Microsoft Technology Licensing, LLC

Microsoft Corporation

Microsoft Corpoartion

Wei-Ying Ma

Zheng Chen

Hua-Jun Zeng

Lei Zhang

Benyu Zhang

Xing Xie

Ji-Rong Wen

Zhiwei Li

Tie-Yan Liu

Mingjing Li

Ming Li

Rui Cai

Feng Jing

Zaiqing Nie

Xin-Jing Wang

Hong-Jiang Zhang

Ruihua Song

Shuming Shi

Ying Li

Yu Chen

Tao Qin

Gui-Rong Xue

Wei Lin Lai

Bin Wang

Li Erran Li

Tarek Najm

Chuang Wang

Hua Chen

Xin Fan

Ming-Yu Wang

Kefeng Deng

Yi-Min Wang

Bin Gao

Hsiao-Wuen Hon

Lee Wang

Chun Liang Yuan

Gu Xu

Jiangming Yang

Yan-Feng Sun

Jin-Lin Chen

Shipeng Li

Jun Zhu

Heung-Yeung Shum

Jue Wang

Jun Yan

Lie Lu

Hua Li

Xian-Sheng Hua

Zheng Zhang

Daniel B Cook

Yu Zheng

Xianfang Wang

Qing Yu

Karen E Fries

Dou Shen

Shipeng Yu

Xiangye Xiao

Randy Hinrichs

Jiang-Ming Yang

Qiang Wu

Ming Zhou

Jing Liu

Ning Liu

Qi Zhang

Yong Rui

Ming Gang Liu

Jian-Tao Sun

Hongjiang Zhang

Di Li

Lei Cui

Yi Li

Rong Xiao

Yan Ke

Furu Wei

Huan Liu

Xiaoming Sun

Bin Lin

Li Tao

Xiaodong Fan

Yuxiao Hu

Yue Liu

Chong Wang

Bin Shi

Pengfei Xu

Aaron Samuel Filner

Jay Franklin McLain

Xuanhui Wang

Yu-Fei Ma

Lei Wu

Gang Luo

Kati Amanda London

Longhao Wang

Zhike Kong

Kuiyuan Yang

Shaohan Huang

Yong Cao

Edward A Fox

Hang Cui

Ming-Feng Tsai

An example environment can automatically generate content for a conversation in response to a presented content item. An example computing device receives information associated with a content item produced by a source system, the content item being accessible to other the computing devices via a network. In response to receiving the information, the computing device identifies a start comment. In response to identifying the start comment, the computing device identifies a reply to the start comment using at least a ranking model.

Automatic conversation creator for news

A method and system for recommending potential contacts to a target user is provided. A recommendation system identifies users who are related to the target user through no more than a maximum degree of separation. The recommendation system identifies the users by starting with the contacts of the target user and identifying users who are contacts of the target user's contacts, contacts of those contacts, and so on. The recommendation system then ranks the identified users, who are potential contacts for the target user, based on a likelihood that the target user will want to have a direct relationship with the identified users. The recommendation system then presents to the target user a ranking of the users who have not been filtered out.

Recommending contacts in a social network

An image chat application generates comments to images based on features of the images. In one example, the image chat application searches through a repository of stored image-comment pairs to identify a stored image that is similar to the image, and generates a comment to the image based on an identified stored image-comment pair. In another example, the image chat application may identify and tag particular objects that dominate an image, and may generate a comment to the image based on characteristics of those particular objects. In this second example, the image chat application further generates a comment to the image based on comments previously associated with the identified tag.

Identifying image comments from similar images

Many users make use of search engines to locate desired internet content by submitting search queries. For example, a user may search for photos, applications, websites, videos, documents, and/or information regarding people, places, and things. Unfortunately, search engines may provide a plethora of information that a user may be left to sift through to find relevant content. Accordingly, one or more systems and/or techniques for organizing search results are disclosed herein. In particular, user generated content, such as photos, may be retrieved based upon a search query. The user generated content may be grouped into clusters of user generated content having similar features. Search results of the search query may be obtained and organized based upon comparing the search results with the clusters. The organized search results and/or a table of content including the clusters may be presented to provide an enhanced user experience.

Organizing search results based upon clustered content

Method and system for providing a user interface for presenting images of clusters of an image search result is provided. The user interface system displays the search result in a cluster/view form using a cluster panel and a view panel. The cluster panel contains a cluster area for each cluster. The view panel may contain thumbnails of images of the search result in a list view or a mix view. When a user selects a cluster area from the cluster panel, the user interface system displays a list view of thumbnails for that cluster in the view panel. The user interface system may display a thumbnail list near a cluster area of the cluster panel. The thumbnail list contains mini-thumbnails of the images of the selected cluster. The user interface system may also display a detail view of an image in the view panel when a user selects an image.

User interface for viewing clusters of images

Systems and methods for automatically inserting advertisements into source video content playback streams are described. In one aspect, the systems and methods communicate a source video content playback stream to a video player to present source video to a user. During playback of the source video, and in response to receipt of a request from the user to navigate portions of the source video (e.g., a user command to fast forward the source video, rewind the source video, or other action), the systems and methods dynamically define a video advertisement clip insertion point (e.g., and insertion point based on a current playback position). The systems and methods then insert a contextually relevant and/or targeted video advertisement clip into the playback stream for presentation to the user.

Automatically inserting advertisements into source video content playback streams

Techniques for identifying near-duplicates of a media object and associating metadata of the near-duplicates with the media object are described herein. One or more devices implementing the techniques are configured to identify the near duplicates based at least on similarity attributes included in the media object. Metadata is then extracted from the near-duplicates and is associated with the media object as descriptors of the media object to enable discovery of the media object based on the descriptors.

Associating media with metadata of near-duplicates

Many users make use of search engines to locate desired internet content by submitting search queries. For example, a user may search for photos, applications, websites, videos, documents, and/or information regarding people, places, and things. Unfortunately, search engines may provide a plethora of information that a user may be left to sift through to find relevant content. Accordingly, one or more systems and/or techniques for organizing search results are disclosed herein. In particular, user generated content, such as photos, may be retrieved based upon a search query. The user generated content may be grouped into clusters of user generated content having similar features. Search results of the search query may be obtained and organized based upon comparing the search results with the clusters. The organized search results and/or a table of content based upon the clusters may be presented to provide an enhanced user experience.

Techniques are described for online real time text to image translation suitable for virtually any submitted query. Semantic classes and associated analogous items for each of the semantic classes are determined for the submitted query. One or more requests are formulated that are associated with analogous items. The requests are used to obtain web based images and associated surrounding text. The web based images are used to obtain associated near-duplicate images. The surrounding text of images is analyzed to create high-quality text associated with each semantic class of the submitted query. One or more query dependent classifiers are trained online in real time to remove noisy images. A scoring function is used to score the images. The images with the highest score are returned as a query response.

Text to image translation

A large web page is analyzed and partitioned into smaller sub-pages so that a user can navigate the web page on a small form factor device. The user can browse the sub-pages to find and read information in the content of the large web page. The partitioning can be performed at a web server, an edge server, at the small form factor device, or can be distributed across one or more such devices. The analysis leverages design habits of a web page author to extract a representation structure of an authored web page. The extracted representation structure includes high level structure using several markup language tag selection rules and low level structure using visual boundary detection in which visual units of the low level structure are provided by clustering markup language tags. User viewing habits can be learned to display favorite parts of a web page.

Small form factor web browsing

Described herein is a technology that facilitates efficient large-scale similarity-based retrieval. In several embodiments documents, images, and/or other multimedia files are compactly represented and efficiently indexed to enable robust search using a long-query in a large-scale corpus. As described herein, these techniques include performing decomposition of a file, e.g., an image, a document containing an image, or a document-like representation of an image. The techniques use dimension reduction to obtain three parts, low-dimensional representations (major semantics), file specific terms (minor semantics), and background words, representing the major semantics in a feature vector and the minor semantics as keywords. Using the techniques described, file vectors are matched in a topic model and the results ranked based on the keywords.

Long-query retrieval

A method and system for providing a user interface for presenting images of clusters of an image search result is provided. The user interface system displays the search result in a cluster/view form using a cluster panel and a view panel. The cluster panel contains a cluster area for each cluster. The view panel may contain thumbnails of images of the search result in a list view or a mix view. When a user selects a cluster area from the cluster panel, the user interface system displays a list view of thumbnails for that cluster in the view panel. The user interface system may display a thumbnail list near a cluster area of the cluster panel. The thumbnail list contains mini-thumbnails of the images of the selected cluster. The user interface system may also display a detail view of an image in the view panel when a user selects an image.

A method and system for identifying information about people is provided. The information system identifies groups of people that have relationships based on their relationships to documents or more generally to objects. The information system initially is provided with an indication of which people have which relationships to which documents. The information system then identifies clusters of people based on having a relationship to the same objects. The information system may also identify clusters of related objects associated with a cluster of people. When a user wants to identify information about a person, the user can provide the name of that person to the information system. The information system then can retrieve and display the names of the other people who are in the same cluster as the person.

Method and system for mining information based on relationships

Growing community of inventors

Beijing, China

Wei-Ying Ma