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公开(公告)号：US20210035313A1
公开(公告)日：2021-02-04
申请号：US17060504
申请日：2020-10-01
Applicant: SAS Institute Inc.
Inventor： Hamza Mustafa Ghadyali , Kedar Shriram Prabhudesai , Jonathan Lee Walker , Xunlei Wu , Xingqi Du , Bahar Biller , Mohammadreza Nazari , Afshin Oroojlooyjadid , Alexander Richard Phelps , Davood Hajinezhad , Varunraj Valsaraj , Jorge Manuel Gomes da Silva , Jinxin Yi
Abstract: A computing system responsive to obtaining original image data, detects a set of data point(s), in the original image data, that indicates an object. The system determines, based on the set of data point(s), a set of pixels associated with the object in the original image data. The system generates an alternative visual identifier for the object that provides a unique identifier for the set of pixels absent in the original image data. The system generates, autonomously from intervention by any user of the computing system, pixel information to conceal feature(s) of the object. The system obtains modified image data comprising the alternative visual identifier. The modified image data further comprises the feature(s) of the object in the original image data visually concealed in the modified image data according to the pixel information. The system outputs an image representation of a trajectory of the object through the modified image data.
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公开(公告)号：US11080602B1
公开(公告)日：2021-08-03
申请号：US17177694
申请日：2021-02-17
Applicant: SAS Institute Inc.
Inventor： Afshin Oroojlooyjadid , Mohammadreza Nazari , Davood Hajinezhad , Jorge Manuel Gomes da Silva
Abstract: A computing system trains a reinforcement learning model comprising multiple different attention model components. The reinforcement learning model trains on training data of a first environment (e.g., a first traffic intersection). The reinforcement learning model trains by training a state attention computer model on the training data that weighs each of respective inputs of a respective state. The reinforcement learning model trains by training an action attention computer model that determines a probability of switching from a first action to a second action of the first set of the multiple candidate actions (e.g., changing traffic colors of traffic lights).
Alternatively, or additionally, a computing system generates an indication of a selected outcome according to the reinforcement learning model and sends a selection output to the second environment (e.g., a second traffic intersection with more lanes than the first traffic intersection) to implement the selected action in the second environment.-
公开(公告)号：US11176691B2
公开(公告)日：2021-11-16
申请号：US17060260
申请日：2020-10-01
Applicant: SAS Institute Inc.
Inventor： Hamza Mustafa Ghadyali , Kedar Shriram Prabhudesai , Mohammadreza Nazari , Bahar Biller , Afshin Oroojlooyjadid , Alexander Richard Phelps , Jonathan Lee Walker , Xunlei Wu , Xingqi Du , Davood Hajinezhad , Varunraj Valsaraj , Jorge Manuel Gomes da Silva , Jinxin Yi
Abstract: A computing system obtains image data representing images. Each of the images is captured at different time points of a physical environment. The physical environment comprises a first object and a second object. The computing system executes a control system to augment the physical environment. The control system detects a group forming in the images. The control system tracks an aspect of a movement, of a given object, in the group. The control system simulates the physical environment and the movement, of the given object, in the group in a simulated environment. The control system evaluates simulated actions in the simulated environment for a predefined objective for the physical environment. The predefined objective is related to an interaction between objects in the group. The control system generates based on evaluated simulated actions and autonomously from involvement by any user of the control system, an indication to augment the physical environment.
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公开(公告)号：US11055861B2
公开(公告)日：2021-07-06
申请号：US17060957
申请日：2020-10-01
Applicant: SAS Institute Inc.
Inventor： Mohammadreza Nazari , Afshin Oroojlooyjadid , Alexander Richard Phelps , Davood Hajinezhad , Bahar Biller , Jonathan Lee Walker , Hamza Mustafa Ghadyali , Kedar Shriram Prabhudesai , Xunlei Wu , Xingqi Du , Jorge Manuel Gomes da Silva , Varunraj Valsaraj , Jinxin Yi
Abstract: A computing system receives historical data. The historical data comprises physical actions taken in an experiment in a physical environment. The experiment comprises user-defined stages. The historical data comprises a recorded outcome, according to user-defined performance indicator(s) related to the user-defined stages, for each physical action taken in the experiment. The system generates, by a discrete event simulator, a computing representation of a simulated environment of the physical environment. The simulated environment comprises processing stages. The system obtains simulation data. The simulation data comprises simulated actions taken by the discrete event simulator. The simulation data comprises a predicted outcome, according to user-defined performance indicator(s) related to the processing stages, for each simulated action taken by the discrete event simulator. The system validates accuracy of the discrete event simulator at predicting the recorded outcome in the experiment. The system trains a computing agent according to a sequential decision-making algorithm.
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Patent Agency Ranking
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：US11176692B2
公开(公告)日：2021-11-16
申请号：US17060504
申请日：2020-10-01
Applicant: SAS Institute Inc.
Inventor： Hamza Mustafa Ghadyali , Kedar Shriram Prabhudesai , Jonathan Lee Walker , Xunlei Wu , Xingqi Du , Bahar Biller , Mohammadreza Nazari , Afshin Oroojlooyjadid , Alexander Richard Phelps , Davood Hajinezhad , Varunraj Valsaraj , Jorge Manuel Gomes da Silva , Jinxin Yi
Abstract: A computing system responsive to obtaining original image data, detects a set of data point(s), in the original image data, that indicates an object. The system determines, based on the set of data point(s), a set of pixels associated with the object in the original image data. The system generates an alternative visual identifier for the object that provides a unique identifier for the set of pixels absent in the original image data. The system generates, autonomously from intervention by any user of the computing system, pixel information to conceal feature(s) of the object. The system obtains modified image data comprising the alternative visual identifier. The modified image data further comprises the feature(s) of the object in the original image data visually concealed in the modified image data according to the pixel information. The system outputs an image representation of a trajectory of the object through the modified image data.
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公开(公告)号：US20210082129A1
公开(公告)日：2021-03-18
申请号：US17060957
申请日：2020-10-01
Applicant: SAS Institute Inc.
Inventor： Mohammadreza Nazari , Afshin Oroojlooyjadid , Alexander Richard Phelps , Davood Hajinezhad , Bahar Biller , Jonathan Lee Walker , Hamza Mustafa Ghadyali , Kedar Shriram Prabhudesai , Xunlei Wu , Xingqi Du , Jorge Manuel Gomes da Silva , Varunraj Valsaraj , Jinxin Yi
Abstract: A computing system receives historical data. The historical data comprises physical actions taken in an experiment in a physical environment. The experiment comprises user-defined stages. The historical data comprises a recorded outcome, according to user-defined performance indicator(s) related to the user-defined stages, for each physical action taken in the experiment. The system generates, by a discrete event simulator, a computing representation of a simulated environment of the physical environment. The simulated environment comprises processing stages. The system obtains simulation data. The simulation data comprises simulated actions taken by the discrete event simulator. The simulation data comprises a predicted outcome, according to user-defined performance indicator(s) related to the processing stages, for each simulated action taken by the discrete event simulator. The system validates accuracy of the discrete event simulator at predicting the recorded outcome in the experiment. The system trains a computing agent according to a sequential decision-making algorithm.
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公开(公告)号：US20210019528A1
公开(公告)日：2021-01-21
申请号：US17060260
申请日：2020-10-01
Applicant: SAS Institute Inc.
Inventor： Hamza Mustafa Ghadyali , Kedar Shriram Prabhudesai , Mohammadreza Nazari , Bahar Biller , Afshin Oroojlooyjadid , Alexander Richard Phelps , Jonathan Lee Walker , Xunlei Wu , Xingqi Du , Davood Hajinezhad , Varunraj Valsaraj , Jorge Manuel Gomes da Silva , Jinxin Yi
Abstract: A computing system obtains image data representing images. Each of the images is captured at different time points of a physical environment. The physical environment comprises a first object and a second object. The computing system executes a control system to augment the physical environment. The control system detects a group forming in the images. The control system tracks an aspect of a movement, of a given object, in the group. The control system simulates the physical environment and the movement, of the given object, in the group in a simulated environment. The control system evaluates simulated actions in the simulated environment for a predefined objective for the physical environment. The predefined objective is related to an interaction between objects in the group. The control system generates based on evaluated simulated actions and autonomously from involvement by any user of the control system, an indication to augment the physical environment.
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