Microsoft Technology Licensing, LLC

Andrew James Peacock

Megan Marie Quintero

Linda Chen Gray

Thomas Coolidge Stanton

Sherry Pei-chen Lin

Shahil Soni

Amir Netz

Christina Storm

Cheryl Couris

Giovanni Moises Della-Libera

Kevin Grealish

David John Nettleton

Michael Justin Flasko

Mark Wade Heninger

Chiu Ying Cheung

Sonia P Carlson

Paula M Bach

John M Sanders

David Paul Limont

Lin Zhong

David Gray

David John Brennan

Russell Lee Simpson, Jr

Vlad Riscutia

Srinivasa Reddy Manda

Zohaib Haider Ali

Pedro Ardila

Akashdeep Khudia

An electronic mail (email) system generates an email user interface display for composing an email message. A context-sensitive widget display is surfaced, on the user interface display, in-line with other message content. A widget that generates the widget display interacts with a remote data service to surface data from the remote data service, on the widget display.

Integrating an application for surfacing data on an electronic message pane

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

Systems for determining and presenting consensus based on evaluating a message conversation are described. A consensus determination application may automatically parse each message of a series of messages related to a topic, using natural language processing or similar methods, to determine one or more suggestions and corresponding opinions for the one or more suggestions contained within those messages. The consensus for each of the one or more suggestions may be presented to a user. By viewing the automatically-determined consensus, the user may, without reading and evaluating all of the messages within the conversation, understand the level of consensus regarding the topic across the group of message participants. Accordingly, user burden is reduced and users may more effectively debate ideas and present suggestions with fewer misunderstandings, leading to an overall better user experience.

Determining consensus among message participants based on 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

Integrating an application for surfacing data on an email message pane

An electronic mail (email) system generates an email user interface display, that allows the user to enter message content. A context-sensitive calendar preview can be triggered and displayed on the email user interface display. The email system detects a user drag and drop input dragging at least a portion of the message content onto the calendar preview, and automatically generates a calendar event based upon the detected drag and drop input, and the particular portion of the message content dragged onto the context-sensitive calendar preview.

Calendar event scheduling from email

Mismatches between schema elements of a data set and a job are identified automatically. Furthermore, the mismatches can be presented visually in conjunction with an interactive visual workspace configured to support diagrammatic authoring of data transformation pipelines. After a data set is connected to a job, one or more mismatches can be determined and presented in context with the workspace. In addition, schema elements can be reconfigured by way of interaction with a visual representation of schema elements to resolve mismatches.

Automatic schema mismatch detection

Job execution can be scheduled and monitored. Execution of a job results in generation of one or more data slices, which can be consumed as input by one or more related jobs. Data slices can be presented in an interactive view. Selection of a data slice in the view can trigger automatic identification of related data slices based on dependencies between data slices. Each data slice can also include and be presented with a status indicating the availability of corresponding data, such as pending, ready, or failed. Furthermore, selection of a data slice that failed to generate successfully can trigger identification of related data slices that failed or are predicted to fail to generate successfully.

Job scheduling and monitoring in a distributed computing environment

Techniques and technologies for smart support systems are described. In at least some embodiments, a method comprises: receiving an indication of a user invoking a support facility due to a problem experienced with a software application; obtaining user-related information including at least one of an age of the user or at least one indication of proficiency of the user with operation of the software application; obtaining problem-related information related to the problem experienced with the software application; and selecting a starting point of a support path based at least partially on the problem-related information and the user-related including at least one of an age of the user or at least one indication of proficiency of the user with operation of the software application, the support path defining one or more operations intended to address the problem experienced with the software application.

Smart support

A technique is described for efficiently detecting a wireless beacon signal emitted from a beacon-emitting mechanism, and thereby reducing consumption of power. The technique operates by selectively increasing detection activity when a detection event is likely to occur, and decreasing it otherwise. The technique leverages calendar information as one factor in determining when a detection event is likely to occur. The technique can also vary the detection activity based on location information (identifying the location of a user) and/or motion information (describing the motion of the user).

Detecting a wireless signal based on context

A view of data transformation jobs can be presented by way of a user interface. Related jobs can subsequently be identified automatically after a job is selected based on data dependencies between jobs. Execution status can also be determined and presented such that successful and failed execution of jobs, for example, can be differentiated. Furthermore, selection of a job run that failed to execute successfully can trigger identification of related jobs runs that failed or are predicted to fail to execute successfully.

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Seattle, WA, United States of America

Andrew James Peacock