Autocompletion using previously submitted query data

    公开(公告)号:US09740780B1

    公开(公告)日:2017-08-22

    申请号:US14556981

    申请日:2014-12-01

    Applicant: Google Inc.

    CPC classification number: G06F17/30864 G06F17/3064

    Abstract: A computer-implemented method for processing query information includes receiving query information at a server system. The query information includes a portion of a query from a search requestor. The method also includes obtaining a set of predicted queries relevant to the portion of the search requestor query based upon the portion of the query from the search requestor and data indicative of search requestor behavior relative to previously submitted queries. The method also includes providing the set of predicted queries to the search requestor.

    Query formulation and search in the context of a displayed document
    3.
    发明授权
    Query formulation and search in the context of a displayed document 有权
    在显示文档的上下文中查询公式和搜索

    公开(公告)号:US09342601B1

    公开(公告)日:2016-05-17

    申请号:US13774711

    申请日:2013-02-22

    Applicant: Google Inc.

    Abstract: Technology described herein enhances a user's search experience by providing refined search results that are relevant to a displayed document. Contextual search results are obtained which identify a list of documents responsive to a formulated query that is based on the user's search query, as well as one or more supplemental terms that are based on content in the displayed document during user entry of the search query. The contextual search results are then “refined” by re-ranking the documents in the list, based on the similarity between the user's original search query and terms in these documents. This re-ranking enables contextual search results to be provided that are also highly relevant to the user's informational need.

    Abstract translation: 本文描述的技术通过提供与所显示的文档相关的精细搜索结果增强了用户的搜索体验。 获得上下文搜索结果,其识别响应于基于用户的搜索查询的配方查询的文档列表,以及基于在用户输入搜索查询期间显示的文档中的内容的一个或多个补充术语。 基于用户原始搜索查询与这些文档中的条款之间的相似性,将上下文搜索结果重新排列在列表中的文档中,进行“精炼”。 这种重新排序使得能够提供与用户的信息需求高度相关的上下文搜索结果。

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