AUDIENCE COMPARISON
    1.
    发明申请
    AUDIENCE COMPARISON 审中-公开

    公开(公告)号:US20170357988A1

    公开(公告)日:2017-12-14

    申请号:US15180582

    申请日:2016-06-13

    IPC分类号: G06Q30/02 G06F3/0482

    CPC分类号: G06Q30/0204 G06F3/0484

    摘要: Systems and methods are disclosed herein for providing a user interface representing differences between segments of end users. The systems and methods receive user input on a user interface identifying a first segment, the first segment being a subset of the end users having a particular characteristic, determine differences between the first segment and a second segment, and represent, on the user interface, the differences between the first segment and the second segment based on relative significances of the differences. The marketer using the user interface is able to quickly and easily identify the metrics, dimensions, and/or relationships to other segments that most distinguish the compared segments from one another.

    DETECTING NOVEL ASSOCIATIONS IN LARGE DATASETS

    公开(公告)号:US20180349466A1

    公开(公告)日:2018-12-06

    申请号:US15611563

    申请日:2017-06-01

    摘要: Certain embodiments involve determining and outputting correlations between metrics in large-scale web analytics datasets. For example, a processor identifies pairs of data metrics in a web analytics data set and determines a Maximal Information Coefficient (MIC) score for each pair of data metrics that indicates a strength of a correlation between the pair of data metrics. The processor generates an interactive user interface that graphically displays each pair of correlated data metrics having an MIC score above a threshold and the interactive user interface indicates the strength of the correlation between each displayed pair of correlated data metrics. The processor receives user input indicating an adjustment to the threshold and modifies the interactive user interface in response to receiving the user input by adding pairs of correlated data metrics to, or removing pairs of correlated metrics from, the interactive user interface based on the adjustment to the threshold.

    IDENTIFICATION OF READING ORDER TEXT SEGMENTS WITH A PROBABILISTIC LANGUAGE MODEL

    公开(公告)号:US20180267956A1

    公开(公告)日:2018-09-20

    申请号:US15462684

    申请日:2017-03-17

    IPC分类号: G06F17/27

    摘要: A computer implemented method and system identifies correct structured reading-order sequence of text segments that are extracted from a file structured in a portable document format. A probabilistic language model is generated from a large text corpus to comprise observed word sequence patterns for a given language. The language model measures whether splicing together a first text segment with another continuation text segment results in a phrase that is more likely than a phrase resulting from splicing together the first text segment with other continuation text segments. Sets of text segments are provided to the probabilistic model, where the sets of text segments comprise a first set including the first text segment and a first continuation text segment. A second set includes the first text segment and a second continuation text segment. A score is obtained for each set of text segments. The score is indicative of a likelihood of the set providing a correct structured reading-order sequence. The probabilistic language model may be generated in accordance with a Recurrent Neural Network or an n-gram model.

    CONTENT PRESENTATION BASED ON A MULTI-TASK NEURAL NETWORK

    公开(公告)号:US20170251081A1

    公开(公告)日:2017-08-31

    申请号:US15053448

    申请日:2016-02-25

    摘要: Techniques for predictively selecting a content presentation in a client-server computing environment are described. In an example, a content management system detects an interaction of a client with a server and accesses client features. Reponses of the client to potential content presentations are predicted based on a multi-task neural network. The client features are mapped to input nodes and the potential content presentations are associated with tasks mapped to output nodes of the multi-task neural network. The tasks specify usages of the potential content presentations in response to the interaction with the server. In an example, the content management system selects the content presentation from the potential content presentations based on the predicted responses. For instance, the content presentation is selected based on having the highest likelihood. The content management system provides the content presentation to the client based on the task corresponding to the content presentation.