Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Xiaojiang Huang

Spyridon Matsoukas

Nikko Strom

Chenlei Guo

Xing Fan

Yu Gu

Vasiliy Radostev

Divya Jetley

Casey Stuart Smith

Yi Luo

Hong Hong

Jie Bao

Xiaocheng Deng

Muddu Krishna Chintha

Priti Bisaria

A system may be configured to receive and process various signals to generate a natural language description of a user's environment, called situational context data. The signals may include sensor data, device status, user activity, user input, and/or inferences made using such data. The situational context data may express a user-centric description of the user's environment; for example: 'User is taking a walk in the park on a sunny afternoon' or “activity: driving location: highway”, etc. The system may send the situational context data to various system components that may, for example, process speech, select applications/skills for handling user inputs, and/or that implement those applications/skills. The applications/skills may use the situational context data to provide recommendations, generate responses, and/or perform actions that are more relevant to the user's current environment.

Determining device context

Embodiments build a knowledge base that includes a list of acronyms and their expansions. The list of acronyms may be associated with a particular organization, e.g. a product team, such that the acronym may have a different meaning to a different organization. In some embodiments, acronyms and their expansions are extracted from artifacts associated with the organization, e.g. documents, emails, attachments, calendar items, etc. Multiple potential definitions identified within the artifacts may be ranked based on contextual data extracted from the artifacts, e.g. who authored the artifact, when was the artifact modified, how often did the author use the acronym, an author's rank in the organization, how long has an author been part of the organization, an author's relationship to other authors, etc. By basing the analysis on artifacts associated with the organization the resulting definitions may be more accurate than if broader resources, such as dictionary definitions, were used.

Generating an acronym index by mining a collection of document artifacts

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Bellevue, WA, United States of America

Xiaojiang Huang