Microsoft Technology Licensing, LLC

Nikitha Rao

Chetan Bansal

Nachiappan Nagappan

Thomas Michael Josef Zimmermann

Mark Alistair Wilson-Thomas

Dung B Nguyen

Curtis Dean Anderson

Sumit Kumar

Manish Shetty Molahalli

Zhongyan Guan

David Boyd Ludwig, Iv

Casey Jo Gossard

Junia Anna George

Systems and methods for automatic recognition of entities related to cloud incidents are described. A method, implemented by at least one processor, for processing cloud incidents related information, including entity names and entity values associated with incidents having a potential to adversely impact products or services offered by a cloud service provider is provided. The method may include using at least one processor, processing the cloud incidents related information to convert at least words and symbols corresponding to a cloud incident into machine learning formatted data. The method may further include using a machine learning pipeline, processing at least a subset of the machine learning formatted data to recognize entity names and entity values associated with the cloud incident.

Automatic recognition of entities related to cloud incidents

Identification of content gaps based on relative user-selection rates between multiple discrete content sources. A system analyzes search log activity to determine whether users that are conducting particular types of search activities are ultimately selecting and relying upon content resources from a predefined content source of interest or, alternatively, whether such users are unsatisfied with the predefined content source of interest and are instead relying upon other third-party content sources. This particular type of analysis provides valuable insights into whether content gaps exist within the predefined content source of interest.

Identification of content gaps based on relative user-selection rates between multiple discrete content sources

Embodiments promote searcher productivity and efficient search engine usage by using extraquery context to detect a searcher's intent, and using detected intent to match searches to well-suited search providers. Extraquery context may include cursor location, open files, and other editing information, tool state, tool configuration or environment, project metadata, and other information external to actual search query text. Search intent may be code (seeking snippets) or non-code (seeking documentation), and sub-intents may be distinguished for different kinds of documentation or different programming languages. Search provider capabilities may reflect input formats such as natural language or logical operator usage, or content scope such as web-wide or local, or other search provider technical characteristics. Search intent detection permits efficient and effective use of a single search box for a wide variety of different searches for different kinds of results, thereby simplifying a development tool user interface.

Extraquery context-aided search intent detection

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Bengaluru, India

Nikitha Rao