Amazon Technologies, Inc.

Deepak Uttam Shah

Venkata Snehith Cherukuri

Ravi Kiran Rachakonda

Joseph White

Vinodth Kumar Mohanam

Maziyar Khorasani

Lalithkumar Rajendran

Prashant J Thakare

Joseph Pedro Tavares

Prathyusha Nadella

Janita Shah

Charles James Torbert

Michael Trinh

Gang Lan

Venkat Chakravarthy

Mckay Clawson

Vijay Shankar Tennety

Karthik Parameswaran

Ratika Anand

Parathan Sivagnanasundaram

Liyun Xiu

Saksham Papreja

Nidhi Mendiratta

Adit Jamdar

This disclosure describes, in part, context-based device arbitration techniques to select a voice-enabled device from multiple voice-enabled devices to provide a response to a command included in a speech utterance of a user. In some examples, the context-driven arbitration techniques may include determining a ranked list of voice-enabled devices that are ranked based on audio signal metric values for audio signals generated by each voice-enabled device, and iteratively moving through the list to determine, based on device states of the voice-enabled devices, whether one of the voice-enabled devices can perform an action responsive to the command. If the voice-enabled devices that detected the speech utterance are unable to perform the action responsive to the command, all other voice-enabled devices associated with an account may be analyzed to determine whether one of the other voice-enabled devices can perform the action responsive to the command in the speech utterance.

Context-based device arbitration

Techniques for providing device functionalities using device components are described. A system receives a system-generated directive from a skill system and determines a workflow to execute. The system implements a response orchestrator that operates based on the workflow that includes interception points where cross-cutting functionalities can be invoked as pluggable components. The interception points occur pre-system-generated directive, pre-device-facing directive, post-device-facing directive generation, post-device-facing directive dispatch, and the like. The system supports asynchronous execution, conditional execution, and sequential execution of components. Data determined by the cross functionality components can be used by other components for processing.

Response orchestrator for natural language interface

Systems and methods for contextual awareness in dynamic device groups are disclosed. For example, a dynamic device group may be generated while output of content is occurring. When a user provides user input to alter the output of the content, contextual data indicating the devices in the dynamic device group when the user input is received may be generated and utilized by an application to determine which devices are to receive a command to perform an action responsive to the user input.

Contextual awareness in dynamic device groups

This disclosure describes techniques for identifying a voice-enabled device from a group of voice-enabled devices to respond to a speech utterance of a user. A speech-processing system may receive an audio signal representing the speech utterance captured in an environment of a voice-enabled device, and identify another voice-enabled device located in the environment. The system may analyze the audio signal using a different natural-language-understanding model for each of the voice-enabled devices to identify an intent for each of the voice-enabled devices to respond to the speech utterance. The system may determine confidence scores that the intents are responsive to the speech utterance, and select the intent with the highest confidence score. The system may use the selected intent to generate a command for the corresponding voice-enabled device to respond to the user.

Device selection from audio data

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Milpitas, CA, United States of America

Deepak Uttam Shah