Patents by Inventor Konstantin Vladimirovich Anisimovich

Konstantin Vladimirovich Anisimovich 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).

  • Differential classification using multiple neural networks
    Patent number: 11157779
    Abstract: A classification engine generates, using a weighted graph, a plurality of sets of confused graphemes based on recognition data for a plurality of document images; receives an input grapheme image associated with a document image comprising a plurality of grapheme images; determines a set of recognition options for the input grapheme image, where the set of recognition options comprises a set of target characters that are similar to the input grapheme image; identifies a neural network trained to recognize a first set of confused graphemes, where the first set of confused graphemes comprises at least a portion of the set of recognition options for the input grapheme image; and determines a grapheme class for the input grapheme image using the identified neural network.
    Type: Grant
    Filed: February 14, 2020
    Date of Patent: October 26, 2021
    Assignee: ABBYY Production LLC
    Inventors: Aleksey Alekseevich Zhuravlev, Vladimir Rybkin, Konstantin Vladimirovich Anisimovich, Azat Aydarovich Davletshin
  • Using autoencoders for training natural language text classifiers
    Patent number: 11087093
    Abstract: Systems and methods for using autoencoders for training natural language classifiers. An example method comprises: producing, by a computer system, a plurality of feature vectors, wherein each feature vector represents a natural language text of a text corpus, wherein the text corpus comprises a first plurality of annotated natural language texts and a second plurality of un-annotated natural language texts; training, using the plurality of feature vectors, an autoencoder represented by an artificial neural network; producing, by the autoencoder, an output of the hidden layer, by processing a training data set comprising the first plurality of annotated natural language texts; and training, using the training data set, a text classifier that accepts an input vector comprising the output of the hidden layer and yields a degree of association, with a certain text category, of a natural language text utilized to produce the output of the hidden layer.
    Type: Grant
    Filed: October 11, 2019
    Date of Patent: August 10, 2021
    Assignee: ABBYY Production LLC
    Inventors: Konstantin Vladimirovich Anisimovich, Evgenii Mikhailovich Indenbom, Ivan Ivanovich Ivashnev
  • USING AUTOENCODERS FOR TRAINING NATURAL LANGUAGE TEXT CLASSIFIERS
    Publication number: 20200042600
    Abstract: Systems and methods for using autoencoders for training natural language classifiers. An example method comprises: producing, by a computer system, a plurality of feature vectors, wherein each feature vector represents a natural language text of a text corpus, wherein the text corpus comprises a first plurality of annotated natural language texts and a second plurality of un-annotated natural language texts; training, using the plurality of feature vectors, an autoencoder represented by an artificial neural network; producing, by the autoencoder, an output of the hidden layer, by processing a training data set comprising the first plurality of annotated natural language texts; and training, using the training data set, a text classifier that accepts an input vector comprising the output of the hidden layer and yields a degree of association, with a certain text category, of a natural language text utilized to produce the output of the hidden layer.
    Type: Application
    Filed: October 11, 2019
    Publication date: February 6, 2020
    Inventors: Konstantin Vladimirovich Anisimovich, Evgenii Mikhailovich Indenbom, Ivan Ivanovich Ivashnev
  • Using autoencoders for training natural language text classifiers
    Patent number: 10474756
    Abstract: Systems and methods for using autoencoders for training natural language classifiers. An example method comprises: producing, by a computer system, a plurality of feature vectors, wherein each feature vector represents a natural language text of a text corpus, wherein the text corpus comprises a first plurality of annotated natural language texts and a second plurality of un-annotated natural language texts; training, using the plurality of feature vectors, an autoencoder represented by an artificial neural network; producing, by the autoencoder, an output of the hidden layer, by processing a training data set comprising the first plurality of annotated natural language texts; and training, using the training data set, a text classifier that accepts an input vector comprising the output of the hidden layer and yields a degree of association, with a certain text category, of a natural language text utilized to produce the output of the hidden layer.
    Type: Grant
    Filed: December 22, 2017
    Date of Patent: November 12, 2019
    Assignee: ABBYY Production LLC
    Inventors: Konstantin Vladimirovich Anisimovich, Evgenii Mikhailovich Indenbom, Ivan Ivanovich Ivashnev
  • USING AUTOENCODERS FOR TRAINING NATURAL LANGUAGE TEXT CLASSIFIERS
    Publication number: 20190179896
    Abstract: Systems and methods for using autoencoders for training natural language classifiers. An example method comprises: producing, by a computer system, a plurality of feature vectors, wherein each feature vector represents a natural language text of a text corpus, wherein the text corpus comprises a first plurality of annotated natural language texts and a second plurality of un-annotated natural language texts; training, using the plurality of feature vectors, an autoencoder represented by an artificial neural network; producing, by the autoencoder, an output of the hidden layer, by processing a training data set comprising the first plurality of annotated natural language texts; and training, using the training data set, a text classifier that accepts an input vector comprising the output of the hidden layer and yields a degree of association, with a certain text category, of a natural language text utilized to produce the output of the hidden layer.
    Type: Application
    Filed: December 22, 2017
    Publication date: June 13, 2019
    Inventors: Konstantin Vladimirovich Anisimovich, Evgenii Mikhailovich Indenbom, Ivan Ivanovich Ivashnev
  • Multi-stage recognition of named entities in natural language text based on morphological and semantic features
    Patent number: 10007658
    Abstract: Systems and methods for multi-stage recognition of named entities based on morphological and semantic features of natural language texts.
    Type: Grant
    Filed: June 23, 2016
    Date of Patent: June 26, 2018
    Assignee: ABBYY PRODUCTION LLC
    Inventors: Konstantin Vladimirovich Anisimovich, Evgeny Mihaylovich Indenbom, Valery Igorevich Novitskiy
  • PRODUCING TRAINING SETS FOR MACHINE LEARNING METHODS BY PERFORMING DEEP SEMANTIC ANALYSIS OF NATURAL LANGUAGE TEXTS
    Publication number: 20180113856
    Abstract: Systems and methods for producing training sets for machine learning methods by performing deep semantic analysis of natural language texts.
    Type: Application
    Filed: December 6, 2016
    Publication date: April 26, 2018
    Inventors: Konstantin Vladimirovich Anisimovich, Vladimir Pavlovich Selegey, Ruslan Victorovich Garashchuk
  • MULTI-STAGE RECOGNITION OF NAMED ENTITIES IN NATURAL LANGUAGE TEXT BASED ON MORPHOLOGICAL AND SEMANTIC FEATURES
    Publication number: 20170364503
    Abstract: Systems and methods for multi-stage recognition of named entities based on morphological and semantic features of natural language texts.
    Type: Application
    Filed: June 23, 2016
    Publication date: December 21, 2017
    Inventors: Konstantin Vladimirovich Anisimovich, Evgeny Mihaylovich Indenbom, Valery Igorevich Novitskiy