Microsoft Technology Licensing, LLC

Nitin Pande

Vipindeep Vangala

Nishchay Kumar

Madan Gopal Jhanwar

Suryanarayana Shastri

Tulasi Menon

Swati Valecha

Deepinder S Gill

Rashi Anand

Ankur Hayatnagarkar

Sravanth Venkata Madhu Kurumaddali

Prabuddh Jaiswal

Sandeep Kadiyala

Sibabrata Paladhi

Raunak Oberoi

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

An intelligent task assistant program can identify tasks associated with a user over a reporting period based on an analysis of, and inferences drawn from, data associated with one or more personal information programs, such as an electronic calendar application, an electronic reminder application, an electronic collaborative application, and/or an electronic communication application. The task assistant may also prioritize the identified tasks based on content associated with the tasks. Some or all of the prioritized tasks can be provided to an output device for perception by the user.

Providing task assistance to a user

A computerized personal assistant communicatively couples to a computer database including a plurality of available skills for the computerized personal assistant. The computerized personal assistant recognizes a current context of the user. The computerized personal assistant operates a previously-trained machine learning classifier to assess a match confidence for a candidate skill, the match confidence indicating a quality of match between the current context and a reference context previously associated with the candidate skill. The computerized personal assistant executes instructions defining an assistive action associated with the candidate skill responsive to the match confidence exceeding a predefined match confidence threshold. The computerized personal assistant executes the instructions defining a complementary help action associated with the candidate skill responsive to the match confidence not exceeding the predefined match confidence threshold.

Skill discovery for computerized personal assistant

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Hyderabad Telangana, India

Nitin Pande