Microsoft Technology Licensing, LLC

Mingquan Xue

Dheepak Ramaswamy

Nakul Garg

Kannan Dhanasekaran

Liming Chen

Yu Meng Li

Krishna Kumar Parthasarathy

Martin Gagne

Yonghua Yang

Jinhao Li

Parul Manek

David Charles Oliver

Dustin G Friesenhahn

Daniel Keith Winter

Ming-wei Wang

Sean Squires

Nicholas Anthony Buelich, Ii

Christopher Paul Holder

Hai Liu

Hani Saliba

Brandon V Taylor

Yuri Rychikhin

Le-Wu Tung

Gagandeep S Kohli

Raj B Merchant

Mitika Gupta

Micaela Osuji

Kristopher William Paries

Krishna Kant Gupta

Chunxu Li

Tho Van Nguyen

Anamika Bedi

Qisheng Chen

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 instantiating 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 are received and sent to the machine learning model. A selection of one or more directories and an instruction to apply the machine learning model are sent to the machine learning model.

Interface for machine teaching modeling

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

A unified document retention management system exposes a retention policy management portal to enable a user to selectively prescribe a unified retention policy to multiple productivity platforms to deploy the unified retention policy across the user selected productivity platforms. Data retention rules of the unified retention policy may define actions to be performed with respect to files that satisfy certain predefined conditions. An exemplary data retention rule prescribes a retention age threshold to files that have been tagged with a particular label. Files may be manually labeled by users and/or may be automatically labeled by the system in accordance with one or more smart label rules. The retention age threshold may be enforced for such tagged files by preventing the files from being deleted and/or modified prior to them reaching the retention age threshold. Then the files may automatically be purged from system storage upon reaching the retention age threshold.

Unified document retention management system

A data file is encrypted with a file-specific encryption key and sent to a remote data storage system. The file-specific encryption key is encrypted with a master key. The encrypted file-specific encryption key and the master key are both stored remotely from the encrypted file and they are stored remotely from one another.

Securely storing data in a data storage system

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

Detailed herein is a technology which, among other things, identifies and provides an audience-appropriate version of a document, upon request. In one approach to this technology, a request for the document is received at a document-specific access point associated with that document. This document request is associated with specific file access authorizations. These specific file access authorizations are used to identify one of the available versions of the document. The version of the document so identified is then retrieved.

Permission-based document server

Mapping a server file to a corresponding locally stored copy of a server file and redirecting an application to a server file or to a corresponding local copy of the server file are provided for opening a most up-to-date and/or most reliable version of the desired file.

Redirection to local copies of server-based files

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Redmond, WA, United States of America

Mingquan Xue