Patents by Inventor Jonathan Rayner

Jonathan Rayner 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).

  • Patent number: 12142973
    Abstract: A switched reluctance electrical motor comprises a rotor having a substantially circular cylindrical envelope with a diameter in a range of 50 mm to 300 mm and a length in the range of 20 mm to 250 mm. The rotor comprises a plurality of radially extending rotor teeth. A switched reluctance electrical motor further comprises a stator surrounding the rotor and comprising a plurality of stator poles. The rotor teeth are circumferentially-spaced apart from each other to define slots between adjacent teeth that, expressed in normalised angular and radial coordinates, have a cross-sectional profile transverse to an axis of rotation of the rotor, lying within a well-defined polygonal region.
    Type: Grant
    Filed: October 26, 2023
    Date of Patent: November 12, 2024
    Assignee: Monumo Limited
    Inventors: Daniel Bates, Kevin Bersch, William Gallafent, Jaroslaw Pawel Rzepecki, Alexey Kostin, Jonathan Rayner, Markus Kaiser, Nicolas Durrande, Rupert Tombs, Ian Murphy, Xiaoyan Wang, Pierre Guern, Bhaskar Sen
  • Patent number: 12118280
    Abstract: A computer-implemented method of simulating operation of an electric drive unit to predict one or more performance parameters of the electric drive unit is provided. The electric drive unit comprises at least an electric motor. The method comprises obtaining parameters defining physical properties of the electric motor, obtaining parameters defining drive currents for driving the electric motor, processing the obtained parameters using a machine learning module trained a priori to predict a spatially varying electromagnetic and/or mechanical and/or thermal profile within the electric motor during operation, and providing as output a predicted profile for the electric motor, and using the predicted profile to compute the one or more performance parameters of the electric drive unit.
    Type: Grant
    Filed: April 3, 2024
    Date of Patent: October 15, 2024
    Assignee: Monumo Limited
    Inventors: Chun-Ting Lau, Daniel Bates, Kevin Bersch, William Gallafent, Jaroslaw Pawel Rzepecki, Alexey Kostin, Jonathan Rayner, Markus Kaiser, Nicolas Durrande, Rupert Tombs, Ian Murphy
  • Patent number: 11977826
    Abstract: A computer-implemented method of simulating operation of an electric drive unit to predict one or more performance parameters of the electric drive unit is provided. The electric drive unit includes at least an electric motor. The method includes obtaining parameters defining physical properties of the electric motor, obtaining parameters defining drive currents for driving the electric motor, processing the obtained parameters using a machine learning module trained a priori to predict a spatially varying electromagnetic and/or mechanical and/or thermal profile within the electric motor during operation, and providing as output a predicted profile for the electric motor, and using the predicted profile to compute the one or more performance parameters of the electric drive unit.
    Type: Grant
    Filed: August 10, 2023
    Date of Patent: May 7, 2024
    Assignee: Monumo Limited
    Inventors: Chun-Ting Lau, Daniel Bates, Kevin Bersch, William Gallafent, Jaroslaw Pawel Rzepecki, Alexey Kostin, Jonathan Rayner, Markus Kaiser, Nicolas Durrande, Rupert Tombs, Ian Murphy
  • Publication number: 20240107022
    Abstract: Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it to produce a y latent representation; (iii) encoding the y latent representation to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, (vi) processing the quantized z hyperlatent representation to obtain a location entropy parameter ?y, an entropy scale parameter ?y, and a context matrix Ay of the y latent representation; (vii) processing the y latent representation, the location entropy parameter py and the context matrix Ay, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream; and (ix) transmitting the bitstreams.
    Type: Application
    Filed: November 19, 2023
    Publication date: March 28, 2024
    Inventors: Chri BESENBRUCH, Aleksandar CHERGANSKI, Christopher FINLAY, Alexander LYTCHIER, Jonathan RAYNER, Tom RYDER, Jan XU, Arsalan ZAFAR
  • Publication number: 20240070925
    Abstract: A method of training one or more neural networks, the one or more neural networks being for use in lossy image or video encoding, transmission and decoding, the method comprising steps including: receiving an input image at a first computer system; encoding the input image using a first neural network and decoding the latent representation using a second neural network to produce an output image; at least one of the plurality of layers of the first or second neural network comprises a transformation; and the method further comprises the steps of: evaluating a difference between the output image and the input image and evaluating a function based on an output of the transformation; updating the parameters of the first neural network and the second neural network based on the evaluated difference and the evaluated function; and repeating the above steps.
    Type: Application
    Filed: August 30, 2023
    Publication date: February 29, 2024
    Inventors: Chris FINLAY, Jonathan RAYNER, Jan XU, Christian BESENBRUCH, Arsalan ZAFAR, Sebastjan CIZEL, Vira KOSHKINA
  • Patent number: 11843777
    Abstract: Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it to produce a y latent representation; (iii) encoding the y latent representation to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, (vi) processing the quantized z hyperlatent representation to obtain a location entropy parameter ?y, an entropy scale parameter ?y, and a context matrix Ay of the y latent representation; (vii) processing the y latent representation, the location entropy parameter ?y and the context matrix Ay, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream; and (ix) transmitting the bitstreams.
    Type: Grant
    Filed: February 3, 2023
    Date of Patent: December 12, 2023
    Assignee: DEEP RENDER LTD.
    Inventors: Chri Besenbruch, Aleksandar Cherganski, Christopher Finlay, Alexander Lytchier, Jonathan Rayner, Tom Ryder, Jan Xu, Arsalan Zafar
  • Publication number: 20230179768
    Abstract: Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it to produce a y latent representation; (iii) encoding the y latent representation to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, (vi) processing the quantized z hyperlatent representation to obtain a location entropy parameter ?y, an entropy scale parameter ?y, and a context matrix Ay of the y latent representation; (vii) processing the y latent representation, the location entropy parameter ?y and the context matrix Ay, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream; and (ix) transmitting the bitstreams.
    Type: Application
    Filed: February 3, 2023
    Publication date: June 8, 2023
    Inventors: Chri BESENBRUCH, Aleksandar CHERGANSKI, Christopher FINLAY, Alexander LYTCHIER, Jonathan RAYNER, Tom RYDER, Jan XU, Arsalan ZAFAR
  • Patent number: 11606560
    Abstract: Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it to produce a y latent representation; (iii) encoding the y latent representation to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, (vi) processing the quantized z hyperlatent representation to obtain a location entropy parameter ?y, an entropy scale parameter ?y, and a context matrix Ay of the y latent representation; (vii) processing the y latent representation, the location entropy parameter ?y and the context matrix Ay, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream; and (ix) transmitting the bitstreams.
    Type: Grant
    Filed: May 19, 2022
    Date of Patent: March 14, 2023
    Assignee: DEEP RENDER LTD.
    Inventors: Chri Besenbruch, Aleksandar Cherganski, Christopher Finlay, Alexander Lytchier, Jonathan Rayner, Tom Ryder, Jan Xu, Arsalan Zafar
  • Patent number: 11558620
    Abstract: Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it using an encoder trained neural network, to produce a y latent representation; (iii) encoding the y latent representation using a hyperencoder trained neural network, to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation using a predetermined entropy parameter to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, using predetermined entropy parameters; (vi) processing the quantized z hyperlatent representation using a hyperdecoder trained neural network to obtain a location entropy parameter ?y, an entropy scale parameter ?y, and a context matrix Ay of the y latent representation; (vii) processing the y latent representation, the location entropy parameter ?y and the context matrix Ay, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent r
    Type: Grant
    Filed: May 10, 2022
    Date of Patent: January 17, 2023
    Assignee: DEEP RENDER LTD.
    Inventors: Chri Besenbruch, Aleksandar Cherganski, Christopher Finlay, Alexander Lytchier, Jonathan Rayner, Tom Ryder, Jan Xu, Arsalan Zafar
  • Patent number: 11544881
    Abstract: A method for lossy image or video encoding, transmission and decoding, the method comprising the steps of: receiving an input image at a first computer system; encoding the first input training image using a first trained neural network to produce a latent representation; performing a quantization process on the latent representation to produce a quantized latent; entropy encoding the quantized latent using a probability distribution, wherein the probability distribution is defined using a tensor network; transmitting the entropy encoded quantized latent to a second computer system; entropy decoding the entropy encoded quantized latent using the probability distribution to retrieve the quantized latent; and decoding the quantized latent using a second trained neural network to produce an output image, wherein the output image is an approximation of the input training image.
    Type: Grant
    Filed: May 19, 2022
    Date of Patent: January 3, 2023
    Assignee: DEEP RENDER LTD.
    Inventors: Chris Finlay, Jonathan Rayner, Chri Besenbruch, Arsalan Zafar
  • Publication number: 20220286682
    Abstract: Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it using an encoder trained neural network, to produce a y latent representation; (iii) encoding the y latent representation using a hyperencoder trained neural network, to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation using a predetermined entropy parameter to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, using predetermined entropy parameters; (vi) processing the quantized z hyperlatent representation using a hyperdecoder trained neural network to obtain a location entropy parameter ?y, an entropy scale parameter ?y, and a context matrix Ay of the y latent representation; (vii) processing the y latent representation, the location entropy parameter ?y and the context matrix Ay, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent r
    Type: Application
    Filed: May 19, 2022
    Publication date: September 8, 2022
    Inventors: Chri BESENBRUCH, Aleksandar CHERGANSKI, Christopher FINLAY, Alexander LYTCHIER, Jonathan RAYNER, Tom RYDER, Jan XU, Arsalan ZAFAR
  • Publication number: 20220272345
    Abstract: Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it using an encoder trained neural network, to produce a y latent representation; (iii) encoding the y latent representation using a hyperencoder trained neural network, to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation using a predetermined entropy parameter to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, using predetermined entropy parameters; (vi) processing the quantized z hyperlatent representation using a hyperdecoder trained neural network to obtain a location entropy parameter ?y, an entropy scale parameter ?y, and a context matrix Ay of the y latent representation; (vii) processing the y latent representation, the location entropy parameter ?y and the context matrix Ay, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent r
    Type: Application
    Filed: May 10, 2022
    Publication date: August 25, 2022
    Inventors: Chri BESENBRUCH, Aleksandar CHERGANSKI, Christopher FINLAY, Alexander LYTCHIER, Jonathan RAYNER, Tom RYDER, Jan XU, Arsalan ZAFAR
  • Patent number: 9796457
    Abstract: A buoyant element includes an elongate buoyant body that defines: (i) a major side; (ii) a minor side that is parallel to the major side; (iii) axial ends connecting the major and minor sides at an angle of substantially 45° from the orthogonal spanning the major and minor sides; and (iv) means for connecting adjacent buoyant elements together. The length of the major side is twice the length of the minor side.
    Type: Grant
    Filed: November 13, 2013
    Date of Patent: October 24, 2017
    Inventor: Jonathan Rayner Tacon
  • Publication number: 20150307163
    Abstract: A buoyant element includes an elongate buoyant body that defines: (i) a major side; (ii) a minor side that is parallel to the major side; (iii) axial ends connecting the major and minor sides at an angle of substantially 45° from the orthogonal spanning the major and minor sides; and (iv) means for connecting adjacent buoyant elements together. The length of the major side is twice the length of the minor side.
    Type: Application
    Filed: November 13, 2013
    Publication date: October 29, 2015
    Inventor: Jonathan Rayner TACON
  • Publication number: 20060177819
    Abstract: The present invention provides compositions useful in and methods for producing populations of infectious, replication-defective alphavirus replicon particles that contain no replication competent alphavirus particles, as determined by passage on cells in culture. The compositions include helper and replicon nucleic acid molecules that can further reduce the predicted frequency for formation of replication-competent virus and can optimize manufacturing strategies and costs.
    Type: Application
    Filed: March 16, 2006
    Publication date: August 10, 2006
    Inventors: Jonathan Smith, Kurt Kamrud, Jonathan Rayner, Sergey Dryga, Ian Caley
  • Patent number: D714713
    Type: Grant
    Filed: May 28, 2013
    Date of Patent: October 7, 2014
    Inventor: Jonathan Rayner Tacon
  • Patent number: D721320
    Type: Grant
    Filed: May 28, 2013
    Date of Patent: January 20, 2015
    Inventor: Jonathan Rayner Tacon