Amazon Technologies, Inc.

Shubham Katiyar

Craig Wesley Howard

Venkatesh Vijayaraghavan

Manigandan Radhakrishnan

Joe Pemberton

Marc John Brooker

Ruhi Sarikaya

Ajay Nair

Young-Bum Kim

David Robert Thomas

David Alexander Dunlap

Rajesh Kumar Pandey

Isaac Joseph Madwed

Oleksii Zakharenko

Yilmaz Can Cecen

Julia Kennedy Nemer

Mohamed Farouk AbdelHady

Arun Kumar Thenappan

Vo Vuong

Meenakshi Vembusubramanian

Jihwan Lee

Narendra Gyanchandani

Hao Yuan

Prasha Shrestha

Gokhan Tur

Chenlei Guo

Xing Fan

Xinhong Zhang

Junqing Shang

Puyang Xu

Arima Vu Ram Thayumanavar

Wangyao Ge

Rushi P Desai

Liyuan Zhang

Lawrence Mariadas Chettiar

Artun Kutchuk

Naushad Zaveri

Methods, systems, and computer-readable media for read-only data store replication to edge locations are disclosed. A read-only data store is replicated to an edge server. A request for content is received at the edge server from a client device. The content is sought from a content cache at the edge server or from an origin server coupled to the edge server. Processing of the request is initiated, comprising encountering an event. The event is associated with a function specified by a customer, and the function specifies the read-only data store as input. The function associated with the event is executed at the edge server, including retrieval of data from the data store. The content is generated based at least in part on execution of the function. The content is sent from the edge server to the client device.

Read-only data store replication to edge locations

Techniques for processing with respect to a user input as contextual information is available are described. A system generates a first task prediction using first context data that is available when a user input is received. The system generates a second task prediction (e.g., updated first task prediction) when second context data is received, and then further generates a third task prediction when third context data is received. Example first context data may include device type information, time information, location, etc. Example second context data may include automatic speech recognition (ASR) data. Example third context data may include natural language understanding (NLU) data. Using the third task prediction, the system generates an output responsive to the user input.

Natural language processing system

Methods, systems, and computer-readable media for customizable event-triggered computation at edge locations are disclosed. A request for content is received at an edge server from a client device. The content is sought from a content cache at the edge server or from an origin server coupled to the edge server. Processing of the request is initiated, comprising encountering an event. The event is associated with a function specified by a customer. The function associated with the event is executed at the edge server using process isolation. The content is generated based at least in part on execution of the function. The content is sent from the edge server to the client device.

Customizable event-triggered computation at edge locations

Devices and techniques are generally described for a speech processing routing architecture. First input data representing an input request may be received. First data including a semantic interpretation of the input request may be determined. Metadata of the first input data may be determined. The metadata may identify an entity associated with the input request. In some examples, a query may be sent to a first component. The query may include the metadata. In some examples, second data that identifies a first skill associated with the entity may be received from the first component. In various examples, the first skill may be selected for processing the first input data based at least in part on the first data and the second data.

Natural language processing routing

An on-demand code execution environment present in points of presence (POPs) and in regions serviced by the POPs is provided herein. For example, a POP may receive a request to execute a task associated with user-defined code. If the POP determines that the computing resources necessary to execute a received task are not available or that the POP should not execute the received task for another reason (e.g., the task is not commonly received and the computing resources needed to execute the task are therefore best allocated for other requests), the POP can forward the task to a region that the POP services for execution by an on-demand code execution environment present in the region. The on-demand code execution environment present in the region can execute the task and forward the results of the execution to the POP for distribution back to a user device that requested the task execution.

Multi-region request-driven code execution system

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.

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

An on-demand code execution environment present in points of presence (POPs) and in regions serviced by the POPs is provided herein. For example, a POP may receive a request to execute a task associated with user-defined code. If the POP determines that the computing resources necessary to execute a received task are not available or that the POP should not execute the received task for another reason (e.g., the task is not commonly received and the computing resources needed to execute the task are therefore best allocated for other requests), the POP can identify a region suitable for executing the task and forward the task to the identified region. An on-demand code execution environment present in the identified region can execute the task and forward the results of the execution to the POP for distribution back to a user device that requested the task execution.

Efficient region selection system for executing request-driven code

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Seattle, WA, United States of America

Shubham Katiyar