United Services Automobile Association (usaa)

Eric J Smith

Vijay Jayapalan

Gregory B Yarbrough

John McChesney TenEyck, Jr

Greg Yarbrough

Charles Lee Oakes, Iii

Justin Dax Haslam

Thomas Bret Buckingham

Maland Keith Mortensen

Minya Liang

John Raymond Harris

Zakery Layne Johnson

Anthony Scott Farnsworth

Ross Andrew Thiele

Joshua Samuel Leonard

Robert Wilson Barner

Joshua S Kerr

Bipin Chadha

Andrew P Jamison

Techniques are described for real time monitoring and tagging of media content generated during a service session between an individual and a service representative (SR). Media content received during a service session may be dynamically tagged with one or more metadata tags during the service session, the tag(s) indicating portion(s) of the media content that potentially include sensitive information. Tagging may be based on a SR's gestures, clicks, keystrokes, mouse-overs, and/or other actions performed in a SR user interface (UI) during the service session. The media content may be redacted based on the tags to remove and/or obfuscate potentially sensitive information.

Dynamic tagging of media for service sessions

Techniques are described for generating metric(s) that predict survey score(s) for a service session. Model(s) may be trained, through supervised or unsupervised machine learning, using training data such as communications from previous service sessions between service representative(s) and individual(s), and survey scores provided by the serviced individual to rate the session on one or more criteria (e.g., survey questions). The model(s) may be trained to output, based on an input session record, metric(s) that each correspond to a survey score that would have been provided by the individual had they completed the survey. The model may be a concatenated model that combines a language model output from a language classifier recurrent neural network, and an acoustic model output from an acoustic feature layer convolutional neural network. Individuals can be clustered according to the metric(s) and/or other factors, and the cluster(s) can be employed for routing incoming service requests.

Learning based metric determination and clustering for service routing

Methods and systems for multiple channel authentication are described. In one embodiment, a request for an interaction is initiated from within a mobile application. The request may include authentication information and contextual information relating to a current exchange between the mobile application and an organization. The user may be authenticated with the authentication information and the request may be routed to a representative based on the contextual information to continue the exchange.

Methods and systems for multiple channel authentication

A method for routing customer service requests to call centers includes collecting data associated with customer service experience between a customer and a call center regarding a completed customer call. The collected data is analyzed to determine a quality of customer service for one or more completed calls between the customer and the call center. A nature of an incoming customer call is determined. The incoming customer call is routed to a call center based upon making reference to the analyzed collected data such that the nature of the customer call matches with corresponding one or more favorable attributes of the call center.

Database allocation and analytics for service call centers

Techniques are described for generating metrics about an individual's experience. One of the method describes providing, by at least one processor, the session record as input to at least one computer-processable model that determines, based on the session record, at least one metric for the service session, the at least one model having been trained, using machine learning and based at least partly on survey data for previous service sessions, to provide the at least one metric associated with the individual's experience. The method includes associating, by at least one processor, the metric of the individual's experience with the individual. The method also includes communicating, by at least one processor, the at least one metric for presentation through a user interface of a computing device.

Learning based metric determination for service sessions

Techniques are described for generating metric(s) that predict survey score(s) for a service session. Model(s) may be trained, through supervised or unsupervised machine learning, using training data from previous service sessions between service representative(s) and individual(s). Training data may include, for previous service session(s), a session record (e.g., audio record) of the session and a set of survey scores provided by the serviced individual to rate the session on one or more criteria (e.g., survey questions). The model(s) may be trained to output, based on an input session record, metric(s) that each correspond to a survey score that would have been provided by the individual had they completed the survey. The model may be a concatenated model that is a combination of a language model output from a language classifier recurrent neural network, and an acoustic model output from an acoustic feature layer convolutional neural network.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for coordinating callers with customer service representatives. One of the methods includes in response to receiving identification information from a caller, selecting a virtual machine from a plurality of virtual machines, the virtual machine configured to generate a virtual user interface for an application executing on the virtual machine. The method includes populating the virtual user interface with information about the caller based on the received identification information. The method includes sharing the virtual user interface with an application executing on a computer of a customer service representative. The method includes connecting the caller with the customer service representative.

Rapid data access

Methods, systems, and apparatus, including computer programs encoded on computer storage media are used for coordinating callers with customer service representatives. One of the methods includes identifying a number of callers. The method also includes dynamically adjusting a number of customer service representatives based on the number of callers.

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Helotes, TX, United States of America

Eric J Smith