Saba Emrani

US Patent:

20180336484, Nov 22, 2018

Filed:

Dec 6, 2017

Appl. No.:

15/833641

Inventors:

- Cary NC, US
Saba Emrani - Santa Clara CA, US
Jorge Manuel Gomes da Silva - Durham NC, US
Ilknur Kaynar Kabul - Apex NC, US

International Classification:

G06N 7/00
G06F 17/18
G06F 17/16
G06N 99/00

Abstract:

A computing device computes a weight matrix to predict a value for a characteristic in a scoring dataset. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

US Patent:

20180239740, Aug 23, 2018

Filed:

Feb 12, 2018

Appl. No.:

15/894002

Inventors:

- Cary NC, US
Jorge Manuel Gomes da Silva - Durham NC, US
Saba Emrani - Santa Clara CA, US
Arin Chaudhuri - Raleigh NC, US

International Classification:

G06F 17/16
G06F 17/18

Abstract:

A computing device detects an abnormal observation vector using a principal components decomposition. The principal components decomposition includes a sparse noise vector scomputed 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 shas a dimension equal to m a number of the plurality of variables. A zero counter time series value ĉis computed using ĉ=Σs[i]. A probability value for ĉis computed using p=ΣH[i]/ΣH[i], where H[i] includes a count of a number of times each value of ĉoccurred 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.

US Patent:

20180239966, Aug 23, 2018

Filed:

Feb 12, 2018

Appl. No.:

15/893959

Inventors:

- Cary NC, US
Jorge Manuel Gomes da Silva - Durham NC, US
Saba Emrani - Santa Clara CA, US
Arin Chaudhuri - Raleigh NC, US

International Classification:

G06K 9/00
G06F 17/16
G06K 9/48
G06K 9/62

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 ν 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.