Patents by Inventor Anindita DUTTA

Anindita DUTTA 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: 11908548
    Abstract: The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.
    Type: Grant
    Filed: May 27, 2022
    Date of Patent: February 20, 2024
    Assignee: Illumina, Inc.
    Inventors: Anindita Dutta, Dorna Kashefhaghighi, Amirali Kia
  • Publication number: 20240055078
    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: July 13, 2023
    Publication date: February 15, 2024
    Inventors: Anindita Dutta, Gery Vessere, Dorna KashefHaghighi, Kishore Jaganathan, Amirali Kia
  • Publication number: 20230407386
    Abstract: Defocus is introduced during sequencing by synthesis by tilt of a flow cell and by variations in flatness of the flow cell. Effects of the defocus are reduced, and base calling quality is improved using techniques relating to dependence of base calling on flow cell tilt. For example, the flow cell surface height is measured throughout the flow cell. A focal height of an imager having a sensor for the sequencing is set, optionally adaptively, one or more times during the sequencing. Each image captured by the sensor is partitioned, e.g., based on differences between focal height and the measured flow cell surface height across areas of the sensor. Filters, e.g., related to defocus correction, are selected based at least in part on the difference between the focal height and the measured flow cell surface height at a particular area of the image being corrected for defocus.
    Type: Application
    Filed: June 9, 2023
    Publication date: December 21, 2023
    Applicants: Illumina, Inc., Illumina Software, Inc.
    Inventors: Stanley Hong, Michael Gallaspy, Merek Siu, Jeffrey Gau, Anindita Dutta, Aathavan Karunakaran, Simon Prince
  • Publication number: 20230340571
    Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can use a machine-learning model to classify or predict a probability of an oligonucleotide probe yielding an accurate genotype call or hybridizing with a target oligonucleotide—based on the oligonucleotide probe's nucleotide-sequence composition. To intelligently identify oligonucleotide probes that are more likely to yield accurate downstream genotyping—or more likely to successfully hybridize with target oligonucleotides—some embodiments of the disclosed machine-learning model include customized layers trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy.
    Type: Application
    Filed: April 26, 2023
    Publication date: October 26, 2023
    Inventors: Sepideh Almasi, Yong Li, Anindita Dutta, Eric Vermaas, Rigoberto Pantoja
  • Publication number: 20230343414
    Abstract: We disclose a computer-implemented method of base calling. The technology disclosed accesses a time series sequence of a read. Respective time series elements in the time series sequence represent respective bases in the read. Then, a composite sequence for the read is generated based on respective aggregate transformations of respective sliding windows of time series elements in the time series sequence. A subject composite element in the composite sequence is generated based on an aggregate transformation of a corresponding window of time series elements in the time series sequence. Then, the composite sequence is processed as an aggregate and generates a base call sequence that has respective base calls for the respective bases in the read.
    Type: Application
    Filed: March 24, 2023
    Publication date: October 26, 2023
    Inventors: Gery Vessere, Anindita Dutta, Gavin Derek Parnaby
  • Publication number: 20230296516
    Abstract: Artificial intelligence driven signal enhancement of sequencing images enables enhanced sequencing by synthesis that determines a sequence of bases in genetic material with any one or more of: improved performance, improved accuracy, and/or reduced cost. A training set of images taken at unreduced and reduced power levels used to excite fluorescence during sequencing by synthesis is used to train a neural network to enable the neural network to recover enhanced images, as if taken at the unreduced power level, from unenhanced images taken at the reduced power level.
    Type: Application
    Filed: February 17, 2023
    Publication date: September 21, 2023
    Applicants: Illumina, Inc., Illumina Software, Inc.
    Inventors: Anindita Dutta, Michael Gallaspy, Jeffrey Gau, Stanley Hong, Aathavan Karunakaran, Simon Prince, Merek Siu, Yina Wang, Rishi Verma
  • Patent number: 11749380
    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: Grant
    Filed: February 19, 2021
    Date of Patent: September 5, 2023
    Assignee: Illumina, Inc.
    Inventors: Anindita Dutta, Gery Vessere, Dorna Kashefhaghighi, Kishore Jaganathan, Amirali Kia
  • Publication number: 20230260096
    Abstract: Artificial intelligence driven enhancement of motion blurred sequencing images enables enhanced sequencing that determines a sequence of bases in genetic material with any one or more of: improved performance, improved accuracy, and/or reduced cost. A training set of images taken after unreduced and reduced movement settling times during sequencing is used to train a neural network to enable the neural network to recover enhanced images, as if taken after the unreduced movement settling time, from unenhanced images taken after the reduced movement settling time.
    Type: Application
    Filed: February 17, 2023
    Publication date: August 17, 2023
    Applicants: Illumina, Inc., Illumina Software, Inc.
    Inventors: Simon Prince, Stanley Hong, Michael Gallaspy, Merek Siu, Jeffrey Gau, Anindita Dutta, Aathavan Karunakaran, Yina Wang, Rishi Verma
  • Publication number: 20230026084
    Abstract: A method of progressively training a base caller is disclosed. The method includes initially training a base caller, and generating labelled training data using the initially trained base caller; and (i) further training the base caller with analyte comprising organism base sequences, and generating labelled training data using the further trained base caller. The method includes iteratively further training the base caller by repeating step (i) for N iterations, which includes further training the base caller for N1 iterations of the N iterations with analyte comprising a first organism base sequence, and further training the base caller for N2 iterations of the N iterations with analyte comprising a second organism base sequence. A complexity of neural network configurations loaded in the base caller monotonically increases with the N iterations, and labelled training data generated during an iteration is used to train the base caller during an immediate subsequent iteration.
    Type: Application
    Filed: June 1, 2022
    Publication date: January 26, 2023
    Applicant: ILLUMINA, INC.
    Inventors: Amirali KIA, Anindita DUTTA
  • Publication number: 20230005253
    Abstract: Techniques for improving artificial intelligence-based base calling are disclosed. The improved techniques can be used to better train artificial intelligence for base calling by reordering of sequencing images, and training of a neural network-based base caller where the temporal logic is effectively “frozen” (or bypassed). In addition, the improved techniques include various combinations, including, for example, combining “normalization” of sequencing images with reordering of sequencing images and/or with effectively “freezing” the temporal logic.
    Type: Application
    Filed: June 13, 2022
    Publication date: January 5, 2023
    Applicant: ILLUMINA, INC.
    Inventors: Anindita DUTTA, Gery VESSERE
  • 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
  • Publication number: 20220415445
    Abstract: A method of progressively training a base caller is disclosed. The method includes iteratively initially training a base caller with analyte comprising a single-oligo base sequence, and generating labelled training data using the initially trained base caller. At operations (i), the base caller is further trained with analyte comprising multi-oligo base sequences, and labelled training data is generated using the further trained base caller. Operations (i) are iteratively repeated to further train the base caller. In an example, during at least one iteration, a complexity of neural network configuration loaded within the base caller is increased. In an example, labelled training data generated during an iteration is used to train the base caller during an immediate subsequent iteration.
    Type: Application
    Filed: June 1, 2022
    Publication date: December 29, 2022
    Applicant: ILLUMINA, INC.
    Inventors: Amirali KIA, Anindita DUTTA
  • Publication number: 20220292297
    Abstract: The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.
    Type: Application
    Filed: May 27, 2022
    Publication date: September 15, 2022
    Applicant: Illumina, Inc.
    Inventors: Anindita DUTTA, Dorna KASHEFHAGHIGHI, Amirali KIA
  • 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: 11347965
    Abstract: The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.
    Type: Grant
    Filed: March 20, 2020
    Date of Patent: May 31, 2022
    Assignee: Illumina, Inc.
    Inventors: Anindita Dutta, Dorna Kashefhaghighi, Amirali Kia
  • Publication number: 20220147760
    Abstract: The technology disclosed relates to artificial intelligence based determination of analyte data for base calling. In particular, the technology disclosed uses input image data that is derived from a sequence of images. Each image in the sequence of images represents an imaged region and depicts intensity emissions indicative of one or more analytes and a surrounding background of the intensity emissions at a respective one of a plurality of sequencing cycles of a sequencing run. The input image data comprises image patches extracted from each image in the sequence of images. The input image data is processed through a neural network to generate an alternative representation of the input image data. The alternative representation is processed through an output layer to generate an output indicating properties of respective portions of the imaged region.
    Type: Application
    Filed: November 17, 2021
    Publication date: May 12, 2022
    Applicant: ILLUMINA, INC.
    Inventors: Anindita DUTTA, Dorna KASHEFHAGHIGHI, Amirali KIA
  • Patent number: 11288576
    Abstract: The technology disclosed predicts quality of base calling during an extended optical base calling process. The base calling process includes pre-prediction base calling process cycles and at least two times as many post-prediction base calling process cycles as pre-prediction cycles. A plurality of time series from the pre-prediction base calling process cycles is given as input to a trained convolutional neural network. The convolutional neural network determines from the pre-prediction base calling process cycles, a likely overall base calling quality expected after post-prediction base calling process cycles. When the base calling process includes a sequence of paired reads, the overall base calling quality time series of the first read is also given as an additional input to the convolutional neural network to determine the likely overall base calling quality after post-prediction cycles of the second read.
    Type: Grant
    Filed: January 5, 2018
    Date of Patent: March 29, 2022
    Assignee: Illumina, Inc.
    Inventors: Anindita Dutta, Amirali Kia
  • Patent number: 11210554
    Abstract: The technology disclosed uses neural networks to determine analyte metadata by (i) processing input image data derived from a sequence of image sets through a neural network and generating an alternative representation of the input image data, the input image data has an array of units that depicts analytes and their surrounding background, (ii) processing the alternative representation through an output layer and generating an output value for each unit in the array, (iii) thresholding output values of the units and classifying a first subset of the units as background units depicting the surrounding background, and (iv) locating peaks in the output values of the units and classifying a second subset of the units as center units containing centers of the analytes.
    Type: Grant
    Filed: March 20, 2020
    Date of Patent: December 28, 2021
    Assignee: Illumina, Inc.
    Inventors: Anindita Dutta, Dorna Kashefhaghighi, Amirali Kia
  • 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: 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