PAGE CONTENT RANKING AND DISPLAY
    1.
    发明申请

    公开(公告)号:US20210141845A1

    公开(公告)日:2021-05-13

    申请号:US16676579

    申请日:2019-11-07

    Abstract: A computer-implemented method comprises analyzing content sections on each of a plurality of open browser pages using natural language processing to identify one or more topics on each of the plurality of open browser pages; calculating a respective relevance score for each of the content sections; grouping each of the plurality of topics into one of a plurality of topic groups; calculating a respective group ranking for each of the plurality of topic groups based on the respective relevance score for each content section. The method further comprises, for each topic group, assigning the respective group ranking to all of the content sections corresponding to the respective topic group; and, for each of the plurality of open browser pages, selecting at least one content section having a highest group ranking and modifying a display of the respective open browser page to direct attention to the selected content section.

    CLASSIFYING SPOKEN CONTENT IN A TELECONFERENCE
    2.
    发明申请
    CLASSIFYING SPOKEN CONTENT IN A TELECONFERENCE 有权
    分类电话内容

    公开(公告)号:US20150199962A1

    公开(公告)日:2015-07-16

    申请号:US14590016

    申请日:2015-01-06

    CPC classification number: G10L15/08 H04M3/568

    Abstract: A method and an apparatus for classifying spoken content in a teleconference for a follower of the teleconference is disclosed. The method comprises: detecting a topic to which the spoken content belongs; determining a (overall) correlation degree between the follower and the spoken content at least according to a correlation degree between the follower and the topic; and classifying the spoken content according to the (overall) correlation degree between the follower and the spoken content. With the method and the apparatus, the correlation degree between the spoken content in the teleconference and the follower of the teleconference can be determined automatically, and the spoken content can be classified according to the correlation degree, so that the follower can selectively pay attention to some spoken contents during the teleconference, which reduces a burden of the follower and improves conference efficiency.

    Abstract translation: 公开了一种用于对电话会议的跟随者的电话会议中的口语内容进行分类的方法和装置。 该方法包括:检测口头内容所属的话题; 至少根据跟随者和主题之间的相关程度确定跟随者和口头内容之间的(总体)相关程度; 并根据跟随者和口头内容之间的(整体)相关程度对口语内容进行分类。 利用该方法和装置,可以自动确定电话会议中的口语内容与电话会议的跟随者之间的相关程度,并且可以根据相关程度对口语内容进行分类,从而可以选择性地关注 在电话会议期间的一些口语内容,减轻了追随者的负担,提高了会议效率。

    Page content ranking and display
    3.
    发明授权

    公开(公告)号:US11144610B2

    公开(公告)日:2021-10-12

    申请号:US16676579

    申请日:2019-11-07

    Abstract: A computer-implemented method comprises analyzing content sections on each of a plurality of open browser pages using natural language processing to identify one or more topics on each of the plurality of open browser pages; calculating a respective relevance score for each of the content sections; grouping each of the plurality of topics into one of a plurality of topic groups; calculating a respective group ranking for each of the plurality of topic groups based on the respective relevance score for each content section. The method further comprises, for each topic group, assigning the respective group ranking to all of the content sections corresponding to the respective topic group; and, for each of the plurality of open browser pages, selecting at least one content section having a highest group ranking and modifying a display of the respective open browser page to direct attention to the selected content section.

    PRIORITY-BASED RENDERING
    4.
    发明申请

    公开(公告)号:US20210034692A1

    公开(公告)日:2021-02-04

    申请号:US16529272

    申请日:2019-08-01

    Abstract: Aspects of the present invention disclose a method, computer program product, and system for content rendering. The method includes one or more processors retrieving at least one user interface (UI) component from a Document Object Model (DOM) tree. The method further includes one or more processors determining a corresponding rendering priority level (RPL) for each of the at least one UI component. The method further includes, in response to determining that the corresponding RPL of a first UI component of the at least one UI component is above a first threshold, one or more processors rendering the first UI component.

    Adaptive language translation using context features

    公开(公告)号:US11947925B2

    公开(公告)日:2024-04-02

    申请号:US16879886

    申请日:2020-05-21

    CPC classification number: G06F40/58 G06N3/044 G06N3/08

    Abstract: A user input in a source language is received. A set of contextual data is received. The user input is encoded into a user input feature vector. The set of contextual data is encoded into a context feature vector. The user input feature vector and the context feature vector are used to generate a fusion vector. An adaptive neural network is trained to identify a second context feature vector, based on the fusion vector. A second user input in the source language is received for translation into a target language. The adaptive neural network is used to determine, based on the second context feature vector, a second user input feature vector. The second user input feature vector is decoded, based on the source language and the target language, into a target language output. A user is notified of the target language output.

    ADAPTIVE LANGUAGE TRANSLATION USING CONTEXT FEATURES

    公开(公告)号:US20210365644A1

    公开(公告)日:2021-11-25

    申请号:US16879886

    申请日:2020-05-21

    Abstract: A user input in a source language is received. A set of contextual data is received. The user input is encoded into a user input feature vector. The set of contextual data is encoded into a context feature vector. The user input feature vector and the context feature vector are used to generate a fusion vector. An adaptive neural network is trained to identify a second context feature vector, based on the fusion vector. A second user input in the source language is received for translation into a target language. The adaptive neural network is used to determine, based on the second context feature vector, a second user input feature vector. The second user input feature vector is decoded, based on the source language and the target language, into a target language output. A user is notified of the target language output.

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