Microsoft Technology Licensing, LLC

Jeffrey L Wight

Dmitriy Meyerzon

Nikita Voronkov

Jan-Ove Almli Karlberg

Åge Andre Kvalnes

Helge Grenager Solheim

Tor Kreutzer

John Michael Winn

John Guiver

Bård Kvalheim

Matteo Venanzi

Alexander Armin Spengler

Vladimir V Gvozdev

Ivan Korostelev

David M Cohen

Runar Bergheim Olsen

Christian Thuv

Kai-Marius Sæther Pedersen

Bjørn Erik Olsrød

Praveen Kumar Reddy Malireddy

Nathaniel Terrell Clinton

Adri Verlaan

Ryan Antoine Nakhoul

Andrew Haon

Age Kvalnes

Andrei-Alin Corodescu

Kristen Kamath

Zhihua Dong

Simon Thoresen Hult

Chia-Jiun Tan

Thuy Thanh Duong

Omar Faruk

Andrei Razvan Popov

Techniques of federation of data during query time are disclosed herein. One example technique includes upon receiving an indication of interaction of a file by a user of a tenant, determining whether automatic replication of the file to a user shard corresponding to the user is permitted according to a company policy or a legal requirement, the user shard being in a second geographic region. The example technique can then include when automatic replication of the file is not permitted, storing the file in a tenant shard corresponding to the tenant in a network storage in the first geographic region and instead of replicating the file to the user shard in the second geographic region, creating, in the user shard in the second geographic region, a file reference that is a pointer to the file stored in the tenant shard in the network storage in the first geographic region.

Federation of data during query time in computing systems

Examples described herein generally relate to a computer system for generating a knowledge graph storing a plurality of entities and to displaying a topic page for an entity in the knowledge graph. The computer system performs a mining of source documents within an enterprise intranet to determine a plurality of entity names. The computer system generates an entity record within the knowledge graph for a mined entity name based on an entity schema and the source documents. The entity record includes attributes aggregated from the source documents. The computer system receives a curation action on the entity record from a first user. The computer system updates the entity record based on the curation action. The computer system displays an entity page including at least a portion of the attributes to a second user based on permissions of the second user to view the source documents.

Enterprise knowledge graph

Systems and methods for providing notification delivery based on utilization of bloom filters are provided. A collaboration system obtains subscriber information for each user of a collaboration system, whereby the subscriber information corresponds to one or more features of content that are relevant to each user. The collaboration system hashes the subscriber information to generate a bloom filter for each user. The collaboration system receives an article to be published, whereby the article comprises a set of features. The set of features is hashed to obtain a hash set. The hashing of the set of features is performed using same hashing functions as that used to generate the bloom filter. The collaboration system compares the hash set to the bloom filter to identify a match, whereby the match indicates a feature of the article matches the subscriber information. The collaboration system generates a list of recipients based on the match.

System of mobile notification delivery utilizing bloom filters

A system may include a processor; and a memory storing instructions that, when executed by the processor, configure the processor to: receive, from a computing device, a request for a set of content, the request associated with a user identifier; query a cached data store to determine that a set of audiences for the user identifier is stale; in response to the determination that the set of audiences is stale: access user properties for the user identifier; and generate the set of audiences for the user identifier based on the user properties; and serve the set of content according to the generated set of audiences.

Scalable system for dynamic user audience determination

Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. The computer system compares source documents within an enterprise intranet to a plurality of templates defining potential entity attributes to identify extracts matching at least one of the plurality of templates. The computer system parses the extracts according to respective templates of the plurality of templates that match the extracts to determine instances. The computer system performs incremental clustering on a number of the instances to determine potential entity names. The computer system queries the knowledge graph with the potential entity names to obtain a set of candidate entity records. The computer system links the potential entity names with at least partial matching ones of the set of candidate entity records to define updated matching candidate entity records. The computer system updates the knowledge graph with the updated matching candidate entity records.

Incremental clustering for enterprise knowledge graph

Among other things, embodiments of the present disclosure help improve the functionality of electronic messaging software and systems by providing selective push notifications to users of such systems in response to new content being published. Embodiments of the present disclosure may provide notifications to some users based on the relevance of published content to such users, while abstaining from notifying users for whom the content is irrelevant.

Organizationally programmable intranet push notifications

Growing community of inventors

Kirkland, WA, United States of America

Jeffrey L Wight