Integrated mounted frame for an unmanned vehicle

    公开(公告)号:USD1040073S1

    公开(公告)日:2024-08-27

    申请号:US29856467

    申请日:2022-10-13

    Abstract: FIG. 1 is a first front perspective view of an integrated mounted frame for an unmanned vehicle, showing our new design;
    FIG. 2 is a second front perspective view of an integrated mounted frame for an unmanned vehicle, showing our new design;
    FIG. 3 is a third front perspective view of an integrated mounted frame for an unmanned vehicle, showing our new design;
    FIG. 4 is a front elevation view of the integrated mounted frame for an unmanned vehicle shown in FIG. 1;
    FIG. 5 is a rear elevation view of the integrated mounted frame for an unmanned vehicle shown in FIG. 1;
    FIG. 6 is a left side elevation view of the integrated mounted frame for an unmanned vehicle shown in FIG. 1;
    FIG. 7 is a right side elevation view of the integrated mounted frame for an unmanned vehicle shown in FIG. 1;
    FIG. 8 is a top plan view of the integrated mounted frame for an unmanned vehicle shown in FIG. 1; and,
    FIG. 9 is a bottom plan view of the integrated mounted frame for an unmanned vehicle shown in FIG. 1.
    The broken lines represent portions of the integrated mounted frame for an unmanned vehicle and form no part of the claimed design.

    Keyword generating method, apparatus, device and storage medium

    公开(公告)号:US11899699B2

    公开(公告)日:2024-02-13

    申请号:US17347448

    申请日:2021-06-14

    CPC classification number: G06F16/3329 G06F16/335 G06F40/20

    Abstract: This application discloses a keyword generating method, an apparatus, a device and a storage medium, which relate to the field of natural language processing in the field of artificial intelligence. A specific implementation scheme includes: inputting a target text into a text processing model, obtaining a word sequence corresponding to the target text, and generating a semantic representation sequence corresponding to the word sequence; making prediction about each semantic representation vector in the semantic representation sequence respectively to obtain a prediction result; and if the prediction result indicates that a word corresponding to the semantic representation vector is capable of triggering a generation of a keyword, outputting the keyword based on the semantic representation vector and the prediction result. This method improves the accuracy of generating keywords.

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