Patents by Inventor Umut Eser
Umut Eser 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: 11971963Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.Type: GrantFiled: May 8, 2019Date of Patent: April 30, 2024Assignee: Quantum-Si IncorporatedInventors: Umut Eser, Michael Meyer
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Patent number: 11967436Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.Type: GrantFiled: May 8, 2019Date of Patent: April 23, 2024Assignee: Quantum-Si IncorporatedInventors: Marylens Hernandez, Umut Eser, Michael Meyer, Henri Lichenstein, Tian Xu, Jonathan M. Rothberg
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Patent number: 11875267Abstract: Techniques for performing a prediction task using a multi-modal statistical model configured to receive input data from multiple modalities including input data from a first modality and input data from a second modality different from the first modality.Type: GrantFiled: October 17, 2022Date of Patent: January 16, 2024Assignee: Quantum-Si IncorporatedInventors: Jonathan M. Rothberg, Umut Eser, Michael Meyer
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Publication number: 20230207062Abstract: A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.Type: ApplicationFiled: December 15, 2022Publication date: June 29, 2023Applicant: Quantum-Si IncorporatedInventors: Jonathan M. Rothberg, Michael Meyer, Umut Eser
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Publication number: 20230052677Abstract: Systems and methods of discovering compounds with biological properties are provided. A first training dataset is obtained, including chemical structures and biological properties. Projections of compounds are obtained by projecting chemical structure information into a latent representation space using encoder weights. Compounds are classified by inputting projections into the classifier using classifier weights. The encoder and classifier are trained by comparing the classification of each compound to actual biological properties and updating the respective weights. A second training dataset is obtained including chemical structures. Projections of compounds are obtained by projecting chemical structure information into a latent representation space using encoder weights. Chemical structures are obtained by inputting projections into a decoder using decoder weights. The decoder is trained by comparing outputted and actual chemical structures and updating the respective weights.Type: ApplicationFiled: January 14, 2021Publication date: February 16, 2023Inventors: Umut ESER, Fabian Alexander WOLF, Nicholas McCartney PLUGIS
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Publication number: 20230039210Abstract: Techniques for performing a prediction task using a multi-modal statistical model configured to receive input data from multiple modalities including input data from a first modality and input data from a second modality different from the first modality.Type: ApplicationFiled: October 17, 2022Publication date: February 9, 2023Applicant: Quantum-Si IncorporatedInventors: Jonathan M. Rothberg, Umut Eser, Michael Meyer
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Patent number: 11538556Abstract: A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.Type: GrantFiled: January 25, 2019Date of Patent: December 27, 2022Assignee: Quantum-Si IncorporatedInventors: Jonathan M. Rothberg, Michael Meyer, Umut Eser
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Publication number: 20220403335Abstract: Systems and methods for associating a compound with physiological conditions are provided. A fingerprint of a compound chemical structure is obtained and inputted to a model that outputs one or more calculated activation scores. Each activation score represents a cellular constituent module in a set of modules, where each module includes a subset of cellular constituents and a first module in the set of modules is associated with the physiological condition. When the activation score for the first module satisfies a threshold criterion, the compound is identified as associated with the physiological condition. In some aspects, each activation score represents a perturbation signature associated with the physiological condition and the compound is identified when the activation score for a first perturbation signature satisfies a threshold criterion. Systems and methods for training a model that associates compounds with physiological conditions are also provided.Type: ApplicationFiled: June 15, 2022Publication date: December 22, 2022Inventors: Fabian Alexander WOLF, Umut ESER, Nicholas McCartney PLUGIS
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Publication number: 20220399084Abstract: Methods of associating a test compound with a compound property. One or more datasets including: for each of a plurality of cell lines and each of a plurality of compounds: for each respective exposure condition: a corresponding response signature for the respective compound in the respective cell line under the respective exposure condition is obtained. A correlation is determined for each unique combination of exposure conditions for a respective pair of compounds based on the corresponding response signature. A weight is determined for each respective pair of compounds based on the determined correlations. A plurality of compound clusters is formed, where each cluster represents compounds that satisfy one or more weight criteria with respect to a particular compound. A compound property of the test compound is determined from properties of one or more compounds in a one or more compound clusters that contain the test compound.Type: ApplicationFiled: June 15, 2022Publication date: December 15, 2022Inventors: Fabian Alexander WOLF, Umut ESER, Nicholas McCartney PLUGIS
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Patent number: 11494589Abstract: Techniques for performing a prediction task using a multi-modal statistical model configured to receive input data from multiple modalities including input data from a first modality and input data from a second modality different from the first modality.Type: GrantFiled: March 8, 2021Date of Patent: November 8, 2022Assignee: Quantum-Si IncorporatedInventors: Jonathan M. Rothberg, Umut Eser, Michael Meyer
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Publication number: 20210192290Abstract: Techniques for performing a prediction task using a multi-modal statistical model configured to receive input data from multiple modalities including input data from a first modality and input data from a second modality different from the first modality.Type: ApplicationFiled: March 8, 2021Publication date: June 24, 2021Applicant: Quantum-Si IncorporatedInventors: Jonathan M. Rothberg, Umut Eser, Michael Meyer
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Patent number: 10956787Abstract: Techniques for performing a prediction task using a multi-modal statistical model configured to receive input data from multiple modalities including input data from a first modality and input data from a second modality different from the first modality.Type: GrantFiled: May 8, 2019Date of Patent: March 23, 2021Assignee: Quantum-Si IncorporatedInventors: Jonathan M. Rothberg, Umut Eser, Michael Meyer
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Publication number: 20200350081Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.Type: ApplicationFiled: May 8, 2019Publication date: November 5, 2020Applicant: Quantum-Si IncorporatedInventors: Marylens Hernandez, Umut Eser, Michael Meyer, Henri Lichenstein, Tian Xu, Jonathan M. Rothberg
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Publication number: 20190371476Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.Type: ApplicationFiled: May 8, 2019Publication date: December 5, 2019Applicant: Quantum-Si IncorporatedInventors: Marylens Hernandez, Umut Eser, Michael Meyer, Henri Lichenstein, Tian Xu, Jonathan M. Rothberg
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Publication number: 20190370616Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.Type: ApplicationFiled: May 8, 2019Publication date: December 5, 2019Applicant: Quantum-Si IncorporatedInventors: Umut Eser, Michael Meyer
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Publication number: 20190347523Abstract: Techniques for performing a prediction task using a multi-modal statistical model configured to receive input data from multiple modalities including input data from a first modality and input data from a second modality different from the first modality.Type: ApplicationFiled: May 8, 2019Publication date: November 14, 2019Applicant: Quantum-Si IncorporatedInventors: Jonathan M. Rothberg, Umut Eser, Michael Meyer
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Publication number: 20190237160Abstract: A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.Type: ApplicationFiled: January 25, 2019Publication date: August 1, 2019Applicant: Quantum-Si IncorporatedInventors: Jonathan M. Rothberg, Michael Meyer, Umut Eser