公开(公告)号：US12293602B1

公开(公告)日：2025-05-06

申请号：US18924261

申请日：2024-10-23

Applicant: SAS Institute, Inc.

Inventor： Kedar Shriram Prabhudesai ,  Hardi Desai ,  Jonathan James McElhinney ,  Jonathan Lee Walker ,  Sanjeev Shyam Heda ,  Andrey Matveenko ,  Varunraj Valsaraj ,  Rik Peter de Ruiter

IPC: G06K9/00 ,  F16P3/14 ,  G06Q50/26 ,  G06T7/00 ,  G06T7/20 ,  G06T7/254 ,  G06T7/70 ,  G06V10/26 ,  G06V10/56 ,  G06V10/70 ,  G06V10/75 ,  G06V10/764 ,  G06V20/40 ,  G06V20/52 ,  G06V40/10 ,  G06V40/20 ,  G08B21/02

Abstract: In some examples, a system can access video data collected from one or more image sensors, the video data showing a region of interest proximate to a machine. The system can execute an object detection model to detect that a person is within the region of interest proximate to the machine based on the video data. The system can detect a motion status of a component of the machine. The system can execute a pose estimation model on the video data to estimate a pose of the person with respect to the machine. The system can detect a safety rule violation based on the pose of the person with respect to the machine, and the motion status of the machine. The system can transmit a signal to a controller of the machine in response to detecting the safety rule violation.

公开(公告)号：US20230267527A1

公开(公告)日：2023-08-24

申请号：US18169592

申请日：2023-02-15

Applicant: SAS Institute Inc.

Inventor： Jonathan Lee Walker ,  Hardi Desai ,  Xuejun Liao ,  Varunraj Valsaraj

IPC: G06Q30/0601 ,  G06N5/04

CPC classification number: G06Q30/0631 ,  G06N5/04

Abstract: The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

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.

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.

公开(公告)号：US11842379B2

公开(公告)日：2023-12-12

申请号：US18169592

申请日：2023-02-15

Applicant: SAS Institute Inc.

Inventor： Jonathan Lee Walker ,  Hardi Desai ,  Xuejun Liao ,  Varunraj Valsaraj

IPC: G06Q30/00 ,  G06Q30/0601 ,  G06N5/04

CPC classification number: G06Q30/0631 ,  G06N5/04

Abstract: The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

公开(公告)号：US11734919B1

公开(公告)日：2023-08-22

申请号：US17988463

申请日：2022-11-16

Applicant: SAS Institute Inc.

Inventor： Daniele Cazzari ,  Hardi Desai ,  Allen Joseph Langlois ,  Jonathan Walker ,  Thomas Tuning ,  Saurabh Mishra ,  Varunraj Valsaraj

IPC: G06V10/94

CPC classification number: G06V10/94

Abstract: A flexible computer architecture for performing digital image analysis is described herein. In some examples, the computer architecture can include a distributed messaging platform (DMP) for receiving images from cameras and storing the images in a first queue. The computer architecture can also include a first container for receiving the images from the first queue, applying an image analysis model to the images, and transmitting the image analysis result to the DMP for storage in a second queue. Additionally, the computer architecture can include a second container for receiving the image analysis result from the second queue, performing a post-processing operation on the image analysis result, and transmitting the post-processing result to the DMP for storage in a third queue. The computer architecture can further include an output container for receiving the post-processing result from the third queue and generating an alert notification based on the post-processing result.