Patents by Inventor Omesh Tickoo

Omesh Tickoo 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: 20210266145
    Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.
    Type: Application
    Filed: October 2, 2020
    Publication date: August 26, 2021
    Applicant: Intel Corporation
    Inventors: Yen-Kuang Chen, Shao-Wen Yang, Ibrahima J. Ndiour, Yiting Liao, Vallabhajosyula S. Somayazulu, Omesh Tickoo, Srenivas Varadarajan
  • Publication number: 20210243012
    Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.
    Type: Application
    Filed: September 11, 2020
    Publication date: August 5, 2021
    Applicant: Intel Corporation
    Inventors: Yen-Kuang Chen, Shao-Wen Yang, Ibrahima J. Ndiour, Yiting Liao, Vallabhajosyula S. Somayazulu, Omesh Tickoo, Srenivas Varadarajan
  • Publication number: 20210209473
    Abstract: The present disclosure provides a machine learning model where each activation node within the model has an adaptive activation function defined in terms of an input and a hyperparameter of the model. Accordingly, each activation node can have a separate of distinct activation function, based on the adaptive activation function where the hyperparameter for each activation node is trained during overall training of the model. Furthermore, the present disclosure provides that a set of adaptive activation functions can be provided for each activation node such that a spike train of activations can be generated.
    Type: Application
    Filed: March 25, 2021
    Publication date: July 8, 2021
    Applicant: Intel Corporation
    Inventors: Julio Cesar Zamora Esquivel, Jesus Adan Cruz Vargas, Nadine L. Dabby, Anthony Rhodes, Omesh Tickoo, Narayan Sundararajan, Lama Nachman
  • Publication number: 20210201047
    Abstract: Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.
    Type: Application
    Filed: March 15, 2021
    Publication date: July 1, 2021
    Inventors: Myung Hwangbo, Krishna Kumar Singh, Teahyung Lee, Omesh Tickoo
  • Publication number: 20210194674
    Abstract: In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images.
    Type: Application
    Filed: August 7, 2020
    Publication date: June 24, 2021
    Applicant: Intel Corporation
    Inventors: Yen-Kuang Chen, Shao-Wen Yang, Ibrahima J. Ndiour, Yiting Liao, Vallabhajosyula S. Somayazulu, Omesh Tickoo, Srenivas Varadarajan
  • Publication number: 20210140793
    Abstract: Various aspects are related to a map representation system including one or more processors configured to: obtain operation task data, the operation task data indicating one or more operation tasks associated with an operation of one or more autonomous agents within a corresponding operating space; and obtain operation task specific map data as a function of the operation task data, the operation task specific map data representing the operating space corresponding to the one or more operation tasks.
    Type: Application
    Filed: December 18, 2020
    Publication date: May 13, 2021
    Inventors: Omesh TICKOO, Rajesh POORNACHANDRAN, David Israel GONZÁLEZ AGUIRRE, John Charles WEAST
  • Publication number: 20210133633
    Abstract: A controller for an automated machine may include including: one or more processors configured to: determine that a group affiliation of the automated machine switched from a first group of automated machines to a second group of automated machines, the first group of automated machines being assigned to one or more first tasks, the second group of automated machines being assigned to one or more second tasks; generate a message for one or more network devices of the second group of automated machines in accordance with a communication protocol, the message including information about a task performing model of the automated machine, the task performing model being based on a result of performing at least one task of the one or more first tasks by the automated machine.
    Type: Application
    Filed: December 22, 2020
    Publication date: May 6, 2021
    Inventors: Rajesh POORNACHANDRAN, Vinayak HONKOTE, Rita H. WOUHAYBI, Omesh TICKOO
  • Publication number: 20210117792
    Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate continuous learning. An example apparatus includes a trainer to train a first Bayesian neural network (BNN) and a second BNN, the first BNN associated with a first weight distribution and the second BNN associated with a second weight distribution. The example apparatus includes a weight determiner to determine a first sampling weight associated with the first BNN and a second sampling weight associated with the second BNN. The example apparatus includes a network sampler to sample at least one of the first weight distribution or the second weight distribution based on a pseudo-random number, the first sampling weight, and the second sampling weight. The example apparatus includes an inference controller to generate an ensemble weight distribution based on the sample.
    Type: Application
    Filed: December 23, 2020
    Publication date: April 22, 2021
    Inventors: Nilesh Ahuja, Mahesh Subedar, Ranganath Krishnan, Ibrahima Ndiour, Omesh Tickoo
  • Publication number: 20210117760
    Abstract: Methods, systems, and apparatus to obtain well-calibrated uncertainty in probabilistic deep neural networks are disclosed. An example apparatus includes a loss function determiner to determine a differentiable accuracy versus uncertainty loss function for a machine learning model, a training controller to train the machine learning model, the training including performing an uncertainty calibration of the machine learning model using the loss function, and a post-hoc calibrator to optimize the loss function using temperature scaling to improve the uncertainty calibration of the trained machine learning model under distributional shift.
    Type: Application
    Filed: December 23, 2020
    Publication date: April 22, 2021
    Inventors: Ranganath Krishnan, Omesh Tickoo, Nilesh Ahuja, Ibrahima Ndiour, Mahesh Subedar
  • Publication number: 20210107153
    Abstract: According to various aspects, controller for an automated machine may include: a processor configured to: compare information about a function of the automated machine with information of a set of tasks available to a plurality of automated machines; negotiate, with the other automated machines of the plurality of automated machines and based on a result of the comparison, which task of the set of tasks is allocated to the automated machine.
    Type: Application
    Filed: December 22, 2020
    Publication date: April 15, 2021
    Inventors: Rajesh Poornachandran, Omesh Tickoo, Anahit Tarkhanyan, Vinayak Honkote, Stanley Mo
  • Publication number: 20210109493
    Abstract: According to various aspects, a controller for an automated machine may include: one or more processors configured to: obtain a message from a further automated machine in accordance with a communication protocol, the message including a first result of a first sensing process that the further automated machine performs; and determine an assessment of the automated machine based on the first result and based on a second result of a second sensing process that the automated machine performs.
    Type: Application
    Filed: December 22, 2020
    Publication date: April 15, 2021
    Inventors: Rajesh Poornachandran, Omesh Tickoo, Vinayak Honkote, John Charles Weast, Rita H. Wouhaybi, Stanley Mo
  • Publication number: 20210081756
    Abstract: An apparatus to facilitate fractional convolutional kernels is disclosed. The apparatus includes one or more processors comprising a convolution circuit of a neural network, the convolution circuit to initialize a set of parameters of a fractional convolutional kernel, the set of parameters comprising at least a fractional derivative parameter that is initialized with a fractional value, and apply the fractional convolutional kernel to input data to convolve the input data to obtain output data.
    Type: Application
    Filed: November 30, 2020
    Publication date: March 18, 2021
    Applicant: Intel Corporation
    Inventors: Julio C. Zamora Esquivel, Jesus Adan Cruz Vargas, Jose Rodrigo Camacho Perez, Paulo Lopez Meyer, Hector A. Cordourier Maruri, Omesh Tickoo
  • Patent number: 10949674
    Abstract: An apparatus for video summarization using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, and a summarizer. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each frame of each activity, wherein the score is based on a plurality of objects in each frame. The summarizer is to summarize the activity segments based on the score for each frame.
    Type: Grant
    Filed: March 11, 2019
    Date of Patent: March 16, 2021
    Assignee: Intel Corporation
    Inventors: Myung Hwangbo, Krishna Kumar Singh, Teahyung Lee, Omesh Tickoo
  • Patent number: 10922536
    Abstract: A mechanism is described for facilitating age classification of humans using image depth and human pose according to one embodiment. A method of embodiments, as described herein, includes facilitating, by one or more cameras of a computing device, capturing of a video stream of a scene having persons, and computing overall-depth torso lengths of the persons based on depth torso lengths of the persons. The method may further include comparing the overall-depth torso lengths with a predetermined threshold value representing a separation age between adults and children, and classifying a first set of the persons as adults if a first set of the overall-depth torso lengths associated with the first set of persons is greater than the threshold value.
    Type: Grant
    Filed: January 21, 2020
    Date of Patent: February 16, 2021
    Assignee: INTEL CORPORATION
    Inventors: Srenivas Varadarajan, Nikita Tiwari, Parual Datta, Andradige Pubudu Madhawa Silva, Omesh Tickoo, Erin Carroll
  • Patent number: 10909017
    Abstract: An apparatus of a multiagent system comprises a processing unit to process a unit of analysis on sensor data received from a sensor, and to provide a latency estimate to process the unit of analysis, and a decision system coupled with the processing unit to execute a decision process in response to an event, wherein the decision system utilizes the latency estimate as part of the decision process.
    Type: Grant
    Filed: December 27, 2017
    Date of Patent: February 2, 2021
    Assignee: Intel Corporation
    Inventors: Willem Beltman, Omesh Tickoo, Jonathan J. Huang, Glen Anderson
  • Patent number: 10887614
    Abstract: Techniques related to applying computer vision to decompressed video are discussed. Such techniques may include generating a region of interest in an individual video frame by translating spatial indicators of a first detected computer vision result from a reference video frame to the individual video frame and applying a greater threshold within the region of interest than outside of the region of interest for computer vision evaluation in the individual frame.
    Type: Grant
    Filed: June 24, 2019
    Date of Patent: January 5, 2021
    Assignee: Intel Corporation
    Inventors: Srenivas Varadarajan, Omesh Tickoo, Vallabhajosyula Somayazulu, Yiting Liao, Ibrahima Ndiour, Shao-Wen Yang, Yen-Kuang Chen
  • Publication number: 20200326667
    Abstract: Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.
    Type: Application
    Filed: June 24, 2020
    Publication date: October 15, 2020
    Inventors: Nilesh Ahuja, Ignacio J. Alvarez, Ranganath Krishnan, Ibrahima J. Ndiour, Mahesh Subedar, Omesh Tickoo
  • Patent number: 10797863
    Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.
    Type: Grant
    Filed: June 29, 2018
    Date of Patent: October 6, 2020
    Assignee: Intel Corporation
    Inventors: Yen-Kuang Chen, Shao-Wen Yang, Ibrahima J. Ndiour, Yiting Liao, Vallabhajosyula S. Somayazulu, Omesh Tickoo, Srenivas Varadarajan
  • Publication number: 20200310532
    Abstract: Generally discussed herein are systems and apparatuses for gesture-based augmented reality. Also discussed herein are methods of using the systems and apparatuses. According to an example a method may include detecting, in image data, an object and a gesture, in response to detecting the object in the image data, providing data indicative of the detected object, in response to detecting the gesture in the image data, providing data indicative of the detected gesture, and modifying the image data using the data indicative of the detected object and the data indicative of the detected gesture.
    Type: Application
    Filed: June 15, 2020
    Publication date: October 1, 2020
    Inventors: Ravishankar R. Iyer, Omesh Tickoo, Glen J. Anderson
  • Patent number: 10778412
    Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.
    Type: Grant
    Filed: June 29, 2018
    Date of Patent: September 15, 2020
    Assignee: Intel Corporation
    Inventors: Yen-Kuang Chen, Shao-Wen Yang, Ibrahima J. Ndiour, Yiting Liao, Vallabhajosyula S. Somayazulu, Omesh Tickoo, Srenivas Varadarajan