Microsoft Technology Licensing, LLC

Peter Bailey

Nikhil Dandekar

Ryen William White

William Ramsey

Li Jiang

Sandy Wong

Nick Craswell

Paul Reinholdtsen

Xiao Lin Zhang

Junaid Ahmed

Walter Sun

Filip Andrzej Radlinski

Sasi Parthasarathy

Adish Singla

Aidan C Crook

Xin Chen

Yipeng Li

Daniel Robert Schwartz

Jeff Huang

Robert L Rounthwaite

Johnson T Apacible

Paul Nathan Bennett

Emre Mehmet Kiciman

Sanaz Ahari

Deana Rae Fuller

Scott Joseph Counts

Aravind Bala

Michael Ching

Hung-Chih Yang

Chad Carson

Shubha Umesh Nabar

Rajeev Kumar Chand

Ho John Lee

Nipoon Malhotra

Daniel T Schwartz

Scott Kirk Imig

Liwei Chen

Sebastian De La Chica

Zachary Gutt

Toan Huynh

Hyun-Ju Seo

Paul Alexander Dow

Shuang Guo

Gayathri Ravichandran Geetha

Aditya Pal

Huizhong Duan

Likhita Krishnamurthy

Generally discussed herein are devices, systems, and methods for contextual messaging in a conference. A method can include receiving, by a first user interface, data indicating content of a message, a recipient of the message, and a condition that, when satisfied during the conference, causes the message to be visually displayed during the audiovisual conference, while the conference is being held via an audiovisual conference application, monitoring user contextual data for whether the condition is satisfied, responsive to the condition being satisfied, augmenting audiovisual conference data from the audiovisual conference application to include the message resulting in augmented audiovisual data, and providing, to the recipient on a second user interface of the audiovisual conference application, a view of the augmented audiovisual data.

Contextual messaging in video conference

Search and browse trails are temporally-ordered sequences of web pages visited by a user during post-search query navigation beginning with a page associated with one of the search results. The trails can provide useful information for a number of search-related purposes. For example, these trails can be used to leverage the post-query behavior of other users to help the current user search more effectively and allow them to make more informed search interaction decisions. The trails can also be used to establish search results and refine search result rankings, select and evaluate deeplinks, and recommend multi-step trails as an alternative to or enhancement for existing search result presentation techniques.

Establishing search results and deeplinks using trails

Systems and methods are provided for performing tasks and related tasks. While a user is performing a task, a group of related tasks is presented. When a new task is selected, a new group of related tasks is provided. This allows a user to perform a series of tasks without having to separately search for each task. As the user moves between tasks, task information items are passed between the tasks so that previously provided information can be preserved.

Related tasks and tasklets for search

Methods, systems, and computer-readable media are provided for updating a search index with new uniform resource locators (URLs) and spiking URLs with increased user interest. History data, provided from browser applications residing on users' computers that indicate URLs accessed by the users, is parsed to identify new/previously unknown URLs. The history data also indicates URLs in which there is increased interest based on a number of recent hits as compared to an average number of hits determined over time. Author postings of new URLs to social networking sites and a quality rating of the authors may also be used to identify and filter new URLs. Search indexes are updated with the new and spiking URL data. As such, lag time between posting of new URLs and spiking of URL interest and inclusion of this data in a search index is greatly decreased.

Updating a search index using reported browser history data

The author ranking technique described herein is a technique to rank authors in social media systems along various dimensions, using a variety of statistical methods for utilizing those dimensions. More particularly, the technique ranks authors in social media systems through a combination of statistical techniques that leverage usage metrics, and social and topical graph characteristics. In various exemplary embodiments, the technique can rank author authority by the following: 1) temporal analysis of link sharing in which authority is computed based on a user's propensity to provide early links to web pages that subsequently become popular; 2) topical authority based on the author's links and content updates in specific topic areas; and 3) popularity and influence based on nodal properties of authors.

Ranking authors in social media systems

Architecture that automatically detects historical search contexts as well as behaviors related to a search query. Machine learning and hand-authored rules are employed to automatically identify search contexts. Historical information likely to be useful in the current context is surfaced. When a user enters a search query or executes another search behavior, past behaviors are exposed which are contextually related to the current behavior. The architecture also provides automatic discovery of historical contexts, features related to the contexts, and training or authoring of a system for classifying behavior into contexts, using some combination of the machine learning and/or hand-authored rules. A runtime system classifies the current user behavior into a context and surfaces contextual information to the user.

Auto-detection of historical search context

Various technologies described herein pertain to using social activity data to personalize ranking of results returned by a computing operation for a user. For each of the results returned by the computing operation, a respective first affinity of the user to a corresponding result and a respective second affinity of the user to the corresponding result can be calculated and used for ranking the results. The respective first affinity of the user to the corresponding result can be calculated based on correlations between social activity data of the user and social activity data of a first group of historical users that clicked the corresponding result. Moreover, the respective second affinity of the user to the corresponding result can be calculated based on correlations between the social activity data of the user and social activity data of a second group of historical users that skipped the corresponding results.

Ranking based on social activity data

Computer-readable media, computer systems, and computing devices facilitate providing a comparison experience to a user in response to a search query. Upon receiving a search query from the user, entities are extracted from the query. The entities are associated with entity classes. The entities, entity classes, previous user behavior, and other information are used to infer whether the user likely is engaging in a comparison task. If the inference indicates that the user likely is engaging in a comparison task, a comparison experience is generated and access to the comparison experience is provided to the user.

Providing comparison experiences in response to search queries

Methods and computer-readable media for determining relevant search categories, such as images, videos, news, health, maps, products, etc., and subcategories that are associated with the search categories based on a query submitted by a user on a search page and displaying the relevant search categories and subcategories on an initial search results page. A query may be inputted by a user and may be received. A set of suggested refinement links, which may include search categories and subcategories, may be identified as being relevant to the query. Further, the subcategories, when selected, may return narrower search results than when a selection is made of a search category. Additionally, the initial search results page may be generated and may include the set of suggested refinement links determined to be relevant to the query, as well as search results.

Relevant navigation with deep links into query

Methods are provided for re-ranking documents based on user-specific features. Search results are received from a non-contextual ranking system such that the search results are not specific toward a particular user, such as the user who submitted the search query. Contextual signals are received and provide user-specific features that are used to re-rank documents so that the most important and relevant documents are listed at the top of the list of search results. Each of the user-specific features are evaluated and compared to determine a new position of each document. A set of contextual search results is then generated based on the new positions.

Ranking contextual signals for search personalization

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Kirkland, WA, United States of America

Peter Bailey