Google Inc.

Synchronoss Technologies, Inc

Jonathan Bloom

Johan Schalkwyk

Trevor D Strohman

Joseph Robert Smarr

Roberto Pieraccini

Jaclyn Konzelmann

David Suendermann

Jackson Liscombe

Grace Li

As part of a dialog session between a user and an automated assistant, implementations can process, using a streaming ASR model, a stream of audio data that captures a portion of a spoken utterance to generate ASR output, process, using an NLU model, the ASR output to generate NLU output, and cause, based on the NLU output, a stream of fulfillment data to be generated. Further, implementations can further determine, based on processing the stream of audio data, audio-based characteristics associated with the portion of the spoken utterance captured in the stream of audio data. Based on the audio-based characteristics and/the stream of NLU output, implementations can determine whether the user has paused in providing the spoken utterance or has completed providing of the spoken utterance. If the user has paused, implementations can cause natural conversation output to be provided for presentation to the user.

Enabling natural conversations with soft endpointing for an automated assistant

A dialog manager for a spoken dialog system. A decision module selects a path from a plurality of alternative paths for a given call, wherein each path implements one of a plurality of strategies for a call flow. A weighting module weights the path selection decision and is connected to a probability estimator for estimating the probability value that a given one of the plurality of paths is the best-performing path.

System and method for optimizing call flows of a spoken dialog system

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Maplewood, NJ, United States of America

Jonathan Bloom