Patents by Inventor SAID KOBEISSI

SAID KOBEISSI has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10346861
    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.
    Type: Grant
    Filed: November 5, 2015
    Date of Patent: July 9, 2019
    Assignee: ADOBE INC.
    Inventors: Wei Zhang, Said Kobeissi, Anandhavelu Natarajan, Shiv Kumar Saini, Ritwik Sinha, Scott Allen Tomko
  • Publication number: 20170288989
    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.
    Type: Application
    Filed: March 30, 2016
    Publication date: October 5, 2017
    Inventors: Ritwik Sinha, Said Kobeissi, Michael Young
  • Publication number: 20170255952
    Abstract: Systems and methods provide for feature selection that combines semantic classification and generative filtering with forward selection. Features from an original feature set are divided into feature subsets corresponding to ranked semantic classes. Additionally, low quality features are removed from consideration. Features are selected for a reduced feature set by iteratively processing the feature subsets using forward selection in an order corresponding to the ranking of the semantic classes. The reduced feature set is used to generate a predictive model.
    Type: Application
    Filed: March 7, 2016
    Publication date: September 7, 2017
    Inventors: Wei Zhang, Shiladitya Bose, Said Kobeissi, Scott Allen Tomko, Jeremy W King
  • Publication number: 20170132516
    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.
    Type: Application
    Filed: November 5, 2015
    Publication date: May 11, 2017
    Inventors: WEI ZHANG, SAID KOBEISSI, ANANDHAVELU NATARAJAN, SHIV KUMAR SAINI, RITWIK SINHA, SCOTT ALLEN TOMKO