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: 20230004749
    Abstract: A system, a method and a non-transitory computer readable storage medium for base calling are described. The base calling method includes processing through a neural network first image data comprising images of clusters and their surrounding background captured by a sequencing system for one or more sequencing cycles of a sequencing run. The base calling method further includes producing a base call for one or more of the clusters of the one or more sequencing cycles of the sequencing run.
    Type: Application
    Filed: August 30, 2022
    Publication date: January 5, 2023
    Applicant: ILLUMINA, INC.
    Inventors: Kishore JAGANATHAN, Anindita DUTTA, Dorna KASHEFHAGHIGHI, John Randall GOBBEL, Amirali KIA
  • Patent number: 11488009
    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 neural 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: Grant
    Filed: October 15, 2018
    Date of Patent: November 1, 2022
    Assignee: Illumina, Inc.
    Inventors: Kishore Jaganathan, Kai-How Farh, Sofia Kyriazopoulou Panagiotopoulou, Jeremy Francis McRae
  • Publication number: 20220301657
    Abstract: A system for base calling includes memory storing a topology of a neural network, a plurality of weights sets, and sensor data for a series of sensing cycles. Sequencing events span temporal progression of the base calling operation through subseries of sensing cycles, and spatial progression of the base calling operation through locations on a biosensor. A configurable processor is configured to load the topology on the configurable processor, select a weight set in dependence upon a subject subseries of sensing cycles and/or a subject location on the biosensor, load subject sensor data for the subject subseries of sensing cycles and the subject location on the processing elements, configure the topology using the selected weight set, and cause the neural network to process the subject sensor data to produce base call classification data for the subject subseries and the subject location.
    Type: Application
    Filed: March 4, 2022
    Publication date: September 22, 2022
    Applicants: Illumina, Inc., Illumina Software, Inc.
    Inventors: Gavin Derek PARNABY, Mark David HAHM, Andrew Christopher DU PREEZ, Dorna KASHEFHAGHIGHI, Kishore JAGANATHAN
  • Patent number: 11436429
    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: Grant
    Filed: March 21, 2020
    Date of Patent: September 6, 2022
    Assignee: Illumina, Inc.
    Inventors: Kishore Jaganathan, Anindita Dutta, Dorna Kashefhaghighi, John Randall Gobbel, Amirali Kia
  • Patent number: 11397889
    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: Grant
    Filed: October 15, 2018
    Date of Patent: July 26, 2022
    Assignee: Illumina, Inc.
    Inventors: Kishore Jaganathan, Kai-How Farh, Sofia Kyriazopoulou Panagiotopoulou, Jeremy Francis McRae
  • Publication number: 20210265017
    Abstract: The technology disclosed relates to artificial intelligence-based base calling. The technology disclosed relates to accessing a progression of per-cycle analyte channel sets generated for sequencing cycles of a sequencing run, processing, through a neural network-based base caller (NNBC), windows of per-cycle analyte channel sets in the progression for the windows of sequencing cycles of the sequencing run such that the NNBC processes a subject window of per-cycle analyte channel sets in the progression for the subject window of sequencing cycles of the sequencing run and generates provisional base call predictions for three or more sequencing cycles in the subject window of sequencing cycles, from multiple windows in which a particular sequencing cycle appeared at different positions, using the NNBC to generate provisional base call predictions for the particular sequencing cycle, and determining a base call for the particular sequencing cycle based on the plurality of base call predictions.
    Type: Application
    Filed: February 19, 2021
    Publication date: August 26, 2021
    Applicant: Illumina, Inc.
    Inventors: Anindita DUTTA, Gery VESSERE, Dorna KASHEFHAGHIGHI, Kishore JAGANATHAN, Amirali KIA
  • Publication number: 20210265009
    Abstract: The technology disclosed relates to artificial intelligence-based base calling of index sequences. The technology disclosed accesses index images generated for the index sequences during index sequencing cycles of a sequencing run. The index images depict intensity emissions generated as a result of nucleotide incorporation in the index sequences during the sequencing run. The technology disclosed normalizes an index image from a current index sequencing cycle based on (i) intensity values of index images from one or more preceding index sequencing cycles, (ii) intensity values of index images from one or more succeeding index sequencing cycles, and (iii) intensity values of index images from the current index sequencing cycle. The technology disclosed processes normalized versions of the index images through a neural network-based base caller and generates a base call for each of the index sequencing cycles, thereby producing index reads for the index sequences.
    Type: Application
    Filed: February 12, 2021
    Publication date: August 26, 2021
    Applicant: Illumina, Inc.
    Inventors: Kishore JAGANATHAN, Amirali KIA
  • Publication number: 20210264267
    Abstract: The technology disclosed relates to a system that comprises a spatial convolution network and a temporal convolution network. The spatial convolution network is configured to process a window of per-cycle sequencing image sets and generate respective per-cycle spatial feature map sets. Trained coefficients of spatial convolution filters in spatial convolution filter banks of respective sequences of spatial convolution filter banks vary between sequences of spatial convolution layers in respective sequences of spatial convolution layers. The temporal convolution network is configured to process the per-cycle spatial feature map sets on a groupwise basis and generate respective per-group temporal feature map sets. Trained coefficients of temporal convolution filters in respective temporal convolution filter banks vary between temporal convolution filter banks in respective temporal convolution filter banks.
    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: 20210265018
    Abstract: The technology disclosed compresses a larger, teacher base caller into a smaller, student base caller. The student base caller has fewer processing modules and parameters than the teacher base caller. The teacher base caller is trained using hard labels (e.g., one-hot encodings). The trained teacher base caller is used to generate soft labels as output probabilities during the inference phase. The soft labels are used to train the student base caller.
    Type: Application
    Filed: February 15, 2021
    Publication date: August 26, 2021
    Applicant: Illumina, Inc.
    Inventors: Anindita DUTTA, Gery VESSERE, Dorna KASHEFHAGHIGHI, Kishore JAGANATHAN, Amirali KIA
  • Publication number: 20210265016
    Abstract: The technology disclosed relates to an artificial intelligence-based method of base calling. In particular, it relates to processing, through a spatial network of a neural network-based base caller, a first window of per-cycle analyte channel sets in for a first window of sequencing cycles of a sequencing run, and generating respective sequences of spatial output sets for respective sequencing cycles in the first window of sequencing cycles, processing, through a compression network of the neural network-based base caller, respective final spatial output sets in the respective sequences of spatial output sets, and generating respective compressed spatial output sets for the respective sequencing cycles in the first window of sequencing cycles, and generating, based on the respective compressed spatial output sets, base call predictions for one or more sequencing cycles in the first window of sequencing cycles.
    Type: Application
    Filed: February 18, 2021
    Publication date: August 26, 2021
    Applicant: Illumina, Inc.
    Inventors: Gery VESSERE, Gavin Derek PARNABY, Anindita DUTTA, Dorna KASHEFHAGHIGHI, Kishore JAGANATHAN, Amirali KIA
  • 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: 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
  • 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