Microsoft Technology Licensing, LLC

Igor Dvorkin

Robert L Rounthwaite

Sudip Roy

Ian M Williams

Andrew Shuman

Mark H Schmidt

Manish Kumar

Matthew Tebbs

Rajanikanth Ageeru

Bonnie Pan

Nishadi Kolonne

Sreekanth Kannepalli

Agnieszka Girling

James Mellers

Evyenia R KaraEftimoglu

Piotr Walaszek

Emanuel Rosu

Vihar Tammana

Taft Sandbakken

Robert Hanley

RajaniKanth Ageeru

Julia Syi

Jason Brinkle

Farheen Omar

Dhruv Sood

Gama Aguilar-Gamez

Mark Schmidt

Evyenia R Karaeftimoglu

Non-limiting examples of the present disclosure describe surfacing a representation of digital artifacts for a user. An operation context associated with a user is identified. The operational context may correspond with an electronic document that is accessed through a productivity application. One or more of a plurality of digital artifacts may be identified based on the identified operational context. An exemplary representation of the one or more digital artifacts is surfaced through a user interface. An exemplary user interface may be a user interface of a productivity application, for example, where the electronic document is being accessed through the productivity application. In alternative examples, an exemplary user interface may be associated with a home service that is executed in association with an operating system (OS). In some instances, an operational context may change resulting in an update to an exemplary representation of digital artifacts. Other examples are also described.

Recall service for productivity applications

The described implementations relate to tunable predicate discovery. One implementation is manifest as a method for obtaining a data set and determining anomaly scores for anomalies of an attribute of interest in the data set. The method can also generate a ranked list of predicates based on the anomaly scores and cause at least one of the predicates of the ranked list to be presented.

Performance anomaly diagnosis

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Redmond, WA, United States of America

Igor Dvorkin