Microsoft Technology Licensing, LLC

Microsoft Corporation

Ramez Naam

Gary William Flake

Oliver Hurst-Hiller

Anoop Gupta

Joshua T Goodman

Eric J Horvitz

Christopher A Meek

John Carlton Platt

Trenholme J Griffin

Bradly A Brunell

William Henry Gates, Iii

Susan Theresa Dumais

Eric David Brill

Kyle G Peltonen

Surajit J Chaudhuri

Brett David Brewer

Stephen L Lawler

Darren Arthur Shakib

Kenneth Alan Moss

Nicole Ashley Hamilton

Julia H Farago

Michael Austin Schechter

Saleel Sathe

James Dai

Alexander G Gounares

Xuedong D Huang

Raymond Edward Ozzie

Hugh Evan Williams

Christopher Bruce Weare

Ziyi Wang

Richard Leigh Mains

Jamie Paul Buckley

Mira Lane

Jeffrey Carey Reynar

Christopher D Payne

Adrian C Klein

Randall Kern

Nicholas A Whyte

Eric Michael Schurman

Marcelo Aizenstein Furman Calbucci

Natala J Menezes

Sreeharsha Kamireddy

Anthony Tran

Kemp Crockett Peterson

Evan Nicklas Wu Malahy

Cory Hicks

Rick Gruenhagen

Jerald Lee Hittle

A method and media are provided for managing feedback data that will be used for ranking search results. A plurality of user feedback data from more than one user can be aggregated into a search index. The user feedback data can be associated with one or more documents within the index such that the one or more documents can be ranked based on the type of feedback data that is aggregated. Once the documents have been ranked, the ranked documents can be provided to a requester.

Using user feedback to rank search results

Architecture that monitors interaction data (e.g., search queries, query results and click-through rates), and provides users with links to other users that fall into similar categories with respect to the foregoing monitored activities (e.g., providing links to individuals and groups that share common interests and/or profiles). A search engine can be interactively coupled with one or more social networks, and that maps individuals and/or groups within respective social networks to subsets of categories associated with searches. A database stores mapped information which can be continuously updated and reorganized as links within the system mapping become stronger or weaker. The architecture can comprise a social network system that includes a database for mapping search-related information to an entity of a social network, and a search component for processing a search query for search results and returning a link to an entity of a social network based on the search query.

Search engine that identifies and uses social networks in communications, retrieval, and electronic commerce

Data management in social networks

Architecture for completing search queries by using artificial intelligence based schemes to infer search intentions of users. Partial queries are completed dynamically in real time. Additionally, search aliasing can also be employed. Custom tuning can be performed based on at least query inputs in the form of text, graffiti, images, handwriting, voice, audio, and video signals. Natural language processing occurs, along with handwriting recognition and slang recognition. The system includes a classifier that receives a partial query as input, accesses a query database based on contents of the query input, and infers an intended search goal from query information stored on the query database. A query formulation engine receives search information associated with the intended search goal and generates a completed formal query for execution.

Machine assisted query formulation

Search results and associated search-result details are provided in response to user queries. Embodiments include identifying a number of search-result details for different indexed search results, identifying one or more detail-attributes for each search-result detail, and identifying one or more user-attributes for a particular user. These search results, associated search-result details, and associated detail-attributes may be stored in a database. Upon receiving a search query from the user, search results may be generated. Search-result details specific to each search result and specific to the end-user may also be generated by identifying the detail-attributes associated with each search-result detail and the user-attributes associated with the end-user. These search results may then be presented to the end-user. Upon receiving an indication of interest in a particular search result, the search result details may then be presented to the end user.

On-demand search result details

The subject disclosure pertains to anonymous network interaction. More specifically, mechanisms are provided to ensure anonymity with respect network interaction such that third parties are unable to determine the source and/or intent of communications. Accordingly, entities may anonymize all outgoing and/or incoming data packets so as to mitigate outside entities from learning about information being sought and/or provided. For example, a user or corporation may employ an anonymizer with respect to web searching so that outside entities are not able to determine what information is attempted to be accessed and by whom.

Anonymous and secure network-based interaction

Voice-based search processing

Sharing of search histories is facilitated with a computer based system and method that allow a searcher to share search results generated without prior authentication of an identity with a search engine. Sharing and collaboration are further facilitated by allowing search history items to be shared with a minimum of additional searcher input. Context for the search history items is provided by associating a search query with an accessed document link without requiring separate tagging of the link by the searcher.

Sharing and collaboration of search findings

A system, method, and medium are provided for providing a personalized answer to a user-defined search query by utilizing the user's search history. A search session is instantiated, and search queries, search results, user selections of search results, and other information is recorded. A search query is received and analyzed to determine whether the query reflects user intent to re-find a search result that the user previously was presented in response to a previously issued search query. Search results pages provided to the user in response to queries include a personalized history answer that represents a previously viewed search result that the user may be attempting to re-find.

History answer for re-finding search results

Architecture that allows a user to search for content or to restrict an online search to a limited number of websites and/or blog sites. Architecture is provided that facilitates data searching via a source component that includes data representative of a website or a blog, and a search component that processes the data to restrict or bias a query based in part on the data. The source component can include one or more of a favorites list, a blogroll, a pointer to an XML file, or a pointer to an OPML (outline processor markup language) file to specify a list of sites or blogs which they frequent. The search component processes a search query and restricts or biases the search by specifying a list of sites or blogs which are contained in the data of the source component, for example, the favorites list, the pointer to an XML file, the pointer to an OPML file, and the blogroll.

Scoping and biasing search to user preferred domains or blogs

The subject disclosure pertains to anonymous network interaction. More specifically, mechanisms are provided to ensure anonymity with respect network interaction such that third parties are unable to determine the source and/or intent of communications. Accordingly, entities can anonymize all outgoing and/or incoming data packets so as to mitigate outside entities from learning about information being sought and/or provided. For example, a user or corporation can employ an anonymizer with respect to web searching so that outside entities are not able to determine what information is attempted to be accessed and by whom.

A system that can analyze a multi-dimensional input thereafter establishing a search query based upon extracted features from the input. In a particular example, an image can be used as an input to a search mechanism. Pattern recognition and image analysis can be applied to the image thereafter establishing a search query that corresponds to features extracted from the image input. The system can also facilitate indexing multi-dimensional searchable items thereby making them available to be retrieved as results to a search query. More particularly, the system can employ text analysis, pattern and/or speech recognition mechanisms to extract features from searchable items. These extracted features can be employed to index the searchable items.

Visual and multi-dimensional search

Architecture is provided for performing multimedia searches and returning media galleries, and then prominently displaying the galleries to a user. The disclosed innovation generates a gallery classifier and then displays found galleries to the user in a new and informative user interface. In one aspect, a system is provided that facilitates searching data for gallery pages. The system includes a gallery classifier that receives web data associated with web pages, and identifies a web page as a gallery page during a search operation. The system also includes a gallery user interface that presents a gallery page and associated content as part of the search process.

Searches over a collection of items through classification and display of media galleries

A system that employs an explicitly and/or implicitly trained model in order to return entity-specific computer-based search results is provided. The innovation can provide for a customized search model that focuses search in connection with achieving information that is meaningful with respect to goals of an entity. The model can be used to modify a search query in accordance with a goal of the entity or to generate the search query thereby returning meaningful and/or targeted results to the user. The system can automatically gather entity-related data thereafter determining or inferring a goal as well as training the model. Moreover, the system can selectively configure (e.g., order, rank, filter) and render results to a user based upon the model.

Entity-specific search model

Growing community of inventors

Seattle, WA, United States of America

Ramez Naam