Patents by Inventor Abde Ali Hunaid Kagalwalla

Abde Ali Hunaid Kagalwalla 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: 11455487
    Abstract: The technology disclosed attenuates spatial crosstalk from sequencing images for base calling. The technology disclosed accesses a section of an image output by a biosensor, where the section of the image includes a plurality of pixels depicting intensity emission values from a plurality of clusters within the biosensor and from locations within the biosensor that are adjacent to the plurality of clusters. The plurality of clusters includes a target cluster. The section of the image is convolved with a convolution kernel, to generate a feature map comprising a plurality of features having a corresponding plurality of feature values. A weighted feature value is assigned to the target cluster, where the weighted feature value is based on one or more features values of the plurality of feature values of the feature map. The weighted feature value assigned to the target cluster is processed, to base call the target cluster.
    Type: Grant
    Filed: October 26, 2021
    Date of Patent: September 27, 2022
    Assignee: Illumina Software, Inc.
    Inventors: Abde Ali Hunaid Kagalwalla, Eric Jon Ojard, Rami Mehio, Gavin Derek Parnaby, Nitin Udpa, Bo Lu, John S. Vieceli
  • Publication number: 20220300772
    Abstract: The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.
    Type: Application
    Filed: May 24, 2022
    Publication date: September 22, 2022
    Applicant: ILLUMINA, INC.
    Inventors: Eric Jon Ojard, Abde Ali Hunaid Kagalwalla, Rami Mehio, Nitin Udpa, Gavin Derek Parnaby, John S. Vieceli
  • Patent number: 11361194
    Abstract: The technology disclosed generates variation correction coefficients on a cluster-by-cluster basis to correct inter-cluster intensity profile variation for improved base calling. An amplification coefficient corrects scale variation. Channel-specific offset coefficients correct shift variation along respective intensity channels. The variation correction coefficients for a target cluster are generated based on combining analysis of historic intensity data generated for the target cluster at preceding sequencing cycles of a sequencing run with analysis of current intensity data generated for the target cluster at a current sequencing cycle of the sequencing run. The variation correction coefficients are then used to correct next intensity data generated for the target cluster at a next sequencing cycle of the sequencing run. The corrected next intensity data is then used to base call the target cluster at the next sequencing cycle.
    Type: Grant
    Filed: October 25, 2021
    Date of Patent: June 14, 2022
    Assignee: ILLUMINA, INC.
    Inventors: Eric Jon Ojard, Abde Ali Hunaid Kagalwalla, Rami Mehio, Nitin Udpa, Gavin Derek Parnaby, John S. Vieceli
  • Publication number: 20220129711
    Abstract: The technology disclosed generates variation correction coefficients on a cluster-by-cluster basis to correct inter-cluster intensity profile variation for improved base calling. An amplification coefficient corrects scale variation. Channel-specific offset coefficients correct shift variation along respective intensity channels. The variation correction coefficients for a target cluster are generated based on combining analysis of historic intensity data generated for the target cluster at preceding sequencing cycles of a sequencing run with analysis of current intensity data generated for the target cluster at a current sequencing cycle of the sequencing run. The variation correction coefficients are then used to correct next intensity data generated for the target cluster at a next sequencing cycle of the sequencing run. The corrected next intensity data is then used to base call the target cluster at the next sequencing cycle.
    Type: Application
    Filed: October 25, 2021
    Publication date: April 28, 2022
    Applicant: ILLUMINA, INC.
    Inventors: Eric Jon OJARD, Abde Ali Hunaid KAGALWALLA, Rami MEHIO, Nitin UDPA, Gavin Derek PARNABY, John S. VIECELI
  • Patent number: 11301982
    Abstract: A method includes identifying a first geometric pattern that failed a design rule check, identifying a second geometric pattern that passed the design rule check, morphing the first geometric pattern based on the second geometric pattern to generate a morphed geometric pattern, wherein the morphed geometric pattern passes the design rule check, and replacing the first geometric pattern with the morphed geometric pattern.
    Type: Grant
    Filed: August 30, 2019
    Date of Patent: April 12, 2022
    Assignee: Intel Corporation
    Inventors: Bikram Baidya, Hale Erten, Allan Gu, John A. Swanson, Vivek K. Singh, Abde Ali Hunaid Kagalwalla, Mengfei Yang-Flint
  • Patent number: 11244440
    Abstract: A method includes, for each data object of a plurality of data objects, performing a measurement on a plurality of instances of the data object to generate a plurality of measurement values for the data object, and generating a distribution of the measurement values for the data object. The method further includes generating an aggregate distribution based on each of the distributions of the measurement values generated for the data objects, and scoring a first data object of the plurality of data objects based on the distribution of the measurement values for the first data object and the aggregate distribution.
    Type: Grant
    Filed: August 30, 2019
    Date of Patent: February 8, 2022
    Assignee: Intel Corporation
    Inventors: Bikram Baidya, Allan Gu, Vivek K. Singh, Abde Ali Hunaid Kagalwalla
  • Patent number: 11176658
    Abstract: A method comprising determining a binary classification value for each of a plurality of data instances based on a first threshold value assigned to each of the plurality of data instances; applying at least one clustering model to a first subset of the plurality of data instances to identify one or more dominant clusters of data instances; determining a second threshold value to assign to a second plurality of data instances that are included within the one or more dominant clusters of data instances; and redetermining a binary classification value for each of the plurality of data instances based on the second threshold value assigned to the second plurality of data instances and the first threshold value, wherein the first threshold value is assigned to at least a portion of data instances of the plurality of data instances that are not included in the second plurality of data instances.
    Type: Grant
    Filed: September 16, 2019
    Date of Patent: November 16, 2021
    Assignee: Intel Corporation
    Inventors: Bikram Baidya, Allan Gu, Vivek K. Singh, Kumara Sastry, Abde Ali Hunaid Kagalwalla
  • Patent number: 10915691
    Abstract: A semantic pattern extraction system can distill tremendous amounts of silicon wafer manufacturing data to generate a small set of simple sentences (semantic patterns) describing physical design geometries that may explain manufacturing defects. The system can analyze many SEM images for manufacturing defects in areas of interest on a wafer. A tagged continuous itemset is generated from the images, with items comprising physical design feature values corresponding to the areas of interest and tagged with the presence or absence of a manufacturing defect. Entropy-based discretization converts the continuous itemset into a discretized one. Frequent set mining identifies a set of candidate semantic patterns from the discretized itemset. Candidate semantic patterns are reduced using reduction techniques and are scored. A ranked list of final semantic patterns is presented to a user. The final semantic patterns can be used to improve a manufacturing process.
    Type: Grant
    Filed: June 28, 2019
    Date of Patent: February 9, 2021
    Assignee: Intel Corporation
    Inventors: Bikram Baidya, Vivek K. Singh, Allan Gu, Abde Ali Hunaid Kagalwalla, Saumyadip Mukhopadhyay, Kumara Sastry, Daniel L. Stahlke, Kritika Upreti
  • Publication number: 20200013161
    Abstract: A method comprising determining a binary classification value for each of a plurality of data instances based on a first threshold value assigned to each of the plurality of data instances; applying at least one clustering model to a first subset of the plurality of data instances to identify one or more dominant clusters of data instances; determining a second threshold value to assign to a second plurality of data instances that are included within the one or more dominant clusters of data instances; and redetermining a binary classification value for each of the plurality of data instances based on the second threshold value assigned to the second plurality of data instances and the first threshold value, wherein the first threshold value is assigned to at least a portion of data instances of the plurality of data instances that are not included in the second plurality of data instances.
    Type: Application
    Filed: September 16, 2019
    Publication date: January 9, 2020
    Inventors: Bikram Baidya, Allan Gu, Vivek K. Singh, Kumara Sastry, Abde Ali Hunaid Kagalwalla
  • Publication number: 20200005451
    Abstract: A method includes, for each data object of a plurality of data objects, performing a measurement on a plurality of instances of the data object to generate a plurality of measurement values for the data object, and generating a distribution of the measurement values for the data object. The method further includes generating an aggregate distribution based on each of the distributions of the measurement values generated for the data objects, and scoring a first data object of the plurality of data objects based on the distribution of the measurement values for the first data object and the aggregate distribution.
    Type: Application
    Filed: August 30, 2019
    Publication date: January 2, 2020
    Applicant: Intel Corporation
    Inventors: Bikram Baidya, Allan Gu, Vivek K. Singh, Abde Ali Hunaid Kagalwalla
  • Publication number: 20190385300
    Abstract: A method includes identifying a first geometric pattern that failed a design rule check, identifying a second geometric pattern that passed the design rule check, morphing the first geometric pattern based on the second geometric pattern to generate a morphed geometric pattern, wherein the morphed geometric pattern passes the design rule check, and replacing the first geometric pattern with the morphed geometric pattern.
    Type: Application
    Filed: August 30, 2019
    Publication date: December 19, 2019
    Applicant: Intel Corporation
    Inventors: Bikram Baidya, Hale Erten, Allan Gu, John A. Swanson, Vivek K. Singh, Abde Ali Hunaid Kagalwalla, Mengfei Yang-Flint
  • Publication number: 20190318059
    Abstract: A semantic pattern extraction system can distill tremendous amounts of silicon wafer manufacturing data to generate a small set of simple sentences (semantic patterns) describing physical design geometries that may explain manufacturing defects. The system can analyze many SEM images for manufacturing defects in areas of interest on a wafer. A tagged continuous itemset is generated from the images, with items comprising physical design feature values corresponding to the areas of interest and tagged with the presence or absence of a manufacturing defect. Entropy-based discretization converts the continuous itemset into a discretized one. Frequent set mining identifies a set of candidate semantic patterns from the discretized itemset. Candidate semantic patterns are reduced using reduction techniques and are scored. A ranked list of final semantic patterns is presented to a user. The final semantic patterns can be used to improve a manufacturing process.
    Type: Application
    Filed: June 28, 2019
    Publication date: October 17, 2019
    Inventors: Bikram Baidya, Vivek K. Singh, Allan Gu, Abde Ali Hunaid Kagalwalla, Saumyadip Mukhopadhyay, Kumara Sastry, Daniel L. Stahlke, Kritika Upreti