Microsoft Technology Licensing, LLC

Gerald Haslhofer

Michael John Novak

Shrey Nitin Shah

Timothy C Franklin

Ryen William White

Marc Wautier

Dileep R Kumar

Micheal Dunn

Aarohi Arora

Sivaramakrishna Mopati

Valentina I Strachan

Eric Anderson

Bryan John Miller

Yu Fan Vincent Chen

Bharath Cheluvaraju

Nabeei Shahzad

Deyuan Ke

Nabeel Shahzad

In non-limiting examples of the present disclosure, systems, methods and devices for generating searchable data structures using observational context data are provided. An indication to process observational context data surfaced by a local computing device may be received. The observational context data may comprise display data or audio data. The observational context data may be processed by a context analysis engine. A determination may be made of a current context type occurring on the local computing device. The current context type may be determined from a plurality of context types, each having a semantic data structure comprising a unique set of entity types. A canvas comprising a searchable data structure of the current context type may be generated and populated with objects having entity types corresponding to the current context type. A displayable representation of the canvas may be displayed.

Context-aware observational entity recognition and capture

Methods and systems are provided that leverage existing information displayed or presented to a user by an app without requiring the integration of new libraries or requiring a user to use a specific app. Rather, methods and systems presented herein identify a task, select an app or multiple apps capable of performing the task, obtain information that may be required by the app or apps, determine a contextual understanding of what is displayed by the selected app or apps, and finally generate and automate the interaction with the app or apps such that the interaction with the app or apps is handled by an automation agent, allowing a user to stay focused on one or more tasks that the user needs to accomplish.

Automating tasks for a user across their mobile applications

A statement in a software code segment for an application that associates the software code segment to a feature is identified. The software code segment includes a first code block and a second code block for the feature. A feature configuration for the feature is read from a manifest that lists a set of features in the application. When a staging option corresponds to a first staging option, the statement is evaluated against the staging option to remove one of the first code block and the second code block in executable code. When the staging option corresponds to a second staging option, the first software code block and the second code block are included in the executable code. The first code block and the second code block are configurable at runtime to be selected or not selected. The first version or the second version of the executable code are output.

Software feature compilation with runtime configuration

The use of logic for routing data. Metadata that is associated with data residing on a first computing system is sent to at least a second computing system. The metadata is structured so that when interpreted by the second system, the second system provides an appearance that the data is local. The first system then receives a communication from the second computing system, demonstrating that a user has interacted with the appearance of data on the second system. Logic may be applied to determine how and when to send the data to the second system. Based on the determination made by applying logic, the data is sent to the second system in a manner that is associated with the identifier for the entire data.

Sharing a file through a metadata representation of the file

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Bellevue, WA, United States of America

Gerald Haslhofer