Amazon Technologies, Inc.

Vijitha Raji

Joe Pemberton

Vikram Kumar Gundeti

Rohan Mutagi

Felix Xiaomeng Wu

Frédéric Johan Georges Deramat

Archit Jain

Miguel Alberdi Lorenzo

Lev Danielyan

Manuel Jesus Leon Rivas

Noel Evans

Michael F Schwartz

Dhruva Lakshmana Rao Batni

Alexander Say Go

Mohammad Kachuee

Sahil Malik

Tara Raj

Jae Young Do

Devices and techniques are generally described for incremental, asynchronous machine learning inference. In various examples, first feature data generated from a first process during a first time period may be input into a first subgraph of a neural network. Second feature data generated from the first process during a second time period may be input into a second subgraph of the neural network. First intermediate data may be generated by the first subgraph based on the first feature data. Second intermediate data may be generated by the second subgraph based on the second feature data. First output data may be generated by the neural network based on the first intermediate data and the second intermediate data.

Machine learning model architecture for incremental asynchronous inference

Techniques for selecting a skill component to process a natural language input are described. When a natural language input is received, natural language understanding (NLU) output data representing the natural language input is generated, and skill components (capable of processing the NLU output data) are determined. Thereafter, rules (for preventing the invocation of skill components) are implemented in a tiered manner, resulting in the determination of a subset of the skill components. The subset of skill components is ranked using a machine learning model(s), and the top-ranked skill component is called to process the NLU output data.

Natural language input routing

Devices and techniques are generally described for a speech processing routing architecture. First input data representing an input request may be received. First data may be sent to a first skill representing a first request for the first skill to evaluate an ability of the first skill to process the first input data. Second data may be sent to a second skill representing a second request for the second skill to evaluate an ability of the second skill to process the first input data. Third data may be received from the first skill indicating a first action performed by the first skill in response to receipt of the first input data. Fourth data may be received from the second skill indicating a second action performed by the second skill. The first skill may be selected for processing the first input data.

Natural language processing routing

Techniques for synchronously outputting content by one or more devices are described. A system may receive a user command and may receive content responsive to the command from an application(s). The content may include various kinds of data (e.g., audio data, image data, video data, etc.). The system may also receive a presentation framework from the application, with the presentation framework indicating how content responsive to the input command should be synchronously output by one or more devices. The system determines one or more devices proximate to the user, determines which of the one or more devices may be used to output content indicated in the presentation framework, and causes the one or more devices to output content in a synchronous manner.

Multi-modality presentation and execution engine

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Sammamish, WA, United States of America

Vijitha Raji