United Services Automobile Association (Usaa)

Jennifer Dawn Ayala

Noe Alberto Martinez

Courtney St Martin

Zachery C Lake

Keegan Patrick Hayes

Jennifer Marie Chandler-Bradley

Mathew Cyriac Kottoor

Sydney Ann Conrad-Cook

Joseph H Louwagie, Iii

Thomas Mercer Harper

Emily Nicole Van Santen

Aspects of the present disclosure are directed to generating voice signatures using validated input from an authenticated user. The voice signature can represent a user's endorsement in relation to one or more electronic document(s). For example, the user can view the electronic document(s) at a client device and provide audio input in relation to the one or more document(s). Implementations of a signature manager can receive the user's audio input, authenticate the user identity, validate the input against one or more validation rules, and generate a voice signature in relation to the electronic document(s). The generated voice signature can then be stored in combination with the electronic document(s), for example in a manner that satisfies an authenticity audit of the electronic document(s).

Authenticated user voice signatures using validated input

Methods and systems described herein are directed to obtaining various types of information that can provide an accounting for an individual's legacy that can be replayed to learn about objects and/or experiences associated with the individual. A personal legacy accountant can generate a legacy profile using automatically obtained public records and personal records supplied by the individual, and afford the individual an opportunity to contextualize records for the profile. Through this contextualization, the individual can selectively define one or more perspectives for her legacy.

Personal legacy accounting and remembrance

Aspects of the present disclosure are directed to prediction models that match stored parameters to a new data analytics task. A data analytics task can refer to a task that analyzes and queries data sources to retrieve target data. Example parameters for a data analytics task include a task description, assigned individuals, relevant databases, relevant data tables, search queries, data table joins, etc. Implementations can store historical data analytics tasks and their task parameters. Prediction model(s) can be provided new data analytics task parameters and predict matching historical data analytics tasks for the new task. The matching data analytics tasks can then be analyzed to select data tables common among them, and the common data tables can be provided to a user. Implementations can also group additional parameters according to a selected common data table and provide the grouped additional parameters to the user.

Matching data analytics parameters using prediction models

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San Antonio, TX, United States of America

Jennifer Dawn Ayala