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公开(公告)号：US11443198B1
公开(公告)日：2022-09-13
申请号：US17522062
申请日：2021-11-09
Applicant: SAS Institute Inc.
Inventor： Xilong Chen , Tao Huang , Jan Chvosta
Abstract: A computing device learns a directed acyclic graph (DAG). An SSCP matrix is computed from variable values defined for observation vectors. A topological order vector is initialized that defines a topological order for the variables. A loss value is computed using the topological order vector and the SSCP matrix. (A) A neighbor determination method is selected. (B) A next topological order vector is determined relative to the initialized topological order vector using the neighbor determination method. (C) A loss value is computed using the next topological order vector and the SSCP matrix. (D) (B) and (C) are repeated until each topological order vector is determined in (B) based on the neighbor determination method. A best topological vector is determined from each next topological order vector based on having a minimum value for the computed loss value. An adjacency matrix is computed using the best topological vector and the SSCP matrix.
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公开(公告)号：US09798575B2
公开(公告)日：2017-10-24
申请号：US14270783
申请日：2014-05-06
Applicant: SAS Institute Inc.
Inventor： Xilong Chen , Mark Roland Little
CPC classification number: G06F9/46 , G01D21/00 , G06F9/455 , G06F17/17 , G06F17/175 , G06F17/18 , G06F17/30286 , G06F19/00 , H04L67/10
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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公开(公告)号：US20250045611A1
公开(公告)日：2025-02-06
申请号：US18751509
申请日：2024-06-24
Applicant: SAS Institute Inc.
Inventor： Xilong Chen , Tao Huang , Jan Chvosta
Abstract: A computing device learns a directed acyclic graph for a plurality of variables. (A) A target variable and zero or more input variables are defined based on a predefined topological order vector and a first index. (B) A machine learning model is trained with observation vectors using the target variable and the input variables. (C) The machine learning model is executed using the observation vectors with the target variable and the input variables to compute a residual vector. (D) The first index is incremented. (E) (A) through (D) are repeated a first plurality of times. A parent set is determined for each variable by comparing the residual vector computed each repetition of (C) to other residual vectors computed on other repetitions of (C). The parent set is output for each variable to describe a directed acyclic graph that defines a hierarchical relationship between the variables.
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公开(公告)号：US20250045355A1
公开(公告)日：2025-02-06
申请号：US18751584
申请日：2024-06-24
Applicant: SAS Institute Inc.
Inventor： Xilong Chen , Tao Huang , Jan Chvosta
IPC: G06F17/18
Abstract: A computing device learns a directed acyclic graph (DAG). (A) A target variable is defined from variables based on a topological order vector and a first index. (B) Input variables are defined from the variables based on the topological order vector and a second index. (C) A machine learning model is trained with observation vectors using the target variable and the input variables. (D) The machine learning model is executed to compute a loss value. (E) The second index is incremented. (F) (B) through (E) are repeated a first plurality of times. (G) The first index is incremented. (H) (A) through (G) are repeated a second plurality of times. A parent set is determined for each variable based on a comparison between the loss value computed each repetition of (D). The parent set is output for each variable to describe the DAG that defines a hierarchical relationship between the variables.
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公开(公告)号：US20220350944A1
公开(公告)日：2022-11-03
申请号：US17730476
申请日：2022-04-27
Applicant: SAS Institute Inc.
Inventor： Xilong Chen , Yang Zhao , Sylvie T. Kabisa , David Bruce Elsheimer
IPC: G06F30/23
Abstract: One example described herein involves a system receiving task data and distribution criteria for a state space model from a client device. The task data can indicate a type of sequential Monte Carlo (SMC) task to be implemented. The distribution criteria can include an initial distribution, a transition distribution, and a measurement distribution for the state space model. The system can generate a set of program functions based on the task data and the distribution criteria. The system can then execute an SMC module to generate a distribution and a corresponding summary, where the SMC module is configured to call the set of program functions during execution of an SMC process and apply the results returned from the set of program functions in one or more subsequent steps of the SMC process. The system can then transmit an electronic communication to the client device indicating the distribution and its corresponding summary.
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Patent Agency Ranking
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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公开(公告)号：US20200293360A1
公开(公告)日：2020-09-17
申请号：US16835854
申请日：2020-03-31
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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公开(公告)号：US12210954B2
公开(公告)日：2025-01-28
申请号：US18530798
申请日：2023-12-06
Applicant: SAS Institute Inc.
Inventor： Sylvie Tchumtchoua Kabisa , Xilong Chen , Gunce Eryuruk Walton , David Bruce Elsheimer , Ming-Chun Chang
Abstract: A point estimate value for an individual is computed using a Bayesian neural network model (BNN) by training a first BNN model that computes a weight mean value, a weight standard deviation value, a bias mean value, and a bias standard deviation value for each neuron of a plurality of neurons using observations. A plurality of BNN models is instantiated using the first BNN model. Instantiating each BNN model of the plurality of BNN models includes computing, for each neuron, a weight value using the weight mean value, the weight standard deviation value, and a weight random draw and a bias value using the bias mean value, the bias standard deviation value, and a bias random draw. Each instantiated BNN model is executed with the observations to compute a statistical parameter value for each observation vector of the observations. The point estimate value is computed from the statistical parameter value.
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公开(公告)号：US12165031B2
公开(公告)日：2024-12-10
申请号：US18529014
申请日：2023-12-05
Applicant: SAS Institute Inc.
Inventor： Sylvie Tchumtchoua Kabisa , Xilong Chen , Gunce Eryuruk Walton , David Bruce Elsheimer , Ming-Chun Chang
Abstract: A treatment model trained to compute an estimated treatment variable value for each observation vector of a plurality of observation vectors is executed. Each observation vector includes covariate variable values, a treatment variable value, and an outcome variable value. An outcome model trained to compute an estimated outcome value for each observation vector using the treatment variable value for each observation vector is executed. A standard error value associated with the outcome model is computed using a first variance value computed using the treatment variable value of the plurality of observation vectors, using a second variance value computed using the treatment variable value and the estimated treatment variable value of the plurality of observation vectors, and using a third variance value computed using the estimated outcome value of the plurality of observation vectors. The standard error value is output.
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公开(公告)号：US11769350B1
公开(公告)日：2023-09-26
申请号：US17969991
申请日：2022-10-20
Applicant: SAS Institute Inc.
Inventor： Ji Shen , Jared Langford Dean , Xilong Chen , Jan Chvosta
IPC: G06V40/20 , G06T9/00 , G06V10/84 , G06N3/042 , G06N3/047 , G06N3/09 , G06T7/246 , G06V10/44 , G06N3/0455
CPC classification number: G06V40/23 , G06N3/042 , G06N3/047 , G06N3/0455 , G06N3/09 , G06T7/246 , G06T9/00 , G06V10/44 , G06V10/85 , G06T2207/20044 , G06T2207/20081
Abstract: A computer system can automatically analyze a video of a physical activity and provide corresponding feedback. For example, the system can receive a video file including image frames showing an entity performing a physical activity that involves a sequence of movement phases. The system can generate coordinate sets by performing image analysis on the image frames. The system can provide the coordinate sets as input to a trained model, the trained model being configured to assign scores and movement phases to the image frames based on the coordinate sets. The system can then select a particular movement phase for which to provide feedback, based on the scores and movement phases assigned to the image frames. The system can generate the feedback for the entity about their performance of the particular movement phase, which may improve the entity's future performance of that particular movement phase.
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