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公开(公告)号：US11436444B1
公开(公告)日：2022-09-06
申请号：US17557298
申请日：2021-12-21
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Xin Jiang Hunt
Abstract: A computing device trains a fair machine learning model. A prediction model is trained to predict a target value. For a number of iterations, a weight vector is computed using the bound value based on fairness constraints defined for a fairness measure type; a weight value is assigned to each observation vector based on the target value and a sensitive attribute value; the prediction model is trained with each weighted observation vector to predict the target value; and a conditional moments vector is computed based on the fairness constraints and the target and sensitive attribute values. Conditional moments difference values are computed. When the conditional moments difference values indicate to adjust the bound value, the bound value is updated and the process is repeated with the bound value replaced with the updated bound value until the conditional moments difference values indicate no further adjustment of the bound value is needed.
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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：US11366699B1
公开(公告)日：2022-06-21
申请号：US17670104
申请日：2022-02-11
Applicant: SAS Institute Inc.
Inventor： Altan Gulcu , Xiaodong Yao
Abstract: Some examples describes herein relate to handling bulk requests for resources. In one example, a system can determine a bulk request parameter-value associated with a bulk request. The system can then predict a baseline benefit value, which can be a benefit value when the bulk request parameter-value is used as a lower boundary for a unit parameter-value. The system can also determine a lower boundary constraint on the unit parameter-value independently of the bulk request parameter-value. The system can then execute an iterative process using the baseline benefit value and the lower boundary constraint. Based on a result of the iterative process, the system can determine whether and how much the bulk request parameter-value should be adjusted. The system may adjust the bulk request parameter-value accordingly or output a recommendation to do so.
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公开(公告)号：US11346751B1
公开(公告)日：2022-05-31
申请号：US17388519
申请日：2021-07-29
Applicant: SAS Institute Inc.
Inventor： Caleb Bridges King , Joseph Albert Morgan , Ryan Adam Lekivetz , Bradley Allen Jones
Abstract: A computing system receives a request for computer-generated likelihood(s) for candidate evaluations of an industrial product set according to a measurement system analysis (MSA). The MSA comprises tests for evaluating, according to a measurement standard, the industrial product set. The request indicates a metric set representing metric(s) each quantifying an estimate of contribution to variation in evaluating the industrial product set according to the MSA. The system generates a design comprising a respective input set for each test of the MSA. The respective input set comprises setting(s) for conducting a test of the MSA. The design is designed to isolate candidate sources for contributing to the variation in evaluating the industrial product set according to the MSA. The system (e.g., prior to the MSA) outputs, based on the metric set and the design, the computer-generated likelihood(s) for the candidate evaluations of the industrial product set according to the MSA.
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公开(公告)号：US11341414B2
公开(公告)日：2022-05-24
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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Patent Agency Ranking
公开(公告)号：US20220083709A1
公开(公告)日：2022-03-17
申请号：US17535745
申请日：2021-11-26
Applicant: SAS Institute Inc.
Inventor： Pradeep Mohan
Abstract: An apparatus includes processor(s) to: receive a request to test goodness-of-fit of a spatial process model; generate a KD tree from observed spatial point dataset including locations within a region at which instances of an event occurred; derive, from the observed spatial point dataset, multiple quadrats into which the region is divided; receive, from multiple processors, current levels of availability of processing resources including quantities of currently available execution threads; select, based on the quantity of currently available execution threads, a subset of the multiple processors to perform multiple iterations of a portion of the test in parallel; provide, to each processor of the subset, the KD tree, the spatial process model, and the multiple quadrats; receive, from each processor of the subset, per-quadrat data portions indicative of results of an iteration; derive a goodness-of-fit statistic from the per-quadrat data portions; and transmit an indication of goodness-of-fit to another device.
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公开(公告)号：US11257396B2
公开(公告)日：2022-02-22
申请号：US17001195
申请日：2020-08-24
Applicant: SAS Institute Inc.
Inventor： Claude Edward Summers, II , Sean Patrick Mealin , Julianna Elizabeth Langston , Gregory David Kraus , Jonathan Tyler Williamson , Lisa Beth Morton Robinson , Jesse Daniel Sookne , Brice Joseph Smith
Abstract: Geospatial data can be converted into audio outputs. For example, a system can receive a dataset indicating geospatial locations of objects within a region. Based on the dataset, the system can generate a virtual map representing the region and including virtual points representing the objects. The virtual points can be spatially positioned at locations in the virtual map corresponding to the geospatial locations of the objects in the region. The system can receive a user input via a user input device for interacting with a particular virtual point among the virtual points in the virtual map. The system can determine one or more sound characteristics for a sound based on receiving the user input. The system can then transmit an audio signal to an audio device for causing the audio device to generate the sound having the one or more sound characteristics, which may assist with exploring the virtual map.
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公开(公告)号：US11231830B2
公开(公告)日：2022-01-25
申请号：US17122349
申请日：2020-12-15
Applicant: SAS Institute Inc.
Inventor： James Byron Morris , Nicholas Akbar Ablitt , Manoj Keshavmurthi Chari
IPC: G06F3/0481 , G06F16/245 , G06F16/901 , G06F16/248 , G06F16/2458 , G06F16/28 , G06F9/451 , G06F3/0482
Abstract: A computing system displays an initial graph with icons. Each icon graphically represents data associated with a respective entity. The first icon is connected in the initial graph to other icon(s). The system receives an indication of a graphical network pattern. The graphical network pattern is defined by a user selection of a second icon in the initial graph and: a user selection of a third icon in the initial graph; or a user selection of a graphical representation in the initial graph of a relationship between the second icon and the third icon. The system sends computer instructions indicating a network pattern query for searching an electronic database for electronic record(s) corresponding to a queried network pattern. The system receives a dataset indicating located electronic record(s) corresponding to the queried network pattern. The system generates output data indicating an output graph for a graphical representation of the located record(s).
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公开(公告)号：US20210326501A1
公开(公告)日：2021-10-21
申请号：US17365083
申请日：2021-07-01
Applicant: SAS Institute Inc.
Inventor： Ryan Adam Lekivetz , Joseph Albert Morgan , Caleb Bridges King , Bradley Allen Jones
Abstract: A computing system receives a request to generate computer-generated data for an experiment. The computer-generated data comprises generated inputs defining setting(s) for a plurality of factors for a design of the experiment. The generated inputs are generated to be representative of a respective design space of different design spaces for the design of the experiment. The system receives first characteristic(s) for specifying generation of the computer-generated data associated with a first design space. The system receives second characteristic(s) for specifying generation of the computer-generated data associated with a second design space. The system, responsive to the request, generates a design suite that comprises the computer-generated data that represents, in a first set of design cases of the design suite, settings constrained by the first design space, and represents, in a second set of design cases of the design suite, settings constrained by the second design space.
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公开(公告)号：US11151463B2
公开(公告)日：2021-10-19
申请号：US17178798
申请日：2021-02-18
Applicant: SAS Institute Inc.
Inventor： Xu Chen , Jorge Manuel Gomes da Silva , Brett Alan Wujek
Abstract: Data is classified using semi-supervised data. Sparse coefficients are computed using a decomposition of a Laplacian matrix. (B) Updated parameter values are computed for a dimensionality reduction method using the sparse coefficients, the Laplacian matrix, and a plurality of observation vectors. The updated parameter values include a robust estimator of a decomposition matrix determined from the decomposition of the Laplacian matrix. (B) is repeated until a convergence parameter value indicates the updated parameter values for the dimensionality reduction method have converged. A classification matrix is defined using the sparse coefficients and the robust estimator of the decomposition of the Laplacian matrix. The target variable value is determined for each observation vector based on the classification matrix. The target variable value is output for each observation vector of the plurality of unclassified observation vectors and is defined to represent a label for a respective unclassified observation vector.
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