Amazon Technologies, Inc.

Swapandeep Singh

Marvin Michael Theimer

Julien Jacques Ellie

Colin Watson

Ullas Sankhla

Paul Anderson

Brian Dahmen

Arjun Raman

Yunhan Chen

Kerry Hart

Yuxin Xie

Suchi Nandini

Fengjia Xiong

Shu Liu

Rama Krishna Sandeep Pokkunuri

Harshal Pimpalkhute

Omkar Prakash Kurode

Kartik Rustagi

Lefan Zhang

Gouthamamani Venkatesan

John Baker

Ganesh Kumar Gella

Yahor Pushkin

Roger Scott Jenke

Vasanth Philomin

Sai Madhu Bhargav Pallem

Haiyang Sun

Ajay Bhaskar Medury

Jin Hoon Bang

Minaxi Singla

Using a first set of machine learning models, a communication from a user of a restaurant is analyzed at an order coordinator linked via a network to resources of an order management service at a provider network. A response to the communication is prepared using another set of models at the provider network and presented to the user. An order of the user for one or more restaurant menu items is fulfilled, based at least partly on analysis of a second communication received from the user after the response is presented.

Distributed system for automated restaurant order acquisition

Methods, systems, and computer-readable media for orchestration of computations using a remote repository are disclosed. A representation of one or more inputs to a computation is stored in a repository. The computation is assigned to one or more hosts of a plurality of hosts. A representation of program code executable to perform the computation is stored in the repository. A local copy of the one or more inputs is stored on the one or more hosts. The computation is initiated on the one or more hosts using the program code and the local copy of the one or more inputs. The computation is initiated for a plurality of keys. The computation succeeds for one or more keys after the computation has failed for one or more other keys. A representation of one or more outputs of the computation is stored in the repository.

Orchestration of computations using a remote repository

An interruption-handling setting for a category of interactions of an application is determined via a programmatic interface. A set of user-generated input is obtained while presentation to a user of a set of output of the category is in progress. A response to the set of user-generated input is prepared based at least in part on the interruption-handling setting.

Customized configuration of multimodal interactions for dialog-driven applications

A checkpoint containing identifiers of input blocks of an utterance directed to a dialog-driven application from a client-side component is generated at a dialog-driven application management service. After a connectivity failure and subsequent connectivity re-establishment between the client-side component and the service, a plurality if input blocks including at least some re-transmitted blocks is received at the service. The checkpoint is used to identify at least one input block for which further processing is not performed.

Fault-tolerance techniques for dialog-driven applications

Methods, systems, and computer-readable media for a client-side filesystem for a remote repository are disclosed. One or more files of a repository are sent from a storage service to a client device. The file(s) are obtained by the client using a credential sent by a repository manager. Local copies of the file(s) are accessible via a local filesystem mounted at the client device. One or more new files associated with the repository are generated at the client device. Using the credential, the one or more new files are obtained at the storage service from the client device. The one or more new files are added to the repository.

Client-side filesystem for a remote repository

A resource of a dialog-driven management service is allocated for a first set of requests based on determining that a population of capacity indicators in a throttling data structure exceeds a threshold. One or more capacity indicator deduction iterations associated with the resource are conducted during a time interval for which the resource remains allocated for the first set of requests. In a given iteration, a number of capacity indicators is deducted from the throttling data structure based on a resource throttling setting. A second set of requests is rejected based on the population of the throttling data structure.

Resource management techniques for dialog-driven applications

Methods, systems, and computer-readable media for parameter variations for computations using a remote repository are disclosed. A first computation is assigned to a first set of one or more hosts. The first computation is associated with first parameters including one or more inputs and program code. A second computation is assigned to a second set of one or more hosts and is associated with a second set of parameters. Execution of the first computation is initiated using the first set of hosts and the first set of parameters. Local copies of the input(s) and program code are obtained from a storage service using a TO credential supplied by a repository manager. Execution of the second computation is initiated using the second set of hosts and the second set of parameters as obtained using a credential supplied by the repository manager.

Parameter variations for computations using a remote repository

Growing community of inventors

Seattle, WA, United States of America

Swapandeep Singh