Amazon Technologies, Inc.

Ajay Gopalakrishnan

Sumedha Arvind Kshirsagar

Nishant Kumar

Jeff Bradley Beal

Mukul Vijay Karnik

Kevin Robert Charter

Vishal Parakh

Antoun Joubran Kanawati

Si Yin

Omer Baluch

Kiran-Kumar Muniswamy-Reddy

David Robert Thomas

Vikas Jain

Shishir Sridhar Bharathi

Roy N Harkness

Paul A Larpenteur

Hubert Wong

Vikram Kumar Gundeti

Frederic Johan Georges Deramat

John Daniel Thimsen

Peter Paul Henri Carbon

Julio Delgado Mangas

Jared Scott Lundell

Timothy Andrew Rath

Santosh Kalki

Yoav Srebrnik

Bhupinder Singh Sidana

Kiran-Kumar Muniswamy Reddy

A speech recognition platform configured to receive an audio signal that includes speech from a user and perform automatic speech recognition (ASR) on the audio signal to identify ASR results. The platform may identify: (i) a domain of a voice command within the speech based on the ASR results and based on context information associated with the speech or the user, and (ii) an intent of the voice command. In response to identifying the intent, the platform may perform multiple actions corresponding to this intent. The platform may select a target action to perform, and may engage in a back-and-forth dialog to obtain information for completing the target action. The action may include streaming audio to the device, setting a reminder for the user, purchasing an item on behalf of the user, making a reservation for the user or launching an application for the user.

Outcome-oriented dialogs on a speech recognition platform

A resource management service implements techniques for provisioning a process with computing resources sufficient to process a query. A query is received and computing system resources sufficient to process the query are provisioned. A response to the query is generated by running the process with the provisioned computing system resources.

Resource management service

A speech recognition platform configured to receive an audio signal that includes speech from a user and perform automatic speech recognition (ASR) on the audio signal to identify ASR results. The platform may identify: (i) a domain of a voice command within the speech based on the ASR results and based on context information associated with the speech or the user, and (ii) an intent of the voice command. In response to identifying the intent, the platform may perform a corresponding action, such as streaming audio to the device, setting a reminder for the user, purchasing an item on behalf of the user, making a reservation for the user or launching an application for the user. In some instances, the speech recognition platform engages in a back-and-forth dialog with the user in order to properly fulfill the user's request.

Managing dialogs on a speech recognition platform

Prediction logic analyzes previous data usage activities of a customer process running on a host machine to generate a first prediction indicating that the customer process will request a first data set at a first time. The prediction logic retrieves the first data set from long-term storage and loads the first data set into memory on the host machine in advance of the first time in order to provide the customer process with access to first data set in the memory during a period between the first time and a second time. The prediction logic further generates a second prediction indicating that the customer process will not access the first data set for a threshold period of time after the second time and stores the first data set in the long-term storage at the second time.

Predictive loading and unloading of customer data in memory

Features are disclosed for performing functions in response to user requests. Natural Language Understanding ('NLU') processing may be performed to generate command data that represents a subject of an utterance. The command data may be sent to an application that causes presentation of first output content in a first modality at a first time in response to receiving the command data, and generates second output content in a second modality different from the first modality, wherein the second output content is associated with the first output content. The second output content may be presented in the second modality at a second time subsequent to the first time.

Dialog management with multiple modalities

A data analytics system may receive query definitions from which relationships between datasets may be identified. The query definitions may be analyzed to determine estimated costs and frequencies of combining a first and second dataset. Based on the cost and frequency, a combined dataset may be generated to by joining data from the first and second datasets. The combined dataset may be stored. Queries that comprise instructions to combine the first and second datasets may be processed by instead accessing the combined dataset.

Analysis engine data intake

Features are disclosed for performing functions in response to user requests based on contextual data regarding prior user requests. Users may engage in conversations with a computing device in order to initiate some function or obtain some information. A dialog manager may manage the conversations and store contextual data regarding one or more of the conversations. Processing and responding to subsequent conversations may benefit from the previously stored contextual data by, e.g., reducing the amount of information that a user must provide if the user has already provided the information in the context of a prior conversation. Additional information associated with performing functions responsive to user requests may be shared among applications, further improving efficiency and enhancing the user experience.

Dialog management with multiple applications

Data may be stored using hybrid multidimensional and column-centric storage techniques. A hierarchy of regions of a multidimensional space may be maintained on one or more storage devices. Range information for the hierarchy may be maintained in a column-centric storage. Leaf nodes of the hierarchy may comprise tuple data stored in a column-centric storage. Tuples may be located by identifying candidate regions encompassing the tuple and scanning column-centric stores at the leaf level. Region splitting may be deferred to favor column-centric search characteristics.

Hybrid spatial and column-oriented multidimensional storage structure

Dialog management context sharing

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Seattle, WA, United States of America

Ajay Gopalakrishnan