Patents by Inventor Vinicius Michel Gottin

Vinicius Michel Gottin 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).

  • Publication number: 20190303799
    Abstract: Techniques are provided for online anomaly detection using pairwise agreement in a heterogeneous model ensemble. An exemplary contextual model agreement network comprises nodes and transition edges between the nodes, where each node corresponds to a machine learning model and the transition edges between corresponding pairwise machine learning models encode a level of historical agreement between the pairwise machine learning models. In response to an availability of new data observations: features present in the data observations are extracted; a subset of the machine learning models is selected from the machine learning models based on the extracted features; the historical agreement between the selected machine learning models is compared with a current agreement of the selected machine learning models; and an anomaly is detected in the data observations based on the comparison. The contextual model agreement network is optionally updated based on new data observations.
    Type: Application
    Filed: March 30, 2018
    Publication date: October 3, 2019
    Inventors: Vinícius Michel Gottin, Tiago Salviano Calmon
  • Patent number: 10409931
    Abstract: Techniques are provided for automatic combination of sub-process simulation results with dataset selection based on a fitness under one or more specific scenarios. An exemplary method comprises obtaining an execution map for each sub-process in a sequence that stores results of a given sub-process execution. The results comprise a scenario, a distribution and a distribution fitness value. In response to a user query regarding a target feature and an initial dataset, initial dataset are combined with results selected from the execution map for a first sub-process in the sequence; each available dataset from the previous sub-processes in the sequence is combined with results selected from the execution map for the next sub-process; a probability distribution function (pdf) for the target feature is composed from a combined dataset that represents a simulation of the process and combines results of each of sub-process in the sequence; and the pdf is processed to answer the user query for the target feature.
    Type: Grant
    Filed: November 29, 2016
    Date of Patent: September 10, 2019
    Assignee: EMC IP Holding Company LLC
    Inventors: Vinícius Michel Gottin, Angelo E. M. Ciarlini, André de Almeida Maximo
  • Patent number: 10409817
    Abstract: Methods and apparatus are provided for domain-tailored detection of outliers, patterns, and/or events in data streams. An exemplary method comprises obtaining a domain-dependent definition of (i) data outliers based on predefined outlier criteria; (ii) data patterns based on predefined pattern criteria; and/or (iii) data events based on predefined event criteria; obtaining time series measurement data from a plurality of sensors; determining, substantially simultaneously with the obtaining, whether individual samples satisfy the domain-dependent definitions of the data outliers, data patterns and/or data events; and storing the individual samples with an indication of whether the individual samples satisfy the domain-dependent definitions of the data outliers, data patterns and/or data events. The domain-dependent definitions are optionally specified using a declarative command language.
    Type: Grant
    Filed: March 25, 2016
    Date of Patent: September 10, 2019
    Assignee: EMC Corporation
    Inventors: Jonas F. Dias, Diego Salomone Bruno, André de Almeida Maximo, Adriana Bechara Prado, Vinícius Michel Gottin, Monica Barros
  • Patent number: 10387588
    Abstract: Methods and apparatus are provided for automatic combination of sub-process simulation results and heterogeneous data sources. An exemplary method comprises obtaining, for a process comprised of a sequence of a plurality of sub-processes, an identification of relevant input and output features for each sub-process; obtaining an execution map for each sub-process, wherein each execution map stores results of an execution of a given sub-process; and, in response to a user query regarding a target feature and a user-provided initial scenario: composing a probability distribution function for the target feature representing a simulation of the process based on a sequence of the execution maps, by matching input features of each execution map with features from the initial scenario or the output of previous execution maps; and processing the probability distribution function to answer the user query.
    Type: Grant
    Filed: July 29, 2016
    Date of Patent: August 20, 2019
    Assignee: EMC Corporation
    Inventors: Vinicius Michel Gottin, Angelo E. M. Ciarlini, André de Almeida Maximo, Adriana Bechara Prado, Jaumir Valença da Silveira Junior
  • Patent number: 10339235
    Abstract: Methods and apparatus are provided for performing massively parallel processing (MPP) large-scale combinations of time series data.
    Type: Grant
    Filed: March 23, 2016
    Date of Patent: July 2, 2019
    Assignee: EMC Corporation
    Inventors: Angelo E. M. Ciarlini, Jonas F. Dias, André de Almeida Maximo, Vinícius Michel Gottin, Monica Barros
  • Patent number: 10324845
    Abstract: Techniques are provided for automatic placement of cache operations in a dataflow. An exemplary method obtains a graph representation of a dataflow of operations; determines a number of executions and a computational cost of the operations, and a computational cost of a caching operation to cache a dataset generated by an operation; establishes a dataflow state structure recording values for properties of the dataflow operations for a number of variations of caching various dataflow operations; determines a cache gain factor for dataflow operations as an estimated reduction in the accumulated cost of the dataflow by caching an output dataset of a given operation; determines changes in the dataflow state structure by caching an output dataset of a different operation in the dataflow; and searches the dataflow state structures to determine the output datasets to cache based on a total dataflow execution cost.
    Type: Grant
    Filed: July 28, 2017
    Date of Patent: June 18, 2019
    Assignee: EMC IP Holding Company LLC
    Inventors: Vinicius Michel Gottin, Edward José Pacheco Condori, Jonas F. Dias, Angelo E. M. Ciarlini, Bruno Carlos da Cunha Costa, Wagner dos Santos Vieira, Paulo de Figueiredo Pires, Fábio André Machado Porto, Yania Molina Souto