Patents by Inventor Harsimran Singh Sidhu

Harsimran Singh Sidhu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20220108130
    Abstract: Systems and methods for training machine models with augmented data. An example method includes identifying a set of images captured by a set of cameras while affixed to one or more image collection systems. For each image in the set of images, a training output for the image is identified. For one or more images in the set of images, an augmented image for a set of augmented images is generated. Generating an augmented image includes modifying the image with an image manipulation function that maintains camera properties of the image. The augmented training image is associated with the training output of the image. A set of parameters of the predictive computer model are trained to predict the training output based on an image training set including the images and the set of augmented images.
    Type: Application
    Filed: December 14, 2021
    Publication date: April 7, 2022
    Inventors: Matthew John Cooper, Paras Jagdish Jain, Harsimran Singh Sidhu
  • Publication number: 20220043449
    Abstract: An autonomous control system combines sensor data from multiple sensors to simulate sensor data from high-capacity sensors. The sensor data contains information related to physical environments surrounding vehicles for autonomous guidance. For example, the sensor data may be in the form of images that visually capture scenes of the surrounding environment, geo-location of the vehicles, and the like. The autonomous control system simulates high-capacity sensor data of the physical environment from replacement sensors that may each have lower capacity than high-capacity sensors. The high-capacity sensor data may be simulated via one or more neural network models. The autonomous control system performs various detection and control algorithms on the simulated sensor data to guide the vehicle autonomously.
    Type: Application
    Filed: October 22, 2021
    Publication date: February 10, 2022
    Inventors: Forrest Nelson Iandola, Donald Benton MacMillen, Anting Shen, Harsimran Singh Sidhu, Daniel Paden Tomasello, Rohan Nandkumar Phadte, Paras Jagdish Jian
  • Patent number: 11205093
    Abstract: Systems and methods for training machine models with augmented data. An example method includes identifying a set of images captured by a set of cameras while affixed to one or more image collection systems. For each image in the set of images, a training output for the image is identified. For one or more images in the set of images, an augmented image for a set of augmented images is generated. Generating an augmented image includes modifying the image with an image manipulation function that maintains camera properties of the image. The augmented training image is associated with the training output of the image. A set of parameters of the predictive computer model are trained to predict the training output based on an image training set including the images and the set of augmented images.
    Type: Grant
    Filed: October 10, 2019
    Date of Patent: December 21, 2021
    Assignee: Tesla, Inc.
    Inventors: Matthew John Cooper, Paras Jagdish Jain, Harsimran Singh Sidhu
  • Patent number: 11157014
    Abstract: An autonomous control system combines sensor data from multiple sensors to simulate sensor data from high-capacity sensors. The sensor data contains information related to physical environments surrounding vehicles for autonomous guidance. For example, the sensor data may be in the form of images that visually capture scenes of the surrounding environment, geo-location of the vehicles, and the like. The autonomous control system simulates high-capacity sensor data of the physical environment from replacement sensors that may each have lower capacity than high-capacity sensors. The high-capacity sensor data may be simulated via one or more neural network models. The autonomous control system performs various detection and control algorithms on the simulated sensor data to guide the vehicle autonomously.
    Type: Grant
    Filed: December 27, 2017
    Date of Patent: October 26, 2021
    Assignee: Tesla, Inc.
    Inventors: Forrest Nelson Iandola, Donald Benton MacMillen, Anting Shen, Harsimran Singh Sidhu, Daniel Paden Tomasello, Rohan Nandkumar Phadte, Paras Jagdish Jain
  • Publication number: 20200401136
    Abstract: An autonomous control system generates synthetic data that reflect simulated environments. Specifically, the synthetic data is a representation of sensor data of the simulated environment from the perspective of one or more sensors. The system generates synthetic data by introducing one or more simulated modifications to sensor data captured by the sensors or by simulating the sensor data for a virtual environment. The autonomous control system uses the synthetic data to train computer models for various detection and control algorithms. In general, this allows autonomous control systems to augment training data to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment.
    Type: Application
    Filed: June 8, 2020
    Publication date: December 24, 2020
    Inventors: Forrest Nelson Iandola, Donald Benton MacMillen, Anting Shen, Harsimran Singh Sidhu, Paras Jagdish Jain
  • Patent number: 10678244
    Abstract: An autonomous control system generates synthetic data that reflect simulated environments. Specifically, the synthetic data is a representation of sensor data of the simulated environment from the perspective of one or more sensors. The system generates synthetic data by introducing one or more simulated modifications to sensor data captured by the sensors or by simulating the sensor data for a virtual environment. The autonomous control system uses the synthetic data to train computer models for various detection and control algorithms. In general, this allows autonomous control systems to augment training data to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment.
    Type: Grant
    Filed: March 23, 2018
    Date of Patent: June 9, 2020
    Assignee: Tesla, Inc.
    Inventors: Forrest Nelson Iandola, Donald Benton MacMillen, Anting Shen, Harsimran Singh Sidhu, Paras Jagdish Jain
  • Publication number: 20200117953
    Abstract: Systems and methods for training machine models with augmented data. An example method includes identifying a set of images captured by a set of cameras while affixed to one or more image collection systems. For each image in the set of images, a training output for the image is identified. For one or more images in the set of images, an augmented image for a set of augmented images is generated. Generating an augmented image includes modifying the image with an image manipulation function that maintains camera properties of the image. The augmented training image is associated with the training output of the image. A set of parameters of the predictive computer model are trained to predict the training output based on an image training set including the images and the set of augmented images.
    Type: Application
    Filed: October 10, 2019
    Publication date: April 16, 2020
    Inventors: Matthew John Cooper, Paras Jain, Harsimran Singh Sidhu
  • Publication number: 20200074304
    Abstract: A neural network architecture is used that reduces the processing load of implementing the neural network. This network architecture may thus be used for reduced-bit processing devices. The architecture may limit the number of bits used for processing and reduce processing to prevent data overflow at individual calculations of the neural network. To implement this architecture, the number of bits used to represent inputs at levels of the network and the related filter masks may also be modified to ensure the number of bits of the output does not overflow the resulting capacity of the reduced-bit processor. To additionally reduce the load for such a network, the network may implement a “starconv” structure that permits the incorporation of nearby nodes in a layer to balance processing requirements and permit the network to learn from context of other nodes.
    Type: Application
    Filed: September 3, 2019
    Publication date: March 5, 2020
    Inventors: Forrest Nelson Iandola, Harsimran Singh Sidhu, Yiqi Hou
  • Publication number: 20200034710
    Abstract: A model training and implementation pipeline trains models for individual embedded systems. The pipeline iterates through multiple models and estimates the performance of the models. During a model generation stage, the pipeline translates the description of the model together with the model parameters into an intermediate representation in a language that is compatible with a virtual machine. The intermediate representation is agnostic or independent to the configuration of the target platform. During a model performance estimation stage, the pipeline evaluates the performance of the models without training the models. Based on the analysis of the performance of the untrained models, a subset of models is selected. The selected models are then trained and the performance of the trained models are analyzed. Based on the analysis of the performance of the trained models, a single model is selected for deployment to the target platform.
    Type: Application
    Filed: July 25, 2019
    Publication date: January 30, 2020
    Inventors: Harsimran Singh Sidhu, Paras Jagdish Jain, Daniel Paden Tomasello, Forrest Nelson Iandola
  • Publication number: 20180275658
    Abstract: An autonomous control system generates synthetic data that reflect simulated environments. Specifically, the synthetic data is a representation of sensor data of the simulated environment from the perspective of one or more sensors. The system generates synthetic data by introducing one or more simulated modifications to sensor data captured by the sensors or by simulating the sensor data for a virtual environment. The autonomous control system uses the synthetic data to train computer models for various detection and control algorithms. In general, this allows autonomous control systems to augment training data to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment.
    Type: Application
    Filed: March 23, 2018
    Publication date: September 27, 2018
    Inventors: Forrest Nelson Iandola, Donald Benton MacMillen, Anting Shen, Harsimran Singh Sidhu, Paras Jagdish Jain
  • Publication number: 20180188733
    Abstract: An autonomous control system combines sensor data from multiple sensors to simulate sensor data from high-capacity sensors. The sensor data contains information related to physical environments surrounding vehicles for autonomous guidance. For example, the sensor data may be in the form of images that visually capture scenes of the surrounding environment, geo-location of the vehicles, and the like. The autonomous control system simulates high-capacity sensor data of the physical environment from replacement sensors that may each have lower capacity than high-capacity sensors. The high-capacity sensor data may be simulated via one or more neural network models. The autonomous control system performs various detection and control algorithms on the simulated sensor data to guide the vehicle autonomously.
    Type: Application
    Filed: December 27, 2017
    Publication date: July 5, 2018
    Inventors: Forrest Nelson Iandola, Donald Benton MacMillen, Anting Shen, Harsimran Singh Sidhu, Daniel Paden Tomasello, Rohan Nandkumar Phadte, Paras Jagdish Jain