Wells Fargo Bank, N.a.

Vodafone IP Licensing Limited

Volkmar Scharf-Katz

Akhlaq M Khan

Peter Bordow

Julio Jiron

Richard Orlando Toohey

Thomas E Bell

Jeff J Stapleton

Ramesh Yarlagadda

Dennis Emmanuel Montenegro

Oleksiy Ignatyev

Sadie Salim

Margaret S Honeycutt

Samuel B Martin

Kevin Tristan Scarr

Miriam Felecia Clark

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

An process analysis computing system receives input data that identifies a subject-matter expert (SME) process, such as an SME process that requires detailed information regarding a very large quantity of information about the SME process. In the process analysis computing system, a scenario selection engine extracts process factors and user factors from the input data. Based on the process factors and user factors, the scenario selection engine determines initial schedule data describing event stages and milestone stages included in the SME process, and also generates user-specific process schedule data based on a modification of the initial schedule data. A user-specific execution engine in the process analysis computing system receives the user-specific process schedule data. Based on a milestone stage dependency in the user-specific process schedule data, the process analysis computing system controls access to a requested resource associated with the SME process.

Process modeling techniques for providing user-specific process schedule data

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

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

Methods of predicting a change of users of a network enabled device and a network and network device configured to perform the methods. A present device category and a present device demographic profile are associated with the network enabled device. The present device category is one of a plurality of predefined categories each having a demographic profile associated therewith. The method includes: detecting a network event corresponding to the network enabled device; determining an event category corresponding to the detected network event, the event category being one of the plurality of predefined categories; and comparing the event category with the present device category. When the event category differs from the present device category, determining a new demographic profile based on the present device demographic profile and the demographic profile associated with the event category; and comparing the new demographic profile with the present device demographic profile. When the new demographic profile differs from the present device demographic profile, setting forth an alert predicting a change of users of the network enabled device.

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San Francisco, CA, United States of America

Volkmar Scharf-Katz