Amazon Technologies, Inc.

Nicholas Adam Cummings

Prashant J Thakare

Ratika Anand

Parathan Sivagnanasundaram

Casey Stuart Smith

Faqin Zhong

Ganesh Prabu Ravi

Narenda Gyanchandani

Chih-Shin Wang

Nhat Vu Doan

Jalaj Kumar

Shiladitya Roy

Xu Zhang

Veer Yuganter Singh

Sabrina Chandrasekaran

Rohit Bhattacharjee

Nikhil Gupta

Chaitanya Krishna Reddy Konda

Alekya Jonnala

Sol Jee Cho

David Z Chen

Pranav Kumar Singh

Techniques for resuming a skill session are described. A system receives a user input, determines a skill to execute with respect to the user input, opens a skill session, and causes the skill to execute. After the skill executes, the skill indicates the skill session is to be placed in a background state, resulting in the user being able to conduct one or more skill sessions while the skill processes in the background state. Sometime after placing the skill session in the background state, the skill requests the system to resume the skill session. If the system determines the skill session is to be resumed, the system sends data to the skill indicating that the skill session will be resumed. Once the skill session is to be resumed, the system places the skill session in an active state, and calls the skill to resume processing of the skill session.

Skill session backgrounding and resumption

Techniques for performing speech processing using multi-modal widget information are described. A system may receive input data corresponding to a user input. The system may also receive widget context data corresponding to one or more multi-modal widgets active at a device. The system may use the widget context data to perform natural language understanding (NLU) processing with respect to the user input, and for selecting a skill component for responding to the user input. The system may send a widget identifier to the skill component when invoking the skill to respond to the user input.

Speech processing and multi-modal widgets

In some disclosed embodiments, first input data corresponding to a first natural language input may be received and processed to determine at least a first natural language understanding (NLU) hypothesis for the first natural language input. First session data identifying a first skill corresponding to the first NLU hypothesis may be determined and used to obtain first visual content corresponding to the first skill. Second session data identifying a second skill may also be determined in response to the input data and be used to obtain second visual content corresponding to the second skill. The device may output a first graphical user interface (GUI) element including the first visual content and a second GUI element including the second visual content. Second input data corresponding to a second input may be received from the device and used to determine, using the second session data, that the second input corresponds to an intent to invoke the second skill.

Multiple results presentation

Techniques for session-based device grouping are described. In an example, a computer system receives first data requesting an output, determines a function to provide content data based at least in part on the first data, determines a session identifier of a session associated with execution of the function, and determines session data associated with the session identifier. The session data includes a first device identifier of a first device, an indication that the first device is outputting the content data, and a second device identifier of a second device. The computer system also causes, based at least in part on the session data including the second device identifier, the second device to output the content data.

Session-based device grouping

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Lynnwood, WA, United States of America

Nicholas Adam Cummings