Microsoft Technology Licensing, LLC

David Raskino

Steven Wayne Ickman

Lili Cheng

William L Portnoy

Christopher Clayton McConnell

Thomas Matthew Laird-McConnell

Gregory Scott Melander

Matthew Bret MacLaurin

Steve Ickman

Matthew Graham Dyor

Christian James Colando

Paul Andrew Steckler

Richard Ignacio Zaragoza

Eric Steven Anderson

Richard Daniel Marshall

Tom Laird-McConnell

Craig Jensen

Bradley Phillip Gibson

Rajeev V Karunakaran

Christopher C Mcconnell

Making an information retrieval process public, so that it can be followed by others, allows capturing of an interest graph that allows people to learn more about shared interests with other people. This also allows items of interest to a trusted resource (such as an expert) to be identified. These items can then be brought to the attention of other users that share the same interest as the expert. In addition, by keeping track of what particular content a user has already seen, the system can bring items of interest to the user's attention, where the user has not yet seen those items.

Identifying unseen content of interest

Various technologies described herein pertain to developing a web application using a scripting language-based framework. A call can be received via an interface of the scripting language-based framework. The call can manage a control included in the scripting language-based framework. Further, the call can be provided by an application that references the scripting language based framework. Moreover, a user interface can be generated based on the control managed by the call, and backend cloud-based services provided by a distributed data store on a cloud platform can be accessed based on the control managed by the call.

Lightweight framework for web applications

A topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of a topic input in the topic feed. The topic feed is distributed and includes the topic input. Experts in subject matter can be identified based on the user's topic input and/or text in the topic feed that the followers interact with.

Identifying and recommending experts using shared posts and interactions

The description relates to visual post builders. One example can receive a user search topic relating to a collage. This example can concurrently present a collage template and search results for the search topic. The collage template can be manifest as a grid of equal sized rectangles. The example can automatically populate a user selection from the search results into one or more rectangles of the collage template. The example can also automatically populate additional user selections into remaining rectangles of the collage template until all of the rectangles of the collage template are populated or the user indicates that the collage is complete.

Visual post builder

Generating and updating event presentation user interfaces. Embodiments include identifying a first geographical location of a first computing device corresponding to a first invitee to an event, and a second geographical location of a second computing device corresponding to a second invitee. An event presentation user interface is generated, to include a first invitee presence indicator indicating that the first invitee is within a predetermined distance from a geographical location of the event, and a second invitee presence indicator indicating the second invitee is outside of the predetermined distance from the geographical location of the event. Based at least on the second computing device having moved to a third geographical location that is within the predetermined distance from the geographical location of the event, the second invitee presence indicator is updated to indicate that the second invitee is within the predetermined distance from the geographical location of the event.

Event planning within social networks

Social networks often permit users to create events, and to invite one or more contacts of the user to as guests. However, conventional social networks may not sufficiently facilitate users in planning an event for which one or more event details are to be determined. According to the techniques presented herein, a social network may permit users to create an event plan, and may permit guests to submit event plan suggestions for various event plan details of the event plan. The social network may also assist the guests in selecting among the event plan suggestions, e.g., by accepting and tallying votes by the guests among alternative event plan suggestions, by providing event plan suggestions based on search engine results, and by presenting maps of event plan suggestions involving locations. The social network may also accept RSVPs, and may notify guests once a guest arrives at a location of the event.

A dynamic digital montage system assists a user in creating and publishing a montage about one or more specified topics. The system receives an identification of one or more topics of interest from the user, and based on the subject information, collects data from dynamic and static data sources for display in dedicated sections of a montage. The data is presented in one of a number of predefined but editable layouts for presentation to a user. In one implementation, at least one of the montage components is associated with a dynamic data source and is updated when new data is provided by the dynamic data source. In another implementation, the data sources attached to the montage components are analyzed on a periodic basis, and based on the analysis, data source assignments and/or the montage layout is revised.

Dynamic digital montage

A set of stories may be related in a set of messages (e.g., news articles, weblog posts, or messages exchanged in a social network). Presented herein are techniques for automatically generating a summary of respective stories that may be used as a headline or title. These techniques involve identifying the entities referenced in each message, clustering the messages based on similarly referenced entities to generate a cluster of messages associated with each story, and identifying facts of the story that appear in many of the messages, which may be used to generate a perspective-independent summary of the story. Additionally, metadata regarding each message may be detected (e.g., the entities involved in each story, a meta-story of the story, and the perspective of the author in relating the story), and may be used to fulfill requests to filter the set of stories and/or messages based on these criteria.

Automatic story summarization from clustered messages

When a user enters an information retrieval query, a topic feed generator automatically generates a topic feed for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of the query. A feed distributor component automatically distributes the topic feed, which includes the query, to the identified recipients. A search component executes the search and returns results, corresponding to the query, to the user.

Sharing public search queries and interactions

When a user enters an information retrieval query, a topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of the query. The topic feed is distributed and includes the query. Experts in subject matter can be identified based on the user's queries and other text in the topic feed that the followers interact with.

Identifying and recommending experts using shared queries and interactions

When a user enters an information retrieval query, topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of the query. The topic feed is distributed and includes the query. The user's interests are tracked based on text in the user's queries and other text in the topic feed that the user interacts with.

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Kirkland, WA, United States of America

David Raskino