Disclosed are example methods, systems, and devices for sensor data processing for monitoring devices. These techniques include receiving scan data from a computing device that corresponds to a scan by an imaging device and analyzing the scan data to determine that the scan data corresponds to a manufactured article. An indication can be transmitted with a request for audiovisual data corresponding to an operation of the manufactured article, and the audiovisual data can be received from an imaging device or a microphone of the computing device. A sound sample corresponding to the operation of the manufactured article is extracted, and a status metric for the article is generated based on a comparison of the sound sample to a sound signature in an identity profile of the manufactured article. The status of the manufactured article can then be determined.

Sensor data processing for monitoring device performance

Disclosed are example methods, systems, and devices that allow for executing machine-learning models for real-time and secure analysis of digital metrics. The techniques include generating metrics for identity elements stored in digital profiles of users. A subset of profiles can be identified that have metrics that fall below a predetermined thresholds, with which a training dataset can be generated. Machine-learning models can be executed over the training dataset to train an artificial intelligence agent that receives digital profiles as input and outputs translational elements corresponding to identity elements in the digital profiles. After training, additional profiles can be input to the machine-learning models of the artificial intelligence agent to identify a second subset of digital profiles with corresponding metrics. Electronic messages corresponding to the second subset can be generated and transmitted to one or more computing devices identified in the second subset of digital profiles.

Machine-learning for real-time and secure analysis of digital metrics

Disclosed are example methods, systems, and devices that allow for secure generation of authentication datasets from network activity. The techniques include accessing secured data sources to generate a first dataset of secured data, and extracting information from one or more unsecured data sources to generate a second dataset comprising a second dataset. A third set of data elements can be generated from the first and second datasets, and may be utilized to authenticate credentials that can be utilized to access secured data via a network. The techniques can transmit indications that credentials are invalid if the credentials fail to satisfy aspects of the third dataset.

Secure generation of authentication datasets from network activity

Secure digital authorization via generated datasets

A method comprises receiving a cash level indicator from a third-party server. The cash level indicator indicates an amount of cash in each of a plurality of cash storage devices, where the plurality of cash storage devices receive cash from a plurality of delivery agents. Each of the cash storage devices is arranged within or on a vehicle operated by one of the plurality of delivery agents. The method further comprises receiving a cash request from a user device. The method also includes selecting one of the plurality of cash storage devices based on the cash level indicator and location data of delivery agent devices of the delivery agents. The method also comprises sending a message comprising location data of the user device, the selected delivery agent device being one of the delivery agent devices, wherein the selected cash storage device is associated with the selected delivery agent device.

Ride sharing with cash delivery

Disclosed are example methods, systems, and devices that allow for secure multi-verification of biometric data in a distributed computing environment. The techniques include receiving a request to grant authorization to a second user. The request can include biometric data of the first user and second user. An authorization token can be generated based on the request, which can be transmitted to a second computing device of the second user. A second request can be received from a third computing device that includes the authorization token and third biometric data. The second request can be verified based on the authorization token, the third biometric data, and provenance data, and an indication that the grant of authorization to the second user is verified can be transmitted to the first, second, or third computing devices.

Secure multi-verification of biometric data in a distributed computing environment

Systems, methods, and computer-readable storage media utilized for event-based authentication. One method includes recognizing an event and receiving, from a device, a withdrawal request of a user, wherein the withdrawal request includes a captured feature by the device. The method further includes determining that the user is associated with the recognized event using at least a geographic proximity between the event and the withdrawal request, wherein the geographic proximity is a given radius around a location of the event, and wherein the device is within the given radius and adjusting authentication rules based on the determination that the user is associated with the recognized event, wherein the adjustment of the authentication rules includes adjusting a matching threshold between the captured feature and a reference feature. The method further includes processing the withdrawal request for the user based on the adjusted authentication rules.

Event-based authentication

A computer system is described that is configured to enable customers of an organization to self-monitor account activity and modify account access settings via a contact center of the organization. The contact center control system is configured to provide an interactive call history associated with the customer's accounts with the organization. Using the call history, the customer may perform self-monitoring of calls into the contact center that access the customer's accounts, and notify the organization if any of the calls appear suspicious. The contact center control system is configured provide a user interface through which the customer may modify account access settings including changing which types of authentication methods are enabled to access the customer's accounts, and blocking account access by callers from certain phone numbers. The contact center control system may allow the customer to turn off all access to the customer's accounts via the contact center.

Customer self-help control system for contact centers

A call screening computing system is described that is configured to perform voice captcha and real-time monitoring of calls into a contact center of an organization. The call screening computing system includes a chat bot configured to operate as an AI-based call screener. The chat bot is configured to perform voice captcha by sending a random question to a user device placing a call into the contact center, and analyzing the received answer to determine whether a user of the user device is human or a robot. The chat bot is configured to, based on the user being human, determine whether the user is a legitimate customer of the organization by generating and presenting authentication challenges to the user device. The chat bot may be configured to monitor and interact with a conversation between the user and an agent of the organization during the call into the contact center.

Voice captcha and real-time monitoring for contact centers

Embodiments of the disclosure are directed to authenticating a user at an electronic computing device. Information is received from a defined zone of an environment surrounding the user. A level of authentication is calculated for the user based on the information from the defined zone of the environment. When the level of authentication is greater than or equal to a predetermined threshold, the user is authenticated at the electronic computing device to perform a requested activity.

Defined zone of authentication

Methods and systems of authenticating a user based on an event, such as a natural disaster, a gathering of people, and mass activities of people. The method comprises recognizing, by a financial institution computing system, an event; receiving, by the financial institution computing system, a transaction request of a user; determining, by the financial institution computing system, that the user is associated with the recognized event; adjusting, by the financial institution computing system, authentication rules for the user; and processing, by the financial institution computing system, the transaction request for the user based on the adjusted authentication rules.

Event based authentication

Growing community of inventors

San Bruno, CA, United States of America

Julio Jiron