Microsoft Technology Licensing, LLC

Rashi Anand

Vipindeep Vangala

Nishchay Kumar

Beethika Tripathi

Rajesh Gunda

Daraksha Parveen

Venkata Madhu Sravanth Kurumaddali

Sapna Grover

Pankaj Khanzode

Deepinder S Gill

Ankur Hayatnagarkar

Jatin Kakkar

Nitin Pande

Abhineet Mishra

Sravanth Venkata Madhu Kurumaddali

Neha Singh

Rishabh Malhotra

Gangula Rama Rohit Reddy

One example provides, on a computing device, a method comprising iteratively receiving, from one or more collaborative communications applications, user data comprising communications of a user, analyzing the received user data to extract contextual information regarding the user, and sending the contextual information to a contextual information data store for the user. The method further comprises receiving a search query originating from within the collaborative communications application, parsing the search query to identify a referential search term, querying the contextual information data store for the user with the referential search term to identify previously stored contextual information regarding the user associated with the referential search term, and sending the previously stored contextual information identified to the collaborative communications application from which the search query was received.

Contextual search in collaborative communications applications

A computing system obtain a keyword and an identifier for a user of a content authoring application. Based upon the keyword and identifier for the user, the computing system walks a user graph comprising nodes connected by edges. The walk comprises identifying seed nodes in the user graph representing at least one topic that corresponds to the keyword and identifying second level nodes in the user graph that are connected to the seed nodes. The second level nodes represent first content that is associated with the user. The computing system transmits contextual data that is based upon the first content to the content authoring application. The contextual data is processed and formatted and is included in second content presentable by the content authoring application. The contextual data may be used to autogenerate the second content without user input. The second content may be modified by the user as desired.

Artificial intelligence driven personalization for content authoring applications

A computing system obtains text that relates to an experience of a user and determines a search intent based upon the text and a context of the user, where the context is determined based upon activity history of the user in a plurality of applications. The computing system identifies potential keywords in the text and identifies a search domain in a plurality of search domains based upon the potential keywords. The computing system computes a confidence score for each of the potential keywords based upon the search domain, the context, and prior search queries of the user. The computing system identifies keywords from amongst the potential keywords based upon the confidence scores and executes a search over an index based upon the keywords, where the index indexes user content of the user and content of an enterprise. The computing system presents search results for the search to the user.

Proactive contextual and personalized search query identification

A computing system obtains a document that includes text. The computing system identifies a fact referenced in the text of the document, where the fact includes a fact name and a fact value. The computing system determines a topic of the document based upon the text of the document. The computing system identifies a factoid stored in a data store based upon the topic and the fact name, where the factoid includes the fact name and a second fact value, and further where the factoid has been generated based upon second text of a second document. While the document is being displayed to a user, the computing system causes a message to be displayed to the user, where the message prompts the user to accept or reject replacement of the fact value in the document with the second fact value.

Computing system for detecting and updating stale information

A graph data structure for an artificial intelligence knowledge base includes a plurality of user-centric facts associated with a user. Each user-centric fact has an application-agnostic data format and includes a subject graph node, an object graph node, and an edge connecting the subject graph node to the object graph node. The graph data structure is designed to accommodate facts from different application-specific data providers using the same application-agnostic data format even when the different application-specific data providers use different native data formats.

User-centric artificial intelligence knowledge base

Techniques for providing a recommendation engine for personal digital assistant functionality on hardware devices. In an aspect, sensor data and other signals for a user are collected and processed to extract user patterns. A user profile is further constructed using machine learning techniques. The insights obtained from the user patterns and user profile are combined by digital service routines to generate customized recommendations for users. In a further aspect, user feedback may be utilized to improve the accuracy and relevance of the recommendations.

Hybrid sensor centric recommendation engine

Growing community of inventors

Hyderabad Telangana, India

Rashi Anand