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公开(公告)号：US11950933B1
公开(公告)日：2024-04-09
申请号：US18527070
申请日：2023-12-01
Applicant: SAS Institute Inc.
Inventor： Carol Wagih Sadek , Yuwei Liao , Arin Chaudhuri
CPC classification number: A61B5/7207 , A61B5/0006 , A61B5/02438 , A61B5/725 , A61B5/726 , G16H40/67 , G16H50/70 , A61B5/0004 , A61B5/0022
Abstract: A heart-rate detection system can receive heartbeat data generated by a wearable heart-rate sensor worn by a wearer. The system can then execute a noise-reduction process for reducing noise in the heartbeat data. The noise-reduction process can involve applying a lowpass filter to the heartbeat data, generating wavelet coefficients by applying a wavelet transform to the filtered heartbeat data, and generating a reduced set of wavelet coefficients by thresholding the wavelet coefficients. An inverse wavelet signal can then be generated by applying an inverse wavelet transform to the reduced set of wavelet coefficients. R-peaks can be identified by performing peak detection on the instantaneous amplitudes of the data points in the inverse wavelet signal. A heart rate curve can then be generated based on the R-peaks and modified by applying a Hampel filter. Heartbeat data can then be generated based on the modified heart rate curve for output.
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公开(公告)号：US20230394109A1
公开(公告)日：2023-12-07
申请号：US18198537
申请日：2023-05-17
Applicant: SAS Institute Inc.
Inventor： Kevin L. SCOTT , Deovrat Vijay Kakde , Arin Chaudhuri , Sergiy Peredriy
IPC: G06F17/16
CPC classification number: G06F17/16
Abstract: Anomalies in a target object can be detected and diagnosed using improved Mahalanobis-Taguchi system (MTS) techniques. For example, an anomaly detection and diagnosis (ADD) system can receive a set of measurements associated with attributes of a target object. A Mahalanobis distance (MD) can be determined using a generalized inverse matrix. An abnormal condition can be detected when the MD is greater than a predetermined threshold value. The ADD system can determine an importance score for each measurement of a corresponding attribute. The attribute whose measurement has the highest importance score can be determined to be responsible for the abnormal condition.
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公开(公告)号：US11314844B1
公开(公告)日：2022-04-26
申请号：US17504634
申请日：2021-10-19
Applicant: SAS Institute Inc.
Inventor： Hansi Jiang , Arin Chaudhuri
IPC: G06F17/16
Abstract: A singular value decomposition (SVD) is computed of a first matrix to define a left matrix, a diagonal matrix, and a right matrix. The left matrix, the diagonal matrix, and the right matrix are updated using an arrowhead matrix structure defined from the diagonal matrix and by adding a next observation vector to a last row of the first matrix. The updated left matrix, the updated diagonal matrix, and the updated right matrix are updated using a diagonal-plus-rank-one (DPR1) matrix structure defined from the updated diagonal matrix and by removing an observation vector from a first row of the first matrix. Eigenpairs of the DPR1 matrix are computed based on whether a value computed from the updated left matrix is positive or negative. The left matrix updated in (C), the diagonal matrix updated in (C), and the right matrix updated in (C) are output.
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14.发明申请公开(公告)号：US20190042891A1
公开(公告)日：2019-02-07
申请号：US16055336
申请日：2018-08-06
Applicant: SAS INSTITUTE Inc.
Inventor： Yuwei Liao , Deovrat Vijay Kakde , Arin Chaudhuri , Hansi Jiang , Carol Wagih Sadek , Seung Hyun Kong
CPC classification number: G06K9/6269 , G06F17/16 , G06F17/18 , G06K9/6215 , G06K9/66 , G06N7/00 , G06N20/00 , G06N20/10
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.
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15.发明授权公开(公告)号：US09990592B2
公开(公告)日：2018-06-05
申请号：US15583067
申请日：2017-05-01
Applicant: SAS Institute Inc.
Inventor： Sergiy Peredriy , Deovrat Vijay Kakde , Arin Chaudhuri
CPC classification number: G06N99/005 , G06F17/30958
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.
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Patent Agency Ranking
16.发明授权Kernel parameter selection in support vector data description for outlier identification 有权内核参数选择支持向量数据描述异常值识别公开(公告)号：US09536208B1
公开(公告)日：2017-01-03
申请号：US15096552
申请日：2016-04-12
Applicant: SAS Institute Inc.
Inventor： Deovrat Vijay Kakde , Arin Chaudhuri , Seung Hyun Kong , Maria Jahja , Hansi Jiang , Jorge Manuel Gomes da Silva
IPC: G06N99/00
CPC classification number: G06N99/005
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.
Abstract translation: 计算机可读介质被配置为确定支持向量数据描述（SVDD）。 对于内核参数的多个值中的每一个，计算使用内核函数为SVDD模型定义的目标函数的最佳值，读取的多个数据点和用于内核参数的相应值，以定义多个 的支持向量集。 针对目标函数计算多个一阶导数值，作为与内核参数的连续值相关联的计算的最优值之间的差。 针对目标函数计算多个二阶导数值作为与内核参数的连续值相关联的所计算的多个一阶导数值之间的差。 选择核心参数值，其中所计算的多个二阶导数值首先超过零。
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公开(公告)号：US20160239749A1
公开(公告)日：2016-08-18
申请号：US14987982
申请日：2016-01-05
Applicant: SAS INSTITUTE INC.
Inventor： Sergiy Peredriy , Yung-Hsin Chien , Arin Chaudhuri , Ann Mary McGuirk , Yongqiao Xiao
CPC classification number: G06N5/048 , G06F16/283 , G06Q10/04 , G06Q30/0202
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.
Abstract translation: 提供计算机实现的系统和方法来预测输出。 与对象相关联的全局输出分数近似。 基于循环方面组件和运动预测来预测组的输出。 基于相关对象组的预测输出和特定对象的近似全局输出分数来计算输出预测。
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公开(公告)号：US11846979B1
公开(公告)日：2023-12-19
申请号：US18198537
申请日：2023-05-17
Applicant: SAS Institute Inc.
Inventor： Kevin L. Scott , Deovrat Vijay Kakde , Arin Chaudhuri , Sergiy Peredriy
IPC: G06F17/16
CPC classification number: G06F17/16
Abstract: Anomalies in a target object can be detected and diagnosed using improved Mahalanobis-Taguchi system (MTS) techniques. For example, an anomaly detection and diagnosis (ADD) system can receive a set of measurements associated with attributes of a target object. A Mahalanobis distance (MD) can be determined using a generalized inverse matrix. An abnormal condition can be detected when the MD is greater than a predetermined threshold value. The ADD system can determine an importance score for each measurement of a corresponding attribute. The attribute whose measurement has the highest importance score can be determined to be responsible for the abnormal condition.
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公开(公告)号：US11120072B1
公开(公告)日：2021-09-14
申请号：US17182424
申请日：2021-02-23
Applicant: SAS Institute Inc.
Inventor： Kai Shen , Haoyu Wang , Arin Chaudhuri
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.
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公开(公告)号：US10509847B1
公开(公告)日：2019-12-17
申请号：US16411214
申请日：2019-05-14
Applicant: SAS Institute Inc.
Inventor： Zekun Xu , Deovrat Vijay Kakde , Arin Chaudhuri
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.
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