Patents by Inventor Deepak Khosla

Deepak Khosla 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: 20220414460
    Abstract: Training an encoder is provided. The method comprises inputting a current state of a number of aircraft into a recurrent layer of a neural network, wherein the current state comprises a reduced state in which a value of a specified parameter is missing. An action applied to the aircraft is input into the recurrent layer concurrently with the current state. The recurrent layer learns a value for the parameter missing from current state, and the output of the recurrent layer is input into a number of fully connected hidden layers. The hidden layers, according to the current state, learned value, and current action, determine a residual output that comprises an incremental difference in the state of the aircraft resulting from the current action.
    Type: Application
    Filed: March 18, 2022
    Publication date: December 29, 2022
    Inventors: Sean Soleyman, Yang Chen, Fan Hin Hung, Deepak Khosla, Navid Naderializadeh
  • Publication number: 20220414283
    Abstract: Training a compressive encoder is provided. The method comprises calculating a difference between a current state of an aircraft and a previous state. The current state comprises a reduced state wherein the value of a specified parameter is missing. The difference is input into compressive layers of a neural network comprising an encoder. The compressive layers learn, according to the difference, a value for the missing parameter. The current state and learned value are concurrently fed into hidden layers of a fully connected neural network comprising a decoder. An action applied to the aircraft is input into the hidden layers concurrently with the current state and learned value. The hidden layers, according to the current state, learned value, and current action, determine a residual output that comprises an incremental difference in the state of the aircraft resulting from the current action.
    Type: Application
    Filed: March 18, 2022
    Publication date: December 29, 2022
    Inventors: Sean Soleyman, Yang Chen, Fan Hin Hung, Deepak Khosla, Navid Naderializadeh
  • Publication number: 20220414422
    Abstract: A computer-implemented method for predicting behavior of aircraft is provided. The method comprises inputting a current state of a number of aircraft into a number of hidden layers of a neural network, wherein the neural network is fully connected. An action applied to the aircraft is input into the hidden layers concurrently with the current state. The hidden layers, according to the current state and current action, determine a residual output that comprises an incremental difference in the state of the aircraft resulting from the current action. A skip connection feeds forward the current state of the aircraft, and the residual output is added to the current state to determine a next state of the aircraft.
    Type: Application
    Filed: March 18, 2022
    Publication date: December 29, 2022
    Inventors: Sean Soleyman, Yang Chen, Fan Hin Hung, Deepak Khosla, Navid Naderializadeh
  • Publication number: 20220413496
    Abstract: Training adversarial aircraft controllers is provided. The method comprises inputting current observed states of a number of aircraft into a world model encoder, wherein each current state represents a state of a different aircraft, and wherein each current state comprises a missing parameter value. A number of adversarial control actions for the aircraft are input into the world model encoder concurrently with the current observed state, wherein the adversarial control actions are generated by competing neural network controllers. The world model encoder generates a learned observation from the current observed states and adversarial control actions, wherein the learned observation represents the missing parameter value from the current observed states. The learned observation and current observed states are input into the competing neural network controllers, wherein each current observed state is fed into a respective controller.
    Type: Application
    Filed: March 18, 2022
    Publication date: December 29, 2022
    Inventors: Sean Soleyman, Yang Chen, Fan Hin Hung, Deepak Khosla, Navid Naderializadeh
  • Publication number: 20220404490
    Abstract: A method, apparatus and computer program product are provided to generate a model of one or more objects relative to a vehicle. In the context of a method, radar information is received in the form of in-phase quadrature (IQ) data and the IQ data is converted to one or more first range-doppler maps. The method further includes evaluating the one or more first range-doppler maps with a machine learning model to generate the model that captures the detection of the one or more objects relative to the vehicle. A corresponding apparatus and computer program product are also provided.
    Type: Application
    Filed: March 8, 2022
    Publication date: December 22, 2022
    Applicant: THE BOEING COMPANY
    Inventors: Nick Shadbeh EVANS, William K. LEACH, Deepak KHOSLA, Leon NGUYEN, Michelle D. WARREN
  • Publication number: 20220404831
    Abstract: An example method for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft can involve: determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action to a simulated environment; generating and sending to both the simulated environment and the MLA, randomly-sampled values for each of a set of parameters of the second aircraft different from predetermined fixed values for the set of parameters; receiving an observation of the simulated environment and a reward signal at the MLA, the observation including information about the simulated environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action based on the randomly-sampled values; and updating the MLA based on the observation of the simulated environment, the reward signal, and the randomly-sampled values.
    Type: Application
    Filed: May 11, 2022
    Publication date: December 22, 2022
    Inventors: Sean Soleyman, Deepak Khosla, Ram Longman
  • Patent number: 11516165
    Abstract: Methods and systems for revoking electronic messages. One method includes storing, for each of a plurality of forwarded messages, a record in a data store, each record including a link to an original message for the forwarded message, and receiving a request to revoke a forwarded message. In response to receiving the request, the method includes identifying an original message the forwarded message via a record stored in the data store and notifying, with an electronic processor, a user associated with the original message of the request to revoke the forwarded message. In response to receiving an instruction revoking the original source message from the user, the method includes identifying each forward of the original message via records stored in the data store and revoking the original message and each message associated with each record stored in the data store including a link to the original message.
    Type: Grant
    Filed: April 10, 2019
    Date of Patent: November 29, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Sukanya Rajagopal, Vikhyat Khosla, Aayushi Joshi, Nikhil Maryala, Manohar Kumar, Rakesh Midha, Arun Rajappa, Deepak Kumar Pratinidhi, Rajiv Kumar
  • Publication number: 20220375222
    Abstract: Described is a system and method for accurate image and/or video scene classification. More specifically, described is a system that makes use of a specialized convolutional-neural network (hereafter CNN) based technique for the fusion of bottom-up whole-image features and top-down entity classification. When the two parallel and independent processing paths are fused, the system provides an accurate classification of the scene as depicted in the image or video.
    Type: Application
    Filed: July 14, 2022
    Publication date: November 24, 2022
    Inventors: Ryan M. Uhlenbrock, Deepak Khosla, Yang Chen, Fredy Monterroza
  • Patent number: 11481634
    Abstract: A device includes a control input generator and a neural network trainer. A flight simulator is configured to generate first state data responsive to a first control input from the control input generator and to provide the first state data to a first neural network to generate a candidate second control input. The control input generator is also configured to select, based on a random value, a second control input from between the candidate second control input and a randomized offset control input that is based on a random offset applied to the first control input. The flight simulator is configured to generate second state data responsive to the second control input from the control input generator. The neural network trainer is configured to update weights of the first neural network based, at least in part, on the first state data and the second state data.
    Type: Grant
    Filed: August 29, 2019
    Date of Patent: October 25, 2022
    Assignee: THE BOEING COMPANY
    Inventors: Yang Chen, Deepak Khosla, Kevin Martin
  • Patent number: 11455893
    Abstract: A method includes obtaining multiple sets of trajectory data, each descriptive of trajectories of two or more objects (e.g., first and second objects). The method also includes generating transformed trajectory data based on the trajectory data. Each set of transformed trajectory data is descriptive of the trajectories of the two or more objects in a normalized reference frame in which a movement path of the first object is constrained. The method further includes generating feature data, performing a clustering operation based on the feature data to generate a set of trajectory clusters, and generating training data based on the set of trajectory clusters. The method further includes using the training data to train a machine learning classifier to classify particular trajectory patterns.
    Type: Grant
    Filed: March 12, 2020
    Date of Patent: September 27, 2022
    Assignee: THE BOEING COMPANY
    Inventors: Nigel Stepp, Sean Soleyman, Deepak Khosla
  • Patent number: 11423651
    Abstract: Described is a system and method for accurate image and/or video scene classification. More specifically, described is a system that makes use of a specialized convolutional-neural network (hereafter CNN) based technique for the fusion of bottom-up whole-image features and top-down entity classification. When the two parallel and independent processing paths are fused, the system provides an accurate classification of the scene as depicted in the image or video.
    Type: Grant
    Filed: February 8, 2017
    Date of Patent: August 23, 2022
    Assignee: HRL LABORATORIES, LLC
    Inventors: Ryan M. Uhlenbrock, Deepak Khosla, Yang Chen, Fredy Monterroza
  • Publication number: 20220230348
    Abstract: Apparatuses and methods train a model and then use the trained model to determine a global three dimensional (3D) position and orientation of a fiduciary marker. In the context of an apparatus for training a model, a wider field-of-view sensor is configured to acquire a static image of a space in which the fiducial marker is disposed and a narrower field-of-view sensor is configured to acquire a plurality of images of at least a portion of the fiducial marker. The apparatus also includes a pan-tilt unit configured to controllably alter pan and tilt angles of the narrower field-of-view sensor during image acquisition. The apparatus further includes a control system configured to determine a transformation of position and orientation information determined from the images acquired by the narrower field-of-view sensor to a coordinate system for the space for which the static image is acquired by the wider field-of-view sensor.
    Type: Application
    Filed: October 1, 2021
    Publication date: July 21, 2022
    Applicant: THE BOEING COMPANY
    Inventors: David James HUBER, Deepak KHOSLA, Yang CHEN, Brandon COURTER, Luke Charles INGRAM, Jacob MOORMAN, Scott RAD, Anthony Wayne BAKER
  • Publication number: 20220212811
    Abstract: Aspects of the disclosure provide fuel receptacle and boom tip position and pose estimation for aerial refueling. A video frame is received and within the video frame, aircraft keypoints for an aircraft to be refueled are determined. Based on at least the aircraft keypoints, a position and pose of a fuel receptacle on the aircraft is determined. Within the video frame, a boom tip keypoint for a boom tip of an aerial refueling boom is also determined. Based on at least the boom tip keypoint, a position and pose of the boom tip is determined. Based on at least the position and pose of the fuel receptacle and the position and pose of the boom tip, the aerial refueling boom is controlled to engage the fuel receptacle. Some examples overlay projections of an aircraft model on displayed video for a human observer.
    Type: Application
    Filed: December 29, 2021
    Publication date: July 7, 2022
    Inventors: Deepak Khosla, Leon Nguyen, William Kentarou Kaneko Leach, Justin C. Hatcher, James L. Clayton, Yifan Yang, Paul S. Idell, Fan Hin Hung
  • Publication number: 20220215571
    Abstract: A system for refining a six degrees of freedom pose estimate of a target object based on a one-dimensional measurement includes a camera and a range-sensing device. The range-sensing device is configured to determine an actual distance measured between the range-sensing device and an actual point of intersection. The range-sensing device projects a line-of-sight that intersects with the target object at the actual point of intersection. The system also includes one or more processors in electronic communication with the camera and the range-sensing device and a memory coupled to the processors. The memory stores data into one or more databases and program code that, when executed by the processors, causes the system to predict the six degrees of freedom pose estimate of the target object. The system also determines a revised six degrees of freedom pose estimate of the target object based on at least an absolute error.
    Type: Application
    Filed: December 20, 2021
    Publication date: July 7, 2022
    Inventors: William K. Leach, Leon Nguyen, Fan Hung, Yang Chen, Deepak Khosla, Haden H. Smith
  • Publication number: 20220107628
    Abstract: A system is provided. The system includes a first platform including a first platform level agent configured to direct one or more actions of the first platform based on at least one of a selected target or a selected goal. The system also includes a computer system in communication with the first platform level agent. The computer system programmed to a) execute a supervisor level agent configured to select at least one of a target or a goal for one or more platforms including the first platform, b) receive targeting information including one or more targets, c) receive platform information for the one or more platforms, d) select, by the supervisor level agent, a target of the one or more targets based on the target information and the platform information, and e) transmit, to the first platform level agent, the selected target.
    Type: Application
    Filed: September 23, 2021
    Publication date: April 7, 2022
    Inventors: Navid Naderializadeh, Sean Soleyman, Fan Hin Hung, Deepak Khosla
  • Publication number: 20210397179
    Abstract: A method includes receiving, by machine-learning logic, observations indicative of a states associated with a first and second group of vehicles arranged within an engagement zone during a first interval of an engagement between the first and the second group of vehicles. The machine-learning logic determines actions based on the observations that, when taken simultaneously by the first group of vehicles during the first interval, are predicted by the machine-learning logic to result in removal of one or more vehicles of the second group of vehicles from the engagement zone during the engagement. The machine-learning logic is trained using a reinforcement learning technique and on simulated engagements between the first and second group of vehicles to determine sequences of actions that are predicted to result in one or more vehicles of the second group being removed from the engagement zone. The machine-learning logic communicates the plurality of actions to the first group of vehicles.
    Type: Application
    Filed: June 22, 2020
    Publication date: December 23, 2021
    Inventors: Joshua G. Fadaie, Richard Hanes, Chun Kit Chung, Sean Soleyman, Deepak Khosla
  • Patent number: 11150670
    Abstract: Apparatus and methods for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft are described. Training of the MLA can include: the MLA determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action from the MLA; after sending the first-aircraft action, receiving an observation of the environment and a reward signal at the MLA, the observation including information about the environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action, the reward signal indicating a score of performance of the first-aircraft action based on dynamic and kinematic properties of the second aircraft; and updating the MLA based on the observation of the environment and the reward signal.
    Type: Grant
    Filed: May 28, 2019
    Date of Patent: October 19, 2021
    Assignee: The Boeing Company
    Inventors: Deepak Khosla, Kevin R. Martin, Sean Soleyman, Ignacio M. Soriano, Michael A. Warren, Joshua G. Fadaie, Charles Tullock, Yang Chen, Shawn Moffit, Calvin Chung
  • Publication number: 20210287554
    Abstract: A method includes obtaining multiple sets of trajectory data, each descriptive of trajectories of two or more objects (e.g., first and second objects). The method also includes generating transformed trajectory data based on the trajectory data. Each set of transformed trajectory data is descriptive of the trajectories of the two or more objects in a normalized reference frame in which a movement path of the first object is constrained. The method further includes generating feature data, performing a clustering operation based on the feature data to generate a set of trajectory clusters, and generating training data based on the set of trajectory clusters. The method further includes using the training data to train a machine learning classifier to classify particular trajectory patterns.
    Type: Application
    Filed: March 12, 2020
    Publication date: September 16, 2021
    Inventors: Nigel Stepp, Sean Soleyman, Deepak Khosla
  • Patent number: 11055872
    Abstract: Described is a system for real-time object recognition. The system extracts a candidate target region representing a candidate object from an input image of a scene based on agglomeration of channel features. The candidate target region is classified using a trained convolutional neural network (CNN) classifier, resulting in an initial classified object. A multi-target tracker is used for tracking the classified objects for final classification of each classified object, resulting in a final output, and a device is controlled based on the final output.
    Type: Grant
    Filed: January 30, 2018
    Date of Patent: July 6, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Yang Chen, Deepak Khosla, Ryan M. Uhlenbrock
  • Publication number: 20210147079
    Abstract: Described is a system for autonomous behavior generation. The system includes both a high-level controller and a low-level controller. The high-level controller receives observations from an environment and, using a neural net, selects a high-level behavior based on the observations from the environment. The low-level controller generates an output command for a scripted action based on the selected one high-level behavior. After generating the output command, the system can implement an action, such as causing a device to perform the scripted action.
    Type: Application
    Filed: October 6, 2020
    Publication date: May 20, 2021
    Inventors: Sean Soleyman, Deepak Khosla