Patents by Inventor Sergiy Peredriy

Sergiy Peredriy 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: 9990592
    Abstract: A computing device determines a kernel parameter value for a support vector data description for outlier identification. A first candidate optimal kernel parameter value is computed by computing a first optimal value of a first objective function that includes a kernel function for each of a plurality of kernel parameter values from a starting kernel parameter value to an ending kernel parameter value using an incremental kernel parameter value. The first objective function is defined for a SVDD model using observation vectors to define support vectors. A number of the observation vectors is a predefined sample size. The predefined sample size is incremented by adding a sample size increment. A next candidate optimal kernel parameter value is computed with an incremented number of vectors until a computed difference value is less than or equal to a predefined convergence value.
    Type: Grant
    Filed: May 1, 2017
    Date of Patent: June 5, 2018
    Assignee: SAS Institute Inc.
    Inventors: Sergiy Peredriy, Deovrat Vijay Kakde, Arin Chaudhuri
  • Patent number: 9830558
    Abstract: A computing device determines an SVDD to identify an outlier in a dataset. First and second sets of observation vectors of a predefined sample size are randomly selected from a training dataset. First and second optimal values are computed using the first and second observation vectors to define a first set of support vectors and a second set of support vectors. A third optimal value is computed using the first set of support vectors updated to include the second set of support vectors to define a third set of support vectors. Whether or not a stop condition is satisfied is determined by comparing a computed value to a stop criterion. When the stop condition is not satisfied, the first set of support vectors is defined as the third set of support vectors, and operations are repeated until the stop condition is satisfied. The third set of support vectors is output.
    Type: Grant
    Filed: June 17, 2016
    Date of Patent: November 28, 2017
    Assignee: SAS Institute Inc.
    Inventors: Arin Chaudhuri, Deovrat Vijay Kakde, Maria Jahja, Wei Xiao, Seung Hyun Kong, Hansi Jiang, Sergiy Peredriy
  • Publication number: 20170323221
    Abstract: A computing device determines an SVDD to identify an outlier in a dataset. First and second sets of observation vectors of a predefined sample size are randomly selected from a training dataset. First and second optimal values are computed using the first and second observation vectors to define a first set of support vectors and a second set of support vectors. A third optimal value is computed using the first set of support vectors updated to include the second set of support vectors to define a third set of support vectors. Whether or not a stop condition is satisfied is determined by comparing a computed value to a stop criterion. When the stop condition is not satisfied, the first set of support vectors is defined as the third set of support vectors, and operations are repeated until the stop condition is satisfied. The third set of support vectors is output.
    Type: Application
    Filed: June 17, 2016
    Publication date: November 9, 2017
    Inventors: Arin Chaudhuri, Deovrat Vijay Kakde, Maria Jahja, Wei Xiao, Seung Hyun Kong, Hansi Jiang, Sergiy Peredriy
  • Publication number: 20170236074
    Abstract: A computing device determines a kernel parameter value for a support vector data description for outlier identification. A first candidate optimal kernel parameter value is computed by computing a first optimal value of a first objective function that includes a kernel function for each of a plurality of kernel parameter values from a starting kernel parameter value to an ending kernel parameter value using an incremental kernel parameter value. The first objective function is defined for a SVDD model using observation vectors to define support vectors. A number of the observation vectors is a predefined sample size. The predefined sample size is incremented by adding a sample size increment. A next candidate optimal kernel parameter value is computed with an incremented number of vectors until a computed difference value is less than or equal to a predefined convergence value.
    Type: Application
    Filed: May 1, 2017
    Publication date: August 17, 2017
    Inventors: Sergiy Peredriy, Deovrat Vijay Kakde, Arin Chaudhuri
  • Patent number: 9639809
    Abstract: A computing device identifies outliers. Support vectors, Lagrange constants, a center threshold value, an upper control limit value, and a lower control limit value are received that define a normal operating condition of a system. The center threshold value, the upper control limit value, and the lower control limit value are computed from the vectors and the Lagrange constants. A first plurality of observation vectors is received for a predefined window length. A window threshold value and a window center vector are computed. A window distance value is computed between the window center vector and the support vectors. Based on comparisons between the computed values and the received values, the first plurality of observation vectors is identified as an outlier relative to the normal operating condition of the system. When the first plurality of observation vectors are identified as the outlier, an alert is output.
    Type: Grant
    Filed: December 23, 2016
    Date of Patent: May 2, 2017
    Assignee: SAS Institute Inc.
    Inventors: Deovrat Vijay Kakde, Sergiy Peredriy, Arin Chaudhuri, Anya M. McGuirk
  • Publication number: 20160239749
    Abstract: Computer-implemented systems and methods are provided for predicting outputs. Global output fractions associated with an object are approximated. Outputs for a group are predicted based upon a cyclical aspect component and a movement prediction. An output prediction is calculated based upon the predicted outputs for a related object group and the approximated global output fraction for a particular object.
    Type: Application
    Filed: January 5, 2016
    Publication date: August 18, 2016
    Applicant: SAS INSTITUTE INC.
    Inventors: Sergiy Peredriy, Yung-Hsin Chien, Arin Chaudhuri, Ann Mary McGuirk, Yongqiao Xiao
  • Publication number: 20100106561
    Abstract: Computer-implemented systems and methods are provided for forecasting product sales. Market shares associated with a product are estimated. Sales for a share group are forecast based upon a seasonality component and a trend prediction. A product sales forecast is calculated based upon the forecasted sales for a share group and the estimated product market share.
    Type: Application
    Filed: October 28, 2008
    Publication date: April 29, 2010
    Inventors: Sergiy Peredriy, Yung-Hsin Chien, Arin Chaudhuri, Ann Mary McGuirk, Yongqiao Xiao