Patents by Inventor Aleksandr E. Petrov

Aleksandr E. Petrov 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: 20200394509
    Abstract: A method of training a neural network includes receiving a text corpus containing a labeled portion and an unlabeled portion, extracting local n-gram features and a sequence of the local n-gram features from the text corpus, processing the text corpus, using convolutional layers, according to the local n-gram features to determine capsule parameters of capsules configured to preserve the sequence of the local n-gram features, performing a forward-oriented dynamic routing between the capsules using the capsule parameters to extract global characteristics of the text corpus, and processing the text corpus according to the global characteristics using a long short-term memory layer to extract global sequential text dependencies from the text corpus, wherein parameters of the neural network are updated according to the local n-gram features, the capsule parameters, global characteristics, and global sequential text dependencies.
    Type: Application
    Filed: June 14, 2019
    Publication date: December 17, 2020
    Inventors: JOHN J. THOMAS, ALEKSANDR E. PETROV, WANTING WANG, MAXIME ALLARD
  • Patent number: 10740380
    Abstract: In a general purpose computer, a method of extracting snippets includes receiving textual content and a plurality of available topics, dividing the textual content into a plurality of snippets, converting each of the snippets to a vector, determining a distance between coadjacent snippets of the plurality of snippets in the textual content, determining an update to the plurality of snippets by merging each of the pairs of coadjacent snippets having a respective distance less than a second threshold, wherein an updated plurality of snippets includes merged snippets, generating a plurality of clusters from the updated plurality of snippets, each cluster associated with one topic selected from the plurality of available topics, and generating, for each of the snippets of the updated plurality of snippets, an affinity score for each of the clusters, each affinity score measuring an assignment strength of a given snippet to a given cluster, and a dominant topic among the at least one identified topic.
    Type: Grant
    Filed: May 24, 2018
    Date of Patent: August 11, 2020
    Assignee: International Business Machines Corporation
    Inventors: Ricardo Balduino, Avijit Chatterjee, Vinay R. Dandin, Aleksandr E. Petrov, John Thomas
  • Publication number: 20200097808
    Abstract: A computer-implemented mechanism is disclosed. The mechanism includes receiving a data signal, and comparing the data signal to one or more predefined patterns to determine one or more long/short term predictor scores. A discount factor is generated in response to the long/short term predictor scores. A set of expected rewards is generated. The set of expected rewards correspond to an action set specific to the data signal. The set of expected rewards are generated according to reinforced learning. The set of expected rewards are adjusted based on the discount factor. A selected action is selected from the action set based on the set of expected rewards. The selected action is initiated.
    Type: Application
    Filed: September 21, 2018
    Publication date: March 26, 2020
    Inventors: John J. Thomas, Aleksandr E. Petrov, Aishwarya Srinivasan, Avijit Chatterjee
  • Publication number: 20190362021
    Abstract: In a general purpose computer, a method of extracting snippets includes receiving textual content and a plurality of available topics, dividing the textual content into a plurality of snippets, converting each of the snippets to a vector, determining a distance between coadjacent snippets of the plurality of snippets in the textual content, determining an update to the plurality of snippets by merging each of the pairs of coadjacent snippets having a respective distance less than a second threshold, wherein an updated plurality of snippets includes merged snippets, generating a plurality of clusters from the updated plurality of snippets, each cluster associated with one topic selected from the plurality of available topics, and generating, for each of the snippets of the updated plurality of snippets, an affinity score for each of the clusters, each affinity score measuring an assignment strength of a given snippet to a given cluster, and a dominant topic among the at least one identified topic.
    Type: Application
    Filed: May 24, 2018
    Publication date: November 28, 2019
    Inventors: RICARDO BALDUINO, AVIJIT CHATTERJEE, VINAY R. DANDIN, ALEKSANDR E. PETROV, JOHN THOMAS
  • Patent number: 9836520
    Abstract: A method, system and computer-usable medium are disclosed for enhancing a classification system to include an automatic classification validation system. The automatic classification validation system takes the classification results and automatically validates them for correctness. More specifically, the automatic classification validation system analyzes the data objects in the categories and, if any outliers are identified, then determines a context of the data object from a plurality of records contained within a certain category to determine the context. The classification validation system then uses the context of the data object to validate the classification.
    Type: Grant
    Filed: February 12, 2014
    Date of Patent: December 5, 2017
    Assignee: International Business Machines Corporation
    Inventors: Romina M. J. Jose, Jason D. LaVoie, Kavita Patil, Aleksandr E. Petrov, Jeffrey R. Pratt, Edward T. Winchester, Kristin A. Witherspoon
  • Publication number: 20150227591
    Abstract: A method, system and computer-usable medium are disclosed for enhancing a classification system to include an automatic classification validation system. The automatic classification validation system takes the classification results and automatically validates them for correctness. More specifically, the automatic classification validation system analyzes the data objects in the categories and, if any outliers are identified, then determines a context of the data object from a plurality of records contained within a certain category to determine the context. The classification validation system then uses the context of the data object to validate the classification.
    Type: Application
    Filed: February 12, 2014
    Publication date: August 13, 2015
    Applicant: International Business Machines Corporation
    Inventors: Romina M. J. Jose, Jason D. LaVoie, Kavita Patil, Aleksandr E. Petrov, Jeffrey R. Pratt, Edward T. Winchester, Kristin A. Witherspoon