Amazon Technologies, Inc.

Kelly James Vanee

Rahul Gupta

Spyridon Matsoukas

Chengwei Su

Sankaranarayanan Ananthakrishnan

Shirin Saleem

Kavya Ravikumar

Timothy Thomas Gray

Kynan Dylan Antos

Jenny Toi Wah Lam

Jeremy Richard Hill

Melanie Chie Bomke Gens

Jonhenry A Righter

Michal Grzegorz Kurpanik

Joe Virgil Fernandez

John Will Crimmins

John Pelak

Sarveshwar Nigam

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

A natural language processing system that can determine an overall score for a natural language hypothesis using hypothesis-specific component scores from different aspects of NLU processing. The individual component scores may be weighted by weights trained to optimize the overall scores relative to each other. Each domain of the system may be configured with a separate component that determines the overall score with respect to the domain. Natural language hypotheses can be ranked using the overall score either within a specific domain or on a cross-domain basis.

Scoring of natural language processing hypotheses

A dock device connects participating devices such as a tablet device and an audio activated device, allowing them to operate as a single device. These participating devices may be associated with different accounts, each account being associated with particular 'speechlets' or data processing functions. A natural language understanding (NLU) system uses NLU models to process text obtained from an automatic speech recognition (ASR) system to determine a set of possible intents. A second set of possible intents may then be generated that is limited to those possible intents that correspond to the speechlets associated with the docked device. The intents within the second set of possible intents are ranked, and the highest ranked intent may be deemed to be the intent of the user. Command data corresponding to the highest ranked intent may be generated and used to perform the action associated with that intent.

Natural language understanding with affiliated devices

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Shoreline, WA, United States of America

Kelly James Vanee