Patents by Inventor Arin Chaudhuri

Arin Chaudhuri 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: 11120072
    Abstract: A computer transforms high-dimensional data into low-dimensional data. (A) A distance matrix is computed from observation vectors. (B) A kernel matrix is computed from the distance matrix using a bandwidth value. (C) The kernel matrix is decomposed using an eigen decomposition to define eigenvalues. (D) A predefined number of largest eigenvalues are selected from the eigenvalues. (E) The selected largest eigenvalues are summed. (F) A next bandwidth value is computed based on the summed eigenvalues. (A) through (F) are repeated with the next bandwidth value until a stop criterion is satisfied. Each observation vector of the observation vectors is transformed into a second space using a kernel principal component analysis with the next bandwidth value and the kernel matrix. The second space has a dimension defined by the predefined number of first eigenvalues. Each transformed observation vector is output.
    Type: Grant
    Filed: February 23, 2021
    Date of Patent: September 14, 2021
    Assignee: SAS Institute Inc.
    Inventors: Kai Shen, Haoyu Wang, Arin Chaudhuri
  • Patent number: 11036981
    Abstract: A computing system determines if an event has occurred. A first window is defined that includes a subset of a plurality of observation vectors modeled as an output of an autoregressive causal system. A magnitude adjustment vector is computed from a mean computed for a matrix of magnitude values that includes a column for each window of a plurality of windows. The first window is stored in a next column of the matrix of magnitude values. Each cell of the matrix of magnitude values includes an estimated power spectrum value for a respective window and a respective frequency. A second matrix of magnitude values is updated using the magnitude adjustment vector. Each cell of the second matrix of magnitude values includes an adjusted power spectrum value for the respective window and the respective frequency. A peak is detected from the next column of the second matrix of magnitude values.
    Type: Grant
    Filed: February 4, 2021
    Date of Patent: June 15, 2021
    Assignee: SAS INSTITUTE INC.
    Inventors: Yuwei Liao, Anya Mary McGuirk, Byron Davis Biggs, Arin Chaudhuri, Allen Joseph Langlois, Vincent L. Deters
  • Patent number: 10984075
    Abstract: A computer transforms high-dimensional data into low-dimensional data. A distance is computed between a selected observation vector and each observation vector of a plurality of observation vectors, a nearest neighbors are selected using the computed distances, and a first sigmoid function is applied to compute a distance similarity value between the selected observation vector and each of the selected nearest neighbors where each of the computed distance similarity values is added to a first matrix. The process is repeated with each observation vector of the plurality of observation vectors as the selected observation vector. An optimization method is executed with an initial matrix, the first matrix, and a gradient of a second sigmoid function that computes a second distance similarity value between the selected observation vector and each of the nearest neighbors to transform each observation vector of the plurality of observation vectors into the low-dimensional space.
    Type: Grant
    Filed: October 13, 2020
    Date of Patent: April 20, 2021
    Assignee: SAS Institute Inc.
    Inventors: Yu Liang, Arin Chaudhuri, Haoyu Wang
  • 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
  • Patent number: 10303954
    Abstract: A computing device updates an estimate of one or more principal components for a next observation vector. An initial observation matrix is defined with first observation vectors. A number of the first observation vectors is a predefined window length. Each observation vector of the first observation vectors includes a plurality of values. A principal components decomposition is computed using the initial observation matrix. The principal components decomposition includes a sparse noise vector s, a first singular value decomposition vector U, and a second singular value decomposition vector v for each observation vector of the first observation vectors. A rank r is determined based on the principal components decomposition. A next principal components decomposition is computed for a next observation vector using the determined rank r. The next principal components decomposition is output for the next observation vector and monitored to determine a status of a physical object.
    Type: Grant
    Filed: February 12, 2018
    Date of Patent: May 28, 2019
    Assignee: SAS INSTITUTE INC.
    Inventors: Wei Xiao, Jorge Manuel Gomes da Silva, Saba Emrani, Arin Chaudhuri
  • 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: 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
  • 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
  • Patent number: 10157319
    Abstract: A computing device detects an abnormal observation vector using a principal components decomposition. The principal components decomposition includes a sparse noise vector st computed for the observation vector that includes a plurality of values, wherein each value is associated with a variable to define a plurality of variables. The sparse noise vector st has a dimension equal to m a number of the plurality of variables. A zero counter time series value ?t is computed using ?t=?i=1mst[i]. A probability value for ?t is computed using p=?i=?t+1m+1Hc[i]/?i=0m+1Hc[i], where Hc[i] includes a count of a number of times each value of ?t occurred for previous observation vectors. The probability value is compared with a predefined abnormal observation probability value. An abnormal observation indicator is set when the probability value indicates the observation vector is abnormal. The observation vector is output when the probability value indicates the observation vector is abnormal.
    Type: Grant
    Filed: February 12, 2018
    Date of Patent: December 18, 2018
    Assignee: SAS Institute Inc.
    Inventors: Wei Xiao, Jorge Manuel Gomes da Silva, Saba Emrani, Arin Chaudhuri
  • Publication number: 20180239740
    Abstract: A computing device detects an abnormal observation vector using a principal components decomposition. The principal components decomposition includes a sparse noise vector st computed for the observation vector that includes a plurality of values, wherein each value is associated with a variable to define a plurality of variables. The sparse noise vector st has a dimension equal to m a number of the plurality of variables. A zero counter time series value ?t is computed using ?t=?i=1mst[i]. A probability value for ?t is computed using p=?i=?t+1m+1Hc[i]/?i=0m+1Hc[i], where Hc[i] includes a count of a number of times each value of ?t occurred for previous observation vectors. The probability value is compared with a predefined abnormal observation probability value. An abnormal observation indicator is set when the probability value indicates the observation vector is abnormal. The observation vector is output when the probability value indicates the observation vector is abnormal.
    Type: Application
    Filed: February 12, 2018
    Publication date: August 23, 2018
    Inventors: Wei Xiao, Jorge Manuel Gomes da Silva, Saba Emrani, Arin Chaudhuri
  • Publication number: 20180239966
    Abstract: A computing device updates an estimate of one or more principal components for a next observation vector. An initial observation matrix is defined with first observation vectors. A number of the first observation vectors is a predefined window length. Each observation vector of the first observation vectors includes a plurality of values. A principal components decomposition is computed using the initial observation matrix. The principal components decomposition includes a sparse noise vector s, a first singular value decomposition vector U, and a second singular value decomposition vector ? for each observation vector of the first observation vectors. A rank r is determined based on the principal components decomposition. A next principal components decomposition is computed for a next observation vector using the determined rank r. The next principal components decomposition is output for the next observation vector and monitored to determine a status of a physical object.
    Type: Application
    Filed: February 12, 2018
    Publication date: August 23, 2018
    Inventors: Wei Xiao, Jorge Manuel Gomes da Silva, Saba Emrani, Arin Chaudhuri
  • 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
  • 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