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公开(公告)号：US10535422B2
公开(公告)日：2020-01-14
申请号：US16507769
申请日：2019-07-10
Applicant: SAS Institute Inc.
Inventor： Ryan Adam Lekivetz , Caleb Bridges King , Joseph Albert Morgan , Bradley Allen Jones
Abstract: A computing device obtains a metric N indicating a quantity of a plurality of test cases for an output design of an experiment Each element of a test case of the output design is a test condition for testing one of factors for the experiment. The computing device obtains input indicating a quantity p of an indicated plurality of factors for the output design. The computing device determines whether there are stored instructions for generating an initial screening design for the experiment. The computing device responsive to determining that there are stored instructions, selects, using the stored instructions, the initial screening design for the experiment. The computing device determines whether to modify the initial screening design based on modification criteria comprising a secondary criterion, the metric N, and/or the quantity p. The computing device outputs an indication of the updated screening design for the output design of the experiment.
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公开(公告)号：US10521734B2
公开(公告)日：2019-12-31
申请号：US16404789
申请日：2019-05-07
Applicant: SAS Institute Inc.
Inventor： Xu Chen , Jorge Manuel Gomes da Silva
Abstract: A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix used to select a label for each observation. Unique combinations of observations selected from the unlabeled observations are defined. A marginal distribution value is computed from the label distribution probability matrix. A joint distribution value is computed between observations included in each combination. A mutual information value is computed for each combination as a combination of the marginal distribution value and the joint distribution value computed for the respective combination. A predefined number of observation vector combinations is selected from the combinations that have highest values for the computed mutual information value. Labeled observation vectors are updated to include each observation vector included in the selected observation vector combinations with a respective obtained label.
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公开(公告)号：US20190385611A1
公开(公告)日：2019-12-19
申请号：US16434210
申请日：2019-06-07
Applicant: SAS Institute Inc.
IPC: G10L15/26 , G10L15/197 , G06F17/27 , G06F17/28 , G06N20/00
Abstract: A system determines user intent from text. A conversation element is received. An intent is determined by matching a domain independent relationship and a domain dependent term determined from the received conversation element to an intent included in an intent database that stores a plurality of intents and by inputting the matched intent into a trained classifier that computes a likelihood that the matched intent is the intent of the received conversation element. An action is determined based on the determined intent. A response to the received conversation element is generated based on the determined action and output.
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公开(公告)号：US10510022B1
公开(公告)日：2019-12-17
申请号：US16451228
申请日：2019-06-25
Applicant: SAS Institute Inc.
Inventor： Ricky Dee Tharrington, Jr. , Xin Jiang Hunt , Ralph Walter Abbey
IPC: G06N20/00 , G06N5/04 , G06F17/16 , G06F16/245
Abstract: Systems and methods for machine learning, models, and related explainability and interpretability are provided. A computing device determines a contribution of a feature to a predicted value. A feature computation dataset is defined based on a selected next selection vector. A prediction value is computed for each observation vector included in the feature computation dataset using a trained predictive model. An expected value is computed for the selected next selection vector based on the prediction values. The feature computation dataset is at least a partial copy of a training dataset with each variable value replaced in each observation vector included in the feature computation dataset based on the selected next selection vector. Each replaced variable value is replaced with a value included in a predefined query for a respective variable. A Shapley estimate value is computed for each variable.
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公开(公告)号：US20190377774A1
公开(公告)日：2019-12-12
申请号：US16433192
申请日：2019-06-06
Applicant: SAS Institute Inc.
Inventor： Mahesh V. Joshi
IPC: G06F17/18
Abstract: A computing device provides distributed estimation of an empirical distribution function. A boundary cumulative distribution function (CDF) value is defined at a start of each region of a plurality of regions. An accuracy value is defined for each region. (a) First equal proportion bins are computed for a first sample of a first marginal variable using the defined boundary CDF value for each region. (b) Second equal proportion bins are computed for the first sample of the first marginal variable within each region based on the defined accuracy value for each region. (c) The computed second equal proportion bins are added as an empirical distribution function (EDF) for the first marginal variable. (d) (a) to (c) are repeated for each remaining sample of the first marginal variable. (e) (a) to (d) are repeated with each remaining marginal variable of a plurality of marginal variables as the first marginal variable.
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Patent Agency Ranking
公开(公告)号：US20190354410A1
公开(公告)日：2019-11-21
申请号：US16531506
申请日：2019-08-05
Applicant: SAS Institute Inc.
Inventor： Ruth Ellen Baldasaro , Jennifer Lee Hargrove , Edward Lew Rowe , Emily Louise Chapman-McQuiston
IPC: G06F9/50 , G06F16/906 , G06N3/08
Abstract: Exemplary embodiments relate to systems for building a model of changes to data items when information the data items is limited or not directly observed. Exemplary embodiments allow properties of the data items to be inferred using a single data structure and creates a highly granular log of changes to the data item. Using this data structure, the time-varying nature of changes to the data item can be determined. The data structure may be used to identify characteristics associated with a regularly-performed action, to examine how adherence to the action affects a system, and to identify outcomes of non-adherence. Fungible data items may be mapped to a remediable condition or remedy class. This may be accomplished by automatically deriving conditions and remedial information from available information, matching the conditions to remedial classes or types via a customizable mapping, and then calculating adherence for the condition on the available information.
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87.发明授权公开(公告)号：US10482353B2
公开(公告)日：2019-11-19
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
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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公开(公告)号：US10360500B2
公开(公告)日：2019-07-23
申请号：US15946331
申请日：2018-04-05
Applicant: SAS Institute Inc.
Inventor： Mustafa Onur Kabul , Lawrence E. Lewis
Abstract: A computing system provides distributed training of a neural network model. Explore phase options, exploit phase options, a subset of a training dataset, and a validation dataset are distributed to a plurality of computing devices. (a) Execution of the model by the computing devices is requested using the subset stored at each computing device. (b) A first result of the execution is received from a computing device. (c) Next configuration data for the neural network model is selected based on the first result and distributed to the computing device. (a) to (c) is repeated until an exploration phase is complete. (d) Execution of the neural network model is requested. (e) A second result is received. (f) Next configuration data is computed based on the second result and distributed to the computing device. (d) to (f) is repeated until an exploitation phase is complete. The next configuration data defines the model.
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公开(公告)号：US10338994B1
公开(公告)日：2019-07-02
申请号：US16165331
申请日：2018-10-19
Applicant: SAS Institute Inc.
Inventor： Jingrui Xie , Yue Li , Yung-Hsin Chien , Pu Wang
Abstract: In some examples, a processing device can receive prediction data representing a prediction. The processing device can also receive files defining abnormal data-point patterns to be identified in the prediction data. The processing device can identify at least one abnormal data-point pattern in the prediction data by executing customizable program-code in the files. The processing device can determine an override process that corresponds to the at least one abnormal data-point pattern in response to identifying the at least one abnormal data-point pattern in the prediction data. The processing device can execute the override process to generate a corrected version of the prediction data. The processing device can then adjust one or more computer parameters based on the corrected version of the prediction data.
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公开(公告)号：US10303954B2
公开(公告)日：2019-05-28
申请号：US15893959
申请日：2018-02-12
Applicant: SAS Institute Inc.
Inventor： Wei Xiao , Jorge Manuel Gomes da Silva , Saba Emrani , Arin Chaudhuri
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.
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