Patents by Inventor Aaron Russell
Aaron Russell 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: 11980901Abstract: A fluid dispenser includes a housing, a pump, an outlet nozzle, a reservoir, a liquid passage, an air passage, and a refill container. The pump and reservoir are attached to the housing, and both the outlet nozzle and the reservoir are in fluid communication with the pump. The reservoir has at least one engagement member, and the liquid passage and the air passage are located in the engagement member. The refill container has at least one sealing member, and the refill container is configured to be releasably attached to the reservoir such that the refill container is in fluid communication with the reservoir. When the refill container is attached to the reservoir, the engagement member engages the sealing member to cause the liquid passage and the air passage to be in fluid communication with the refill container.Type: GrantFiled: April 25, 2022Date of Patent: May 14, 2024Assignee: GOJO Industries, Inc.Inventors: Aaron D. Marshall, Nick E. Ciavarella, Donald Russell Harris
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Patent number: 11964989Abstract: Compounds that inhibit KRas G12D. In particular, compounds that inhibit the activity of KRas G12D, pharmaceutical compositions comprising the compounds and methods of use therefor, and in particular, methods of treating cancer. The compounds have a general structure represented by Formula (I): or a pharmaceutically acceptable salt thereof.Type: GrantFiled: July 20, 2022Date of Patent: April 23, 2024Assignees: Mirati Therapeutics, Inc., Array BioPharma Inc.Inventors: Xiaolun Wang, Aaron Craig Burns, James Gail Christensen, John Michael Ketcham, John David Lawson, Matthew Arnold Marx, Christopher Ronald Smith, Shelley Allen, James F. Blake, Mark Joseph Chicarelli, Joshua Ryan Dahlke, Donghua Dai, Jay Bradford Fell, John Peter Fischer, Macedonio J. Mejia, Brad Newhouse, Phong Nguyen, Jacob Matthew O'Leary, Spencer Pajk, Martha E. Rodriguez, Pavel Savechenkov, Tony P. Tang, Guy P.A. Vigers, Qian Zhao, Dean Russell Kahn, John Gaudino, Michael Christopher Hilton
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Patent number: 11911688Abstract: A game employing user-modifiable game components, such as cards in a collectable card game, employs various features to provide user-modifiability, including sleeves, transparent cards, stickers, and other elements. Electronic versions of the game and various other features are included, including tracking of history associated with such components.Type: GrantFiled: June 2, 2020Date of Patent: February 27, 2024Assignee: WIZARDS OF THE COAST LLCInventors: Frank Gilson, Cormac Russell, Paul Sottosanti, Randy Buehler, Ramon Arjona, Karl Robert Gutschera, Brandon Anthony Bozzi, Aaron Joel Forsythe
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Publication number: 20230314337Abstract: Methods of inspecting a material include moving the material and identifying a defect location of a defect. Methods include moving a camera along a second travel path in a second travel direction such that a field of view (FOV) of the camera moves relative to the material along the second travel path to match the defect location. Methods include passing the defect through the field of view (FOV) as the material moves and capturing images of the defect with the camera as the material moves and the defect moves through the FOV. The images include a first image of a first major surface, a second image of a second major surface, and a third image of an intermediate portion of the material between the first major surface and the second major surface. Methods include reviewing the plurality of images to characterize the defect.Type: ApplicationFiled: August 3, 2021Publication date: October 5, 2023Inventors: Earle William Gillis, Aaron Russell Greenbaum
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Patent number: 11741098Abstract: The present invention relates to methods and systems for storing and querying database entries with neuromorphic computers. The system is comprised of a plurality of encoding subsystems that convert database entries and search keys into vector representations, a plurality of associative memory subsystems that match vector representations of search keys to vector representations of database entries using spike-based comparison operations, a plurality of binding subsystems that update retrieved vector representations during the execution of hierarchical queries, a plurality of unbinding subsystems that extract information from retrieved vector representations, a plurality of cleanup subsystems that remove noise from these retrieved representations, and one or more input search key representations that propagates spiking activity through the associative memory, binding, unbinding, cleanup, and readout subsystems to retrieve database entries matching the search key.Type: GrantFiled: July 15, 2020Date of Patent: August 29, 2023Assignee: APPLIED BRAIN RESEARCH INC.Inventors: Aaron Russell Voelker, Christopher David Eliasmith, Peter Blouw
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Publication number: 20230152082Abstract: An apparatus can comprise an illumination source and at least one wave front sensor that positioned in a first region. A reflector can be positioned in a second region. A measurement plane can be positioned between the first region and the second region. The reflector can be configured to reflect the light. The at least one wave front sensor can be configured to detect the light. Methods of measuring a feature of a glass-based substrate can comprise emitting light from the illumination source. Methods can comprise transmitting the light through a thickness of the glass-based substrate. Method can comprise transmitting the light through a target location of a first major surface of the glass-based substrate. Methods can comprise detecting the light with the at least one wave front sensor and generating a signal based on the detected light.Type: ApplicationFiled: June 10, 2021Publication date: May 18, 2023Inventors: Earle William Gillis, Aaron Russell Greenbaum
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Publication number: 20220172053Abstract: A method is described for designing systems that provide efficient implementations of feed-forward, recurrent, and deep networks that process dynamic signals using temporal filters and static or time-varying nonlinearities. A system design methodology is described that provides an engineered architecture. This architecture defines a core set of network components and operations for efficient computation of dynamic signals using temporal filters and static or time-varying nonlinearities. These methods apply to a wide variety of connected nonlinearities that include temporal filters in the connections. Here we apply the methods to synaptic models coupled with spiking and/or non-spiking neurons whose connection parameters are determined using a variety of methods of optimization.Type: ApplicationFiled: December 20, 2021Publication date: June 2, 2022Applicant: Applied Brain Research Inc.Inventors: Aaron Russell Voelker, Christopher David Eliasmith
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Publication number: 20220138382Abstract: The present invention relates to methods and systems for simulating and predicting dynamical systems with vector symbolic representations of continuous spaces. More specifically, the present invention specifies methods for simulating and predicting such dynamics through the definition of temporal fractional binding, collection, and decoding subsystems that collectively function to both create vector symbolic representations of multi-object trajectories, and decode these representations to simulate or predict the future states of these trajectories. Systems composed of one or more of these temporal fractional binding, collection, and decoding subsystems are combined to simulate or predict the behavior of at least one dynamical system that involves the motion of at least one object.Type: ApplicationFiled: November 5, 2021Publication date: May 5, 2022Inventors: Aaron Russell VOELKER, Christopher David ELIASMITH, Peter BLOUW, Terrance STEWART, NICOLE SANDRA-YAFFE DUMONT
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Publication number: 20220083867Abstract: The present invention relates to methods and systems for using neural networks to simulate dynamical systems for purposes of solving optimization problems. More specifically, the present invention defines methods and systems that perform a process of “synaptic descent” for performing “synaptic descent”, wherein the state of a given synapse in a neural network is a variable being optimized, the input to the synapse is a gradient defined with respect to this state, and the synapse implements the computations of an optimizer that performs gradient descent over time. Synapse models regulate the dynamics of a given neural network by governing how the output of one neuron is passed as input to another, and since the process of synaptic descent performs gradient descent with respect to state variables defining these dynamics, it can be harnessed to evolve the neural network towards a state or sequence of states that encodes the solution to an optimization problem.Type: ApplicationFiled: September 14, 2021Publication date: March 17, 2022Inventors: Aaron Russell VOELKER, Christopher David ELIASMITH
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Patent number: 11238345Abstract: Neural network architectures, with connection weights determined using Legendre Memory Unit equations, are trained while optionally keeping the determined weights fixed. Networks may use spiking or non-spiking activation functions, may be stacked or recurrently coupled with other neural network architectures, and may be implemented in software and hardware. Embodiments of the invention provide systems for pattern classification, data representation, and signal processing, that compute using orthogonal polynomial basis functions that span sliding windows of time.Type: GrantFiled: March 6, 2020Date of Patent: February 1, 2022Assignee: Applied Brain Research Inc.Inventors: Aaron Russell Voelker, Christopher David Eliasmith
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Patent number: 11238337Abstract: A method is described for designing systems that provide efficient implementations of feed-forward, recurrent, and deep networks that process dynamic signals using temporal filters and static or time-varying nonlinearities. A system design methodology is described that provides an engineered architecture. This architecture defines a core set of network components and operations for efficient computation of dynamic signals using temporal filters and static or time-varying nonlinearities. These methods apply to a wide variety of connected nonlinearities that include temporal filters in the connections. Here we apply the methods to synaptic models coupled with spiking and/or non-spiking neurons whose connection parameters are determined using a variety of methods of optimization.Type: GrantFiled: August 22, 2016Date of Patent: February 1, 2022Assignee: Applied Brain Research Inc.Inventors: Aaron Russell Voelker, Christopher David Eliasmith
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Publication number: 20210342668Abstract: Recurrent neural networks are efficiently mapped to hardware computation blocks specifically designed for Legendre Memory Unit (LMU) cells, Projected LSTM cells, and Feed Forward cells. Iterative resource allocation algorithms are used to partition recurrent neural networks and time multiplex them onto a spatial distribution of computation blocks, guided by multivariable optimizations for power, performance, and accuracy. Embodiments of the invention provide systems for low power, high performance deployment of recurrent neural networks for battery sensitive applications such as automatic speech recognition (ASR), keyword spotting (KWS), biomedical signal processing, and other applications that involve processing time-series data.Type: ApplicationFiled: April 29, 2021Publication date: November 4, 2021Inventors: Gurshaant Singh Malik, Aaron Russell VOELKER, Christopher David ELIASMITH
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Publication number: 20210133190Abstract: The present invention relates to methods and systems for storing and querying database entries with neuromorphic computers. The system is comprised of a plurality of encoding subsystems that convert database entries and search keys into vector representations, a plurality of associative memory subsystems that match vector representations of search keys to vector representations of database entries using spike-based comparison operations, a plurality of binding subsystems that update retrieved vector representations during the execution of hierarchical queries, a plurality of unbinding subsystems that extract information from retrieved vector representations, a plurality of cleanup subsystems that remove noise from these retrieved representations, and one or more input search key representations that propagates spiking activity through the associative memory, binding, unbinding, cleanup, and readout subsystems to retrieve database entries matching the search key.Type: ApplicationFiled: July 15, 2020Publication date: May 6, 2021Inventors: Aaron Russell VOELKER, Christopher David ELIASMITH, Peter Blouw
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Publication number: 20210133568Abstract: The present invention relates to methods of sparsifying signals over time in multi-bit spiking neural networks, methods of training and converting these networks by interpolating between spiking and non-spiking regimes, and their efficient implementation in digital hardware. Four algorithms are provided that encode signals produced by nonlinear functions, spiking neuron models, supplied as input to the network, and any linear combination thereof, as multi-bit spikes that may be compressed and adaptively scaled in size, in order to balance metrics including the desired accuracy of the network and the available energy in hardware.Type: ApplicationFiled: October 30, 2020Publication date: May 6, 2021Inventor: Aaron Russell Voelker
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Publication number: 20210089912Abstract: Neural network architectures, with connection weights determined using Legendre Memory Unit equations, are trained while optionally keeping the determined weights fixed. Networks may use spiking or non-spiking activation functions, may be stacked or recurrently coupled with other neural network architectures, and may be implemented in software and hardware. Embodiments of the invention provide systems for pattern classification, data representation, and signal processing, that compute using orthogonal polynomial basis functions that span sliding windows of time.Type: ApplicationFiled: March 6, 2020Publication date: March 25, 2021Inventors: Aaron Russell VOELKER, Christopher David ELIASMITH
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METHODS AND SYSTEMS FOR ENCODING AND PROCESSING VECTOR-SYMBOLIC REPRESENTATIONS OF CONTINUOUS SPACES
Publication number: 20200302281Abstract: The present invention relates to methods and systems for encoding and processing representations that include continuous structures using vector-symbolic representations. The system is comprised of a plurality of binding subsystems that implement a fractional binding operation, a plurality of unbinding subsystems that implement a fractional unbinding operation, and at least one input symbol representation that propagates activity through a binding subsystem and an unbinding subsystem to produce a high-dimensional vector representation of a continuous space.Type: ApplicationFiled: March 18, 2020Publication date: September 24, 2020Inventors: Aaron Russell VOELKER, Christopher David ELIASMITH, Brent KOMER, Terrence STEWART -
Publication number: 20180053090Abstract: A method is described for designing systems that provide efficient implementations of feed-forward, recurrent, and deep networks that process dynamic signals using temporal filters and static or time-varying nonlinearities. A system design methodology is described that provides an engineered architecture. This architecture defines a core set of network components and operations for efficient computation of dynamic signals using temporal filters and static or time-varying nonlinearities. These methods apply to a wide variety of connected nonlinearities that include temporal filters in the connections. Here we apply the methods to synaptic models coupled with spiking and/or non-spiking neurons whose connection parameters are determined using a variety of methods of optimization.Type: ApplicationFiled: August 22, 2016Publication date: February 22, 2018Inventors: Aaron Russell VOELKER, Christopher David ELIASMITH
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Patent number: D838865Type: GrantFiled: February 16, 2016Date of Patent: January 22, 2019Assignee: HALLIBURTON ENERGY SERVICES, INC.Inventors: Ajish Potty, Antonio Recio, III, Christopher R. Parton, Aaron Russell, Mike Phillips
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Patent number: D1022714Type: GrantFiled: May 21, 2021Date of Patent: April 16, 2024Assignee: Apple Inc.Inventors: Jody Akana, Molly Anderson, Bartley K. Andre, Shota Aoyagi, Anthony Michael Ashcroft, Marine C. Bataille, Jeremy Bataillou, Kevin Will Chen, Andrew Patrick Clymer, Clara Geneviève Marine Courtaigne, Markus Diebel, Alan C. Dye, Aurelio Guzmán, M. Evans Hankey, Julian Hoenig, Richard P. Howarth, Jonathan P. Ive, Julian Jaede, Duncan Robert Kerr, Aaron Mathew Melim, Marc A. Newson, Peter Russell-Clarke, Benjamin Andrew Shaffer, Joe Sung-Ho Tan, Clement Tissandier, Jacob Weiss, Eugene Antony Whang, Rico Zörkendörfer
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Patent number: D1026900Type: GrantFiled: May 20, 2022Date of Patent: May 14, 2024Assignee: Apple Inc.Inventors: Jody Akana, Molly Anderson, Bartley K. Andre, Shota Aoyagi, Marine C. Bataille, Kevin Will Chen, Abidur Rahman Chowdhury, Andrew Patrick Clymer, Clara Geneviève Marine Courtaigne, Markus Diebel, Alexandre B. Girard, Jonathan Gomez Garcia, Aurelio Guzmán, M. Evans Hankey, Anne-Marie Heck, Moises Hernandez Hernandez, Richard P. Howarth, Julian Jaede, Duncan Robert Kerr, Kainoa Kwon-Perez, Nicolas Pedro Lylyk, Aaron Mathew Melim, Peter Russell-Clarke, Benjamin Andrew Shaffer, Clement Tissandier