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).
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Publication number: 20240249800Abstract: A computerized method for forecasting a future conformation of a molecular system based on a current conformation of the molecular system comprises (a) receiving the current conformation in a trained machine-learning model that has been previously trained to map a plurality of conformations received to a corresponding plurality of conformations proposed; (b) mapping the current conformation to a proposed conformation via the trained machine-learning model, wherein the proposed conformation is appended to a Markov chain; and (c) returning the proposed conformation as the future conformation.Type: ApplicationFiled: March 7, 2023Publication date: July 25, 2024Applicant: Microsoft Technology Licensing, LLCInventors: Leon Immanuel KLEIN, Yue Kwang FOONG, Tor Erlend FJELDE, Bruno Kacper MLODOZENIEC, Marc Manuel Johannes BROCKSCHMIDT, Reinhard Sebastian Bernhard NOWOZIN, Frank NOE, Ryota TOMIOKA
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Patent number: 12019705Abstract: 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: GrantFiled: April 5, 2021Date of Patent: June 25, 2024Assignee: Microsoft Technology Licensing, LLCInventors: Ioan Alexandru Stefanovici, Benn Charles Thomsen, Alexander Lloyd Gaunt, Antony Ian Taylor Rowstron, Reinhard Sebastian Bernhard Nowozin
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Patent number: 11741357Abstract: 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: GrantFiled: June 17, 2019Date of Patent: August 29, 2023Assignee: Microsoft Technology Licensing, LLCInventors: 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
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Patent number: 11710080Abstract: 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: GrantFiled: June 17, 2019Date of Patent: July 25, 2023Assignee: Microsoft Technology Licensing, LLCInventors: 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
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Publication number: 20210224355Abstract: 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: ApplicationFiled: April 5, 2021Publication date: July 22, 2021Applicant: Microsoft Technology Licensing, LLCInventors: Ioan Alexandru STEFANOVICI, Benn Charles Thomsen, Alexander Lloyd Gaunt, Antony Ian Taylor Rowstron, Reinhard Sebastian Bernhard Nowozin
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Patent number: 10970363Abstract: 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: GrantFiled: October 17, 2017Date of Patent: April 6, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Ioan Alexandru Stefanovici, Benn Charles Thomsen, Alexander Lloyd Gaunt, Antony Ian Taylor Rowstron, Reinhard Sebastian Bernhard Nowozin
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Patent number: 10832163Abstract: 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: GrantFiled: October 28, 2016Date of Patent: November 10, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Jamie Daniel Joseph Shotton, Toby Sharp, Pushmeet Kohli, Reinhard Sebastian Bernhard Nowozin, John Michael Winn, Antonio Criminisi
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Patent number: 10768825Abstract: 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: GrantFiled: November 12, 2019Date of Patent: September 8, 2020Assignee: Microsoft Technology Licensing, LLCInventors: 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
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Patent number: 10719239Abstract: 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: GrantFiled: May 16, 2018Date of Patent: July 21, 2020Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: 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
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Publication number: 20200104702Abstract: 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: ApplicationFiled: June 17, 2019Publication date: April 2, 2020Inventors: 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
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Publication number: 20200105381Abstract: 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: ApplicationFiled: June 17, 2019Publication date: April 2, 2020Inventors: 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
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Publication number: 20200081619Abstract: 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: ApplicationFiled: November 12, 2019Publication date: March 12, 2020Applicant: Microsoft Technology Licensing, LLCInventors: 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
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Publication number: 20190354283Abstract: 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: ApplicationFiled: May 16, 2018Publication date: November 21, 2019Applicant: Microsoft Technology Licensing, LLCInventors: 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
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Patent number: 10417575Abstract: 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: GrantFiled: December 14, 2012Date of Patent: September 17, 2019Assignee: Microsoft Technology Licensing, LLCInventors: Reinhard Sebastian Bernhard Nowozin, Po-Ling Loh
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Publication number: 20190114307Abstract: 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: ApplicationFiled: October 17, 2017Publication date: April 18, 2019Applicant: Microsoft Technology Licensing, LLCInventors: Ioan Alexandru STEFANOVICI, Benn Charles THOMSEN, Alexander Lloyd GAUNT, Antony Ian Taylor ROWSTRON, Reinhard Sebastian Bernhard NOWOZIN
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Patent number: 10110881Abstract: 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: GrantFiled: October 30, 2014Date of Patent: October 23, 2018Assignee: Microsoft Technology Licensing, LLCInventors: Jamie Daniel Joseph Shotton, Toby Sharp, Jonathan James Taylor, Pushmeet Kohli, Shahram Izadi, Andrew William Fitzgibbon, Reinhard Sebastian Bernhard Nowozin
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Publication number: 20170147947Abstract: 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: ApplicationFiled: October 28, 2016Publication date: May 25, 2017Inventors: Jamie Daniel Joseph Shotton, Toby Sharp, Pushmeet Kohli, Reinhard Sebastian Bernhard Nowozin, John Michael Winn, Antonio Criminisi
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Patent number: 9489639Abstract: 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: GrantFiled: November 13, 2013Date of Patent: November 8, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Jamie Daniel Joseph Shotton, Toby Sharp, Pushmeet Kohli, Reinhard Sebastian Bernhard Nowozin, John Michael Winn, Antonio Criminisi
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Patent number: 9430817Abstract: 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: GrantFiled: November 12, 2013Date of Patent: August 30, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Kevin Schelten, Reinhard Sebastian Bernhard Nowozin, Jeremy Jancsary, Carsten Curt Eckard Rother
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Patent number: 9396523Abstract: 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: GrantFiled: July 24, 2013Date of Patent: July 19, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Jeremy Jancsary, Reinhard Sebastian Bernhard Nowozin, Carsten Curt Eckard Rother