Patents by Inventor Deovrat Vijay Kakde

Deovrat Vijay Kakde 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: 10684618
    Abstract: A computing system detects an event. (A) A frequency spectrum is computed using a Fourier transform. (B) (A) is repeated a predefined plurality of times with successive windows of observation vectors. Each window of the successive windows includes a subset of the observation vectors. The successive windows include successive subsets selected sequentially in time. (C) An average frequency spectrum is computed from the frequency spectrum computed the predefined plurality of times. (D) A plurality of segmented average frequency spectra is computed from the computed average frequency spectrum. Each segmented average frequency spectrum of the plurality of segmented average frequency spectra is computed for a frequency band of a plurality of predefined frequency bands. (E) When an event has occurred is determined based on the computed plurality of segmented average frequency spectra and a predefined threshold value.
    Type: Grant
    Filed: December 4, 2019
    Date of Patent: June 16, 2020
    Assignee: SAS INSTITUTE INC.
    Inventors: Anya M. McGuirk, Yuwei Liao, Byron Davis Biggs, Deovrat Vijay Kakde
  • Patent number: 10656638
    Abstract: A frequency spectrum is computed using a Fourier transform a predefined plurality of times with successive windows of observation vectors, wherein each window of the successive windows includes a subset of observation vectors, wherein the successive windows include successive subsets selected sequentially in time. An average frequency spectrum is computed. A plurality of segmented average frequency spectra is computed, wherein each segmented average frequency spectrum is computed for a frequency band of predefined frequency bands. A distance value is computed using a trained support vector data description model with the segmented average frequency spectra. When an event has occurred is determined based on a comparison between the distance value and a predefined threshold. A vector is computed from the segmented average frequency spectra using t-stochastic neighbor embedding. When an event has occurred based on the comparison, an event indicator is presented on a graph using the vector.
    Type: Grant
    Filed: December 4, 2019
    Date of Patent: May 19, 2020
    Assignee: SAS INSTITUTE INC.
    Inventors: Anya M. McGuirk, Yuwei Liao, Byron Davis Biggs, Deovrat Vijay Kakde
  • Patent number: 10558207
    Abstract: A computing system detects an event. (A) A frequency spectrum of observation vectors is computed using a Fourier transform. Each observation vector includes a sensor value. (B) (A) is repeated a predefined plurality of times with successive windows of the observation vectors. Each window of the successive windows includes a subset of the observation vectors. The successive windows include successive subsets selected sequentially in time. (C) An average frequency spectrum is computed from the frequency spectrum computed the predefined plurality of times. (D) A predefined noise filter is applied to the average frequency spectrum to define a filtered frequency spectrum. (E) A distance value is computed between the filtered frequency spectrum and a predefined reference spectrum using a distance computation function. (F) When an event has occurred is determined based on a comparison between the computed distance value and a predefined distance threshold.
    Type: Grant
    Filed: August 23, 2019
    Date of Patent: February 11, 2020
    Assignee: SAS INSTITUTE INC.
    Inventors: Anya M. McGuirk, Yuwei Liao, Byron Davis Biggs, Deovrat Vijay Kakde
  • Patent number: 10509847
    Abstract: A computing device determines hyperparameter values for outlier detection. An LOF score is computed for observation vectors using a neighborhood size value. Outlier observation vectors are selected from the observation vectors. Outlier mean and outlier variance values are computed of the LOF scores of the outlier observation vectors. Inlier observation vectors are selected from the observation vectors that have highest computed LOF scores of the observation vectors that are not included in the outlier observation vectors. Inlier mean and inlier variance values are computed of the LOF scores of the inlier observation vectors. A difference value is computed using the outlier mean and variance values and the inlier mean and variance values. The process is repeated with each neighborhood size value of a plurality of neighborhood size values. A tuned neighborhood size value is selected as the neighborhood size value associated with an extremum value of the difference value.
    Type: Grant
    Filed: May 14, 2019
    Date of Patent: December 17, 2019
    Assignee: SAS Institute Inc.
    Inventors: Zekun Xu, Deovrat Vijay Kakde, Arin Chaudhuri
  • Patent number: 10482353
    Abstract: A computing device determines a bandwidth parameter value for outlier detection or data classification. A mean pairwise distance value is computed between observation vectors. A tolerance value is computed based on a number of observation vectors. A scaling factor value is computed based on a number of observation vectors and the tolerance value. A Gaussian bandwidth parameter value is computed using the mean pairwise distance value and the scaling factor value. An optimal value of an objective function is computed that includes a Gaussian kernel function that uses the computed Gaussian bandwidth parameter value. The objective function defines a support vector data description model using the observation vectors to define a set of support vectors. The Gaussian bandwidth parameter value and the set of support vectors are output for determining if a new observation vector is an outlier or for classifying the new observation vector.
    Type: Grant
    Filed: August 6, 2018
    Date of Patent: November 19, 2019
    Assignee: SAS INSTITUTE INC.
    Inventors: Yuwei Liao, Deovrat Vijay Kakde, Arin Chaudhuri, Hansi Jiang, Carol Wagih Sadek, Seung Hyun Kong
  • Publication number: 20190095400
    Abstract: A Gaussian similarity matrix is computed between observation vectors. An inverse Gaussian similarity matrix is computed from the Gaussian similarity matrix. A row sum vector is computed that includes a row sum value computed from each row of the inverse Gaussian similarity matrix. (a) A new observation vector is selected. (b) An acceptance value is computed for the new observation vector using the set of boundary support vectors, the row sum vector, and the new observation vector. (c) (a) and (b) are repeated when the computed acceptance value is less than or equal to zero. (d) An incremental vector is computed from the inverse Gaussian similarity matrix and the new observation vector when the computed acceptance value is greater than zero. (e) the selected new observation vector is output as an outlier observation vector when a maximum value of the incremental vector is less than a first predefined tolerance value.
    Type: Application
    Filed: July 9, 2018
    Publication date: March 28, 2019
    Inventors: Hansi Jiang, Wenhao Hu, Haoyu Wang, Deovrat Vijay Kakde, Arin Chaudhuri
  • Publication number: 20190042977
    Abstract: A computing device employs machine learning and determines a bandwidth parameter value for a support vector data description (SVDD). A mean pairwise distance value is computed between observation vectors. A scaling factor value is computed based on a number of the plurality of observation vectors and a predefined tolerance value. A Gaussian bandwidth parameter value is computed using the computed mean pairwise distance value and the computed scaling factor value. An optimal value of an objective function is computed that includes a Gaussian kernel function that uses the computed Gaussian bandwidth parameter value. The objective function defines a SVDD model using the plurality of observation vectors to define a set of support vectors. The computed Gaussian bandwidth parameter value and the defined a set of support vectors are output for determining if a new observation vector is an outlier.
    Type: Application
    Filed: February 2, 2018
    Publication date: February 7, 2019
    Inventors: Arin Chaudhuri, Deovrat Vijay Kakde, Carol Wagih Sadek, Seung Hyun Kong, Laura Lucia Gonzalez
  • Publication number: 20190042891
    Abstract: A computing device determines a bandwidth parameter value for outlier detection or data classification. A mean pairwise distance value is computed between observation vectors. A tolerance value is computed based on a number of observation vectors. A scaling factor value is computed based on a number of observation vectors and the tolerance value. A Gaussian bandwidth parameter value is computed using the mean pairwise distance value and the scaling factor value. An optimal value of an objective function is computed that includes a Gaussian kernel function that uses the computed Gaussian bandwidth parameter value. The objective function defines a support vector data description model using the observation vectors to define a set of support vectors. The Gaussian bandwidth parameter value and the set of support vectors are output for determining if a new observation vector is an outlier or for classifying the new observation vector.
    Type: Application
    Filed: August 6, 2018
    Publication date: February 7, 2019
    Inventors: Yuwei Liao, Deovrat Vijay Kakde, Arin Chaudhuri, Hansi Jiang, Carol Wagih Sadek, Seung Hyun Kong
  • 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
  • Patent number: 9536208
    Abstract: A computer-readable medium is configured to determine a support vector data description (SVDD). For each of a plurality of values for a kernel parameter, an optimal value of an objective function defined for an SVDD model using a kernel function, a read plurality of data points, and a respective value for the kernel parameter is computed to define a plurality of sets of support vectors. A plurality of first derivative values are computed for the objective function as a difference between the computed optimal values associated with successive values for the kernel parameter. A plurality of second derivative values are computed for the objective function as a difference between the computed plurality of first derivative values associated with successive values for the kernel parameter. A kernel parameter value is selected where the computed plurality of second derivative values first exceeds zero.
    Type: Grant
    Filed: April 12, 2016
    Date of Patent: January 3, 2017
    Assignee: SAS Institute Inc.
    Inventors: Deovrat Vijay Kakde, Arin Chaudhuri, Seung Hyun Kong, Maria Jahja, Hansi Jiang, Jorge Manuel Gomes da Silva
  • Patent number: 8671364
    Abstract: Techniques to present hierarchical information as orthographic projections are described. An apparatus may comprise an orthographic projection application arranged to manage a three dimensional orthographic projection of hierarchical information. The orthographic projection application may comprise a hierarchical information component operative to receive hierarchical information representing multiple nodes at different hierarchical levels, and parse the hierarchical information into a tree data structure, an orthographic generator component operative to generate a graphical tile for each node, arrange graphical tiles for each hierarchical level into graphical layers, and arrange the graphical layers in a vertical stack, and an orthographic presentation component operative to present a three dimensional orthographic projection of the hierarchical information with the stack of graphical layers each having multiple graphical tiles. Other embodiments are described and claimed.
    Type: Grant
    Filed: September 26, 2011
    Date of Patent: March 11, 2014
    Assignee: SAS Institute, Inc.
    Inventors: Deovrat Vijay Kakde, Arindam Chakrabarti
  • Publication number: 20130080978
    Abstract: Techniques to present hierarchical information as orthographic projections are described. An apparatus may comprise an orthographic projection application arranged to manage a three dimensional orthographic projection of hierarchical information. The orthographic projection application may comprise a hierarchical information component operative to receive hierarchical information representing multiple nodes at different hierarchical levels, and parse the hierarchical information into a tree data structure, an orthographic generator component operative to generate a graphical tile for each node, arrange graphical tiles for each hierarchical level into graphical layers, and arrange the graphical layers in a vertical stack, and an orthographic presentation component operative to present a three dimensional orthographic projection of the hierarchical information with the stack of graphical layers each having multiple graphical tiles. Other embodiments are described and claimed.
    Type: Application
    Filed: September 26, 2011
    Publication date: March 28, 2013
    Applicant: SAS INSTITUTE INC.
    Inventors: Deovrat Vijay Kakde, Arindam Chakrabarti