Microsoft Technology Licensing, LLC

Yu Been Lee

Shikha Devesh Desai

Susan Michele Hendrich

Anav Silverman

Alyssa Rachel Mayo

Penelope Ann Collisson

Patricia Hendricks Balik

Gwenyth Alanna Vabalis Hardiman

Jesse Clay Satterfield

Michelle Elena Keslin

Theresa Ann Estrada

Allison Anne Whilden

James Squires Masson

Olga Veselova

Scott H W Snyder

Ronald Di Sandro

Ashwini Purohit

Enrique J Dominguez

The present disclosure relates to processing operations configured to tailor notifications of productivity feature suggestions based on predictive relevance to a context associate with user access to an electronic document. Machine learning modeling executes a contextual evaluation of user access to predictively determine relevance of a suggestion that relates to: 1) a confidence in the quality of the suggestion; and 2) a timing prediction as to the urgency for surfacing the suggestion to the user so that the suggestion is most applicable. Example notifications are proactive interruptions that aim to aid processing efficiency in task execution as well as an improve user interface experience when users work with an application/service and/or an application platform that comprises a suite of applications/services. A manner in which the notification is presented may vary based on the confidence in the relevance of the suggestion and timing relevance for interrupting a user's workflow.

Proactive notification of relevant feature suggestions based on contextual analysis

The present disclosure relates to processing operations configured to identify and present productivity features that are contextually relevant for user access to an electronic document. In doing so, signal data is evaluated to determine a context associated with user access to an electronic document and insights, from the determined context, are utilized to rank productivity features for relevance to a user workflow. As an example, an intelligent learning model is trained and implemented to identify what productivity features are most relevant to a current task of a user. Productivity features are identified and ranked for contextual relevance. A notification comprising one or more ranked productivity features is presented to a user. In one example, the notification is presented through a user interface of an application/service. For instance, a user interface pane is surfaced to present suggestions. However, in alternative examples, notification of ranked productivity features is presented through different modalities.

Relevance ranking of productivity features for determined context

Systems, components, devices, and methods for performing inline commands within a content authoring tool are provided. A non-limiting example is a method for generating replacement content within a content authoring canvas. The method includes the steps of receiving a trigger entry in the content authoring canvas and receiving a search text entry and a second trigger entry. The method also includes the steps of generating a search query based on the search text entry and receiving query results based on the generated search query. The query results include at least one option for replacement content. The method also includes the step of proposing replacement content based on the at least one option for replacement content.

Content authoring inline commands

Embodiments include notes application features including notes page preview and other features, but the embodiments are not so limited. In an embodiment, a computer-based method can be configured to generate a notes page preview based on a source notes page, wherein the notes page preview includes one or more of a title, a thumbnail, and/or a snippet generated from the source notes page. A handheld device of an embodiment includes a notes application interface configured in part to display and dynamically update one or more notes page previews, wherein each notes page preview includes a plurality of preview display areas including a title display area, a thumbnail display area, and/or a snippet display area. Other embodiments are included.

Previews of electronic notes

Automatic generation of a document template based on recognized composition element patterns in documents associated with a user is provided. Composition elements used in documents are typically unique to a particular user or to a group of users. An automated template generation system detects composition element patterns in documents associated with the user, and generates and provides one or more useful and relevant templates comprising composition elements consistent with subsets of composition elements used in previously created documents. Generating a document template based on composition element patterns in documents associated with the user improves the user experience, reduces the use of bandwidth, memory, and processing power to reuse composition elements between documents. Additionally, providing user-relevant templates helps to ensure consistency across documents created by the user.

Automatic template generation based on previous documents

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Bellevue, WA, United States of America

Yu Been Lee