Microsoft Technology Licensing, LLC

Ranganath Kondapally

Vipindeep Vangala

Pankaj Khanzode

Madan Gopal Jhanwar

Beethika Tripathi

Daraksha Parveen

Jimish Bhayani

Priyam Bakliwal

Naveen Sethia

Vijayendra Gopalrao Vasu

Sree Hari Nagaralu

Jatin Kakkar

Pavan Kumar Dasari

Venkata Sai Ravali Busetty

Karthikeyan Raman

Techniques for extracting and ranking multiple topic descriptions based on source contexts and subsequently selecting individual topic descriptions to surface based on recipient contexts. More specifically, a mining platform may extract, from a set of source documents making up a corpus, topic descriptions for various topics that are relevant to an enterprise. The mining platform may further rank the extracted topic descriptions based on a source context of those documents from which individual topic descriptions are extracted. Subsequently, when users access enterprise documents including term-usage instances of topics for which one or more topic descriptions have been extracted and ranked, a description serving module may select a topic description that is contextually appropriate for a recipient view the enterprise documents.

Extracting and surfacing contextually relevant topic descriptions

An entity interlinkage platform may receive a corpus of enterprise documents and user profile data that corresponds to an individual user. The entity interlinkage platform utilizes topic extraction models to extract entities from the corpus and further utilizes the user profile data to generate a knowledge graph that includes interlinkages between the extracted entities. The entity interlinkage platform may identify a multitude of topic descriptions and corresponding topic terms from source documents that an access control list permits an individual user to access. Then, based on the user profile data, the entity interlinkage platform may generate a knowledge graph that is tailored for the individual user's specific purposes within the enterprise. For example, the knowledge graph may be generated based on knowledge graph preferences (which may be defined by the individual user) indicating preferred topic description types and/or levels of detail in association with specific topics and/or categories of topics.

Generating user-specific entity interlinkages of extracted enterprise topic descriptions

Systems, methods, and computer-readable media are presented for generating notifications for a person according to user-defined notification templates. In contrast to presenting notifications according to a default notification template, the disclosed subject matter is directed to generating a notification regarding a notification signal according to a user-defined notification template. Upon receiving a notification signal, a determination is made as to whether a first user-defined notification template from a template store is applicable to the notification signal. Upon determining that the user-defined notification template is applicable to the received notification signal, a notification corresponding to the notification signal is generated and presented according to the user-defined notification template.

User-defined notification templates

Growing community of inventors

Hyderabad Telangana, India

Ranganath Kondapally