Amazon Technologies, Inc.

Rajesh Kumar Pandey

Isaac Joseph Madwed

David Robert Thomas

Joe Pemberton

Julia Kennedy Nemer

Arun Kumar Thenappan

Ruhi Sarikaya

Young-Bum Kim

Shubham Katiyar

Mohamed Farouk AbdelHady

Jihwan Lee

Arijit Ganguly

Christopher Kakovitch

Prasha Shrestha

Luben Karavelov

Hao Yuan

Rashmi Tonge

Steven Mack Saunders

Arima Vu Ram Thayumanavar

Wangyao Ge

Siddharth Mohan Misra

Systems and methods are described for the chained execution of a set of code in an on-demand network code execution system. A user may provide a set of code for execution in the on-demand network code execution system and the system may determine that the set of code comprises multiple chained tasks. The system may provide the set of code to a first virtual machine instance for execution of a first task. The system may obtain an indication that the first task has been executed. The results of the execution of the first task may be sent to a second virtual machine instance, via a push or pull, for execution of a second task. Based on identifying that the first task has been executed, the system may instruct the second virtual machine instance to execute the second task.

Chained tasks for execution in an on-demand network code execution system

Systems and methods are described for execution of multiple tasks associated with a set of code in an on-demand network code execution system. A user may provide a set of code that is associated with the multiple tasks. The system may generate a first virtual machine instance for execution of a first task. The system may determine that a second task is associated with the first task and may identify a location of the first virtual machine instance. The system may further identify a second virtual machine instance for execution of the second task based on the location of the first virtual machine instance. For example, the system may identify the first virtual machine instance from a plurality of pre-generated virtual machine instances and/or may generate the first virtual machine instance.

Collocated virtual machine instances in an on-demand network code execution system

Techniques for intelligently selecting a component to execute with respect to a natural language user input are described. A natural language processing (NLP) system may receive first data representing a natural language input. The NLP system may determine first and second scores representing first and second confidences that first and second components are to be invoked to perform actions responsive to the natural language input, respectively. Based on the first and second scores, the NLP system may determine further information is needed to determine which of the first or second component is to be invoked. The NLP system may query a user for the further information. Based on the further information, the NLP system may determine third and fourth scores representing third and fourth confidences that the first and second components are to be invoked to perform actions responsive to the natural language input, respectively. The NLP system may determine the third score is greater than the fourth score and, based thereon, cause the first component to perform an action responsive to the original natural language input.

Natural language input processing

Devices and techniques are generally described for a speech processing routing architecture. In various examples, first data comprising a first feature definition is received. The first feature definition may include a first indication of first source data and first instructions for generating feature data using the first source data. In various examples, the feature data may be generated according to the first feature definition. In some examples, a speech processing system may receive a first request to process a first utterance. The feature data may be retrieved from a non-transitory computer-readable memory. The speech processing system may determine a first skill for processing the first utterance based at least in part on the feature data.

Natural language processing routing

Devices and techniques are generally described for using user feedback to determine routing decisions in a speech processing system. In various examples, first data representing a first utterance may be received. Second data representing a first semantic interpretation of the first utterance may be determined. A first intent data processing application may be selected for processing the second data. Feedback data may be determined related to the first intent data processing application processing the second data. Third data representing a semantic interpretation of a second utterance may be received, wherein the first semantic interpretation is the same as the second semantic interpretation. A second intent data processing application may be determined for processing the third data based at least in part on the feedback data.

Natural language processing

Routing of natural language inputs to speech processing applications

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Bothell, WA, United States of America

Rajesh Kumar Pandey