Patents by Inventor Kishore JAGANATHAN

Kishore JAGANATHAN 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: 20210264266
    Abstract: The technology disclosed relates to a system that comprises a spatial convolution network and a bus network. The spatial convolution network is configured to process a window of per-cycle sequencing image sets on a cycle-by-cycle basis by separately processing respective per-cycle sequencing image sets through respective spatial processing pipelines to generate respective per-cycle spatial feature map sets for respective sequencing cycles. The bus network is configured to form buses between spatial convolution layers within the respective spatial processing pipelines. The buses are configured to cause respective per-cycle spatial feature map sets generated by two or more spatial convolution layers in a particular sequence of spatial convolution layer for a particular sequencing cycle to combine into a combined per-cycle spatial feature map set, and provide the combined per-cycle spatial feature map set as input to another spatial convolution layer in the particular sequence of spatial convolution layer.
    Type: Application
    Filed: February 19, 2021
    Publication date: August 26, 2021
    Applicant: Illumina, Inc.
    Inventors: Anindita DUTTA, Gery VESSERE, Dorna KASHEFHAGHIGHI, Gavin Derek PARNABY, Kishore JAGANATHAN, Amirali KIA
  • Publication number: 20200327377
    Abstract: The technology disclosed assigns quality scores to bases called by a neural network-based base caller by (i) quantizing classification scores of predicted base calls produced by the neural network-based base caller in response to processing training data during training, (ii) selecting a set of quantized classification scores, (iii) for each quantized classification score in the set, determining a base calling error rate by comparing its predicted base calls to corresponding ground truth base calls, (iv) determining a fit between the quantized classification scores and their base calling error rates, and (v) correlating the quality scores to the quantized classification scores based on the fit.
    Type: Application
    Filed: March 20, 2020
    Publication date: October 15, 2020
    Applicant: Illumina, Inc.
    Inventors: Kishore JAGANATHAN, John Randall GOBBEL, Amirali KIA
  • Publication number: 20200302224
    Abstract: The technology disclosed processes a first input through a first neural network and produces a first output. The first input comprises first image data derived from images of analytes and their surrounding background captured by a sequencing system for a sequencing run. The technology disclosed processes the first output through a post-processor and produces metadata about the analytes and their surrounding background. The technology disclosed processes a second input through a second neural network and produces a second output. The second input comprises third image data derived by modifying second image data based on the metadata. The second image data is derived from the images of the analytes and their surrounding background. The second output identifies base calls for one or more of the analytes at one or more sequencing cycles of the sequencing run.
    Type: Application
    Filed: March 21, 2020
    Publication date: September 24, 2020
    Applicant: Illumina, Inc.
    Inventors: Kishore JAGANATHAN, Anindita DUTTA, Dorna KASHEFHAGHIGHI, John Randall GOBBEL, Amirali KIA
  • Publication number: 20200302297
    Abstract: The technology disclosed processes input data through a neural network and produces an alternative representation of the input data. The input data includes per-cycle image data for each of one or more sequencing cycles of a sequencing run. The per-cycle image data depicts intensity emissions of one or more analytes and their surrounding background captured at a respective sequencing cycle. The technology disclosed processes the alternative representation through an output layer and producing an output and base calls one or more of the analytes at one or more of the sequencing cycles based on the output.
    Type: Application
    Filed: March 20, 2020
    Publication date: September 24, 2020
    Applicant: Illumina, Inc.
    Inventors: Kishore JAGANATHAN, John Randall GOBBEL, Amirali KIA
  • Publication number: 20190197401
    Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.
    Type: Application
    Filed: October 15, 2018
    Publication date: June 27, 2019
    Applicant: Illumina, Inc.
    Inventors: Kishore JAGANATHAN, Kai-How FARH, Sofia KYRIAZOPOULOU PANAGIOTOPOULOU, Jeremy Francis McRAE
  • Publication number: 20190114547
    Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.
    Type: Application
    Filed: October 15, 2018
    Publication date: April 18, 2019
    Applicant: Illumina, Inc.
    Inventors: Kishore JAGANATHAN, Kai-How FARH, Sofia KYRIAZOPOULOU PANAGIOTOPOULOU, Jeremy Francis McRAE
  • Publication number: 20190114391
    Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.
    Type: Application
    Filed: October 15, 2018
    Publication date: April 18, 2019
    Applicant: Illumina, Inc.
    Inventors: Kishore JAGANATHAN, Kai-How FARH, Sofia KYRIAZOPOULOU PANAGIOTOPOULOU, Jeremy Francis McRAE