Patents by Inventor Richard D. Lawrence
Richard D. Lawrence 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: 20240150676Abstract: Methods employing detergent compositions effective for reducing hard water scale and accumulation on hard surfaces, namely within food, beverage and pharmaceutical applications are disclosed. The detergent compositions employ phosphinosuccinic acid adducts in combination with an alkalinity source and optionally polymers, surfactants and/or oxidizers, providing alkaline compositions having a pH between about 10 and 13.5.Type: ApplicationFiled: November 21, 2023Publication date: May 9, 2024Inventors: Carter M. Silvernail, Erik C. Olson, Michel M. Lawrence, Richard D. Johnson, Steven J. Lange
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Patent number: 11952578Abstract: The disclosure provides novel corn, tomato, and soybean U6, U3, U2, U5, and 7SL snRNA promoters which are useful for CRISPR/Cas-mediated targeted gene modifications in plants. The disclosure also provides methods for use for U6, U3, U2, U5, and 7SL promoters in driving expression of sgRNA polynucleotides which function in a CRISPR/Cas system of targeted gene modification in plants. The disclosure also provides methods of genome modification by insertion of blunt-end DNA fragments at a site of genomic cleavage.Type: GrantFiled: December 26, 2022Date of Patent: April 9, 2024Assignee: Monsanto Technology LLCInventors: Brent Brower-Toland, Andrei Y. Kouranov, Rosemarie Kuehn, Richard J. Lawrence, Ervin D. Nagy, Linda Rymarquis, Veena Veena
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Patent number: 11100411Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.Type: GrantFiled: May 25, 2017Date of Patent: August 24, 2021Assignee: International Business Machines CorporationInventors: Richard D. Lawrence, Estepan Meliksetian, Prem Melville, Claudia Perlich, Karthik Subbian
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Publication number: 20170262759Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.Type: ApplicationFiled: May 25, 2017Publication date: September 14, 2017Inventors: Richard D. Lawrence, Estepan Meliksetian, Prem Melville, Claudia Perlich, Karthik Subbian
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Patent number: 9684868Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.Type: GrantFiled: May 7, 2015Date of Patent: June 20, 2017Assignee: International Business Machines CorporationInventors: Richard D. Lawrence, Estepan Meliksetian, Prem Melville, Claudia Perlich, Karthik Subbian
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Patent number: 9477929Abstract: Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. A graph-based transfer learning framework propagates label information from a source domain to a target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bipartite graph. An iterative algorithm renders the framework scalable to large-scale applications. The framework propagates the label information to both features irrelevant to the source domain and unlabeled examples in the target domain via common features in a principled way.Type: GrantFiled: September 14, 2012Date of Patent: October 25, 2016Assignee: International Business Machines CorporationInventors: Jingrui He, Richard D. Lawrence, Yan Liu
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Publication number: 20150235137Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.Type: ApplicationFiled: May 7, 2015Publication date: August 20, 2015Inventors: Richard D. Lawrence, Estepan Meliksetian, Prem Melville, Claudia Perlich, Karthik Subbian
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Patent number: 9031888Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.Type: GrantFiled: August 10, 2011Date of Patent: May 12, 2015Assignee: International Business Machines CorporationInventors: Richard D. Lawrence, Estepan Meliksetian, Prem Melville, Claudia Perlich, Karthik Subbian
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Patent number: 8990128Abstract: A system and method a Multi-Task Multi-View (M2TV) learning problem. The method uses the label information from related tasks to make up for the lack of labeled data in a single task. The method further uses the consistency among different views to improve the performance. It is tailored for the above complicated dual heterogeneous problems where multiple related tasks have both shared and task-specific views (features), since it makes full use of the available information.Type: GrantFiled: June 5, 2012Date of Patent: March 24, 2015Assignee: International Business Machines CorporationInventors: Jingrui He, David C. Gondek, Richard D. Lawrence, Enara C. Vijil
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Patent number: 8909643Abstract: A method, system and computer program product for inferring topic evolution and emergence in a set of documents. In one embodiment, the method comprises forming a group of matrices using text in the documents, and analyzing these matrices to identify a first group of topics as evolving topics and a second group of topics as emerging topics. The matrices includes a first matrix X identifying a multitude of words in each of the documents, a second matrix W identifying a multitude of topics in each of the documents, and a third matrix H identifying a multitude of words for each of the multitude of topics. These matrices are analyzed to identify the evolving and emerging topics. In an embodiment, the documents form a streaming dataset, and two forms of temporal regularizers are used to help identify the evolving topics and the emerging topics in the streaming dataset.Type: GrantFiled: December 9, 2011Date of Patent: December 9, 2014Assignee: International Business Machines CorporationInventors: Saha Ankan, Arindam Banerjee, Shiva P. Kasiviswanathan, Richard D. Lawrence, Prem Melville, Vikas Sindhwani, Edison L. Ting
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Patent number: 8856050Abstract: A novel domain adaption/transfer learning method applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain.Type: GrantFiled: January 13, 2011Date of Patent: October 7, 2014Assignee: International Business Machines CorporationInventors: Vijil E. Chenthamarakshan, Richard D. Lawrence, Yan Liu, Dan Zhang
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Patent number: 8856052Abstract: A novel domain adaption/transfer learning method applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain.Type: GrantFiled: September 14, 2012Date of Patent: October 7, 2014Assignee: International Business Machines CorporationInventors: Vijil E. Chenthamarakshan, Richard D. Lawrence, Yan Liu, Dan Zhang
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Publication number: 20130325756Abstract: A system and method a Multi-Task Multi-View (M2TV) learning problem. The method uses the label information from related tasks to make up for the lack of labeled data in a single task. The method further uses the consistency among different views to improve the performance. It is tailored for the above complicated dual heterogeneous problems where multiple related tasks have both shared and task-specific views (features), since it makes full use of the available information.Type: ApplicationFiled: June 5, 2012Publication date: December 5, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Jingrui He, David C. Gondek, Richard D. Lawrence, Enara C. Vijil
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Patent number: 8583639Abstract: A method for automatically determining an Internet home page corresponding to a named entity identified by a specified descriptor including building a trained machine-learning model, generating candidate matches from the specified descriptor, wherein each candidate match includes an Internet address, extracting content-based features from websites associated with the Internet addresses of the candidate matches, determining a model score for each candidate match based on the content-based features using the trained machine-learning model, and determining a match from among the candidate matches according to the scores, wherein the match is returned as the Internet home page corresponding to the named entity.Type: GrantFiled: February 19, 2008Date of Patent: November 12, 2013Assignee: International Business Machines CorporationInventors: Upendra Chitnis, Wojciech Gryc, Ildar Khabibrakhmanov, Richard D. Lawrence, Prem Melville, Cezar Pendus
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Publication number: 20130151525Abstract: A method, system and computer program product for inferring topic evolution and emergence in a set of documents. In one embodiment, the method comprises forming a group of matrices using text in the documents, and analyzing these matrices to identify evolving topics and emerging topics. The matrices includes a matrix X identifying a multitude of words in each of the documents, a matrix W identifying a multitude of topics in each of the documents, and a matrix H identifying a multitude of words for each of the multitude of topics. These matrices are analyzed to identify the evolving and emerging topics. In an embodiment, two forms of temporal regularizers are used to help identify the evolving and emerging topics. In another embodiment, a two stage approach involving detection and clustering is used to help identify the evolving and emerging topics.Type: ApplicationFiled: September 14, 2012Publication date: June 13, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Saha Ankan, Arindam Banerjee, Shiva P. Kasiviswanathan, Richard D. Lawrence, Prem Melville, Vikas Sindhwani, Edison L. Ting
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Publication number: 20130151520Abstract: A method, system and computer program product for inferring topic evolution and emergence in a set of documents. In one embodiment, the method comprises forming a group of matrices using text in the documents, and analyzing these matrices to identify a first group of topics as evolving topics and a second group of topics as emerging topics. The matrices includes a first matrix X identifying a multitude of words in each of the documents, a second matrix W identifying a multitude of topics in each of the documents, and a third matrix H identifying a multitude of words for each of the multitude of topics. These matrices are analyzed to identify the evolving and emerging topics. In an embodiment, the documents form a streaming dataset, and two forms of temporal regularizers are used to help identify the evolving topics and the emerging topics in the streaming dataset.Type: ApplicationFiled: December 9, 2011Publication date: June 13, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Saha Ankan, Arindam Banerjee, Shiva P. Kasiviswanathan, Richard D. Lawrence, Prem Melville, Vikas Sindhwani, Edison L. Ting
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Publication number: 20130041860Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.Type: ApplicationFiled: August 10, 2011Publication date: February 14, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Richard D. Lawrence, Estepan Meliksetian, Prem Melville, Claudia Perlich, Karthik Subbian
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Publication number: 20130018828Abstract: A first mapping function automatically maps a plurality of documents each with a concept of ontology to create a documents-to-ontology distribution. An ontology-to-class distribution that maps concepts in the ontology to class labels, respectively, is received, and a classifier is generated that labels a selected document with an associated class identified based on the documents-to-ontology distribution and the ontology-to-class distribution.Type: ApplicationFiled: September 14, 2012Publication date: January 17, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Jingrui He, Richard D. Lawrence, Prem Melville, Vikas Sindhwani, Vijil E. Chenthamarakshan
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Publication number: 20130018827Abstract: A first mapping function automatically maps a plurality of documents each with a concept of ontology to create a documents-to-ontology distribution. An ontology-to-class distribution that maps concepts in the ontology to class labels, respectively, is received, and a classifier is generated that labels a selected document with an associated class identified based on the documents-to-ontology distribution and the ontology-to-class distribution.Type: ApplicationFiled: July 15, 2011Publication date: January 17, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Jingrui He, Richard D. Lawrence, Prem Melville, Vikas Sindhwani, Vijil E. Chenthamarakshan
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Publication number: 20130013540Abstract: Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. A graph-based transfer learning framework propagates label information from a source domain to a target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bipartite graph. An iterative algorithm renders the framework scalable to large-scale applications. The framework propagates the label information to both features irrelevant to the source domain and unlabeled examples in the target domain via common features in a principled way.Type: ApplicationFiled: September 14, 2012Publication date: January 10, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Jingrui He, Richard D. Lawrence, Yan Liu