Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Adobe, Inc.

Freescale Semiconductor,inc.

Amit Gupta

Vaibhav Jain

Carlos Yeung

Christopher M Vaughan

Somasundaram Perianayagam

James Christopher Sorenson

Akshat Vig

Rishabh Jain

Avronil Bhattacharjee

Cindy Kwan

Alexander Richard Keyes

Christophe Marle

Oliver Wallace

Peter Zhivkov

Setty Venkateshaiah

Naresh Sundaram

Johannes Gehrke

Sharatkumar Nagesh Kuppahally

Sangya Singh

Palak Agrawal

Benjamin Donald Wood

Stefano Stefani

Balaji Krishnamurthy

Douglas Brian Terry

Naveen Prakash Goel

Mayur Hemani

Satish Joseph Thomas

Sarat Chandra Subramaniam

James Charles Kleewein

Nicholas Gordon

Ravi Math

Tom Laird-McConnell

Ladislau Conceicao

Marcelo Daniel Truffat

Chase Kernan

Ashish Sharma

Vipul Aggarwal

Arunpriyaa Nachimuthu

Kirtan Modi

Amit Purohit

Robert James Victor

Alexander Marliave Hehmeyer

Nathan Pellegrom Riley

Giselli Panontini DeSouza Sana

Shashi R Singaravel

Alfons A Staerk

Remi Paucher

Meet Patel

Karanjeet Singh

Apoorva Gupta

Sagar Mundra

Ankur Sial

Harpreet Neelu

Akshay Madan

Tate Andrew Certain

Rajesh Ramanathan

Eran Shtiegman

Amritansh Raghav

Kevin D Morrison

Rui Liang

Blaine J Carpenter

Jonathan C Hawkins

James B Eifert

Ye Tian

Nikhil P Bobde

Kelly Elizabeth Rollin

Jeffrey M Cooperstein

Thomas S Bouchard

Jehoda Refaeli

Robert William Serr

Thomas W Liston

Manu Vasandani

Ming-Chuan Wu

Jason Andrew Cook

Ryan Thomas Murphy

Niranjan Shivanand Kumbi

Sri Srinivasan

Ajay Awatramani

Scott Alan Huddleston

Mark McClelland

Lewis Bruck

William L Looney

Tony Petrossian

Santosh Kumar

Jeffrey V Johnson

Nipun Poddar

Li Qing Xia

Nathan S Premo

Dmitry Gorn

Skye Lee Pazuchanics

Michael John Eckert

Pankaj Gupta

Thomas McNeill

Santhosh Kumar Misro

Ginger Elizabeth Tien

Reddy Sreekanth

Aditya Bindal

Mark Aiken

Mala Munisamy

A data set may be replicated to another storage location according to a schema that projects a subset of the data set according to a replication technique. An additional replication technique, different from the first replication technique, that also projects a subset of the data set to a third storage location is performed. The correctness of the additional replication technique may be verified with regard to the data set to generate a performance report for the additional replication technique.

Verifying performance of different replication techniques for data set projections

Techniques for synchronizing device group data across different device control applications are described. A device group may include two or more smart home devices that may be controlled/supported by different device control applications. A user may set up device groups, including the same devices, in the different device control applications. A system may include a group synchronization service that synchronizes (e.g., merges) the multiple device groups across the different device control applications. The groups may be synchronized based on matching group names, matching devices included in the group, etc. After the device groups are synchronized, modifications made by the user to a device group in one device control application may be propagated to other device control applications.

Device group synchronization

Inferences may be obtained to handle access requests at a non-relational database system. An access request may be received at a non-relational database system. The non-relational database system may determine that the access request uses a machine learning model to complete the access request. The non-relational database system may cause an inference to be generated using data items for the access request as input to the machine learning model. The access request may be completed using the generated inference.

Obtaining inferences to perform access requests at a non-relational database system

Devices, systems, and methods are provided for managing advertisement exposure. The method may include receiving, by a first device, from a second device, an audio fingerprint based on presentation of first media content using a first media channel at a first time; determining, using automatic content recognition, that the audio fingerprint represents a portion of a first advertisement; determining that the first advertisement was presented at a second time using a second media channel and the second device; determining that a first number of times that the first advertisement has been presented to the user exceeds a first threshold number and that a second number of times that the first advertisement has been presented to the user does not exceed a second threshold number; refraining from selecting the first advertisement at a third time; and selecting the first advertisement for presentation at a fourth time.

Enhanced cross-media channel deterministic and modeled presentation frequency limits

Updates to projected data subsets may be ordered using conditional operations. When updates to a data set are received, a determination is made according to a schema for a projected subset of the data set as to whether the update is to be propagated to the projected subset of the data set. If the update is to be propagated, a comparison of a version identifier for the update may be made with a version identifier for the subset to determine whether performance of the update is consistent with an ordering of updates performed to the first data set. If the comparison indicates the performance of the update is consistent, then the update may be performed to the projected subset. If not, then the update may not be performed.

Ordering updates to secondary indexes using conditional operations

Offline building of a projected data subset may be performed. A request to create a data set that is a projected subset of data from a source data set may be received. A data store separate from the data store storing the source data set may store a copy of the source data set that is used to replicate items to the projected subset of data according to a schema for the projected data subset. Updates made to the source data set may also be replicated to the projected data subset according to the schema. Conflicts between replicated items and replicated updates to the projected data set may be resolved by comparing a version identifier for the replicated update and replicated item to determine what to store in the projected data subset.

Offline index builds for database tables

A client of a distributed database service may conveniently enable replication of data from a source table to a destination (e.g., a different service/database table) by submitting a request enable replication for the source table. In response to the request to enable replication, the database service enables replication for the source table. To replicate a change, the database service may modify a format of the change based on a data storage format of the destination that is different than a data storage format of the database service. The database service monitors the performance of the replication and based on the performance, the database service may automatically modify the capacity to perform the replication of changes to the destination (e.g., by assigning additional resources to replication).

Replicating changes from a database to a destination and modifying replication capacity

Non-relational database operations are invoked to provide results consistent with commands specified in a relational query language. A request to access data in a non-relational database is received that includes one or more commands specified in a query language for accessing data according to a relational data scheme. The request may be evaluated to determine one or more Application Programming Interfaces (APIs) to invoke operations at the non-relational database that provide a result consistent with the commands specified in the query language. The determined APIs are executed and a result returned for the request based on the execution of the APIs.

Invoking supported non-relational database operations to provide results consistent with commands specified in a relational query language

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing deep learning to intelligently determine compression settings for compressing a digital image. For instance, the disclosed system utilizes a neural network to generate predicted perceptual quality values for compression settings on a compression quality scale. The disclosed system fits the predicted compression distortions to a perceptual distortion characteristic curve for interpolating predicted perceptual quality values across the compression settings on the compression quality scale. Additionally, the disclosed system then performs a search over the predicted perceptual quality values for the compression settings along the compression quality scale to select a compression setting based on a perceptual quality threshold. The disclosed system generates a compressed digital image according to compression parameters for the selected compression setting.

Compressing digital images utilizing deep perceptual similarity

A distributed database maintains a table on a first plurality of partitions. A request to restore the table to a point-in-time is received. The database determines, based on log data of the partitions, a maximum version number of an operation processed by the partitions. The log data is processed to exclude, from the restoration, operations whose transactions were started after the point-in-time, by setting the version number of those operations to be greater than the maximum version number. The log data is then applied to a second plurality of partitions, where the version number of each applied operation is less than or equal to the determined maximum version number.

Transactionally consistent point-in-time restore

A method performed by a computing system comprises accessing group subscription data that is associated with a user and identifies a plurality of different groups, each group being hosted by a corresponding client system. The group subscription data indicates that the user is subscribed to receive resource updates from the plurality of different groups. An aggregated set of resource updates is generated by aggregating resource updates associated with resources in the plurality of different groups. An integrated view renders the aggregated set of resource updates and includes an action user input mechanism representing a computing action to be performed in a corresponding one of the groups associated with at least one resource update. Based on user actuation of the action user input mechanism, a machine instruction instructs the client system, that hosts the corresponding group, to perform the computing action.

Integration of client system groups

Compressing digital images utilizing deep learning-based perceptual similarity

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing intelligent sectioning and selective document reflow for section-based printing. For example, the disclosed systems can intelligently identify document objects (e.g., document structures and sections) within a digital document by utilizing a machine-learning model. In so doing, the disclosed systems can identify document-object types and document-object locations for the document objects in the digital document. In turn, the disclosed systems can provide, for display within a dynamic printing interface, selectable document sections comprising the identified document objects. In response to a user selection of one or more of the selectable document sections, the disclosed system can generate a modified digital document for printing by reflowing the identified document objects in accordance with the user selection. In some cases, reflowing comprises removing unselected document objects and/or repositioning one or more of the selected document objects.

Utilizing intelligent sectioning and selective document reflow for section-based printing

Growing community of inventors

Redmond, WA, United States of America

Amit Gupta