Amazon Technologies, Inc.

Kerry Hammil

Rohit Prasad

Ruhi Sarikaya

Spyridon Matsoukas

Nikko Strom

Stephen Frederick Potter

Young-Bum Kim

Anna Chen Santos

Joseph Pedro Tavares

Frédéric Johan Georges Deramat

Christopher Schindler

David Sanchez

Jared Strawderman

Sarah Castle

Chenlei Guo

Xing Fan

Timothy D Twerdahl

Bartek Gulik

Bharath Bhimanaik Kumar

Simon Peter Reavely

Alexander Thomas Loeb

Archit Jain

Sixing Lu

Huinan Ren

Hyungseo Park

Puyang Xu

Bradford Jason Snow

Robert O'Neal Copeland

Jerrin Thomas Elanjikal

Bartosz Gulik

Zheng Ma

Timothy Twerdahl

Dinesh Malla

Devices and techniques are generally described for arbitration between LLM-based and intent-based natural language processing flows. In various examples, an automatic speech recognition (ASR) component may generate first ASR output data representing the first natural language input. A first machine learning model may select an LLM-based natural language processing flow. The first machine learning model may be trained to select between at least the LLM-based and non-LLM-based natural language processing flows. The first ASR output data may be processed using the LLM-based processing flow. The LLM-based processing flow may generate first executable data.

Language model arbitration for natural language processing

Techniques for generating tasks to be completed in order to perform an action responsive to a user input and, for a given task, shortlisting available components to those that are relevant for the task are described. The system processes a user input to determine tasks to be completed in order to perform an action responsive to the user input. The system determines a priority of the tasks and selects a top-ranked task. The system determines descriptions of processing performable by components that are semantically similar to the current task, and requests a description of the function the corresponding components would perform for the current task. Based on the received descriptions, the system selects one or more components to perform the task. Thereafter, the system causes the action to be performed and outputs a response to the user input.

Natural language processing

Systems, methods, and computer-readable media are disclosed for dynamic voice search transitioning. Example methods may include receiving, by a computer system in communication with a display, a first incoming voice data indication, initiating a first user interface theme for presentation at a display, wherein the first user interface theme is a default user interface theme, and receiving first voice data. Example methods may include sending the first voice data to a remote server for processing, receiving an indication from the remote server to initiate a second user interface theme, and initiating the second user interface theme for presentation at the display.

Dynamic voice search transitioning

Techniques for limiting natural language processing performed on input data are described. A system receives input data from a device. The input data corresponds to a command to be executed by the system. The system determines applications likely configured to execute the command. The system performs named entity recognition and intent classification with respect to only the applications likely configured to execute the command.

Natural language speech processing application selection

Techniques for performing command processing are described. A system receives, from a device, input data corresponding to a command. The input data may originate as audio data, as text data, or as other data. The system determines NLU processing results corresponding to the input data. The NLU processing results may be associated with multiple speechlets. The system also determines NLU confidences for the NLU processing results for each speechlet. The system sends NLU processing results and an indication to provide potential results to a portion of the multiple speechlets, and receives potential results from the portion of the speechlets. The system also receives indications whether the speechlets need to be re-called if the speechlets are selected to execute with respect to the command. The system ranks the portion of the speechlets based at least in part on the NLU processing results as well as the potential results provided by the portion of the speechlets. The system may also rank the portion of the applications using other data. The system causes content provided by the highest ranked speechlet to be output to a user.

System command processing

Techniques for performing command processing are described. A system receives, from a device, input data corresponding to a command. The system determines NLU processing results associated with multiple applications. The system also determines NLU confidences for the NLU processing results for each application. The system sends NLU processing results to a portion of the multiple applications, and receives output data or instructions from the portion of the applications. The system ranks the portion of the applications based at least in part on the NLU processing results associated with the portion of the applications as well as the output data or instructions provided by the portion of the applications. The system may also rank the portion of the applications using other data. The system causes content corresponding to output data or instructions provided by the highest ranked application to be output to a user.

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Seattle, WA, United States of America

Kerry Hammil