Microsoft Technology Licensing, LLC

Steve Tullis

Uhl Albert

Aaron Lamar Wilson

John S Campbell

Carlos Augusto Otero

Daniel Yancy Parish

Viswanath Vadlamani

John Bruno

David Gustafson

A smart fill feature is provided, which may be utilized to provide suggestions to a user and to prepopulate fill results in a spreadsheet. Characteristics associated with a set of data may be recognized and used to help determine fill results Suggestions may be determined from internal and or/external data sources, and may use contextual information in a data set to determine what a user may be trying to accomplish. External data sources may include one or more Internet data sources or internal corporate databases, providing a large amount of data from which to search for potential fill results. A split column command may be used to split a column into two or more columns. Data may be split between columns or combined by various methods, which may include, by example, by direct manipulation via tokens, or via a slider UI.

Method and system for providing autofill of data

Smart fill

The present disclosure describes a meta-application framework that enables data to be associated with a class of applications called a meta-application. A meta-application entity may store attributes in common across multiple platforms. A platform entity may store attributes in common across multiple devices and/or operating systems. A device entity may be comprised by a particular set of hardware features and an operating system entity may be comprised of a particular set of software feature. Data associated with the meta-application framework may be accessed directly via data manipulation operations or alternatively by application operations via an application programming interface or via the aforementioned data manipulation operations. Applications may then identify application specific behavior at the appropriate level of detail.

Meta-application framework

A data visualization application transforms visualized data through visual analytics. A detected gesture and associated visualization are processed through a visual analytics engine of the application. The visual analytics engine determines attributes for a new visualization based on the contextual information of the gesture and the visualization. The analytics engine dynamically builds an action based on the attributes. Execution of the action generates the new visualization which is rendered for presentation.

Transforming visualized data through visual analytics based on interactivity

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Redmond, WA, United States of America

Steve Tullis