Patents by Inventor Raja Bala

Raja Bala 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).

  • Patent number: 12002265
    Abstract: A method includes defining a model for a liquid while the liquid is positioned at least partially within a nozzle of a printer. The method also includes synthesizing video frames of the liquid using the model to produce synthetic video frames. The method also includes generating a labeled dataset that includes the synthetic video frames and corresponding model values. The method also includes receiving real video frames of the liquid while the liquid is positioned at least partially within the nozzle of the printer. The method also includes generating an inverse mapping from the real video frames to predicted model values using the labeled dataset. The method also includes reconstructing the liquid in the real video frames based at least partially upon the predicted model values.
    Type: Grant
    Filed: June 24, 2021
    Date of Patent: June 4, 2024
    Assignee: XEROX CORPORATION
    Inventors: Robert R. Price, Raja Bala, Svyatoslav Korneev, Christoforos Somarakis, Matthew Shreve, Adrian Lew, Palghat Ramesh
  • Patent number: 11983171
    Abstract: A method of labeling a dataset includes inputting a testing set comprising a plurality of input data samples into a plurality of pre-trained machine learning models to generate a set of embeddings output by the plurality of pre-trained machine learning models. The method further includes performing an iterative cluster labeling algorithm that includes generating a plurality of clusterings from the set of embeddings, analyzing the plurality of clusterings to identify a target embedding with a highest duster quality, analyzing the target embedding to determine a compactness for each of the plurality of clusterings of the target embedding, and identifying a target cluster among the plurality of clusterings of the target embedding based on the compactness. The method further includes assigning pseudo-labels to the subset of the plurality of input data samples that are members of the target duster.
    Type: Grant
    Filed: July 7, 2023
    Date of Patent: May 14, 2024
    Assignee: Xerox Corporation
    Inventors: Matthew Shreve, Francisco E. Torres, Raja Bala, Robert R. Price, Pei Li
  • Patent number: 11983394
    Abstract: Embodiments described herein provide a system for generating semantically accurate synthetic images. During operation, the system generates a first synthetic image using a first artificial intelligence (AI) model and presents the first synthetic image in a user interface. The user interface allows a user to identify image units of the first synthetic image that are semantically irregular. The system then obtains semantic information for the semantically irregular image units from the user via the user interface and generates a second synthetic image using a second AI model based on the semantic information. The second synthetic image can be an improved image compared to the first synthetic image.
    Type: Grant
    Filed: November 23, 2022
    Date of Patent: May 14, 2024
    Assignee: Xerox Corporation
    Inventors: Raja Bala, Sricharan Kallur Palli Kumar, Matthew A. Shreve
  • Patent number: 11958112
    Abstract: A three-dimensional (3D) printer includes a nozzle and a camera configured to capture a real image or a real video of a liquid metal while the liquid metal is positioned at least partially within the nozzle. The 3D printer also includes a computing system configured to perform operations. The operations include generating a model of the liquid metal positioned at least partially within the nozzle. The operations also include generating a simulated image or a simulated video of the liquid metal positioned at least partially within the nozzle based at least partially upon the model. The operations also include generating a labeled dataset that comprises the simulated image or the simulated video and a first set of parameters. The operations also include reconstructing the liquid metal in the real image or the real video based at least partially upon the labeled dataset.
    Type: Grant
    Filed: June 24, 2021
    Date of Patent: April 16, 2024
    Assignee: XEROX CORPORATION
    Inventors: Robert R. Price, Raja Bala, Svyatoslav Korneev, Christoforos Somarakis, Matthew Shreve, Adrian Lew, Palghat Ramesh
  • Patent number: 11945169
    Abstract: A 3D printer includes a nozzle configured to jet a drop of liquid metal therethrough. The 3D printer also includes a light source configured to illuminate the drop with a pulse of light. A duration of the pulse of light is from about 0.0001 seconds to about 0.1 seconds. The 3D printer also includes a camera configured to capture an image, video, or both of the drop. The 3D printer also includes a computing system configured to detect the drop in the image, the video, or both. The computing system is also configured to characterize the drop after the drop is detected. Characterizing the drop includes determining a size of the drop, a location of the drop, or both in the image, the video, or both.
    Type: Grant
    Filed: May 27, 2021
    Date of Patent: April 2, 2024
    Assignee: XEROX CORPORATION
    Inventors: Vijay Kumar Baikampady Gopalkrishna, Raja Bala, Palghat Ramesh, David Allen Mantell, Peter Michael Gulvin, Mark A. Cellura
  • Patent number: 11948306
    Abstract: At least one input image comprising curvilinear features is received. Latent representations of the input images are learned using a trained deep neural network. At least one boundary estimate is determined based on the latent representations. At least one segmentation estimate of the at least one input image is determined based on the latent representations. The at least one image is mapped to output segmentation maps based on the segmentation estimate and the at least one boundary estimate.
    Type: Grant
    Filed: October 8, 2021
    Date of Patent: April 2, 2024
    Assignee: XEROX CORPORATION
    Inventors: Raja Bala, Xuelu Li
  • Publication number: 20240071132
    Abstract: A method of image annotation includes obtaining a candidate annotation map for an annotation task for an image from each of a set of annotation models wherein each of the candidate annotation maps includes suggested annotations for the image, receiving user selections or modifications of at least one of the suggested annotations from one or more of the candidate annotation maps, and generating a final annotation map based on the user selections or modifications from the one or more of the candidate annotation maps.
    Type: Application
    Filed: November 6, 2023
    Publication date: February 29, 2024
    Inventors: Matthew Shreve, Raja Bala, Jeyasri Subramanian
  • Patent number: 11886759
    Abstract: A method operates a three-dimensional (3D) metal object manufacturing system to compensate for displacement errors that occur during object formation. In the method, image data of a metal object being formed by the 3D metal object manufacturing system is generated prior to completion of the metal object and compared to original 3D object design data of the object to identify one or more displacement errors. For the displacement errors outside a predetermined difference range, the method modifies machine-ready instructions for forming metal object layers not yet formed to compensate for the identified displacement errors and operates the 3D metal object manufacturing system using the modified machine-ready instructions.
    Type: Grant
    Filed: October 1, 2019
    Date of Patent: January 30, 2024
    Assignee: Xerox Corporation
    Inventors: David A. Mantell, Christopher T. Chungbin, Daniel R. Cormier, Scott J. Vader, Zachary S. Vader, Viktor Sukhotskiy, Raja Bala, Walter Hsiao
  • Publication number: 20240012853
    Abstract: A system and method provide extractions of regions of interest from images hand annotated by reviewers by lifting annotations from images, filtering out text labels, reconstructing continuous closed boundaries, and marking the contained region.
    Type: Application
    Filed: July 8, 2022
    Publication date: January 11, 2024
    Applicant: Palo Alto Research Center Incorporated
    Inventors: Robert R. Price, Raja Bala
  • Publication number: 20230401829
    Abstract: A method of labeling data and training a model is provided. The method includes obtaining a set of images. The set of images includes a first subset and a second subset. The first subset is associated with a first set of labels. The method also includes generating a set of pseudo labels for the set of images and a second set of labels for the second subset based on the first subset, the second subset, a first machine learning model, and a domain adaption model. The method further includes generating second machine learning model. The second machine learning model is generated based on the set of images, the set of pseudo labels, the first set of labels, and the second set of labels. The second set of labels is updated based on one or more inferences generated by the second machine learning model.
    Type: Application
    Filed: June 8, 2022
    Publication date: December 14, 2023
    Inventors: Qun Liu, Matthew Shreve, Raja Bala
  • Patent number: 11808680
    Abstract: A method includes illuminating a drop with a pulse of light from a light source. A duration of the pulse of light is from about 0.0001 seconds to about 0.1 seconds. The method also includes capturing an image, video, or both of the drop. The method also includes detecting the drop in the image, the video, or both. The method also includes characterizing the drop after the drop is detected. Characterizing the drop includes determining a size of the drop, a location of the drop, or both in the image, the video, or both.
    Type: Grant
    Filed: May 27, 2021
    Date of Patent: November 7, 2023
    Assignee: XEROX CORPORATION
    Inventors: Vijay Kumar Baikampady Gopalkrishna, Raja Bala, Palghat Ramesh, David Allen Mantell, Peter Michael Gulvin, Mark A. Cellura
  • Patent number: 11810396
    Abstract: A method of image annotation includes selecting a plurality of annotation models related to an annotation task for an image, obtaining a candidate annotation map for the image from each of the plurality of annotation models, and selecting at least one of the candidate annotation maps to be displayed via a user interface, the candidate annotation maps comprising suggested annotations for the image. The method further includes receiving user selections or modifications of at least one of the suggested annotations from the candidate annotation map and generating a final annotation map based on the user selections or modifications.
    Type: Grant
    Filed: April 16, 2021
    Date of Patent: November 7, 2023
    Assignee: Xerox Corporation
    Inventors: Matthew Shreve, Raja Bala, Jeyasri Subramanian
  • Publication number: 20230350880
    Abstract: A method of labeling a dataset includes inputting a testing set comprising a plurality of input data samples into a plurality of pre-trained machine learning models to generate a set of embeddings output by the plurality of pre-trained machine learning models. The method further includes performing an iterative cluster labeling algorithm that includes generating a plurality of clusterings from the set of embeddings, analyzing the plurality of clusterings to identify a target embedding with a highest duster quality, analyzing the target embedding to determine a compactness for each of the plurality of clusterings of the target embedding, and identifying a target cluster among the plurality of clusterings of the target embedding based on the compactness. The method further includes assigning pseudo-labels to the subset of the plurality of input data samples that are members of the target duster.
    Type: Application
    Filed: July 7, 2023
    Publication date: November 2, 2023
    Inventors: Matthew Shreve, Francisco E. Torres, Raja Bala, Robert R. Price, Pei Li
  • Patent number: 11760000
    Abstract: A method includes capturing a video of a plurality of drops being jetted through a nozzle of a printer. The method also includes measuring a signal proximate to the nozzle based at least partially upon the video. The method also includes determining one or more metrics that characterize a behavior of the drops based at least partially upon the signal.
    Type: Grant
    Filed: May 7, 2021
    Date of Patent: September 19, 2023
    Assignee: XEROX CORPORATION
    Inventors: Raja Bala, Vijay Kumar Baikampady Gopalkrishna, Palghat Ramesh, David Allen Mantell, Peter Michael Gulvin, Mark A. Cellura
  • Patent number: 11741693
    Abstract: One embodiment facilitates generating synthetic data objects using a semi-supervised GAN. During operation, a generator module synthesizes a data object derived from a noise vector and an attribute label. The system passes, to an unsupervised discriminator module, the data object and a set of training objects which are obtained from a training data set. The unsupervised discriminator module calculates: a value indicating a probability that the data object is real; and a latent feature representation of the data object. The system passes the latent feature representation and the attribute label to a supervised discriminator module. The supervised discriminator module calculates a value indicating a probability that the attribute label given the data object is real. The system performs the aforementioned steps iteratively until the generator module produces data objects with a given attribute label which the unsupervised and supervised discriminator modules can no longer identify as fake.
    Type: Grant
    Filed: November 29, 2017
    Date of Patent: August 29, 2023
    Assignee: Palo Alto Research Center Incorporated
    Inventors: Sricharan Kallur Palli Kumar, Raja Bala, Jin Sun, Hui Ding, Matthew A. Shreve
  • Patent number: 11724441
    Abstract: A 3D printer includes a nozzle and a camera configured to capture an image, a video, or both of a plurality of drops of liquid metal being jetted through the nozzle. The 3D printer also includes a computing system configured to measure a signal proximate to the nozzle based at least partially upon the image, the video, or both. The computing system is also configured to determine one or more metrics that characterize a behavior of the drops based at least partially upon the signal.
    Type: Grant
    Filed: May 7, 2021
    Date of Patent: August 15, 2023
    Assignees: PALO ALTO RESEARCH CENTER INCORPORATED, XEROX CORPORATION
    Inventors: Raja Bala, Vijay Kumar Baikampady Gopalkrishna, Palghat Ramesh, David Allen Mantell, Peter Michael Gulvin, Mark A. Cellura
  • Patent number: 11714802
    Abstract: A method of labeling a dataset of input samples for a machine learning task includes selecting a plurality of pre-trained machine learning models that are related to a machine learning task. The method further includes processing a plurality of input data samples through each of the pre-trained models to generate a set of embeddings. The method further includes generating a plurality of clusterings from the set of embeddings. The method further includes analyzing, by a processing device, the plurality of clusterings to extract superclusters. The method further includes assigning pseudo-labels to the input samples based on analysis.
    Type: Grant
    Filed: April 2, 2021
    Date of Patent: August 1, 2023
    Assignee: Palo Alto Research Center Incorporated
    Inventors: Matthew Shreve, Francisco E. Torres, Raja Bala, Robert R. Price, Pei Li
  • Patent number: 11645770
    Abstract: One embodiment can provide a system for detecting occlusion at an orifice of a three-dimensional (3D) printer nozzle while the printer nozzle is jetting liquid droplets. During operation, the system uses one or more cameras to capture an image of the orifice of the printer nozzle while the 3D printer nozzle is jetting liquid droplets. The system performs an image-analysis operation on the captured image to identify occluded regions within the orifice of the 3D printer nozzle, compute an occlusion fraction based on the determined occluded regions, and generate an output based on the computed occlusion fraction, thereby facilitating effective maintenance of the 3D printer.
    Type: Grant
    Filed: November 17, 2021
    Date of Patent: May 9, 2023
    Assignee: Palo Alto Research Center Incorporated
    Inventors: Vijay Kumar Baikampady Gopalkrishna, Raja Bala
  • Publication number: 20230110806
    Abstract: At least one input image comprising curvilinear features is received. Latent representations of the input images are learned using a trained deep neural network. At least one boundary estimate is determined based on the latent representations. At least one segmentation estimate of the at least one input image is determined based on the latent representations. The at least one image is mapped to output segmentation maps based on the segmentation estimate and the at least one boundary estimate.
    Type: Application
    Filed: October 8, 2021
    Publication date: April 13, 2023
    Inventors: Raja Bala, Xuelu Li
  • Publication number: 20230090801
    Abstract: Embodiments described herein provide a system for generating semantically accurate synthetic images. During operation, the system generates a first synthetic image using a first artificial intelligence (AI) model and presents the first synthetic image in a user interface. The user interface allows a user to identify image units of the first synthetic image that are semantically irregular. The system then obtains semantic information for the semantically irregular image units from the user via the user interface and generates a second synthetic image using a second AI model based on the semantic information. The second synthetic image can be an improved image compared to the first synthetic image.
    Type: Application
    Filed: November 23, 2022
    Publication date: March 23, 2023
    Applicant: Palo Alto Research Center Incorporated
    Inventors: Raja Bala, Sricharan Kallur Palli Kumar, Matthew A. Shreve