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公开(公告)号：US11062219B1
公开(公告)日：2021-07-13
申请号：US17106488
申请日：2020-11-30
Applicant: SAS Institute Inc.
Inventor： Joshua David Griffin , Riadh Omheni , Yan Xu
Abstract: A computer solves a nonlinear optimization problem. An optimality check is performed for a current solution to an objective function that is a nonlinear equation with constraint functions on decision variables. When the performed optimality check indicates that the current solution is not an optimal solution, a barrier parameter value is updated, and a Lagrange multiplier value is updated for each constraint function based on a result of a complementarity slackness test. The current solution to the objective function is updated using a search direction vector determined by solving a primal-dual linear system that includes a dual variable for each constraint function and a step length value determined for each decision variable and for each dual variable. The operations are repeated until the optimality check indicates that the current solution is the optimal solution or a predefined number of iterations has been performed.
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公开(公告)号：US20210174207A1
公开(公告)日：2021-06-10
申请号：US17158062
申请日：2021-01-26
Applicant: SAS Institute Inc.
Inventor： Ryan Jeremy Parker , Clayton Adam Barker , Christopher Michael Gotwalt
Abstract: An analytic system provides direct functional principal component analysis. (A) A next group variable value is selected from values of a group variable. (B) Explanatory variable values of observations having the selected next group variable value are sorted in ascending order. (C) The response variable value associated with each sorted explanatory variable value is stored in a next row of a data matrix. (D) (A) through (C) are repeated. (E) An eigenfunction index is incremented. (F) An FPCA is performed using the data matrix to define an eigenfunction for the eigenfunction index. (G) (E) and (F) are repeated. (H) FPCA results from the performed FPCA are presented within a window of a display. The FPCA results include an eigenvalue and an eigenfunction associated with the eigenvalue for each functional principal component identified from the performed FPCA in (F).
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公开(公告)号：US20210158171A1
公开(公告)日：2021-05-27
申请号：US17165226
申请日：2021-02-02
Applicant: SAS Institute Inc.
Inventor： Nancy Anne Rausch , Roger Jay Barney , John P. Trawinski
Abstract: An apparatus includes processor(s) to: receive a request for a data catalog; in response to the request specifying a structural feature, analyze metadata of multiple data sets for an indication of including it, and to retrieve an indicated degree of certainty of detecting it for data sets including it; in response to the request specifying a contextual aspect, analyze context data of the multiple data sets for an indication of being subject to it, and to retrieve an indicated degree of certainty concerning it for data sets subject to it; selectively include each data set in the data catalog based on the request specifying a structural feature and/or a contextual aspect, and whether each data set meets what is specified; for each data set in the data catalog, generate a score indicative of the likelihood of meeting what is specified; and transmit the data catalog to the requesting device.
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公开(公告)号：US20210157595A1
公开(公告)日：2021-05-27
申请号：US17105695
申请日：2020-11-27
Applicant: SAS Institute Inc.
Abstract: An apparatus includes a processor core to: receive a request to execute application code including a trigger instruction and an instruction block that reads a row of data values from a data structure and outputs a data value from a function using the row as input, wherein the data structure is divided into multiple portions and the trigger instruction indicates that multiple instances of the instruction block are to be executed concurrently; and in response to the request, and to identification of the instruction block and trigger instruction: generate multiple instances of a support block that causes independent repetitive execution of each instance of the instruction block until all rows of the corresponding portion of the data structure are used as input; assign instances of the instruction and support blocks to multiple processor cores; and provide each instance of the instruction block with the corresponding portion of the data structure.
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公开(公告)号：US10984075B1
公开(公告)日：2021-04-20
申请号：US17069293
申请日：2020-10-13
Applicant: SAS Institute Inc.
Inventor： Yu Liang , Arin Chaudhuri , Haoyu Wang
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.
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Patent Agency Ranking
公开(公告)号：USD915458S1
公开(公告)日：2021-04-06
申请号：US29674055
申请日：2018-12-19
Applicant: SAS Institute Inc.
Designer： Michael Ryan Chipley , Steven Todd Barlow
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公开(公告)号：US10965706B2
公开(公告)日：2021-03-30
申请号：US17037892
申请日：2020-09-30
Applicant: SAS Institute Inc.
Inventor： Bryan C. Harris , Alexius Kofi Ameyaw Boakye, Jr. , Sean Riley Dyer , Christopher Francis Smith
Abstract: A computing device determines a peer group identifier and supplements netflow records with the peer group identifier. An authentication event block object is received that was sent to a first source window. The authentication event block object includes a user identifier, an IP address, and a peer group identifier. Members of the peer group are identified based on an expected network activity behavior. The user identifier and the peer group identifier are stored in association with the IP address in a cache. A netflow event block object sent to the first source window is received that includes a netflow packet IP address. Netflow data is parsed from the netflow event block object into a netflow record. When the stored IP address matches the netflow packet IP address, the netflow record is supplemented with the user identifier and the peer group identifier. The supplemented netflow record is output to summary data.
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公开(公告)号：US10956835B2
公开(公告)日：2021-03-23
申请号：US16297952
申请日：2019-03-11
Applicant: SAS Institute Inc.
Inventor： Rui Shi , Guixian Lin , Xiangqian Hu , Yan Xu
Abstract: A computing device compresses a gradient boosting tree predictive model. A gradient boosting tree predictive model is trained using a plurality of observation vectors. Each observation vector includes an explanatory variable value of an explanatory variable and a response variable value for a response variable. The gradient boosting tree predictive type model is trained to predict the response variable value of each observation vector based on a respective explanatory variable value of each observation vector. The trained gradient boosting tree predictive model is compressed using a compression model with a predefined penalty constant value and with a predefined array of coefficients to reduce a number of trees of the trained gradient boosting tree predictive model. The compression model minimizes a sparsity norm loss function. The compressed, trained gradient boosting tree predictive model is output for predicting a new response variable value from a new observation vector.
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公开(公告)号：US10949747B1
公开(公告)日：2021-03-16
申请号：US16950145
申请日：2020-11-17
Applicant: SAS Institute Inc.
Inventor： Majid Jahani , Joshua David Griffin , Seyedalireza Yektamaram , Wenwen Zhou
Abstract: A computer trains a neural network model. (A) Observation vectors are randomly selected from a plurality of observation vectors. (B) A forward and backward propagation of a neural network is executed to compute a gradient vector and a weight vector. (C) A search direction vector is computed. (D) A step size value is computed. (E) An updated weight vector is computed. (F) Based on a predefined progress check frequency value, second observation vectors are randomly selected, a progress check objective function value is computed given the weight vector, the step size value, the search direction vector, and the second observation vectors, and based on an accuracy test, the mini-batch size value is updated. (G) (A) to (F) are repeated until a convergence parameter value indicates training of the neural network is complete. The weight vector for a next iteration is the computed updated weight vector.
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公开(公告)号：US20210073023A1
公开(公告)日：2021-03-11
申请号：US16952375
申请日：2020-11-19
Applicant: SAS Institute Inc.
Inventor： Xilong Chen , Mark Roland Little
Abstract: Techniques to manage virtual classes for statistical tests are described. An apparatus may comprise a simulated data component to generate simulated data for a statistical test, statistics of the statistical test based on parameter vectors to follow a probability distribution, a statistic simulator component to simulate statistics for the parameter vectors from the simulated data with a distributed computing system comprising multiple nodes each having one or more processors capable of executing multiple threads, the simulation to occur by distribution of portions of the simulated data across the multiple nodes of the distributed computing system, and a distributed control engine to control task execution on the distributed portions of the simulated data on each node of the distributed computing system with a virtual software class arranged to coordinate task and sub-task operations across the nodes of the distributed computing system. Other embodiments are described and claimed.
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