Patents by Inventor Murat C. Ganiz

Murat C. Ganiz 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).

  • Systems and methods for data transformation using higher order learning
    Patent number: 8572071
    Abstract: Disclosed is a method and apparatus for transforming data in vector form. Each vector is composed of a set of attributes that are either boolean or have been mapped to boolean form. The vectors may or may not fall into categories assigned by a subject matter expert (SME). If categories exist, the categorical labels divide the vectors into subsets. The first transformation calculates a prior probability for each attribute based on the links between attributes in each subset of the vectors. The second transformation computes a new numeric value for each attribute based on the links between attributes in each subset of the vectors. The third transformation operates on vectors that have not been categorized. Based on the automatic selection of categories from the attributes, this transformation computes a new numeric value for each attribute based on the links between attributes in each subset of the vectors.
    Type: Grant
    Filed: June 9, 2010
    Date of Patent: October 29, 2013
    Assignee: Rutgers, The State University of New Jersey
    Inventors: William M. Pottenger, Nikita Lytkin, Murat C. Ganiz
  • SYSTEMS AND METHODS FOR DATA TRANSFORMATION USING HIGHER ORDER LEARNING
    Publication number: 20100312727
    Abstract: Disclosed is a method and apparatus for transforming data in vector form. Each vector is composed of a set of attributes that are either boolean or have been mapped to boolean form. The vectors may or may not fall into categories assigned by a subject matter expert (SME). If categories exist, the categorical labels divide the vectors into subsets. The first transformation calculates a prior probability for each attribute based on the links between attributes in each subset of the vectors. The second transformation computes a new numeric value for each attribute based on the links between attributes in each subset of the vectors. The third transformation operates on vectors that have not been categorized. Based on the automatic selection of categories from the attributes, this transformation computes a new numeric value for each attribute based on the links between attributes in each subset of the vectors.
    Type: Application
    Filed: June 9, 2010
    Publication date: December 9, 2010
    Inventors: William M. Pottenger, Nikita Lytkin, Murat C. Ganiz