Microsoft Technology Licensing, LLC

Jichen Yang

Ransom Lloyd Richardson

Benjamin Gustav Wilde

Rui Hu

Michael M Santos

Aleksandr Polyakov

Michael Adam Scarpati

Jenna Hong

Natalie Ann Mionis

Siqing Chen

Yimeng Li

Lucas Rosenblatt

Courtney Sarah Cochrane

William Nain Saez

Yang He

Anish Yatin Pimpley

Sean Gormley T Kelley

Tejprakash Singh Gill

Raymond C Li

Pramod Kumar Gupta

Juan Antonio Karmy Tacla

Priya Tushar Nakhre

Sidhika Varshney

Anshul Basia

Joe A Herzog

David Milićević

Yash Ravi Punjabi

Tracy A Childers

Jinnie Park

Abhijith Asok

Yiquan Xu

Elijah John Scherrer

Amit Dinesh Gupte

The disclosed technology is generally directed to the conversion of text to tables. In one example of the technology, input text that includes at least three rows is received. A plurality of characteristics of the input text is determined. Each characteristic of the plurality of characteristics is associated with a uniformity between the rows of the input text. The plurality of characteristics includes at least one characteristic that is associated with a delimiter count. A determination is made as to whether the input text is suitable for conversion to table based on the plurality of characteristics. Upon determining that the input text is suitable for conversion to a table, a machine learning model is used to convert the input text into a table.

Intelligent table suggestion and conversion for text

Methods, systems, and computer storage media for providing action-recommendations (e.g., save action, collaboration action, and upload action) using cloud system operations in a cloud system. For example, action-recommendations are predicted using usage data of applications and communicated to a user (e.g., via a prompt) such that the user elects to perform or not perform the action-recommendation. In operation, usage data for an application and an action-recommendation profile are accessed. Determining that an action-recommendation should be generated is based on the usage data and the action-recommendation profile. The determination is further based on action scores and thresholds (e.g., save action, collaboration action, upload action). When the action scores meet certain thresholds, the action-recommendation is generated as a save-action-recommendation, a collaboration-action-recommendation, or an upload-action-recommendation. Based on generating the action-recommendation, an action recommendation interface element (e.g., an action prompt or action dialog) is generated to perform a corresponding action.

Prediction-based action-recommendations in a cloud system

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Walpole, MA, United States of America

Jichen Yang