Microsoft Technology Licensing, LLC

Megan Marie Quintero

Andrew James Peacock

Linda Chen Gray

Thomas Coolidge Stanton

Sherry Pei-chen Lin

Roshin Lal Ramesan

Michael Francis Palermiti, Ii

User interaction efficiency is improved by providing automatic enrichment of content with contextually relevant information. While a user is utilizing a productivity application, the system may receive a content item, such as an email or calendar event, to display within an application user interface. The system analyzes and identifies contextually relevant information based on the content item. Thereafter, the system displays the application user interface including the content item which is modified to display the contextually relevant information within the content item.

Auto-enrichment of content

A travel or a promotion based recommendation is provided in association with a social graph of a recipient of a communication. A communication service initiates operations to provide the travel or promotion based recommendation by processing a content of a communication to identify a travel plan of a recipient or a promotion directed to a recipient. A detected shared attribute between the travel plan of the recipient and the travel plan of the acquaintance is assigned a relevancy score. The promotion is processed to determine a trend of the promotion with an acquaintance at a social networking site and an accuracy of the promotion. The shared attribute or the promotion is provided to the recipient.

Providing travel or promotion based recommendation associated with social graph

Automated classification and time-based relevancy prioritization of electronic messages is provided. An electronic mail item is parsed for identifying and extracting attributes for classifying the electronic message and for identifying time-sensitivity associated with the electronic message, and enriched with metadata identifying the classification and the associated time-sensitivity for displaying the electronic message based on the classification and based on relevancy to a receiving user based on the time-sensitivity.

Automated classification and time-based relevancy prioritization of electronic mail items

Non-limiting examples describe sharing of bundled content through an application/service. A first input may be received, through a user interface of the email service. The first input is associated with a selection of a user interface element for sharing of a bundle of emails that comprises a grouping of at least two separate email threads. A second input is received indicating one or more other users for sharing the bundle of emails. The bundle of emails is shared with the one or more other users. Sharing of the bundle of emails creates a sharing permission that enables multi-directional sharing between the user and the one or more other users for current content of the shared bundle of emails and future content added to the shared bundle of emails. The shared bundle of emails is displayed inline with other emails of an email mailbox of the user through the user interface.

Sharing of bundled content

Non-limiting examples describe automated management of rich content previews associated with bundled content of an application/service. As an example, a bundle of content may be a bundle of emails created within an email service. Email content associated with a bundle of emails may be accessed. The email content may be evaluated based on application of machine learning model that: determines an entity type associated with the bundle of emails based on the tag, identifies a state associated with the bundle of emails, and determines relevancy of specific email content in association with a determined entity type and the identified state associated with the bundle of emails. A rich content preview for the bundle of emails may be generated based on an evaluation of the email content.

Rich preview of bundled content

Non-limiting examples describe enhanced searching and filtering of message content by an application/service. Processing operations described herein are applicable to any type of message content including but not limited to bundled content of an email service. A search input may be received through a user interface of an email service. The search input may be for searching emails associated with a mailbox the email service. Email results may be retrieved based on the received search input. The retrieved email results may be categorized into one or more categories. A smart filter may be generated for filtering the retrieved email results. An exemplary smart filter may comprise at least one filter field that corresponds to category specific content extracted from emails of a category of the one or more categories. The email results and the smart filter may be displayed through the user interface of the email service.

Search and filtering of message content

Non-limiting examples describe automated bundling of content on behalf of a user by an application/service. As an example, a bundle of content may be a bundle of emails created on behalf of a user by an email service. Email data associated with a user account of an email service may be accessed. The email data may be evaluated by applying a bundle generation model that that evaluates the emails for bundling associations. An exemplary bundle generation model may evaluate the email data based on: user signal data from actions pertaining to categorization of specific emails, content and metadata corresponding with the email data, and server side metric analysis of activity corresponding with the user account. A bundle of emails may be automatically generated based on an evaluation result from applying the bundle generation model. The bundle of emails may be transmitted for display in a mailbox of the user account.

Automated bundling of content

A server system suitable for organizing files using stickers is provided. The server system determines a plurality of stickers applicable to data presented on a user interface of a client device, and causes presentation of the determined plurality of stickers on the user interface of the client device. The server system receives a selection of a sticker from the plurality of stickers. The server system then applies the selected sticker to the presented data, whereby the application of the selected sticker causes the presented data to be classified based on the selected sticker.

Data organizing stickers

In one example, a user with multiple devices may have the same group of favorited webpages across each device while having the bookmark listing of the favorited pages organized to present a device-specific order. A user device may receive in a roaming bookmark listing profile a bookmark describing a webpage for ready access by a user. A user device may associate the bookmark with a usage data set describing an interaction with the webpage by the user. A user device may examine the usage data set for the webpage in relation to the user device. A user device may present the bookmark in a bookmark listing to a user based upon the usage data set.

Separation of work and personal content

In one example, a user device may implement a browser that identifies and presents dominant content in a bookmarked website or a frequently visited website. The user device may store a page history profile describing interactions with a featured hub of content. The user device may execute a browser to identify a historical focus describing a historical hub element of the featured hub. The user device may determine a current hub element acting as a current focus specific for a user based on the historical focus. The user device may present an element representation of the current hub element.

Extraction of dominant content for link list

A communication is emphasized based on a past interaction related to promoted items. A communication application initiates operations to process a previous communication associated with an item upon receiving a communication from a provider associated with the item. The previous communication includes an order and/or shipping confirmation associated with a past purchase of the item by a recipient of the communication. Next, the item is identified as an item of interest for the recipient based on the previous communication, a recipient attribute, and/or an item attribute. The communication is also assigned to a list of communications to be emphasized based on the identification of the item as the item of interest. Furthermore, the communication is provided to a client application to be presented to the recipient with an emphasis.

Emphasizing communication based on past interaction related to promoted items

In one embodiment, a user device may organize the user bookmark list into a set of categories automatically. The user device may represent a website as a user bookmark. Memory of the user device may associate the user bookmark with the descriptive entity data set for the website. A processing core of the user device may categorize the user bookmark at a view layer based on the descriptive entity data set upon each presentation of a user bookmark list to a user. An output device of the user device may present the user bookmark list to a user.

Automatic grouping of browser bookmarks

Download manager integration with a cloud storage platform is described. In one or more implementations, a web page is navigated to using a web platform. User input to the web page to download content referenced in the web page is detected. For example, the web page may include a download link that is selected by the user to download content such as a media file, word processing file, and so forth. In response to detecting the user input to download the content, a download manager automatically, and without any additional user input, initiates a download of the content to a cloud storage database that is remote from the computing device.

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Seattle, WA, United States of America

Megan Marie Quintero