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).
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Publication number: 20240220584Abstract: 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: ApplicationFiled: November 3, 2023Publication date: July 4, 2024Inventors: Eric Jon OJARD, Abde Ali Hunaid KAGALWALLA, Rami MEHIO, Nitin UDPA, Gavin Derek PARNABY, John S. VIECELI
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Patent number: 11989265Abstract: The technology disclosed extracts intensities from sequencing images for base calling target clusters and attenuates spatial crosstalk from neighboring clusters. The technology disclosed accesses a particular section from a plurality of sections of an image output by a sensor, the particular section of the image including at least one pixel depicting intensity emission values from a target cluster and neighboring clusters located across the sensor, and convolves the particular section of the image with a corresponding convolution kernel in a plurality of convolution kernels, to generate a feature map comprising a plurality of feature values. The technology disclosed further assigns a corresponding feature value to the target cluster based on feature values in the plurality of feature values adjoining a center of the target cluster, and processes the corresponding feature value assigned to the target cluster, to base call the target cluster.Type: GrantFiled: September 2, 2022Date of Patent: May 21, 2024Assignee: Illumina, Inc.Inventors: Abde Ali Hunaid Kagalwalla, Eric Jon Ojard, Rami Mehio, Gavin Derek Parnaby, Nitin Udpa, Bo Lu, John S. Vieceli
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Patent number: 11853396Abstract: 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: GrantFiled: January 13, 2023Date of Patent: December 26, 2023Assignee: Illumina, Inc.Inventors: Eric Jon Ojard, Abde Ali Hunaid Kagalwalla, Rami Mehio, Nitin Udpa, Gavin Derek Parnaby, John S Vieceli
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Publication number: 20230259588Abstract: 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: ApplicationFiled: January 13, 2023Publication date: August 17, 2023Inventors: Eric Jon OJARD, Abde Ali Hunaid KAGALWALLA, Rami MEHIO, Nitin UDPA, Gavin Derek PARNABY, John S. VIECELI
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Patent number: 11593595Abstract: 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: GrantFiled: May 24, 2022Date of Patent: February 28, 2023Inventors: Eric Jon Ojard, Abde Ali Hunaid Kagalwalla, Rami Mehio, Nitin Udpa, Gavin Derek Parnaby, John S. Vieceli
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Publication number: 20230018469Abstract: We disclose a system. The system comprises a memory and a runtime logic. The memory stores a plurality of specialist signal profilers. Each specialist signal profiler in the plurality of specialist signal profilers is trained to maximize signal-to-noise ratio of sequenced signals in a particular signal profile detected for analytes in a particular analyte class and characterized in a particular training data set. The runtime logic, having access to the memory, is configured to execute a base calling operation by applying respective specialist signal profilers in the plurality of specialist signal profilers to sequenced signals in respective signal profiles detected for analytes in respective analyte classes during the base calling operation.Type: ApplicationFiled: June 13, 2022Publication date: January 19, 2023Applicant: ILLUMINA SOFTWARE, INC.Inventors: Abde Ali Hunaid KAGALWALLA, Eric Jon OJARD, Rami MEHIO, Gavin Derek PARNABY, Nitin UDPA, John S. VIECELI
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Patent number: 11455487Abstract: 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: GrantFiled: October 26, 2021Date of Patent: September 27, 2022Assignee: Illumina Software, Inc.Inventors: Abde Ali Hunaid Kagalwalla, Eric Jon Ojard, Rami Mehio, Gavin Derek Parnaby, Nitin Udpa, Bo Lu, John S. Vieceli
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Publication number: 20220300772Abstract: 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: ApplicationFiled: May 24, 2022Publication date: September 22, 2022Applicant: ILLUMINA, INC.Inventors: Eric Jon Ojard, Abde Ali Hunaid Kagalwalla, Rami Mehio, Nitin Udpa, Gavin Derek Parnaby, John S. Vieceli
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Patent number: 11361194Abstract: 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: GrantFiled: October 25, 2021Date of Patent: June 14, 2022Assignee: ILLUMINA, INC.Inventors: Eric Jon Ojard, Abde Ali Hunaid Kagalwalla, Rami Mehio, Nitin Udpa, Gavin Derek Parnaby, John S. Vieceli
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Publication number: 20220129711Abstract: 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: ApplicationFiled: October 25, 2021Publication date: April 28, 2022Applicant: ILLUMINA, INC.Inventors: Eric Jon OJARD, Abde Ali Hunaid KAGALWALLA, Rami MEHIO, Nitin UDPA, Gavin Derek PARNABY, John S. VIECELI
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Patent number: 11301982Abstract: 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: GrantFiled: August 30, 2019Date of Patent: April 12, 2022Assignee: Intel CorporationInventors: Bikram Baidya, Hale Erten, Allan Gu, John A. Swanson, Vivek K. Singh, Abde Ali Hunaid Kagalwalla, Mengfei Yang-Flint
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Patent number: 11244440Abstract: 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: GrantFiled: August 30, 2019Date of Patent: February 8, 2022Assignee: Intel CorporationInventors: Bikram Baidya, Allan Gu, Vivek K. Singh, Abde Ali Hunaid Kagalwalla
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Patent number: 11176658Abstract: 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: GrantFiled: September 16, 2019Date of Patent: November 16, 2021Assignee: Intel CorporationInventors: Bikram Baidya, Allan Gu, Vivek K. Singh, Kumara Sastry, Abde Ali Hunaid Kagalwalla
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Patent number: 10915691Abstract: 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: GrantFiled: June 28, 2019Date of Patent: February 9, 2021Assignee: Intel CorporationInventors: Bikram Baidya, Vivek K. Singh, Allan Gu, Abde Ali Hunaid Kagalwalla, Saumyadip Mukhopadhyay, Kumara Sastry, Daniel L. Stahlke, Kritika Upreti
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Publication number: 20200013161Abstract: 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: ApplicationFiled: September 16, 2019Publication date: January 9, 2020Inventors: Bikram Baidya, Allan Gu, Vivek K. Singh, Kumara Sastry, Abde Ali Hunaid Kagalwalla
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Publication number: 20200005451Abstract: 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: ApplicationFiled: August 30, 2019Publication date: January 2, 2020Applicant: Intel CorporationInventors: Bikram Baidya, Allan Gu, Vivek K. Singh, Abde Ali Hunaid Kagalwalla
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Publication number: 20190385300Abstract: 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: ApplicationFiled: August 30, 2019Publication date: December 19, 2019Applicant: Intel CorporationInventors: Bikram Baidya, Hale Erten, Allan Gu, John A. Swanson, Vivek K. Singh, Abde Ali Hunaid Kagalwalla, Mengfei Yang-Flint
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Publication number: 20190318059Abstract: 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: ApplicationFiled: June 28, 2019Publication date: October 17, 2019Inventors: Bikram Baidya, Vivek K. Singh, Allan Gu, Abde Ali Hunaid Kagalwalla, Saumyadip Mukhopadhyay, Kumara Sastry, Daniel L. Stahlke, Kritika Upreti