Patents by Inventor Aurelie C. Lozano
Aurelie C. Lozano 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: 10360527Abstract: A computing system initializes a first frontier to be a root of a multi-dimensional hierarchical data structure representing an entity. The system acquires first data corresponding to the first frontier. The system performs modeling on the first data to obtain a first model and a corresponding first statistic. The system expands a dimension of the first frontier. The system gathers second data corresponding to the expanded frontier. The system applies the data modeling on the second data to obtain a second model and a corresponding second statistic. The system compares the first statistic of the first model and the second statistic of the second model. The system sets the second model to be the first model in response to determining that the second model statistic is better than the first model statistic. The system outputs the first model.Type: GrantFiled: November 10, 2010Date of Patent: July 23, 2019Assignee: International Business Machines CorporationInventors: Naoki Abe, Jing Fu, Michael G. Gemmell, Shubir Kapoor, Floyd S. Kelly, David M. Loehr, Aurelie C. Lozano, Shilpa N. Mahatma, Bonnie K. Ray
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Patent number: 8645304Abstract: Structural changes in causal relationship over time may be detected, for example, by a Markov switching vector autoregressive model that detects and infers the structural changes in the causal graphs.Type: GrantFiled: August 19, 2011Date of Patent: February 4, 2014Assignee: International Business Machines CorporationInventors: Huijing Jiang, Fei Liu, Aurelie C. Lozano
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Patent number: 8626680Abstract: In response to issues of high dimensionality and sparsity in machine learning, it is proposed to use a multiple output regression modeling module that takes into account information on groups of related predictor features and groups of related regressions, both given as input, and outputs a regression model with selected feature groups. Optionally, the method can be employed as a component in methods of causal influence detection, which are applied on a time series training data set representing the time-evolving content generated by community members, output a model of causal relationships and a ranking of the members according to their influence.Type: GrantFiled: December 3, 2010Date of Patent: January 7, 2014Assignee: International Business Machines CorporationInventors: Aurelie C. Lozano, Vikas Sindhwani
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Patent number: 8543533Abstract: A computer model finds an originating community member or members, for instance amongst bloggers, scientific researchers, or phenomena in phenomenological systems. In one embodiment, non-explicit causal relationships may be inferred from comparing blogs as a whole. Influence relationships are derived from a weighted, directed graph output and sources of influence are ranked. This implementation is useful for a variety of applications.Type: GrantFiled: December 3, 2010Date of Patent: September 24, 2013Assignee: International Business Machines CorporationInventors: Aurelie C. Lozano, Vikas Sindhwani
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Publication number: 20130046721Abstract: Structural changes in causal relationship over time may be detected, for example, by a Markov switching vector autoregressive model that detects and infers the structural changes in the causal graphs.Type: ApplicationFiled: August 19, 2011Publication date: February 21, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Huijing Jiang, Fei Liu, Aurelie C. Lozano
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Patent number: 8275721Abstract: Multi-class cost-sensitive boosting based on gradient boosting with “p-norm” cost functionals” uses iterative example weighting schemes derived with respect to cost functionals, and a binary classification algorithm. Weighted sampling is iteratively applied from an expanded data set obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, and where each non-optimally labeled example is given the weight equaling a half times the original misclassification cost for the labeled example times the p?1 norm of the average prediction of the current hypotheses. Each optimally labeled example is given the weight equaling the sum of the weights for all the non-optimally labeled examples for the same instance. Component classification algorithm is executed on a modified binary classification problem. A classifier hypothesis is output, which is the average of all the hypotheses output in the respective iterations.Type: GrantFiled: August 12, 2008Date of Patent: September 25, 2012Assignee: International Business Machines CorporationInventors: Naoki Abe, Aurelie C. Lozano
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Patent number: 8255346Abstract: A “variable group selection” system and method in which constructs are based upon a training data set, a regression modeling module that takes into account information on groups of related predictor variables given as input and outputs a regression model with selected variable groups. Optionally, the method can be employed as a component in methods of temporal causal modeling, which are applied on a time series training data set, and output a model of causal relationships between the multiple times series in the data.Type: GrantFiled: November 11, 2009Date of Patent: August 28, 2012Assignee: International Business Machines CorporationInventors: Naoki Abe, Yan Liu, Aurelie C. Lozano, Saharon Rosset, Grzegorz Swirszcz
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Publication number: 20120143815Abstract: A computer model finds an originating community member or members, for instance amongst bloggers, scientific researchers, or phenomena in phenomenological systems. In one embodiment, non-explicit causal relationships may be inferred from comparing blogs as a whole. Influence relationships are derived from a weighted, directed graph output and sources of influence are ranked. This implementation is useful for a variety of applications.Type: ApplicationFiled: December 3, 2010Publication date: June 7, 2012Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Aurelie C. Lozano, Vikas Sindhwani
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Publication number: 20120143796Abstract: In response to issues of high dimensionality and sparsity in machine learning, it is proposed to use a multiple output regression modeling module that takes into account information on groups of related predictor features and groups of related regressions, both given as input, and outputs a regression model with selected feature groups. Optionally, the method can be employed as a component in methods of causal influence detection, which are applied on a time series training data set representing the time-evolving content generated by community members, output a model of causal relationships and a ranking of the members according to their influence.Type: ApplicationFiled: December 3, 2010Publication date: June 7, 2012Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Aurelie C. Lozano, Vikas Sindhwani
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Publication number: 20120116850Abstract: A computing system initializes a first frontier to be a root of a multi-dimensional hierarchical data structure representing an entity. The system acquires first data corresponding to the first frontier. The system performs modeling on the first data to obtain a first model and a corresponding first statistic. The system expands a dimension of the first frontier. The system gathers second data corresponding to the expanded frontier. The system applies the data modeling on the second data to obtain a second model and a corresponding second statistic. The system compares the first statistic of the first model and the second statistic of the second model. The system sets the second model to be the first model in response to determining that the second model statistic is better than the first model statistic. The system outputs the first model.Type: ApplicationFiled: November 10, 2010Publication date: May 10, 2012Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Naoki Abe, Jing Fu, Michael G. Gemmell, Shubir Kapoor, Floyd S. Kelly, David M. Loehr, Aurelie C. Lozano, Shilpa N. Mahatma, Bonnie K. Ray
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Publication number: 20110112998Abstract: A “variable group selection” system and method in which constructs are based upon a training data set, a regression modeling module that takes into account information on groups of related predictor variables given as input and outputs a regression model with selected variable groups. Optionally, the method can be employed as a component in methods of temporal causal modeling, which are applied on a time series training data set, and output a model of causal relationships between the multiple times series in the data.Type: ApplicationFiled: November 11, 2009Publication date: May 12, 2011Applicant: International Business Machines CorporationInventors: Naoki Abe, Yan Liu, Aurelie C. Lozano, Saharon Rosset, Grzegorz Swirszcz
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Publication number: 20100042561Abstract: Multi-class cost-sensitive boosting based on gradient boosting with “p-norm” cost functionals” uses iterative example weighting schemes derived with respect to cost functionals, and a binary classification algorithm. Weighted sampling is iteratively applied from an expanded data set obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, and where each non-optimally labeled example is given the weight equaling a half times the original misclassification cost for the labeled example times the p?1 norm of the average prediction of the current hypotheses. Each optimally labeled example is given the weight equaling the sum of the weights for all the non-optimally labeled examples for the same instance. Component classification algorithm is executed on a modified binary classification problem. A classifier hypothesis is output, which is the average of all the hypotheses output in the respective iterations.Type: ApplicationFiled: August 12, 2008Publication date: February 18, 2010Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Naoki Abe, Aurelie C. Lozano