Patents by Inventor Amir Kantor
Amir Kantor 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: 11526667Abstract: Embodiments of the present systems and methods may provide techniques for augmenting textual data that may be used for textual classification tasks. Embodiments of such techniques may provide the capability to synthesize labeled data to improve text classification tasks. Embodiments may be specifically useful when only a small amount of data is available, and provide improved performance in such cases. For example, in an embodiment, a method implemented in a computer system may comprise a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise fine-tuning a language model using a training dataset, synthesizing a plurality of samples using the fine-tuned language model, filtering the plurality of synthesized samples, and generating an augmented training dataset comprising the training dataset and the filtered plurality of synthesized sentences.Type: GrantFiled: May 9, 2020Date of Patent: December 13, 2022Assignee: International Business Machines CorporationInventors: Amir Kantor, Ateret Anaby Tavor, Boaz Carmeli, Esther Goldbraich, George Kour, Segev Shlomov, Naama Tepper, Naama Zwerdling
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Patent number: 11455981Abstract: A method, apparatus, and system are provided for resolving conflicts between training data conflicts by retrieving independent training data sets, each comprising a plurality of intents and end-user utterances for use in training one or more classifiers to recognize a corresponding intent from one or more of the end-user utterances, providing a first test end-user utterance associated with a first intent from the first independent training data set to the one or more classifiers to select an output intent generated by the one or more classifiers; identifying a first conflict when the first intent does not match the output intent, and automatically generating, by the system, one or more conflict resolution recommendations for display and selection by an end user to resolve the first conflict.Type: GrantFiled: January 15, 2020Date of Patent: September 27, 2022Assignee: International Business Machines CorporationInventors: David Amid, David Boaz, Tin Kam Ho, Amir Kantor, Luis A. Lastras-Montano, Neil R. Mallinar
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Patent number: 11281867Abstract: An example system includes a processor to receive data for a multi-objective task. The processor is to also perform the multi-objective task on the received data via a trained primal network. The primal network and a dual network are trained for a multi-objective task using a Lagrangian loss function representing a number of objectives. The primal network is trained to minimize the Lagrangian loss function and the dual network is trained to maximize the Lagrangian loss function.Type: GrantFiled: February 3, 2019Date of Patent: March 22, 2022Assignee: International Business Machines CorporationInventors: Amir Kantor, Guy Uziel, Ateret Anaby-Tavor
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Publication number: 20210350076Abstract: Embodiments of the present systems and methods may provide techniques for augmenting textual data that may be used for textual classification tasks. Embodiments of such techniques may provide the capability to synthesize labeled data to improve text classification tasks. Embodiments may be specifically useful when only a small amount of data is available, and provide improved performance in such cases. For example, in an embodiment, a method implemented in a computer system may comprise a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise fine-tuning a language model using a training dataset, synthesizing a plurality of samples using the fine-tuned language model, filtering the plurality of synthesized samples, and generating an augmented training dataset comprising the training dataset and the filtered plurality of synthesized sentences.Type: ApplicationFiled: May 9, 2020Publication date: November 11, 2021Inventors: Amir Kantor, Ateret Anaby Tavor, Boaz Carmeli, Esther Goldbraich, GEORGE KOUR, Segev Shlomov, Naama Tepper, Naama Zwerdling
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Patent number: 11151324Abstract: An example system includes a processor to receive a prefix of conversation and a text input. The processor is to also generate a completed response based on the prefix of conversation and the text input via a trained primal network. The primal network is trained to minimize a Lagrangian loss function representing a number of objectives and a dual network is trained to maximize the Lagrangian loss function.Type: GrantFiled: February 3, 2019Date of Patent: October 19, 2021Assignee: International Business Machines CorporationInventors: Amir Kantor, Guy Uziel, Ateret Anaby-Tavor
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Patent number: 10956816Abstract: A method, computer system, and a computer program product for enhanced rating predictions is provided. The present invention may include receiving a user input. The present invention may then include translating the received user input into an embedding matrix and inputting the embedding matrix into a deep neural network. The present invention may further include generating, by the deep neural network, an output vector, a user bias term and an item bias term based on the embedding matrix. The present invention may then include calculating a predicted rating based on the generated output vector, the generated user bias term and the generated item bias term. The present invention may then include determining an accuracy of the predicted rating.Type: GrantFiled: June 28, 2017Date of Patent: March 23, 2021Assignee: International Business Machines CorporationInventors: Amir Kantor, Oren Sar-Shalom, Guy Uziel
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Patent number: 10817352Abstract: A method comprising operating a computerized chatbot to: calculate first and second scores representing a relevance of input received from a user to functionalities provided by respective first and second modules, respectively, of the chatbot; associate the first and second modules with respective first and second names; introducing the modules to the user using their associated names; selecting a module to interact with the user based on at least one of: a name mentioned by the user and a score and switching between the first and second modules based on the names.Type: GrantFiled: March 27, 2018Date of Patent: October 27, 2020Assignee: International Business Machines CorporationInventors: Amir Kantor, David Amid, David Boaz, Ateret Anaby Tavor
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Publication number: 20200250272Abstract: An example system includes a processor to receive a prefix of conversation and a text input. The processor is to also generate a completed response based on the prefix of conversation and the text input via a trained primal network. The primal network is trained to minimize a Lagrangian loss function representing a number of objectives and a dual network is trained to maximize the Lagrangian loss function.Type: ApplicationFiled: February 3, 2019Publication date: August 6, 2020Inventors: Amir Kantor, Guy Uziel, Ateret Anaby-Tavor
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Publication number: 20200250279Abstract: An example system includes a processor to receive data for a multi-objective task. The processor is to also perform the multi-objective task on the received data via a trained primal network. The primal network and a dual network are trained for a multi-objective task using a Lagrangian loss function representing a number of objectives. The primal network is trained to minimize the Lagrangian loss function and the dual network is trained to maximize the Lagrangian loss function.Type: ApplicationFiled: February 3, 2019Publication date: August 6, 2020Inventors: Amir Kantor, Guy Uziel, Ateret Anaby-Tavor
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Publication number: 20200152174Abstract: A method, apparatus, and system are provided for resolving conflicts between training data conflicts by retrieving independent training data sets, each comprising a plurality of intents and end-user utterances for use in training one or more classifiers to recognize a corresponding intent from one or more of the end-user utterances, providing a first test end-user utterance associated with a first intent from the first independent training data set to the one or more classifiers to select an output intent generated by the one or more classifiers; identifying a first conflict when the first intent does not match the output intent, and automatically generating, by the system, one or more conflict resolution recommendations for display and selection by an end user to resolve the first conflict.Type: ApplicationFiled: January 15, 2020Publication date: May 14, 2020Inventors: David Amid, David Boaz, Tin Kam Ho, Amir Kantor, Luis A. Lastras-Montano, Neil R. Mallinar
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Patent number: 10553202Abstract: A method, apparatus, and system are provided for resolving conflicts between training data conflicts by retrieving independent training data sets, each comprising a plurality of intents and end-user utterances for use in training one or more classifiers to recognize a corresponding intent from one or more of the end-user utterances, providing a first test end-user utterance associated with a first intent from the first independent training data set to the one or more classifiers to select an output intent generated by the one or more classifiers; identifying a first conflict when the first intent does not match the output intent, and automatically generating, by the system, one or more conflict resolution recommendations for display and selection by an end user to resolve the first conflict.Type: GrantFiled: October 31, 2017Date of Patent: February 4, 2020Assignee: International Business Machines CorporationInventors: David Amid, David Boaz, Tin Kam Ho, Amir Kantor, Luis A. Lastras-Montano, Neil R. Mallinar
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Patent number: 10510336Abstract: A method, system, and apparatus are provided for resolving conflicts between training data conflicts by retrieving independent training data sets, each comprising a plurality of intents and end-user utterances for use in training one or more classifiers to recognize a corresponding intent from one or more of the end-user utterances, providing a first test end-user utterance associated with a first intent from the first independent training data set to the one or more classifiers to select an output intent generated by the one or more classifiers; identifying a first conflict when the first intent does not match the output intent, and automatically generating, by the system, one or more conflict resolution recommendations for display and selection by an end user to resolve the first conflict.Type: GrantFiled: June 12, 2017Date of Patent: December 17, 2019Assignee: International Business Machines CorporationInventors: David Amid, David Boaz, Tin Kam Ho, Amir Kantor, Luis A. Lastras-Montano, Neil R. Mallinar
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Publication number: 20190303218Abstract: A method comprising operating a computerized chatbot to: calculate first and second scores representing a relevance of input received from a user to functionalities provided by respective first and second modules, respectively, of the chatbot; associate the first and second modules with respective first and second names; introducing the modules to the user using their associated names; selecting a module to interact with the user based on at least one of: a name mentioned by the user and a score and switching between the first and second modules based on the names.Type: ApplicationFiled: March 27, 2018Publication date: October 3, 2019Inventors: Amir Kantor, David Amid, David Boaz, Ateret Anaby Tavor
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Publication number: 20190266215Abstract: A method comprising using at least one hardware processor for receiving sensory data from at least one physical or virtual sensor. The hardware processor(s) are used for computing a plurality of decision options for configuration of the at least one physical or virtual sensor. The hardware processor(s) are used for computing a plurality of utility functions, and for each utility function: (a) computing a utility value for each decision option, and (b) identifying a first subset of decision options that substantially maximize the computed utility values. The hardware processor(s) are used for selecting at least one cross-function decision option from of the first subsets, wherein the at least one cross-function decision option is included in a substantially maximum number of the first subsets. The hardware processor(s) are used for applying at least one of the at least one cross-function decision options, to at least one physical or virtual sensor.Type: ApplicationFiled: February 27, 2018Publication date: August 29, 2019Inventors: AMIR KANTOR, Michael Masin, Segev Shlomov, Rotem Dror
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Publication number: 20190005383Abstract: A method, computer system, and a computer program product for enhanced rating predictions is provided. The present invention may include receiving a user input. The present invention may then include translating the received user input into an embedding matrix and inputting the embedding matrix into a deep neural network. The present invention may further include generating, by the deep neural network, an output vector, a user bias term and an item bias term based on the embedding matrix. The present invention may then include calculating a predicted rating based on the generated output vector, the generated user bias term and the generated item bias term. The present invention may then include determining an accuracy of the predicted rating.Type: ApplicationFiled: June 28, 2017Publication date: January 3, 2019Inventors: Amir Kantor, Oren Sar-Shalom, Guy Uziel
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Publication number: 20180358000Abstract: A method, system, and apparatus are provided for resolving conflicts between training data conflicts by retrieving independent training data sets, each comprising a plurality of intents and end-user utterances for use in training one or more classifiers to recognize a corresponding intent from one or more of the end-user utterances, providing a first test end-user utterance associated with a first intent from the first independent training data set to the one or more classifiers to select an output intent generated by the one or more classifiers; identifying a first conflict when the first intent does not match the output intent, and automatically generating, by the system, one or more conflict resolution recommendations for display and selection by an end user to resolve the first conflict.Type: ApplicationFiled: June 12, 2017Publication date: December 13, 2018Inventors: David Amid, David Boaz, Tin Kam Ho, Amir Kantor, Luis A. Lastras-Montano, Neil R. Mallinar
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Publication number: 20180358001Abstract: A method, apparatus, and system are provided for resolving conflicts between training data conflicts by retrieving independent training data sets, each comprising a plurality of intents and end-user utterances for use in training one or more classifiers to recognize a corresponding intent from one or more of the end-user utterances, providing a first test end-user utterance associated with a first intent from the first independent training data set to the one or more classifiers to select an output intent generated by the one or more classifiers; identifying a first conflict when the first intent does not match the output intent, and automatically generating, by the system, one or more conflict resolution recommendations for display and selection by an end user to resolve the first conflict.Type: ApplicationFiled: October 31, 2017Publication date: December 13, 2018Inventors: David Amid, David Boaz, Tin Kam Ho, Amir Kantor, Luis A. Lastras-Montano, Neil R. Mallinar