Amazon Technologies, Inc.

John Will Crimmins

Rahul Gupta

Spyridon Matsoukas

Chengwei Su

Shirin Saleem

Melanie Chie Bomke Gens

Sankaranarayanan Ananthakrishnan

Kelly James Vanee

Kavya Ravikumar

John Pelak

A natural language understanding system that can determine an overall score for a natural language hypothesis using hypothesis-specific component scores from different aspects of NLU processing as well as context data describing the context surrounding the utterance corresponding to the natural language hypotheses. The individual component scores may be input into a feature vector at a location corresponding to a type of a device captured by the utterance. Other locations in the feature vector corresponding to other device types may be populated with zero values. The feature vector may also be populated with other values represent other context data. The feature vector may then be multiplied by a weight vector comprising trained weights corresponding to the feature vector positions to determine a new overall score for each hypothesis, where the overall score incorporates the impact of the context data. Natural language hypotheses can be ranked using their respective new overall scores.

Contextual natural language processing

Growing community of inventors

Seattle, WA, United States of America

John Will Crimmins