中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

8 records (took 0.012 seconds)

    Adaptive sampling scheme for imbalanced large scale data
    4.
    发明授权
    Adaptive sampling scheme for imbalanced large scale data 有权

    公开(公告)号：US10346861B2

    公开(公告)日：2019-07-09

    申请号：US14933254

    申请日：2015-11-05

    Applicant: ADOBE INC.

    Inventor： Wei Zhang ,  Said Kobeissi ,  Anandhavelu Natarajan ,  Shiv Kumar Saini ,  Ritwik Sinha ,  Scott Allen Tomko

    IPC: G06N3/08 ,  G06Q30/02

    Abstract: Embodiments of the present invention relate to providing business customers with predictive capabilities, such as identifying valuable customers or estimating the likelihood that a product will be purchased. An adaptive sampling scheme is utilized, which helps generate sample data points from large scale data that is imbalanced (for example, digital website traffic with hundreds of millions of visitors but only a small portion of them are of interest). In embodiments, a stream of sample data points is received. Positive samples are added to a positive list until the desired number of positives is reached and negative samples are added to a negative list until the desired number of negative samples is reached. The positive list and the negative list can then be combined, shuffled, and fed into a prediction model.

    Systems and techniques for determining associations between multiple types of data in large data sets
    5.
    发明授权
    Systems and techniques for determining associations between multiple types of data in large data sets 有权

    公开(公告)号：US10552996B2

    公开(公告)日：2020-02-04

    申请号：US15085210

    申请日：2016-03-30

    Applicant: Adobe Inc.

    Inventor： Ritwik Sinha ,  Said Kobeissi ,  Michael Young

    IPC: G06F15/16 ,  G06T11/20 ,  G06F16/35

    Abstract: Systems and methods disclosed herein identify multivariate relationships that exist across all types data collected from numerous observed users over one or more networks. Electronic data collected from observed users include categorical data and non-categorical/numeric data. To compare and analyze the collected data, a marketing entity converts the numeric data to categorical data via a binning algorithm, which reduces the numeric data into two or more discrete categories. The marketing entity analyzes the data variables to compute pairwise associations on the collected categorical and numeric data (which has been converted to categorical data). The marketing entity also determines hierarchical clusters to group the pairwise associations of data variables based on the strength of the associations. The pairwise relationships and hierarchical clusters are displayed on a user interface.

    GENERATING AND PROVIDING DIMENSION-BASED LOOKALIKE SEGMENTS FOR A TARGET SEGMENT
    8.
    发明申请
    GENERATING AND PROVIDING DIMENSION-BASED LOOKALIKE SEGMENTS FOR A TARGET SEGMENT 有权

    公开(公告)号：US20210224857A1

    公开(公告)日：2021-07-22

    申请号：US16746531

    申请日：2020-01-17

    Applicant: Adobe Inc.

    Inventor： Ritwik Sinha ,  William George ,  Said Kobeissi ,  Raymond Wong ,  Prithvi Bhutani ,  Ilya Reznik ,  Fan Du ,  David Arbour ,  Chris Challis ,  Atanu Sinha ,  Anup Rao

    IPC: G06Q30/02 ,  G06F16/22 ,  G06F3/0482

    Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for generating lookalike segments corresponding to a target segment using decision trees and providing a graphical user interface comprising nodes representing such lookalike segments. Upon receiving an indication of a target segment, for instance, the disclosed systems can generate a lookalike segment from a set of users by partitioning the set of users according to one or more dimensions based on probabilities of subsets of users matching the target segment. By partitioning subsets of users within a node tree, the disclosed systems can identify different subsets of users partitioned according to different dimensions from the set of users. The disclosed systems can further provide a node tree interface comprising a node for the set of users and nodes for subsets of users within one or more lookalike segments.

Patent Agency Ranking