Patents by Inventor Jean Joseph Belanger
Jean Joseph Belanger 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: 11893520Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.Type: GrantFiled: June 7, 2022Date of Patent: February 6, 2024Assignee: Cerebri AI Inc.Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
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Publication number: 20240013314Abstract: Provided is a process including: writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods; writing, with the computing system, a second plurality of classes using object-oriented modelling of governance; scanning, with the computing system, a set of libraries collectively containing both modelling object classes among the first plurality of classes and governance classes among the second plurality of classes to determine class definition information; using, with the computing system, at least some of the class definition information to produce object manipulation functions, wherein the object manipulation functions allow a governance system to access methods and attributes of classes among first plurality of classes or the second plurality of classes to manipulate objects of at least some of the modelling object classes; and using at least some of the class definition information to effectuate access to the object manipulation functions.Type: ApplicationFiled: August 28, 2023Publication date: January 11, 2024Inventors: Alain Charles Briancon, Jean Joseph Belanger, Chris Michael Coovrey, Thejas Narayana Prasad, Mirza Safiullah Baig
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Publication number: 20230334580Abstract: Provided is a process including: writing modelling-object classes using object-oriented modelling of the modelling methods, the modelling-object classes being members of a set of class libraries; writing quality-management classes using object-oriented modelling of quality management, the quality-management classes being members of the set of class libraries; scanning modelling-object classes in the set of class libraries to determine modelling-object class definition information; scanning quality-management classes in the set of class libraries to determine quality-management class definition information; using the modelling-object class definition information and the quality-management class definition information to produce object manipulation functions that allow a quality management system to access methods and attributes of modelling-object classes to manipulate objects of the modelling-object classes; and using the modelling-object class definition information and the quality-management class definitioType: ApplicationFiled: January 6, 2023Publication date: October 19, 2023Inventors: Alain Charles Briancon, Jean Joseph Belanger, Chris Michael Coovrey, Travis Stanton Penn, Divya Karumuri, Valisis Sotiris
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Patent number: 11783375Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.Type: GrantFiled: July 8, 2022Date of Patent: October 10, 2023Assignee: Cerebri AI Inc.Inventors: Alain Charles Briancon, Jean Joseph Belanger, James Cvetan Stojanov, Christopher Michael Coovrey, Pranav Mahesh Makhijani, Gregory Klose, Max Changchun Huang, Mounib Mohamad Ismail, Michael Henry Engeling, Hongshi Li
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Patent number: 11776060Abstract: Provided is a process including: writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods; writing, with the computing system, a second plurality of classes using object-oriented modelling of governance; scanning, with the computing system, a set of libraries collectively containing both modelling object classes among the first plurality of classes and governance classes among the second plurality of classes to determine class definition information; using, with the computing system, at least some of the class definition information to produce object manipulation functions, wherein the object manipulation functions allow a governance system to access methods and attributes of classes among first plurality of classes or the second plurality of classes to manipulate objects of at least some of the modelling object classes; and using at least some of the class definition information to effectuate access to the object manipulation functions.Type: GrantFiled: June 3, 2020Date of Patent: October 3, 2023Assignee: Cerebri AI Inc.Inventors: Alain Charles Briancon, Jean Joseph Belanger, Chris Michael Coovrey, Thejas Narayana Prasad, Mirza Safiullah Baig
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Publication number: 20230206124Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.Type: ApplicationFiled: December 21, 2022Publication date: June 29, 2023Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
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Publication number: 20230101487Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.Type: ApplicationFiled: July 8, 2022Publication date: March 30, 2023Inventors: Alain Charles Briancon, Jean Joseph Belanger, James Cvetan Stojanov, Christopher Michael Coovrey, Pranav Mahesh Makhijani, Gregory Klose, Max Changchun Huang, Mounib Mohamad Ismail, Michael Henry Engeling, Hongshi Li
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Patent number: 11615271Abstract: Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configurType: GrantFiled: June 3, 2020Date of Patent: March 28, 2023Assignee: Cerebri AI Inc.Inventors: Eyal Ben Zion, Alain Charles Briancon, Pranav Mahesh Makhijani, Thejas Narayana Prasad, Sara Amini, Jian Deng, Ngoc Thu Nguyen, Jean Joseph Belanger
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Publication number: 20230080773Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.Type: ApplicationFiled: June 7, 2022Publication date: March 16, 2023Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
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Patent number: 11599752Abstract: Provided is a process including: writing modelling-object classes using object-oriented modelling of the modelling methods, the modelling-object classes being members of a set of class libraries; writing quality-management classes using object-oriented modelling of quality management, the quality-management classes being members of the set of class libraries; scanning modelling-object classes in the set of class libraries to determine modelling-object class definition information; scanning quality-management classes in the set of class libraries to determine quality-management class definition information; using the modelling-object class definition information and the quality-management class definition information to produce object manipulation functions that allow a quality management system to access methods and attributes of modelling-object classes to manipulate objects of the modelling-object classes; and using the modelling-object class definition information and the quality-management class definitioType: GrantFiled: June 3, 2020Date of Patent: March 7, 2023Assignee: Cerebri AI Inc.Inventors: Alain Charles Briancon, Jean Joseph Belanger, Chris Michael Coovrey, Travis Stanton Penn, Divya Karumuri, Valisis Sotiris
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Patent number: 11556846Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.Type: GrantFiled: October 3, 2019Date of Patent: January 17, 2023Assignee: Cerebri AI Inc.Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
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Patent number: 11416896Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.Type: GrantFiled: June 18, 2021Date of Patent: August 16, 2022Assignee: Cerebri AI Inc.Inventors: Alain Charles Briancon, Jean Joseph Belanger, James Cvetan Stojanov, Christopher Michael Coovrey, Pranav Mahesh Makhijani, Gregory Klose, Max Changchun Huang, Mounib Mohamad Ismail, Michael Henry Engeling, Hongshi Li
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Patent number: 11386295Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.Type: GrantFiled: October 3, 2018Date of Patent: July 12, 2022Assignee: Cerebri AI Inc.Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
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Publication number: 20220044283Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.Type: ApplicationFiled: June 18, 2021Publication date: February 10, 2022Inventors: Alain Charles Briancon, Jean Joseph Belanger, James Cvetan Stojanov, Christopher Michael Coovrey, Pranav Mahesh Makhijani, Gregory Klose, Max Changchun Huang, Mounib Mohamad Ismail, Michael Henry Engeling, Hongshi Li
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Publication number: 20210342490Abstract: Provided is a process including: searching code of a machine-learning pipeline to find a first and a second object code sequences performing similar tasks; modifying the code of the machine learning pipeline by inserting a third object code sequence into the code of the machine learning pipeline, the third code sequence being operable to pass control to the first object code sequence; inserting a branch at the end of the first code sequence, the branch being operable to: pass control, upon detection of a first predefined condition, to an instruction following the first object code sequence, and to pass control, upon detection of a second predefined condition, to an instruction following the third object code sequence; and wherein the third code sequence is executed in place of the second object sequence without affecting completion of the tasks.Type: ApplicationFiled: May 4, 2021Publication date: November 4, 2021Inventors: Alain Charles Briancon, Eric Paver Simon, Mirza Safiullah Baig, Jean Joseph Belanger, Michael Henry Engeling, Sathish Kumar Lakshmipathy, Travis Stanton Penn, Bryan Wayne Collins, Arun Prakash, Chris Michael Coovrey, Piyush Sunil Deshmukh, Vasilis Andrew Sotiris, Mounib Mohamad Ismail
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Patent number: 11068942Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.Type: GrantFiled: October 18, 2019Date of Patent: July 20, 2021Assignee: Cerebri AI Inc.Inventors: Alain Charles Briancon, Jean Joseph Belanger, James Cvetan Stojanov, Christopher Michael Coovrey, Pranav Mahesh Makhijani, Gregory Klose, Max Changchun Huang, Mounib Mohamad Ismail, Michael Henry Engeling, Hongshi Li
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Publication number: 20210174257Abstract: Provided is a process including: receiving a data token to be passed from a first node to a second node; retrieving machine learning model attributes from a collection of one or more of the sub-models of a federated machine-learning model; determining based on the machine learning model attributes, that the data token is learning relevant to members of the collection of one or more of the sub-models and, in response, adding the data toke to a training set to be used by at least some members of the collection of one or more of the sub-models; determining a collection of data tokens to transmit from the second node to a third node of the set of nodes participating in a federated machine-learning model; and transmitting the collection of data tokens.Type: ApplicationFiled: December 2, 2020Publication date: June 10, 2021Inventors: Sundeep Pothula, Max Changchun Huang, Thejas Narayana Prasad, Alain Charles Briancon, Jean Joseph Belanger
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Publication number: 20200402084Abstract: Provided is a process including: obtaining, for a plurality of entities, datasets; and orchestrating an object-orientated application or service by: forming a plurality of objects, forming object-oriented labeled datasets based on an event and the attributes of each of the datasets; forming a library or framework of classes with a plurality of object-orientation modelors; and forming a plurality of object-manipulation functions, each function being configured to leverage a respective class among the library or framework of classes.Type: ApplicationFiled: June 3, 2020Publication date: December 24, 2020Inventors: Alain Charles Briancon, Jean Joseph Belanger, Chris Michael Coovrey, Valisis Sotiris, Eric Paver Simon
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Publication number: 20200380417Abstract: Provided is a process including: writing modelling-object classes using object-oriented modelling of the modelling methods, the modelling-object classes being members of a set of class libraries; writing quality-management classes using object-oriented modelling of quality management, the quality-management classes being members of the set of class libraries; scanning modelling-object classes in the set of class libraries to determine modelling-object class definition information; scanning quality-management classes in the set of class libraries to determine quality-management class definition information; using the modelling-object class definition information and the quality-management class definition information to produce object manipulation functions that allow a quality management system to access methods and attributes of modelling-object classes to manipulate objects of the modelling-object classes; and using the modelling-object class definition information and the quality-management class definitioType: ApplicationFiled: June 3, 2020Publication date: December 3, 2020Inventors: Alain Charles Briancon, Jean Joseph Belanger, Chris Michael Coovrey, Travis Stanton Penn, Divya Karumuri, Valisis Sotiris
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Publication number: 20200380416Abstract: Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configurType: ApplicationFiled: June 3, 2020Publication date: December 3, 2020Inventors: Eyal Ben Zion, Alain Charles Briancon, Pranav Mahesh Makhijani, Thejas Narayana Prasad, Sara Amini, Jian Deng, Ngoc Thu Nguyen, Jean Joseph Belanger