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Patent Agency Ranking
公开(公告)号：US11354583B2
公开(公告)日：2022-06-07
申请号：US17225238
申请日：2021-04-08
Applicant: SAS Institute Inc.
Inventor： Hamoon Azizsoltani , Prathaban Mookiah , Weichen Wang , Thomas J. O'Connell
Abstract: Logical rules can be automatically generated for use with event detection systems according to some aspects of the present disclosure. For example, a computing device can extract a group of logical rules from trained decision trees and apply a test data set to the group of logical rules to determine count values corresponding to the logical rules. The computing device can then determine performance metric values based on the count values, select a subset of logical rules from among the group of logical rules based on the performance metric values, and provide at least one logical rule in the subset for use with an event detection system. The event detection system can be configured to detect an event in relation to a target data set that was not used to train the decision trees.
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公开(公告)号：US20220121967A1
公开(公告)日：2022-04-21
申请号：US17225238
申请日：2021-04-08
Applicant: SAS Institute Inc.
Inventor： Hamoon Azizsoltani , Prathaban Mookiah , Weichen Wang , Thomas J. O'Connell
Abstract: Logical rules can be automatically generated for use with event detection systems according to some aspects of the present disclosure. For example, a computing device can extract a group of logical rules from trained decision trees and apply a test data set to the group of logical rules to determine count values corresponding to the logical rules. The computing device can then determine performance metric values based on the count values, select a subset of logical rules from among the group of logical rules based on the performance metric values, and provide at least one logical rule in the subset for use with an event detection system. The event detection system can be configured to detect an event in relation to a target data set that was not used to train the decision trees.
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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：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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