Microsoft Technology Licensing, LLC

Xiaodong Fan

Yan Ke

Ting Cai

Qiaolin Mao

Lei Zhang

Jun Liu

Serge Smirnov

Rui Cai

Junaid Ahmed

Alexander Owen Coll

Wei-Ying Ma

Xian-Sheng Hua

Yang Li

Tie-Yan Liu

Hugh Evan Williams

Arun Sacheti

Xiao Li

Felix Gti Andrew

Parthasarathy Govindarajen

Laurentiu Titi Nedelcu

Richard Junqiang Qian

Minghui Xia

Guihong Cao

Diane Shambaugh

Chris J Burges

Tao Tao

Zicheng Huang

Taifeng Wang

Jiang-Ming Yang

Avinash Vemuluru

Nikita Astrakhantsev

Nektarios Ioannides

Dong Han Wang

Liang Tang

Hong-Qiao Li

Eugene Joonsup So

Julian Manuel Varanda

Alyssa Nicole Ricken

Alexander Jack Sunell

Alexandra Victoria Foote

Elena Usinskiene

Qianyue You

Souvick Sarkar

Adam Jeffrey Curtis

Jeffrey Roger Devries

Yu Jia

Yufei Zhong

Innocent Mutabazi Ndimubanzi

Astha Suneja

Evgeny Podlepaev

Eric Conrad Ranft

Denyong Zhou

Systems and methods for transferring link context to a web browser. An example method includes receiving, by a web browser, a web address from a desktop application based on a selection of a link in document displayed in the desktop application; receiving, by a web browser from the application, link context for the selected link, the link context including an identification of the desktop application and an identification of the document; loading a web page corresponding to the received web address; based on the link context, identifying a browser-based application corresponding to the desktop application; launching the browser-based application within the web browser; transmitting the link context to the browser-based application; and displaying at least a portion of the document within the web browser.

Transferring link context from desktop application to browser

The present disclosure relates to systems and methods for automatically identifying additional content for a webpage. The systems and methods may create a side pane placed next to a webpage to supplement the webpage in a browser. The side pane may provide additional information or additional content to help users consume the webpage. The additional content may provide webpage insights and help the users further explore the webpage.

Automatic identification of additional content for webpages

The present disclosure relates to systems, devices, and methods for a visual exploration of search engine results. The systems, devices, and methods may present, in response to receiving the search engine results for a query to perform a task, a search result webpage with a cascading waterfall layout that includes a plurality of images from a plurality of webpages included in the search results. The search result webpage may also present, adjacent to the plurality of images, structured data corresponding to a selected image from the plurality of images where the structured data includes details from the webpage to complete the task.

Image reranking and presentation for visual exploration

One or more techniques and/or systems are provided for displaying related content associated with browsing a website. For example, a user may access a website to view content provided by the website. As opposed to the user manually searching for interesting content through trial and error, related content that may be interesting to the user may be identified and surfaced to the user. For example, a user profile specifying user interests of the user may be used to identify related content assigned topics corresponding to the user interests (e.g., content provided by the website or other web source). In this way, the related content may be displayed to the user through a content recommendation reader interface. In an example, the content recommendation reader interface may be provided by a web browser so that related content may be identified and/or provided to users of any website from any device.

User interests facilitated by a knowledge base

Techniques for determining user information needs and selecting data based on user information needs are described herein. The present disclosure describes extracting topics of interests to users from multiple sources including search log data and social network website, and assigns a budget to each topic to stipulate the quota of data to be selected for each topic. The present disclosure also describes calculating similarities between gathered data and the topics, and selecting top related data with each topic subject to limit of the budget. A search engine may use the techniques described here to select data for its index.

User information needs based data selection

Web crawling polices are generated based on user web browsing statistics. User browsing statistics are aggregated at the granularity of resource identifier patterns (such as URL patterns) that denote groups of resources within a particular domain or website that share syntax at a certain level of granularity. The web crawl policies rank the resource identifier patterns according to their associated aggregated user browsing statistics. A crawl ordering defined by the web crawl polices is used to download and discover new resources within a domain or website.

Resource download policies based on user browsing statistics

Techniques for index building are described. Clickcounts of respective training URLs may indicate a number of times that corresponding training URLs were clicked in search engine results. A machine learning algorithm implemented on a computer computes a trained model that is then stored. The clickcounts and respective URLs are passed to the machine learning algorithm to train the model to predict probabilities based on feature vectors of URLs. An index of web pages is built for a set of URLs that identify the web pages. Feature vectors for the URLs are computed. Probabilities of the web pages of the URLs being searched in the future by users may be computed by processing the feature vectors with the trained model. The probabilities may be used to determine which of the URLs to include in the index.

User-driven index selection

Functionality is described for forming a summary representation of a video item to help a user decide whether to obtain a full version of the video item. The functionality operates by: (a) receiving a video item; (b) dividing the video item into a plurality of segments; (c) extracting at least one key frame from each of the plurality of segments to form a plurality of key frames; and (d) organizing the video segments into one or more groups corresponding to one or more respective scenes based on the plurality of key frames, to thereby form the representation of the video item. The functionality can be used to communicate search results to a user, to provide a sample of the video item in a message, etc.

Forming a representation of a video item and use thereof

Methods, computer systems, and computer-readable storage media for generating and presenting multi-domain recommendations to a user viewing a Web page are provided. A topic-of-interest is identified on the Web page being viewed by the user. The topic-of-interest is mapped to an entity ID in a multi-domain recommendation data structure. The multi-domain recommendation data structure is used to identify entities, uniform resource locators, and search queries mapped to the entity ID. Multi-domain recommendations are generated using the entities, uniform resource locators, and search queries; the multi-domain recommendations are presented on the Web page being viewed by the user.

Multi-domain recommendations

A pattern tree is constructed based on a plurality of key-value pairs representing portions of a data set. In some implementations, the pattern tree may be used for learning one or more rules for interacting with a source of the data set.

Pattern tree-based rule learning

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Sammamish, WA, United States of America

Xiaodong Fan