Patents by Inventor Reinhard Sebastian Bernhard Nowozin

Reinhard Sebastian Bernhard Nowozin 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: 11741357
    Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise one or more physical conditions of the user; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a circumstance when the user is exhibiting a particular physical condition to output subsequent questions.
    Type: Grant
    Filed: June 17, 2019
    Date of Patent: August 29, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Cheng Zhang, Reinhard Sebastian Bernhard Nowozin, Ameera Patel, Danielle Charlotte Mary Belgrave, Konstantina Palla, Anja Thieme, Iain Edward Buchan, Chao Ma, Sebastian Tschiatschek, Jose Miguel Hernandez Lobato
  • Patent number: 11710080
    Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.
    Type: Grant
    Filed: June 17, 2019
    Date of Patent: July 25, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Cheng Zhang, Reinhard Sebastian Bernhard Nowozin, Ameera Patel, Danielle Charlotte Mary Belgrave, Konstantina Palla, Anja Thieme, Iain Edward Buchan, Chao Ma, Sebastian Tschiatschek, Jose Miguel Hernandez Lobato
  • Publication number: 20210224355
    Abstract: Examples are disclosed that relate to encoding data on a data-storage medium. The method comprises obtaining a representation of a measurement performed on the data-storage medium, the representation being based on a previously recorded pattern of data encoded in the data-storage medium in a layout that defines a plurality of data locations. The method further comprises inputting the representation into a data decoder comprising a trained machine-learning function, and obtaining from the data decoder, for each data location of the layout, a plurality of probability values, wherein each probability value is associated with a corresponding data value and represents the probability that the corresponding data value matches the actual data value in the previously recorded pattern of data at a same location in the layout.
    Type: Application
    Filed: April 5, 2021
    Publication date: July 22, 2021
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Ioan Alexandru STEFANOVICI, Benn Charles Thomsen, Alexander Lloyd Gaunt, Antony Ian Taylor Rowstron, Reinhard Sebastian Bernhard Nowozin
  • Patent number: 10970363
    Abstract: Examples are disclosed that relate to reading stored data. The method comprises obtaining a representation of a measurement performed on a data-storage medium, the representation being based on a previously recorded pattern of data encoded in the data-storage medium in a layout that defines a plurality of data locations. The method further comprises inputting the representation into a data decoder comprising a trained machine-learning function, and obtaining from the data decoder, for each data location of the layout, a plurality of probability values, wherein each probability value is associated with a corresponding data value and represents the probability that the corresponding data value matches the actual data value in the previously recorded pattern of data at a same location in the layout.
    Type: Grant
    Filed: October 17, 2017
    Date of Patent: April 6, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ioan Alexandru Stefanovici, Benn Charles Thomsen, Alexander Lloyd Gaunt, Antony Ian Taylor Rowstron, Reinhard Sebastian Bernhard Nowozin
  • Patent number: 10832163
    Abstract: Memory facilitation using directed acyclic graphs is described, for example, where a plurality of directed acyclic graphs are trained for gesture recognition from human skeletal data, or to estimate human body joint positions from depth images for gesture detection. In various examples directed acyclic graphs are grown during training using a training objective which takes into account both connection patterns between nodes and split function parameter values. For example, a layer of child nodes is grown and connected to a parent layer of nodes using an initialization strategy. In examples, various local search processes are used to find good combinations of connection patterns and split function parameters.
    Type: Grant
    Filed: October 28, 2016
    Date of Patent: November 10, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Jamie Daniel Joseph Shotton, Toby Sharp, Pushmeet Kohli, Reinhard Sebastian Bernhard Nowozin, John Michael Winn, Antonio Criminisi
  • Patent number: 10768825
    Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.
    Type: Grant
    Filed: November 12, 2019
    Date of Patent: September 8, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Antony Ian Taylor Rowstron, Ioan Alexandru Stefanovici, Aaron William Ogus, Douglas Wayne Phillips, Richard John Black, Austin Nicholas Donnelly, Alexander Lloyd Gaunt, Andreas Georgiou, Ariel Gomez Diaz, Serguei Anatolievitch Legtchenko, Reinhard Sebastian Bernhard Nowozin, Benn Charles Thomsen, Hugh David Paul Williams, David Lara Saucedo, Patrick Neil Anderson, Andromachi Chatzieleftheriou, John Christopher Dainty, James Hilton Clegg, Raluca Andreea Diaconu, Rokas Drevinskas, Mengyang Yang
  • Patent number: 10719239
    Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.
    Type: Grant
    Filed: May 16, 2018
    Date of Patent: July 21, 2020
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Antony Ian Taylor Rowstron, Ioan Alexandru Stefanovici, Aaron William Ogus, Douglas Wayne Phillips, Richard John Black, Austin Nicholas Donnelly, Alexander Lloyd Gaunt, Andreas Georgiou, Ariel Gomez Diaz, Serguei Anatolievitch Legtchenko, Reinhard Sebastian Bernhard Nowozin, Benn Charles Thomsen, Hugh David Paul Williams, David Lara Saucedo, Patrick Neil Anderson, Andromachi Chatzieleftheriou, John Christopher Dainty, James Hilton Clegg, Raluca Andreea Diaconu, Rokas Drevinskas, Mengyang Yang
  • Publication number: 20200105381
    Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.
    Type: Application
    Filed: June 17, 2019
    Publication date: April 2, 2020
    Inventors: Cheng ZHANG, Reinhard Sebastian Bernhard NOWOZIN, Ameera PATEL, Danielle Charlotte Mary BELGRAVE, Konstantina PALLA, Anja THIEME, Iain Edward BUCHAN, Chao MA, Sebastian TSCHIATSCHEK, Jose Miguel HERNANDEZ LOBATO
  • Publication number: 20200104702
    Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise one or more physical conditions of the user; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a circumstance when the user is exhibiting a particular physical condition to output subsequent questions.
    Type: Application
    Filed: June 17, 2019
    Publication date: April 2, 2020
    Inventors: Cheng ZHANG, Reinhard Sebastian Bernhard NOWOZIN, Ameera PATEL, Danielle Charlotte Mary BELGRAVE, Konstantina PALLA, Anja THIEME, Iain Edward BUCHAN, Chao MA, Sebastian TSCHIATSCHEK, Jose Miguel HERNANDEZ LOBATO
  • Publication number: 20200081619
    Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.
    Type: Application
    Filed: November 12, 2019
    Publication date: March 12, 2020
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Antony Ian Taylor ROWSTRON, Ioan Alexandru STEFANOVICI, Aaron William OGUS, Douglas Wayne PHILLIPS, Richard John BLACK, Austin Nicholas DONNELLY, Alexander Lloyd GAUNT, Andreas GEORGIOU, Ariel GOMEZ DIAZ, Serguei Anatolievitch LEGTCHENKO, Reinhard Sebastian Bernhard NOWOZIN, Benn Charles THOMSEN, Hugh David Paul WILLIAMS, David LARA SAUCEDO, Patrick Neil ANDERSON, Andromachi CHATZIELEFTHERIOU, John Christopher DAINTY, James Hilton CLEGG, Raluca Andreea DIACONU, Rokas DREVINSKAS, Mengyang YANG
  • Publication number: 20190354283
    Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.
    Type: Application
    Filed: May 16, 2018
    Publication date: November 21, 2019
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Antony Ian Taylor ROWSTRON, Ioan Alexandru STEFANOVICI, Aaron William OGUS, Douglas Wayne PHILLIPS, Richard John BLACK, Austin Nicholas DONNELLY, Alexander Lloyd GAUNT, Andreas GEORGIOU, Ariel GOMEZ DIAZ, Serguei Anatolievitch LEGTCHENKO, Reinhard Sebastian Bernhard NOWOZIN, Benn Charles THOMSEN, Hugh David Paul WILLIAMS, David LARA SAUCEDO, Patrick Neil ANDERSON, Andromachi CHATZIELEFTHERIOU, John Christopher DAINTY, James Hilton CLEGG, Raluca Andreea DIACONU, Rokas DREVINSKAS, Mengyang YANG
  • Patent number: 10417575
    Abstract: Resource allocation for machine learning is described such as for selecting between many possible options, for example, as part of an efficient training process for random decision tree training, for selecting which of many families of models best describes data, for selecting which of many features best classifies items. In various examples samples of information about uncertain options are used to score the options. In various examples, confidence intervals are calculated for the scores and used to select one or more of the options. In examples, the scores of the options may be bounded difference statistics which change little as any sample is omitted from the calculation of the score. In an example, random decision tree training is made more efficient while retaining accuracy for applications not limited to human body pose detection from depth images.
    Type: Grant
    Filed: December 14, 2012
    Date of Patent: September 17, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Reinhard Sebastian Bernhard Nowozin, Po-Ling Loh
  • Publication number: 20190114307
    Abstract: Examples are disclosed that relate to reading stored data. The method comprises obtaining a representation of a measurement performed on a data-storage medium, the representation being based on a previously recorded pattern of data encoded in the data-storage medium in a layout that defines a plurality of data locations. The method further comprises inputting the representation into a data decoder comprising a trained machine-learning function, and obtaining from the data decoder, for each data location of the layout, a plurality of probability values, wherein each probability value is associated with a corresponding data value and represents the probability that the corresponding data value matches the actual data value in the previously recorded pattern of data at a same location in the layout.
    Type: Application
    Filed: October 17, 2017
    Publication date: April 18, 2019
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Ioan Alexandru STEFANOVICI, Benn Charles THOMSEN, Alexander Lloyd GAUNT, Antony Ian Taylor ROWSTRON, Reinhard Sebastian Bernhard NOWOZIN
  • Patent number: 10110881
    Abstract: Model fitting from raw time of flight image data is described, for example, to track position and orientation of a human hand or other entity. In various examples, raw image data depicting the entity is received from a time of flight camera. A 3D model of the entity is accessed and used to render, from the 3D model, simulations of raw time of flight image data depicting the entity in a specified pose/shape. The simulated raw image data and at least part of the received raw image data are compared and on the basis of the comparison, parameters of the entity are computed.
    Type: Grant
    Filed: October 30, 2014
    Date of Patent: October 23, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Jamie Daniel Joseph Shotton, Toby Sharp, Jonathan James Taylor, Pushmeet Kohli, Shahram Izadi, Andrew William Fitzgibbon, Reinhard Sebastian Bernhard Nowozin
  • Publication number: 20170147947
    Abstract: Memory facilitation using directed acyclic graphs is described, for example, where a plurality of directed acyclic graphs are trained for gesture recognition from human skeletal data, or to estimate human body joint positions from depth images for gesture detection. In various examples directed acyclic graphs are grown during training using a training objective which takes into account both connection patterns between nodes and split function parameter values. For example, a layer of child nodes is grown and connected to a parent layer of nodes using an initialization strategy. In examples, various local search processes are used to find good combinations of connection patterns and split function parameters.
    Type: Application
    Filed: October 28, 2016
    Publication date: May 25, 2017
    Inventors: Jamie Daniel Joseph Shotton, Toby Sharp, Pushmeet Kohli, Reinhard Sebastian Bernhard Nowozin, John Michael Winn, Antonio Criminisi
  • Patent number: 9489639
    Abstract: Memory facilitation using directed acyclic graphs is described, for example, where a plurality of directed acyclic graphs are trained for gesture recognition from human skeletal data, or to estimate human body joint positions from depth images for gesture detection. In various examples directed acyclic graphs are grown during training using a training objective which takes into account both connection patterns between nodes and split function parameter values. For example, a layer of child nodes is grown and connected to a parent layer of nodes using an initialization strategy. In examples, various local search processes are used to find good combinations of connection patterns and split function parameters.
    Type: Grant
    Filed: November 13, 2013
    Date of Patent: November 8, 2016
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Jamie Daniel Joseph Shotton, Toby Sharp, Pushmeet Kohli, Reinhard Sebastian Bernhard Nowozin, John Michael Winn, Antonio Criminisi
  • Patent number: 9430817
    Abstract: Blind image deblurring with a cascade architecture is described, for example, where photographs taken on a camera phone are deblurred in a process which revises blur estimates and estimates a blur function as a combined process. In various examples the estimates of the blur function are computed using first trained machine learning predictors arranged in a cascade architecture. In various examples a revised blur estimate is calculated at each level of the cascade using a latest deblurred version of a blurred image. In some examples the revised blur estimates are calculated using second trained machine learning predictors interleaved with the first trained machine learning predictors.
    Type: Grant
    Filed: November 12, 2013
    Date of Patent: August 30, 2016
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Kevin Schelten, Reinhard Sebastian Bernhard Nowozin, Jeremy Jancsary, Carsten Curt Eckard Rother
  • Patent number: 9396523
    Abstract: Image restoration cascades are described, for example, where digital photographs containing noise are restored using a cascade formed from a plurality of layers of trained machine learning predictors connected in series. For example, noise may be from sensor noise, motion blur, dust, optical low pass filtering, chromatic aberration, compression and quantization artifacts, down sampling or other sources. For example, given a noisy image, each trained machine learning predictor produces an output image which is a restored version of the noisy input image; each trained machine learning predictor in a given internal layer of the cascade also takes input from the previous layer in the cascade. In various examples, a loss function expressing dissimilarity between input and output images of each trained machine learning predictor is directly minimized during training. In various examples, data partitioning is used to partition a training data set to facilitate generalization.
    Type: Grant
    Filed: July 24, 2013
    Date of Patent: July 19, 2016
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Jeremy Jancsary, Reinhard Sebastian Bernhard Nowozin, Carsten Curt Eckard Rother
  • Patent number: 9373087
    Abstract: Improved decision tree training in machine learning is described, for example, for automated classification of body organs in medical images or for detection of body joint positions in depth images. In various embodiments, improved estimates of uncertainty are used when training random decision forests for machine learning tasks in order to give improved accuracy of predictions and fewer errors. In examples, bias corrected estimates of entropy or Gini index are used or non-parametric estimates of differential entropy. In examples, resulting trained random decision forests are better able to perform classification or regression tasks for a variety of applications without undue increase in computational load.
    Type: Grant
    Filed: October 25, 2012
    Date of Patent: June 21, 2016
    Assignee: Microsoft Technology Licensing, LLC
    Inventor: Reinhard Sebastian Bernhard Nowozin
  • Patent number: 9344690
    Abstract: Image demosaicing is described, for example, to enable raw image sensor data, where image elements have intensity values in only one of three color channels, to be converted into a color image where image elements have intensity values in three color channels. In various embodiments a trained machine learning component is used to carry out demosaicing optionally in combination with denoising. In some examples the trained machine learning system comprises a cascade of trained regression tree fields. In some examples the machine learning component has been trained using pairs of mosaiced and demosaiced images where the demosaiced images have been obtained by downscaling natural color digital images. For example, the mosaiced images are obtained from the demosaiced images by subsampling according to one of a variety of color filter array patterns.
    Type: Grant
    Filed: January 24, 2014
    Date of Patent: May 17, 2016
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Reinhard Sebastian Bernhard Nowozin, Danyal Khashabi, Jeremy Martin Jancsary, Bruce Justin Lindbloom, Andrew William Fitzgibbon