Microsoft Technology Licensing, LLC

Liming Chen

Mingquan Xue

Yu Meng Li

Krishna Kumar Parthasarathy

Dheepak Ramaswamy

Nakul Garg

Kannan Dhanasekaran

Yonghua Yang

Jinhao Li

Martin Gagne

Yuri Rychikhin

Sean Squires

Nicholas Anthony Buelich, Ii

Systems and methods that enable implementation of content aware file management labeling. Techniques disclosed enable real-time analysis of a data file so that associations between the data file and applicable file management label(s) can be automatically suggested and/or made while a user is working in the data file. A user may deploy an application on a client device to edit a data file. While the user is actively editing the data file, the application may transmit modification data to a content analysis service which analyzes the modification data to determine whether the modifications result in a file management label becoming applicable to the data file. Ultimately, the content analysis service may transmit a verdict to the client device to cause the application to display a labeling suggestion to the user and/or to automatically apply a label to the data file while it is being worked on by the user.

Systems and methods for implementing content aware file management labeling

Techniques configuring a machine learning model include receiving, via a user interface configured to communicate with a machine learning model hosted on a collaborative computing platform, a selection of a file for input to the machine learning model, a selection of content in the file for input to the machine learning model, and instructions for applying the selected content to the machine learning model, which are sent to the machine learning model. As new files are uploaded to the selected directories of the collaborative computing platform, the machine learning model is applied to the uploaded files to classify the files and extract metadata. The extracted metadata and associated classification data are stored in data structures associated with the new files. The data structures are existing data structures of the collaborative computing platform.

Storage and automated metadata extraction using machine teaching

Techniques for labeling data files that enable a file management label to persist on a data file. Variations disclosed herein enable a file management label to be embedded within a data file so that the file management label propagates with the data file when the data file is copied and/or moved between systems and/or workloads. In some embodiments, an application may be configured to write the file management label directly into a stream of the data file. For example, an application client that is run locally on a client device may open the data file by accessing its corresponding stream. While the data file is open within the application client such that a user may edit content of the data file, the application client may be caused to associate the file management label with the data file. Ultimately, the application client may write this association directly into the stream.

Systems and methods for enabling a file management label to persist on a data file

Growing community of inventors

Redmond, WA, United States of America

Liming Chen