Amazon Technologies, Inc.

Weam Abu Zaki

Ying Shi

Lambert Leo Mathias

Rajiv Ramachandran

Jim Huang

Paula Marie Despins

Yu Gan

Kelly Paulson

Fabian Lutz-Frank Wauthier

Kyle Tiffany

Cody Barnhart

Teresa Chen

Systems, methods, and computer-readable media are disclosed for automated server-based content delivery. In one embodiment, an example method may include determining campaign information for a content delivery campaign, the campaign information comprising a first allocation value for first content, and a second allocation value for second content, determining first observed data comprising a first user response rate for the first content and a second user response rate for the second content, determining an exponentiated gradient algorithm for the content delivery campaign, and determining a reallocation amount to reallocate a portion of the first allocation value to the second allocation value using the exponentiated gradient algorithm based at least in part on the first observed data, wherein the reallocation amount maximizes an output of the exponentiated gradient algorithm.

Automated server-based content delivery

Features are disclosed for determining a definition or value of a nonstandard term. A user utterance may be processed into one or more candidate transcriptions. An interpretation of the utterance can be generated from the transcriptions. If the transcription includes a word, phrase, or term that is not recognized or is used in a nonstandard way, one or more data stores may be queried regarding the proper value or definition of the term. If a definition or value is not available in the data stores, the user may be prompted to provide one. The user-supplied definition can be saved for future use, and may be used as a general definition of the term for other users.

Interpreting nonstandard terms in language processing using text-based communications

Features are disclosed for determining an element of a user utterance or user intent in conjunction with one or more related elements of the user utterance or user intent. A user utterance may be transcribed by an automatic speech recognition ('ASR') module, and the results may be provided to a natural language understanding ('NLU') module. The NLU module may perform named entity recognition, intent classification, and/or other processes on the ASR results. In addition, the NLU module may determine or verify the values associated with the recognized named entities using a data store of known values. When two or more named entities are related, their values may be determined and/or verified in conjunction with each other in order to preserve the relationship between them.

Named entity resolution in spoken language processing

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Seattle, WA, United States of America

Weam Abu Zaki