Patents by Inventor Avraham Ever Shinnar
Avraham Ever Shinnar 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: 11720586Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises analyzing identified data for a determined conversion of the identified data, wherein the identified data is input data stored on an external database; automatically converting the analyzed data to a uniform domain by mapping a data route within the analyzed data, predicting a plurality of outcomes based on an application of a plurality of scenarios associated with the mapped data route, ranking the predicted outcomes based on a positive match percentage for the analyzed data, and converting the analyzed data associated with at least one ranked outcome using a relational algebra algorithm; and dynamically transmitting the converted, analyzed data into at least one section of a machine learning data pipeline.Type: GrantFiled: March 26, 2021Date of Patent: August 8, 2023Assignee: International Business Machines CorporationInventors: Kiran A Kate, Martin Hirzel, Avraham Ever Shinnar
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Patent number: 11681510Abstract: Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.Type: GrantFiled: September 26, 2022Date of Patent: June 20, 2023Assignee: International Business Machines CorporationInventors: Julian Timothy Dolby, Martin Hirzel, Kiran A Kate, Louis Mandel, Avraham Ever Shinnar, Kavitha Srinivas
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Publication number: 20230168938Abstract: A computer-implemented method according to one embodiment includes identifying a machine learning pipeline and a plurality of training data batches; creating a plurality of tasks, based on the machine learning pipeline; and determining an order in which the plurality of tasks is executed, utilizing a resource usage-aware approach.Type: ApplicationFiled: November 29, 2021Publication date: June 1, 2023Inventors: Martin Hirzel, Kiran A. Kate, Avraham Ever Shinnar
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Patent number: 11599357Abstract: A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.Type: GrantFiled: January 31, 2020Date of Patent: March 7, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Alan Braz, Martin Hirzel, Avraham Ever Shinnar, Jason Tsay, Todd Mummert
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Publication number: 20230024047Abstract: Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.Type: ApplicationFiled: September 26, 2022Publication date: January 26, 2023Inventors: Julian Timothy Dolby, MARTIN HIRZEL, Kiran A Kate, Louis Mandel, Avraham Ever Shinnar, Kavitha Srinivas
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Publication number: 20220398074Abstract: Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.Type: ApplicationFiled: June 15, 2021Publication date: December 15, 2022Inventors: Julian Timothy Dolby, MARTIN HIRZEL, Kiran A. Kate, Louis Mandel, Avraham Ever Shinnar, Kavitha Srinivas
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Patent number: 11507352Abstract: Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.Type: GrantFiled: June 15, 2021Date of Patent: November 22, 2022Assignee: International Business Machines CorporationInventors: Julian Timothy Dolby, Martin Hirzel, Kiran A Kate, Louis Mandel, Avraham Ever Shinnar, Kavitha Srinivas
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Publication number: 20220309073Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises analyzing identified data for a determined conversion of the identified data, wherein the identified data is input data stored on an external database; automatically converting the analyzed data to a uniform domain by mapping a data route within the analyzed data, predicting a plurality of outcomes based on an application of a plurality of scenarios associated with the mapped data route, ranking the predicted outcomes based on a positive match percentage for the analyzed data, and converting the analyzed data associated with at least one ranked outcome using a relational algebra algorithm; and dynamically transmitting the converted, analyzed data into at least one section of a machine learning data pipeline.Type: ApplicationFiled: March 26, 2021Publication date: September 29, 2022Inventors: Kiran A Kate, MARTIN HIRZEL, Avraham Ever Shinnar
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Publication number: 20220253723Abstract: Embodiments are disclosed for a method. The method includes identifying one or more source code signals in a source code. The method also include generating an amplified code based on the identified signals and the source code. The amplified code is functionally equivalent to the source code. Further, the amplified code includes one or more amplified signals. The method additionally includes providing the amplified code for a machine learning model that is trained to perform a source code relevant task.Type: ApplicationFiled: February 10, 2021Publication date: August 11, 2022Inventors: Julian Timothy Dolby, MARTIN HIRZEL, Kiran A. Kate, Louis Mandel, Avraham Ever Shinnar, Kavitha Srinivas, Jason Tsay
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Publication number: 20210240471Abstract: A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.Type: ApplicationFiled: January 31, 2020Publication date: August 5, 2021Inventors: Alan Braz, Martin Hirzel, Avraham Ever Shinnar, Jason Tsay, Todd Mummert
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Patent number: 10977385Abstract: Methods and systems are provided for configurable and non-invasive protection of private information in a user input to a software application that handles real-time information. A method includes detecting, by a filter in real-time, private information in the user input. The method further includes forming, by the filter, a filtered user input from the user input, by maintaining non-private information from the user input in the filtered user input, extracting and encrypting the private information in the user input and attaching the encrypted private information to the filtered user input, and replacing the private information in the user input with unique identifiers in the filtered user input. The unique identifiers are configured to be exploitable by the software application to achieve an intended function of the software application for the user. The method also includes transmitting, by a communications redirector, the filtered user input over a communication channel.Type: GrantFiled: March 7, 2018Date of Patent: April 13, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Guillaume A. Baudart, Evelyn Duesterwald, Martin Hirzel, Avraham Ever Shinnar, Julian Timothy Dolby
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Patent number: 10679000Abstract: A method and a system for interpreting conversational authoring of information models. The system includes an understanding module, a managing module, and a generating module. The understanding module is configured to understand a natural language input to interpret an output. The managing module is configured to construct an information model based on the output of the understanding module. The generating module configured is to prompt, as a response to the natural language inputs, wherein the natural language inputs determine concepts and relationships of the concepts. The method includes receiving an interactive dialog between a conversational agent and an information model designer in natural language to produce an information model. The method can further include validating the information model using an information model management system. The method can include interpreting the information model with the use of an application.Type: GrantFiled: January 9, 2018Date of Patent: June 9, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Martin Hirzel, Avraham Ever Shinnar, Jerome Simeon
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Publication number: 20190278942Abstract: Methods and systems are provided for configurable and non-invasive protection of private information in a user input to a software application that handles real-time information. A method includes detecting, by a filter in real-time, private information in the user input. The method further includes forming, by the filter, a filtered user input from the user input, by maintaining non-private information from the user input in the filtered user input, extracting and encrypting the private information in the user input and attaching the encrypted private information to the filtered user input, and replacing the private information in the user input with unique identifiers in the filtered user input. The unique identifiers are configured to be exploitable by the software application to achieve an intended function of the software application for the user. The method also includes transmitting, by a communications redirector, the filtered user input over a communication channel.Type: ApplicationFiled: March 7, 2018Publication date: September 12, 2019Inventors: Guillaume A. Baudart, Evelyn Duesterwald, Martin Hirzel, Avraham Ever Shinnar, Julian Timothy Dolby
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Publication number: 20190213244Abstract: A method and a system for interpreting conversational authoring of information models. The system includes an understanding module, a managing module, and a generating module. The understanding module is configured to understand a natural language input to interpret an output. The managing module is configured to construct an information model based on the output of the understanding module. The generating module configured is to prompt, as a response to the natural language inputs, wherein the natural language inputs determine concepts and relationships of the concepts. The method includes receiving an interactive dialog between a conversational agent and an information model designer in natural language to produce an information model. The method can further include validating the information model using an information model management system. The method can include interpreting the information model with the use of an application.Type: ApplicationFiled: January 9, 2018Publication date: July 11, 2019Inventors: Martin Hirzel, Avraham Ever Shinnar, Jerome Simeon