Patents by Inventor Paolo PASTORE
Paolo PASTORE 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: 11694770Abstract: Volatile organic compounds classification by receiving test data associated with detecting volatile organic compounds (VOCs), analyzing the test data according to a set of data features associated with known VOCs, determining a match between each feature of the test data and a corresponding feature of the set of data features, yielding a set of matches, defining a first degree of anomaly for the test data according to the set of matches, and classifying the test data according to the first degree of anomaly.Type: GrantFiled: October 26, 2020Date of Patent: July 4, 2023Assignee: International Business Machines CorporationInventors: Vito Paolo Pastore, Simone Bianco, Nimrod Megiddo, Andrea Fasoli, Aminat Adebiyi, Mohammed Abdi, Alberto Mannari, Luisa Dominica Bozano
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Patent number: 11681951Abstract: A method, a computer system, and a computer program product are provided for federated learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The cluster information may include centroid information per cluster. The aggregator may include a processor. The aggregator may integrate the cluster information to define data classes for machine learning classification. The integrating may include computing a respective distance between centroids of the clusters in order to determine a total number of the data classes. The aggregator may send a deep learning model that includes an output layer that has a total number of nodes equal to the total number of the data classes. The deep learning model may be for the distributed computing devices to perform machine learning classification in federated learning.Type: GrantFiled: August 8, 2022Date of Patent: June 20, 2023Assignee: International Business Machines CorporationInventors: Vito Paolo Pastore, Yi Zhou, Nathalie Baracaldo Angel, Ali Anwar, Simone Bianco
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Patent number: 11682111Abstract: A system and method that identify and classify unknown microorganisms and/or known microorganisms with anomalies are provided. The system and method comprise processing images of microorganisms from an aquatic environment; extracting features from the processed images; an unsupervised partitioning algorithm for identifying and classifying known microorganisms in the aquatic environment based upon the extracted features; and a supervised classifier neural network that is trained with the unsupervised partitioning algorithm and identifies and classifies unknown microorganisms and/or known microorganisms with anomalies.Type: GrantFiled: March 18, 2020Date of Patent: June 20, 2023Assignee: International Business Machines CorporationInventors: Vito Paolo Pastore, Simone Bianco
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Publication number: 20220383132Abstract: A method, a computer system, and a computer program product are provided for federated learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The cluster information may include centroid information per cluster. The aggregator may include a processor. The aggregator may integrate the cluster information to define data classes for machine learning classification. The integrating may include computing a respective distance between centroids of the clusters in order to determine a total number of the data classes. The aggregator may send a deep learning model that includes an output layer that has a total number of nodes equal to the total number of the data classes. The deep learning model may be for the distributed computing devices to perform machine learning classification in federated learning.Type: ApplicationFiled: August 8, 2022Publication date: December 1, 2022Inventors: Vito Paolo Pastore, Yi Zhou, Nathalie Baracaldo Angel, Ali Anwar, Simone Bianco
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Publication number: 20220367011Abstract: Provided is a deep learning algorithm that analyzes fragments of biological sequences. The input for the deep learning algorithm is a biological sequence fragment of unknown origin and the output is the closest known biological genome that could share phenotypic properties with the biological species of unknown origin. The workflow thus has application for genomic classification, identification of mutations within known genomes, and the identification of the closest class for unknown species.Type: ApplicationFiled: May 14, 2021Publication date: November 17, 2022Inventors: Vito Paolo Pastore, Mark Kunitomi, Simone Bianco
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Patent number: 11494700Abstract: A method, a computer system, and a computer program product are provided for federated learning enhanced with semantic learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The aggregator may integrate the cluster information to define classes. The integrating may include identifying any redundant clusters amongst the identified clusters. A number of the classes may correspond to a total number of the clusters from the distributed computing devices reduced by any redundant clusters. A deep learning model may be sent from the aggregator to the distributed computing devices. The deep learning model may include an output layer having nodes that may correspond to the defined classes. The aggregator may receive results of federated learning performed by the distributed computing devices. The federated learning may train the deep learning model.Type: GrantFiled: September 16, 2020Date of Patent: November 8, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Vito Paolo Pastore, Yi Zhou, Nathalie Baracaldo Angel, Ali Anwar, Simone Bianco
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Publication number: 20220276177Abstract: The present invention relates to a colorimetric sensor for measuring pH based on the H coordinate of the HSV color space.Type: ApplicationFiled: August 3, 2020Publication date: September 1, 2022Applicant: UNIVERSITÀ DEGLI STUDI DI PADOVAInventors: Luca CAPPELLIN, Paolo PASTORE, Denis BADOCCO, Andrea PASTORE
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Publication number: 20220262457Abstract: Provided is a data-driven deep-learning based algorithm for synthetic biology applications that makes no assumptions and/or hypotheses on genotype-phenotype interactions. deep-learning based algorithm trains a neural network with morphological features from single genetic modifications and tests the neural network with morphological features from multiple genetic modifications. The trained and tested neural network uses a link between the morphological features caused by the single and multiple gene modifications as input and outputs a genotype-phenotype mapping highlighting perturbation subspaces. The genotype-phenotype mapping is used to select one or more genetic insults as a starting point to engineer cells in synthetic biology applications.Type: ApplicationFiled: February 12, 2021Publication date: August 18, 2022Inventors: Vito Paolo Pastore, Simone Bianco, Wallace Marshall
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Publication number: 20220130491Abstract: Volatile organic compounds classification by receiving test data associated with detecting volatile organic compounds (VOCs), analyzing the test data according to a set of data features associated with known VOCs, determining a match between each feature of the test data and a corresponding feature of the set of data features, yielding a set of matches, defining a first degree of anomaly for the test data according to the set of matches, and classifying the test data according to the first degree of anomaly.Type: ApplicationFiled: October 26, 2020Publication date: April 28, 2022Inventors: Vito Paolo Pastore, Simone Bianco, Nimrod Megiddo, Andrea Fasoli, Aminat Adebiyi, Mohammed Abdi, Alberto Mannari, Luisa Dominica Bozano
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Publication number: 20220083904Abstract: A method, a computer system, and a computer program product are provided for federated learning enhanced with semantic learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The aggregator may integrate the cluster information to define classes. The integrating may include identifying any redundant clusters amongst the identified clusters. A number of the classes may correspond to a total number of the clusters from the distributed computing devices reduced by any redundant clusters. A deep learning model may be sent from the aggregator to the distributed computing devices. The deep learning model may include an output layer having nodes that may correspond to the defined classes. The aggregator may receive results of federated learning performed by the distributed computing devices. The federated learning may train the deep learning model.Type: ApplicationFiled: September 16, 2020Publication date: March 17, 2022Inventors: Vito Paolo Pastore, Yi Zhou, Nathalie Baracaldo Angel, Ali Anwar, Simone Bianco
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Publication number: 20210292805Abstract: A system and method that identify and classify unknown microorganisms and/or known microorganisms with anomalies are provided. The system and method comprise processing images of microorganisms from an aquatic environment; extracting features from the processed images; an unsupervised partitioning algorithm for identifying and classifying known microorganisms in the aquatic environment based upon the extracted features; and a supervised classifier neural network that is trained with the unsupervised partitioning algorithm and identifies and classifies unknown microorganisms and/or known microorganisms with anomalies.Type: ApplicationFiled: March 18, 2020Publication date: September 23, 2021Inventors: Vito Paolo Pastore, Simone Bianco