Microsoft Technology Licensing, LLC

Jignesh Shah

Bernhard Kohlmeier

Madeline Schuster Kleiner

Tomasz Lukasz Religa

Jon Meling

Jan Heier Johansen

Nathaniel George Freier

Vegar Skjærven Wang

Amod Anil Agashe

Paul J Leach

Robert L Rounthwaite

Mark Fishel Novak

Geoffrey John Hulten

Arun K Nanda

Pamela Bhattacharya

Vishal Agarwal

Derik Bjorn Stenerson

Nir Ben Zvi

Sunil P Gottumukkala

Pranav Kukreja

David McPherson

Hongqiao Li

Robyn Sambo

Ramaswamy Ranganathan

Vincent Etter

Daniel Fairweather

Manan Sanghi

Pankaj Gaur

Shuo Li

Yijia Xu

Yating Zheng

Sandip Nath

Duc Mai Thanh Le

HongQiao Li

Riyang Liu

A method and system for one or more application command recommendations may include receiving a search query, the search query being an in-application assistance query, accessing contextual data associated with the search query, providing at least one of the search query and the contextual data as input to a multilingual machine-learning (ML) model to identify one or more application command recommendations, obtaining the one or more application command recommendations as an output from the multilingual ML model, and providing data about the output to the application for display. The multilingual ML model uses a multilingual encoder to provide command recommendations for a plurality of languages.

Unified multilingual command recommendation model

A method and system for intelligently organizing one or more groups of relevant files may include retrieving a user data signal including user-specific data, analyzing the user data signal to identify a parameter relating to file relevance, identifying one or more relevant files in a storage medium based on the parameter at least one of a user category property, a lifecycle stage property, a relevant activity property, or an activity level property of one or more files in the storage medium, organizing the one or more relevant file into the one or more groups of relevant files, and providing for display data relating to the one or more groups of relevant files.

Intelligently identifying and grouping relevant files and providing an event representation for files

Described herein are mechanisms to allow users to access functionality of applications in a suite of applications. In a first aspect, when a query relating to functionality of an application is received from a user, an index containing both top-level and sub-level functionality is searched. Results are ranked using a trained machine learning model using both context describing user interactions and the search results. A subset of the ranked results are presented to the user as options. In a second aspect the index can comprise entries describing functionality from other applications so that results presented to the user can include cross-application functionality. In a third aspect, the index can be searched using the context prior to receiving a query and adjusting the user interface based on the results. In a fourth aspect, the system can recommend other applications and/or devices that are better suited to a user's intent.

Deep command search within and across applications

A method and system for providing one or more suggested application features to a user of an application may include receiving an indication of a user action in a document, accessing contextual document data associated with the document, providing at least one of information about the user action or the contextual document data as input to a machine-learning (ML) model to predict a desired intent for the document, obtaining the predicted desired intent as an output from the ML model, identifying based on the predicted desired intent one or more application features that fulfil the desired intent, and providing data about the one or more application features for display.

Intelligent feature identification and presentation

A method and system for providing a personalized user interface and personalized search suggestions when using an application is disclosed. The method includes receiving a request for providing a personalized user interface such as a personalized menu for an application and examining a history of the user's usage of the application, a history of other user's usage of the application, and/or the content of the current document in the application, before identifying one or more suggested options offered by the application based on one or more of these factors. A list may then be generated and provided for being displayed as part of the personalized user interface. Upon receiving an indication of a user's intent to perform a search within the application, the user's usage history, usage history of users with similar activities, and/or content of the current document may be examined to provide the user with a list of search suggestions.

Personalized user experience and search-based recommendations

A policy that governs access to a resource may be tested against real-world access requests before being used to control access to the resource. In one example, access to a resource is governed by a policy, referred to as an effective policy. When the policy is to be modified or replaced, the modification or replacement may become a test policy. When a request is made to access the resource, the request may be evaluated under both the effective policy and the test policy. Whether access is granted is determined under the effective policy, but the decision that would be made under the test policy is noted, and may be logged. If the test policy is determined to behave acceptably when confronted with real-world access requests, then the current effective policy may be replaced with the test policy.

Testing access policies

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Redmond, WA, United States of America

Jignesh Shah