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公开(公告)号：US11531907B2
公开(公告)日：2022-12-20
申请号：US17854264
申请日：2022-06-30
Applicant: SAS Institute Inc.
Inventor： Afshin Oroojlooyjadid , Mohammadreza Nazari , Davood Hajinezhad , Amirhassan Fallah Dizche , Jorge Manuel Gomes da Silva , Jonathan Lee Walker , Hardi Desai , Robert Blanchard , Varunraj Valsaraj , Ruiwen Zhang , Weichen Wang , Ye Liu , Hamoon Azizsoltani , Prathaban Mookiah
IPC: G06N5/02
Abstract: A computing device trains a machine state predictive model. A generative adversarial network with an autoencoder is trained using a first plurality of observation vectors. Each observation vector of the first plurality of observation vectors includes state variable values for state variables and an action variable value for an action variable. The state variables define a machine state, wherein the action variable defines a next action taken in response to the machine state. The first plurality of observation vectors successively defines sequential machine states to manufacture a product. A second plurality of observation vectors is generated using the trained generative adversarial network with the autoencoder. A machine state machine learning model is trained to predict a subsequent machine state using the first plurality of observation vectors and the generated second plurality of observation vectors. A description of the machine state machine learning model is output.
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公开(公告)号：US20220374732A1
公开(公告)日：2022-11-24
申请号：US17854264
申请日：2022-06-30
Applicant: SAS Institute Inc.
Inventor： Afshin Oroojlooyjadid , Mohammadreza Nazari , Davood Hajinezhad , Amirhassan Fallah Dizche , Jorge Manuel Gomes da Silva , Jonathan Lee Walker , Hardi Desai , Robert Blanchard , Varunraj Valsaraj , Ruiwen Zhang , Weichen Wang , Ye Liu , Hamoon Azizsoltani , Prathaban Mookiah
IPC: G06N5/02
Abstract: A computing device trains a machine state predictive model. A generative adversarial network with an autoencoder is trained using a first plurality of observation vectors. Each observation vector of the first plurality of observation vectors includes state variable values for state variables and an action variable value for an action variable. The state variables define a machine state, wherein the action variable defines a next action taken in response to the machine state. The first plurality of observation vectors successively defines sequential machine states to manufacture a product. A second plurality of observation vectors is generated using the trained generative adversarial network with the autoencoder. A machine state machine learning model is trained to predict a subsequent machine state using the first plurality of observation vectors and the generated second plurality of observation vectors. A description of the machine state machine learning model is output.
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公开(公告)号：US20140372090A1
公开(公告)日：2014-12-18
申请号：US14199409
申请日：2014-03-06
Applicant: SAS Institute Inc.
Inventor： Taiyeong Lee , Ruiwen Zhang , Yongqiao Xiao , Jared Langford Dean
IPC: G06F17/50
CPC classification number: G06Q30/0242 , G06N20/00 , G06Q30/0254
Abstract: A method of selecting a one-class support vector machine (SVM) model for incremental response modeling is provided. Exposure group data generated from first responses by an exposure group receiving a request to respond is received. Control group data generated from second responses by a control group not receiving the request to respond is received. A response is either positive or negative. A one-class SVM model is defined using the positive responses in the control group data and an upper bound parameter value. The defined one-class SVM model is executed with the identified positive responses from the exposure group data. An error value is determined based on execution of the defined one-class SVM model. A final one-class SVM model is selected by validating the defined one-class SVM model using the determined error value.
Abstract translation: 提供了一种选择用于增量响应建模的一类支持向量机（SVM）模型的方法。 接收由接收到响应请求的曝光组的第一响应产生的曝光组数据。 接收到由未接收到响应请求的控制组从第二响应产生的控制组数据。 回应是正面或负面。 使用控制组数据中的正响应和上限参数值定义一类SVM模型。 使用来自曝光组数据的识别的正响应来执行定义的一类SVM模型。 基于定义的一类SVM模型的执行确定错误值。 通过使用确定的误差值验证定义的一类SVM模型来选择最终的一类SVM模型。
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公开(公告)号：US10303818B2
公开(公告)日：2019-05-28
申请号：US15237209
申请日：2016-08-15
Applicant: SAS Institute Inc.
Inventor： Jun Liu , Ruiwen Zhang , Zheng Zhao
Abstract: Processing speeds for generating a model can be enhanced. For example, the model can be generated by using regression coefficient values as weights for independent variables in the model. The regression coefficient values can be determined using a coordinate descent method to find a minimum value of a least absolute shrinkage and selection operator cost function. Each iteration of the coordinate descent method can include determining a starting coordinate based on (i) a previous starting coordinate or a previous regression coefficient value from an immediately prior iteration of the coordinate descent method; (ii) a current regression coefficient value associated with a current iteration of the coordinate descent method; and (iii) a refinement factor configured to minimize a result of a univariate algorithm. Each iteration can also include performing a coordinate descent using the starting coordinate to determine a next regression coefficient value for a next iteration of the coordinate descent method.
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公开(公告)号：US20150269241A1
公开(公告)日：2015-09-24
申请号：US14482726
申请日：2014-09-10
Applicant: SAS Institute Inc.
Inventor： Taiyeong Lee , Shunping Huang , Ruiwen Zhang , Jared Langford Dean
IPC: G06F17/30
CPC classification number: G06F16/285
Abstract: A method of transforming time series data to cluster data is provided. Time series data including a plurality of time series is received. A distance between a first time series of the plurality of time series and each of a remaining set of time series of the plurality of time series is computed pairwise between each of the remaining set of time series of the plurality of time series and the first time series. The computed values of the distance are sorted in increasing value. Gap width values are computed as a difference between successive pairs of the sorted, computed values. Whether a cluster including the received time series data is uniform is determined based on the computed gap width values. Cluster data including the first time series and the remaining set of time series assigned to the cluster is output when the cluster is determined to be uniform.
Abstract translation: 提供了一种将时间序列数据转换为集群数据的方法。 接收包括多个时间序列的时间序列数据。 多个时间序列的第一时间序列与多个时间序列的剩余的一组时间序列之间的距离在多个时间序列的剩余的一组时间序列和第一时间序列的每一个之间成对计算 系列。 距离的计算值按增加值排序。 间隙宽度值被计算为排序的计算值的连续对之间的差。 基于所计算的间隙宽度值来确定包括接收的时间序列数据的群集是否均匀。 当群集被确定为均匀时，输出包括第一时间序列和分配给群集的剩余的时间序列集群的群集数据。
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Patent Agency Ranking
公开(公告)号：US09940343B2
公开(公告)日：2018-04-10
申请号：US14571682
申请日：2014-12-16
Applicant: SAS Institute Inc.
Inventor： Yongqiao Xiao , Taiyeong Lee , Jared Langford Dean , Ruiwen Zhang
IPC: G06F17/30
CPC classification number: G06F17/30327 , G06F17/30289 , G06F17/30339 , G06F17/30345 , G06F17/30507 , G06F17/30587
Abstract: A method of converting data to tree data is provided. A first node memory structure that includes a first value indicator, a first counter value, and a first observation indicator is initialized for a first variable. The first value indicator is initialized with a first value of the first variable selected from first observation data, and the first observation indicator is initialized with a first indicator that indicates the first observation data. The first value of the first variable is compared to a second value of the first variable. The first counter value included in the first node memory structure is incremented when the first value of the first variable matches the second value of the first variable. Corresponding values of second observation data are compared to the identified values from first observation data when the first value of the first variable matches the second value of the first variable. A next observation is read from the data when the identified values match the corresponding values. The tree data is output after a last observation of the data is processed.
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公开(公告)号：US20150324403A1
公开(公告)日：2015-11-12
申请号：US14571682
申请日：2014-12-16
Applicant: SAS Institute Inc.
Inventor： Yongqiao Xiao , Taiyeong Lee , Jared Langford Dean , Ruiwen Zhang
IPC: G06F17/30
CPC classification number: G06F17/30327 , G06F17/30289 , G06F17/30339 , G06F17/30345 , G06F17/30507 , G06F17/30587
Abstract: A method of converting data to tree data is provided. A first node memory structure that includes a first value indicator, a first counter value, and a first observation indicator is initialized for a first variable. The first value indicator is initialized with a first value of the first variable selected from first observation data, and the first observation indicator is initialized with a first indicator that indicates the first observation data. The first value of the first variable is compared to a second value of the first variable. The first counter value included in the first node memory structure is incremented when the first value of the first variable matches the second value of the first variable. Corresponding values of second observation data are compared to the identified values from first observation data when the first value of the first variable matches the second value of the first variable. A next observation is read from the data when the identified values match the corresponding values. The tree data is output after a last observation of the data is processed.
Abstract translation: 提供了一种将数据转换为树数据的方法。 对于第一变量初始化包括第一值指示符，第一计数器值和第一观察指示符的第一节点存储器结构。 利用从第一观测数据选择的第一变量的第一值初始化第一值指示符，并且用指示第一观测数据的第一指示符初始化第一观测指标。 第一个变量的第一个值与第一个变量的第二个值进行比较。 当第一变量的第一值与第一变量的第二值匹配时，包括在第一节点存储器结构中的第一计数器值递增。 当第一变量的第一值与第一变量的第二值匹配时，将第二观测数据的相应值与来自第一观测数据的识别值进行比较。 当识别的值与相应的值匹配时，从数据中读取下一个观察结果。 在最后一次观察数据被处理后输出树数据。
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公开(公告)号：US20150324398A1
公开(公告)日：2015-11-12
申请号：US14571756
申请日：2014-12-16
Applicant: SAS Institute Inc.
Inventor： Yongqiao Xiao , Taiyeong Lee , Jared Langford Dean , Ruiwen Zhang
IPC: G06F17/30
CPC classification number: G06F17/30327 , G06F17/30289 , G06F17/30339 , G06F17/30345 , G06F17/30507 , G06F17/30587
Abstract: A method of creating a contingency table is provided. Whether or not a variable level list exists for a second variable in tree data is determined. When the variable level list exists for the second variable in the tree data, a first node memory structure is determined for the second variable from the variable level list, a first value of a first variable is determined using a first observation indicator and the tree data, and a first counter value is added to the contingency table in association with the first value of the first variable and a first value of the second variable. The first node memory structure includes the first value indicator, the first counter value, and the first observation indicator. The first value indicator indicates a first value of the second variable.
Abstract translation: 提供了创建应急表的方法。 确定树数据中的第二变量是否存在可变等级列表。 当在树数据中存在第二变量的可变等级列表时，从可变级列表确定第二变量的第一节点存储器结构，使用第一观察指示符确定第一变量的第一值，并且树数据 并且第一计数器值与第一变量的第一值和第二变量的第一值相关联地添加到应急表中。 第一节点存储器结构包括第一值指示符，第一计数器值和第一观察指示符。 第一个值指示符表示第二个变量的第一个值。
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公开(公告)号：US11436438B1
公开(公告)日：2022-09-06
申请号：US17559735
申请日：2021-12-22
Applicant: SAS Institute Inc.
Inventor： Ruiwen Zhang , Weichen Wang , Jorge Manuel Gomes da Silva , Ye Liu , Hamoon Azizsoltani , Prathaban Mookiah
Abstract: (A) Conditional vectors are defined. (B) Latent observation vectors are generated using a predefined noise distribution function. (C) A forward propagation of a generator model is executed with the conditional vectors and the latent observation vectors as input to generate an output vector. (D) A forward propagation of a decoder model of a trained autoencoder model is executed with the generated output vector as input to generate a plurality of decoded vectors. (E) Transformed observation vectors are selected from transformed data based on the defined plurality of conditional vectors. (F) A forward propagation of a discriminator model is executed with the transformed observation vectors, the conditional vectors, and the decoded vectors as input to predict whether each transformed observation vector and each decoded vector is real or fake. (G) The discriminator and generator models are updated and (A) through (G) are repeated until training is complete.
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公开(公告)号：US09734179B2
公开(公告)日：2017-08-15
申请号：US14571756
申请日：2014-12-16
Applicant: SAS Institute Inc.
Inventor： Yongqiao Xiao , Taiyeong Lee , Jared Langford Dean , Ruiwen Zhang
IPC: G06F17/30
CPC classification number: G06F17/30327 , G06F17/30289 , G06F17/30339 , G06F17/30345 , G06F17/30507 , G06F17/30587
Abstract: A method of creating a contingency table is provided. Whether or not a variable level list exists for a second variable in tree data is determined. When the variable level list exists for the second variable in the tree data, a first node memory structure is determined for the second variable from the variable level list, a first value of a first variable is determined using a first observation indicator and the tree data, and a first counter value is added to the contingency table in association with the first value of the first variable and a first value of the second variable. The first node memory structure includes the first value indicator, the first counter value, and the first observation indicator. The first value indicator indicates a first value of the second variable.
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