Patents by Inventor Boris ORESHKIN
Boris ORESHKIN 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: 11922294Abstract: Systems and components for use with neural networks. An execution block and a system architecture using that execution block are disclosed. The execution block uses a fully connected stack of layers and one output is a forecast for a time series while another output is a backcast that can be used to determine a residual from the input to the execution block. The execution block uses a waveform generator sub-unit whose parameters can be judiciously selected to thereby constrain the possible set of waveforms generated. By doing so, the execution block specializes its function. The system using the execution block has been shown to be better than the state of the art in providing solutions to the time series problem.Type: GrantFiled: April 21, 2020Date of Patent: March 5, 2024Assignee: ServiceNow Canada Inc.Inventors: Boris Oreshkin, Dmitri Carpov
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Publication number: 20240062446Abstract: A method of generating or modifying poses in an animation of a character are disclosed. Variable numbers and types of supplied inputs are combined into a single input. The variable numbers and types of supplied inputs correspond to one or more effector constraints for one or more joints of the character. The single input is transformed into a pose embedding. The pose embedding includes a machine-learned representation of the single input. The pose embedding is expanded into a pose representation output. The pose representation output includes local rotation data and global position data for the one or more joints of the character.Type: ApplicationFiled: May 23, 2023Publication date: February 22, 2024Inventors: Florent Benjamin Bocquelet, Dominic Laflamme, Boris Oreshkin
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Publication number: 20240054671Abstract: A method of estimating a pose for a custom character is disclosed. A skeleton corresponding to a user-supplied character is received or access. Features of the skeleton of the user-supplied character are computed. A set of betas and a scale value that correspond to a skinned multi-person linear (SMPL) model of the user-supplied skeleton are computed. The pose of the skeleton of the custom character is estimated using the SMPL model.Type: ApplicationFiled: August 14, 2023Publication date: February 15, 2024Inventors: Boris Oreshkin, Florent Benjamin Bocquelet, Vikram Seetharama Voleti, Louis-Simon Ménard
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Patent number: 11829869Abstract: Systems and methods relating to multitask transfer learning. Neural networks are used to accomplish a number of tasks and the results of these tasks are used to determine parameters common to these and other tasks. These parameters can then be used to accomplish other related tasks. In the description, data fitting as well as image related tasks are used. Task conditioning as well as the use of a KL regularizer have greatly improved results when testing the methods of the invention.Type: GrantFiled: July 25, 2019Date of Patent: November 28, 2023Assignee: ServiceNow Canada Inc.Inventors: Alexandre Lacoste, Boris Oreshkin
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Publication number: 20230368451Abstract: A method of optimizing a pose of a character is disclosed. An input is received. The input defines one or more effectors. A pose is generated for the character using a learned inverse kinematics (LIK) machine-learning (ML) component. The LIK ML component is trained using a motion dataset. The generating of the pose is based on one or more criteria. The one or more criteria include explicit intent expressed as the one or more effectors. The generated pose is adjusted using an ordinary inverse kinematics (OIK) component. The OIK component solves an output from the LIK ML component to increase an accuracy at which the explicit intent is reached. A final pose is generated from the adjusted pose. The generating of the final pose includes applying a physics engine (PE) to an output from the OIK component to increase a physics accuracy of the pose.Type: ApplicationFiled: May 15, 2023Publication date: November 16, 2023Inventors: Florent Benjamin Bocquelet, Dominic Laflamme, Boris Oreshkin, Félix Gingras Harvey
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Patent number: 11694382Abstract: A method of generating or modifying poses in an animation of a character are disclosed. Variable numbers and types of supplied inputs are combined into a single input. The variable numbers and types of supplied inputs correspond to one or more effector constraints for one or more joints of the character. The single input is transformed into a pose embedding. The pose embedding includes a machine-learned representation of the single input. The pose embedding is expanded into a pose representation output. The pose representation output includes local rotation data and global position data for the one or more joints of the character.Type: GrantFiled: May 20, 2021Date of Patent: July 4, 2023Assignee: Unity IPR ApSInventors: Florent Benjamin Bocquelet, Dominic Laflamme, Boris Oreshkin
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Patent number: 11526733Abstract: Systems, architectures, and approaches for use with neural networks. An execution block and a system architecture using a novel execution block are disclosed along with how such an execution block can be used. The execution block uses a fully connected stack of layers and parameters of this fully connected stack of layers are shared. The fully connected nature of the block and on-the-fly generated parameters allow for bypassing specialized training data sets. The system may be trained using non-task-specific training data sets and this allows the system to transfer what is learned to execute a different task. Thus, instead of having to obtain a specialized training data set for a specific task, a more generic training data set can be used to train and prepare the system for that specific task. Results have shown that performance is as good as than the state of the art in providing solutions.Type: GrantFiled: April 21, 2020Date of Patent: December 13, 2022Assignee: SERVICENOW CANADA INC.Inventors: Boris Oreshkin, Dmitri Carpov
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Publication number: 20220076472Abstract: A method of generating or modifying poses in an animation of a character are disclosed. Variable numbers and types of supplied inputs are combined into a single input. The variable numbers and types of supplied inputs correspond to one or more effector constraints for one or more joints of the character. The single input is transformed into a pose embedding. The pose embedding includes a machine-learned representation of the single input. The pose embedding is expanded into a pose representation output. The pose representation output includes local rotation data and global position data for the one or more joints of the character.Type: ApplicationFiled: May 20, 2021Publication date: March 10, 2022Inventors: Florent Benjamin Bocquelet, Dominic Laflamme, Boris Oreshkin
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Publication number: 20210133951Abstract: Systems and methods for determining similarities between an input data set and a target data set with the data sets being vector representations of the features of a candidate potential copy and a target original. A feature extraction module receives an image of the potential copy and extracts the features of that candidate. The features of the target original may already be extracted or may be separately extracted. The resulting data sets for the candidate and the original are then passed through a decision module. The decision module determines a level of similarity between the features of the candidate and the features of the original. The output of the decision module provides an indication of this level of similarity and, depending on this level of similarity, an alert may be generated. A report module may be included to provide an explanation regarding the level of similarity.Type: ApplicationFiled: July 11, 2019Publication date: May 6, 2021Applicant: ELEMENT AI INC.Inventors: Boris ORESHKIN, Bahador KHALEGHI, Francois MAILLET, Paul GAGNON
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Publication number: 20200372326Abstract: Systems, architectures, and approaches for use with neural networks. An execution block and a system architecture using a novel execution block are disclosed along with how such an execution block can be used. The execution block uses a fully connected stack of layers and parameters of this fully connected stack of layers are shared. The fully connected nature of the block and on-the-fly generated parameters allow for bypassing specialized training data sets. The system may be trained using non-task-specific training data sets and this allows the system to transfer what is learned to execute a different task. Thus, instead of having to obtain a specialized training data set for a specific task, a more generic training data set can be used to train and prepare the system for that specific task. Results have shown that performance is as good as than the state of the art in providing solutions.Type: ApplicationFiled: April 21, 2020Publication date: November 26, 2020Inventors: Boris ORESHKIN, Dmitri CARPOV
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Publication number: 20200372329Abstract: Systems and components for use with neural networks. An execution block and a system architecture using that execution block are disclosed. The execution block uses a fully connected stack of layers and one output is a forecast for a time series while another output is a backcast that can be used to determine a residual from the input to the execution block. The execution block uses a waveform generator sub-unit whose parameters can be judiciously selected to thereby constrain the possible set of waveforms generated. By doing so, the execution block specializes its function. The system using the execution block has been shown to be better than the state of the art in providing solutions to the time series problem.Type: ApplicationFiled: April 21, 2020Publication date: November 26, 2020Inventors: Boris ORESHKIN, Dmitri CARPOV
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Publication number: 20200143209Abstract: Systems and methods relating to machine learning by using a sample data set to learn a specific task and using that learned task on a query data set. In an image classification implementation, a sample set is used to derive a task representation and the task representation is used with a task embedding network to determine parameters to be used with a neural network to perform the task. Once the parameters have been derived, the sample set and the query set are passed through neural network with the parameters. The results are then compared for similarities.Type: ApplicationFiled: November 7, 2019Publication date: May 7, 2020Inventors: Alexandre LACOSTE, Boris ORESHKIN
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Publication number: 20200034694Abstract: Systems and methods relating to multitask transfer learning. Neural networks are used to accomplish a number of tasks and the results of these tasks are used to determine parameters common to these and other tasks. These parameters can then be used to accomplish other related tasks. In the description, data fitting as well as image related tasks are used. Task conditioning as well as the use of a KL regularizer have greatly improved results when testing the methods of the invention.Type: ApplicationFiled: July 25, 2019Publication date: January 30, 2020Inventors: Alexandre LACOSTE, Boris ORESHKIN