United Services Automobile Association (Usaa)

Uipco, LLC

United States Automobile Association (Usaa)

Sydney Ann Conrad-Cook

Surender Kumar

Janelle Denice Dziuk

Carlos J P Chavez

Courtney St Martin

Zachery C Lake

Robert Lee Black

Edward Manuel Alonzo, Iii

Madhusudhana Rao Abburi

Brian Katz

Roberto Virgillio Jolliffe

Breanna Nicole Allerkamp

Shayla Leigh Callis

Jennifer Dawn Ayala

Jon Dwain McLaughlin

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

Aspects of the present disclosure are directed to systems and methods for preventing spam communications. Implementations can implement an access control list that a communicator (e.g., a user device sending a text message, making a phone call, etc.) must be on in order to complete the communication with a recipient. If they are not on the access control list, the communicator can be prevented from sending the communication to the recipient, redirected to a registration process, or the communication can be tagged with an unverified status flag. In some implementations, the access control list can include a recipient's contacts, contacts of the recipient's contacts, authenticated entities (e.g., users and/or companies), previously verified entities, etc. In some implementations, a recipient can give out a temporary extension that a communicator can enter to complete communications with the recipient for a certain amount of time or for a certain number of uses.

Preventing spam communications

Implementations configure hologram interactions with vehicle passengers. A hologram manager can configure a hologram to interact with vehicle passengers, alleviate danger, and/or guide vehicle passengers to safety. The hologram manager can select an input modality for the hologram according to sensed conditions. For example, the hologram manager can determine a state of impairment for a passenger using sensed data and machine learning model(s). The hologram manager can then select an input modality according to the state of impairment. The generated hologram can be configured by the hologram manager to interact with the passenger using the selected input modality. For example, an instruction manager can generate a set of instructions using sensed and/or gathered information, and the hologram manager can configure the hologram to provide the instructions to the passenger using the input modality.

Configuring hologram interactions with vehicle passengers

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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Van Meter, IA, United States of America

Sydney Ann Conrad-Cook