A domain classifier develops and maintains relevance data about specific domains based on historical relevance data and source context data. Such data may be used to classify the user's interest in attempting to visit a specific domain and thereby redirect the user to a website expected to be aligned with the user's interest. In various implementations, the historical relevance data is derived from source context data and/or post-visit user behavior collected from previous attempts to visit a specific domain. The source context data collected from the current visit may also be used as source context-factors to influence domain classification. Based on such historical and current source context factors, as well as the domain address provided in the user's navigation request, a domain classifier consults the historical relevance data and provides the user with Web content that is identified as likely to be relevant to the user's interests.

Relevancy-based domain classification

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

The present disclosure is directed to systems and methods for determining semantically related terms. Generally, one or more seed terms are received from a user. A system searches a first index comprising a plurality of terms and one or more webpages associated with each term of the plurality of terms to determine a plurality of webpages associated with the seed terms. The system then searches a second index comprising a plurality of webpages and one or more terms associated with each webpage of the plurality of webpages to determine a plurality of potential terms associated with the plurality of webpages associated with the seed terms. At least one term of the plurality of potential terms is suggested to a user.

System and method for determining semantically related terms

A method and apparatus for dynamically modifying search results “on the fly” based on the behavior of the user currently conducting a search. In one embodiment, data regarding user behavior is gathered from virtually any activity, including clicks on links, dwell times, downloads, transactions and cursor movements. Subordinate keywords are generated to reflect the intent of the user as inferred from the user's behavior. Subordinate keywords, as opposed to traditional primary keywords, are keywords that are identified as important to the search, but are not necessarily essential for a matched document. They are automatically generated by the system from a variety of places, such as documents clicked on by the user as well as documents that are skipped. The system uses the subordinate keywords to dynamically re-rank thatched documents and advertisements to best reflect the inferred intent of the user in order to continuously provide the most relevant results.

Dynamic search engine results employing user behavior

Voice-based search processing

A system and method for ranking search results based on a series of attributes derived from the behavior of past searchers is disclosed. The attributes provide a measure of the relevancy between a search query and a URL, file, or other resource based on its relevancy to prior users. The system comprises (1) an attribute database including a plurality of prior search terms or phases; a first set of resources associated with each of the queries; and the attributes, i.e., metrics, characterizing the relevance of the first set of resources to the queries; and (2) a search processor adapted to identify a second set of resources determined to be relevant to a user query; rank each of the second set of resources based on the metrics associated with the query and resource; and provide the user with the search results ranked in accordance the metrics and displayed in a manner to increase the utility of the results for the user.

Reed Sturtevant

Papers

Search engine using user intent