Patents by Inventor Marc Berndl
Marc Berndl 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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Patent number: 12086702Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving graph data representing an input graph comprising a plurality of vertices connected by edges; generating, from the graph data, vertex input data representing characteristics of each vertex in the input graph and pair input data representing characteristics of pairs of vertices in the input graph; and generating order-invariant features of the input graph using a neural network, wherein the neural network comprises: a first subnetwork configured to generate a first alternative representation of the vertex input data and a first alternative representation of the pair input data from the vertex input data and the pair input data; and a combining layer configured to receive an input alternative representation and to process the input alternative representation to generate the order-invariant features.Type: GrantFiled: April 19, 2023Date of Patent: September 10, 2024Assignee: Google LLCInventors: Patrick F. Riley, Marc Berndl
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Publication number: 20240209378Abstract: This disclosure describes methods and compositions for protein and peptide sequencing.Type: ApplicationFiled: October 27, 2023Publication date: June 27, 2024Inventors: Annalisa Marie Pawlosky, Zachary Cutts, Shirley Jing Shao, Michelle Therese Hoerner Dimon, Marc Berndl, Alexander Julian Tran, Diana Terri Wu
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Patent number: 11926820Abstract: This disclosure describes methods and compositions for protein and peptide sequencing.Type: GrantFiled: September 11, 2020Date of Patent: March 12, 2024Assignee: Google LLCInventors: Annalisa Marie Pawlosky, Michael Gibbons, Shirley Jing Shao, Marc Berndl, Michelle Therese Hoerner Dimon, Ali Bashir, Lauren Schiff
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Patent number: 11915134Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.Type: GrantFiled: September 12, 2022Date of Patent: February 27, 2024Assignee: Google LLCInventors: Philip Charles Nelson, Eric Martin Christiansen, Marc Berndl, Michael Frumkin
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Publication number: 20240047010Abstract: A method is provided for determining a sample genome from a plurality of read fragments and a reference genome. The method includes: (i) applying a first putative variant event, selected from a set of candidate variant events, to the sample genome to update the sample genome; (ii) mapping the plurality of read fragments to the updated sample genome; (hi) based on the mapping of the plurality of read fragments to the updated sample genome, determining a first read mapping cost function; and (iv) based on the first read mapping cost function, retaining the updated sample genome and removing the first putative variant event from the set of candidate variant events.Type: ApplicationFiled: February 1, 2022Publication date: February 8, 2024Inventors: Ali BASHIR, Zahra SHAMSI, Wesley Wei QIAN, Tze Way Eugene IE, Jeffrey CHAN, Marc BERNDL, Martin MLADENOV, Lawrence Stephen LANSING
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Publication number: 20240006027Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for obtaining data defining a sequence for an aptamer, the aptamer comprising a string of nucleobases; encoding the data defining the sequence for the aptamer as a neural network input; and processing the neural network input using a neural network to generate an output that characterizes how strongly the aptamer binds to a particular target molecule, wherein the neural network has been configured through training to receive the data defining the sequence and to process the data to generate predicted outputs that characterize how strongly the aptamer binds to the particular target molecule.Type: ApplicationFiled: April 26, 2023Publication date: January 4, 2024Inventors: Michelle Therese Hoerner Dimon, Marc Berndl, Marc Adlai Coram, Brian Trippe, Patrick F. Riley, Philip Charles Nelson
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Patent number: 11834664Abstract: This disclosure describes methods and compositions for protein and peptide sequencing.Type: GrantFiled: September 11, 2020Date of Patent: December 5, 2023Assignee: Google LLCInventors: Annalisa Marie Pawlosky, Zachary Cutts, Shirley Jing Shao, Michelle Therese Hoerner Dimon, Marc Berndl, Alexander Julian Tran, Diana Terri Wu
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Patent number: 11834756Abstract: This disclosure describes methods and compositions for protein and peptide sequencing.Type: GrantFiled: September 11, 2020Date of Patent: December 5, 2023Assignee: Google LLCInventors: Annalisa Marie Pawlosky, Michael Gibbons, Sara Ahadi, Shirley Jing Shao, Anna Le, Ali Bashir, Marc Berndl, Michelle Therese Hoerner Dimon, Lauren Schiff
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Publication number: 20230332220Abstract: Contemporary gene sequencing techniques, including “Next Generation Sequencing” techniques, can include sequencing a plurality of fragments of a target polynucleotide. These fragment sequences are then used to determine a sequence for the target as a whole. This can include aligning the fragment sequences to each other anchor to a reference genome. However, the limitations of existing sequencing techniques, and the often repetitive or otherwise difficult-to-sequence structure of natural polynucleotides, means that it can be difficult and/or expensive to generate accurate sequences. Methods provided herein include inserting polynucleotide ‘barcodes’ into a target polynucleotide prior to fragmentation or other sequencing processes. These inserted barcodes can improve the accuracy of sequences generated for the target by adding ‘noise’ into the target, allowing subsequent sequencing techniques (e.g., alignment, stitching, etc.) to more accurately estimate the target-plus-barcodes sequence.Type: ApplicationFiled: October 1, 2020Publication date: October 19, 2023Inventors: Ali BASHIR, Marc BERNDL, Annalisa PAWLOSKY, Jun KIM
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Publication number: 20230332205Abstract: Contemporary gene sequencing techniques, including “Next Generation Sequencing” techniques, can include sequencing a plurality of fragments of a target polynucleotide. However, tire limitations of existing sequencing techniques, and the often repetitive or otherwise difficult-to-sequence structure of natural polynucleotides, means that it can be difficult and/or expensive to generate accurate sequences. Methods provided herein include inserting dual polynucleotide ‘barcodes,’ along with neighboring primer sequences, into a target polynucleotide prior to other sequencing processes. These inserted barcodes can improve the accuracy of sequences generated for the target by adding ‘noise’ into the target, allowing subsequent sequencing techniques (e.g., alignment, stitching, etc.) to more accurately estimate the target-plus-barcodes sequence.Type: ApplicationFiled: October 1, 2020Publication date: October 19, 2023Inventors: Ali BASHIR, Marc BERNDL, Annalisa PAWLOSKY, Jun KIM
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Publication number: 20230334293Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving graph data representing an input graph comprising a plurality of vertices connected by edges; generating, from the graph data, vertex input data representing characteristics of each vertex in the input graph and pair input data representing characteristics of pairs of vertices in the input graph; and generating order-invariant features of the input graph using a neural network, wherein the neural network comprises: a first subnetwork configured to generate a first alternative representation of the vertex input data and a first alternative representation of the pair input data from the vertex input data and the pair input data; and a combining layer configured to receive an input alternative representation and to process the input alternative representation to generate the order-invariant features.Type: ApplicationFiled: April 19, 2023Publication date: October 19, 2023Inventors: Patrick F. Riley, Marc Berndl
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Publication number: 20230193245Abstract: This disclosure provides methods and compositions for making and using a protein or peptide array.Type: ApplicationFiled: June 29, 2020Publication date: June 22, 2023Inventors: Asmamaw Wassie, Annalisa Marie Pawlosky, Mariya Chavarha, Phillip Jess, Marc Berndl
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Patent number: 11670400Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for obtaining data defining a sequence for an aptamer, the aptamer comprising a string of nucleobases; encoding the data defining the sequence for the aptamer as a neural network input; and processing the neural network input using a neural network to generate an output that characterizes how strongly the aptamer binds to a particular target molecule, wherein the neural network has been configured through training to receive the data defining the sequence and to process the data to generate predicted outputs that characterize how strongly the aptamer binds to the particular target molecule.Type: GrantFiled: January 24, 2020Date of Patent: June 6, 2023Assignee: Google LLCInventors: Michelle Therese Hoerner Dimon, Marc Berndl, Marc Adlai Coram, Brian Trippe, Patrick F. Riley, Philip Charles Nelson
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Patent number: 11663447Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving graph data representing an input graph comprising a plurality of vertices connected by edges; generating, from the graph data, vertex input data representing characteristics of each vertex in the input graph and pair input data representing characteristics of pairs of vertices in the input graph; and generating order-invariant features of the input graph using a neural network, wherein the neural network comprises: a first subnetwork configured to generate a first alternative representation of the vertex input data and a first alternative representation of the pair input data from the vertex input data and the pair input data; and a combining layer configured to receive an input alternative representation and to process the input alternative representation to generate the order-invariant features.Type: GrantFiled: August 24, 2021Date of Patent: May 30, 2023Assignee: Google LLCInventors: Patrick F. Riley, Marc Berndl
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Publication number: 20230114552Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.Type: ApplicationFiled: September 12, 2022Publication date: April 13, 2023Inventors: Philip Charles Nelson, Eric Martin Christiansen, Marc Berndl, Michael Frumkin
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Patent number: 11443190Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.Type: GrantFiled: June 18, 2020Date of Patent: September 13, 2022Assignee: Google LLCInventors: Philip Charles Nelson, Eric Martin Christiansen, Marc Berndl, Michael Frumkin
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Patent number: 11334770Abstract: The present disclosure relates to phenotype analysis of cellular image data using a deep metric network. One example embodiment includes a method. The method includes receiving a target image of a target biological cell having a target phenotype. The method also includes obtaining a semantic embedding associated with the target image. The semantic embedding is generated using a machine-learned, deep metric network model. Further, the method includes obtaining, for each of a plurality of candidate images of candidate biological cells each having a respective candidate phenotype, a semantic embedding associated with the respective candidate image. In addition, the method includes identifying, for each of the semantic embeddings, common morphological variations and reducing, for each of the semantic embeddings based on the identified common morphological variations, effects of nuisances. Even further, the method includes determining, by the computing device, a similarity score for each candidate image.Type: GrantFiled: August 3, 2020Date of Patent: May 17, 2022Assignee: Google LLCInventors: Dale M. Ando, Marc Berndl
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Patent number: 11205113Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving graph data representing an input graph comprising a plurality of vertices connected by edges; generating, from the graph data, vertex input data representing characteristics of each vertex in the input graph and pair input data representing characteristics of pairs of vertices in the input graph; and generating order-invariant features of the input graph using a neural network, wherein the neural network comprises: a first subnetwork configured to generate a first alternative representation of the vertex input data and a first alternative representation of the pair input data from the vertex input data and the pair input data; and a combining layer configured to receive an input alternative representation and to process the input alternative representation to generate the order-invariant features.Type: GrantFiled: July 29, 2019Date of Patent: December 21, 2021Assignee: Google LLCInventors: Patrick F. Riley, Marc Berndl
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Publication number: 20210383196Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving graph data representing an input graph comprising a plurality of vertices connected by edges; generating, from the graph data, vertex input data representing characteristics of each vertex in the input graph and pair input data representing characteristics of pairs of vertices in the input graph; and generating order-invariant features of the input graph using a neural network, wherein the neural network comprises: a first subnetwork configured to generate a first alternative representation of the vertex input data and a first alternative representation of the pair input data from the vertex input data and the pair input data; and a combining layer configured to receive an input alternative representation and to process the input alternative representation to generate the order-invariant features.Type: ApplicationFiled: August 24, 2021Publication date: December 9, 2021Inventors: Patrick F. Riley, Marc Berndl
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Publication number: 20210171937Abstract: This disclosure describes methods and compositions for protein and peptide sequencing.Type: ApplicationFiled: September 11, 2020Publication date: June 10, 2021Inventors: Annalisa Marie Pawlosky, Michael Gibbons, Shirley Jing Shao, Marc Berndl, Michelle Therese Hoerner Dimon, Ali Bashir