North Carolina State University

Sas Institute Inc.

Livewire Acquisition, Inc.

Bradford Wayne Mott

James Allen Cox

Samuel Paul Leeman-Munk

James Curtis Lester, Ii

William Bares

Luke Zettlemoyer

Wookhee Min

James Lester

Electronic communications can be normalized using neural networks. For example, an electronic representation of a noncanonical communication can be received. A normalized version of the noncanonical communication can be determined using a normalizer including a neural network. The neural network can receive a single vector at an input layer of the neural network and transform an output of a hidden layer of the neural network into multiple values that sum to a total value of one. Each value of the multiple values can be a number between zero and one and represent a probability of a particular character being in a particular position in the normalized version of the noncanonical communication. The neural network can determine the normalized version of the noncanonical communication based on the multiple values. Whether the normalized version should be output can be determined based on a result from a flagger including another neural network.

Normalizing electronic communications using a neural-network normalizer and a neural-network flagger

A customer communication is responded to by receiving an utterance from the customer at an agent that executes on a data processing system. The agent uses a knowledge base that includes information extracted from one or more exemplary conversations to generate a response to the received utterance. The agent then sends the generated response to the customer.

Methods, systems, and computer program products for providing automated customer service via an intelligent virtual agent that is trained using customer-agent conversations

Growing community of inventors

Apex, NC, United States of America

Bradford Wayne Mott