Patents by Inventor Ivan Jelev Tashev
Ivan Jelev Tashev 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: 20240329751Abstract: This document relates to employing tongue gestures to control a computing device, and training machine learning models to detect tongue gestures. One example relates to a method or technique that can include receiving one or more motion signals from an inertial sensor. The method or technique can also include detecting a tongue gesture based at least on the one or more motion signals, and outputting the tongue gesture.Type: ApplicationFiled: May 21, 2024Publication date: October 3, 2024Applicant: Microsoft Technology Licensing, LLCInventors: Raymond Michael WINTERS, IV, Tan GEMICIOGLU, Thomas Matthew GABLE, Yu-Te WANG, Ivan Jelev TASHEV
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Publication number: 20240268741Abstract: The description relates to self-dispensing electrodes. One example can include a curved hollow tube configured to hold a flowable conductive material and a selective retention mechanism positioned on the curved hollow tube and configured to retain the flowable conductive material in the hollow tube unless a force is imparted on the curved hollow tube.Type: ApplicationFiled: February 10, 2023Publication date: August 15, 2024Applicant: Microsoft Technology Licensing, LLCInventors: Yu-Te WANG, Ivan Jelev TASHEV
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Patent number: 12019808Abstract: This document relates to employing tongue gestures to control a computing device, and training machine learning models to detect tongue gestures. One example relates to a method or technique that can include receiving one or more motion signals from an inertial sensor. The method or technique can also include detecting a tongue gesture based at least on the one or more motion signals, and outputting the tongue gesture.Type: GrantFiled: December 6, 2022Date of Patent: June 25, 2024Assignee: Microsoft Technology Licensing, LLCInventors: Raymond Michael Winters, IV, Tan Gemicioglu, Thomas Matthew Gable, Yu-Te Wang, Ivan Jelev Tashev
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Publication number: 20240085985Abstract: This document relates to employing tongue gestures to control a computing device, and training machine learning models to detect tongue gestures. One example relates to a method or technique that can include receiving one or more motion signals from an inertial sensor. The method or technique can also include detecting a tongue gesture based at least on the one or more motion signals, and outputting the tongue gesture.Type: ApplicationFiled: December 6, 2022Publication date: March 14, 2024Applicant: Microsoft Technology Licensing, LLCInventors: Raymond Michael WINTERS, IV, Tan GEMICIOGLU, Thomas Matthew GABLE, Yu-Te WANG, Ivan Jelev TASHEV
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Patent number: 11847261Abstract: A computer device is provided that includes a display device, and a sensor system configured to be mounted adjacent to a user's head and to measure an electrical potential near one or more electrodes of the sensor system. The computer device further includes a processor configured to present a periodic motion-based visual stimulus having a changing motion that is frequency-modulated for a target frequency or code-modulated for a target code, detect changes in the electrical potential via the one or more electrodes, identify a corresponding visual evoked potential feature in the detected changes in electrical potential that corresponds to the periodic motion-based visual stimulus, and recognize a user input to the computing device based on identifying the corresponding visual evoked potential feature.Type: GrantFiled: July 27, 2022Date of Patent: December 19, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Andrew D. Wilson, Hakim Si Mohammed, Christian Holz, Adrian Kuo Ching Lee, Ivan Jelev Tashev, Hannes Gamper, Edward Bryan Cutrell, David Emerson Johnston, Dimitra Emmanouilidou, Mihai R. Jalobeanu
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Publication number: 20230190196Abstract: A force-controlled electroencephalogram (EEG) monitoring device maintains a constant pressure between electrodes and the scalp of a user thereby increasing user comfort. Arms on the EEG monitoring device position the electrodes in contact with specific regions on the head of the user. The dimension, shape, and curvature of the arms affect the amount of force with which an electrode is held in contact with the user's scalp. The amount of pressure may be different for different regions of the user's head to achieve a balance between comfort and conductivity. The amount of pressure may be further modulated by the use of spring-loaded electrode holders that allow an electrode to move relative to the holder. To further improve user comfort, the tips of the electrodes may be hemispherical rather than pointed. The EEG monitoring device can be used as input for a brain-computer interface (BCI).Type: ApplicationFiled: December 17, 2021Publication date: June 22, 2023Inventors: Yu-Te WANG, Ivan Jelev TASHEV, Teresa Elizabeth LASCALA
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Publication number: 20230190175Abstract: A semi-dry electrode combines advantages of wet electrodes and dry electrodes by use of a rotatable ball to apply a conductive gel at the tip of the electrode in a manner similar to how a ballpen applies ink. A reservoir in the semi-dry electrode contains the conductive gel that is applied by the ball to the skin of the user. This creates a thin film of conductive gel at the tip of the semi-dry electrode which reduces impedance and increases the signal-to-noise (SNR) ratio. Directly applying the conductive gel from within the electrode itself reduces mess and improves user convenience. The semi-dry electrode may be used in a lightweight electroencephalography (EEG) monitoring device to detect brain activity. The brain activity may be used as input for a brain-computer interface (BCI).Type: ApplicationFiled: December 17, 2021Publication date: June 22, 2023Inventors: Yu-Te WANG, Ivan Jelev TASHEV
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Patent number: 11593633Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved real-time audio processing. One method including: constructing a deep neural network model, including a plurality of at least one-bit neurons, configured to output a predicted label of audio data, the plurality of at least one-bit neurons arranged in a plurality of layers, including at least one hidden layer, and being connected by a plurality of connections, each connection having at least a one-bit weight, wherein one or both of the plurality of at least one-bit neurons and the plurality of connections have a reduced bit precision; receiving a training data set, the training data set including audio data; training the deep neural network model using the training data set; and outputting a trained deep neural network model configured to output a predicted label of real-time audio data.Type: GrantFiled: April 13, 2018Date of Patent: February 28, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Ivan Jelev Tashev, Shuayb M Zarar, Matthai Philipose, Jong Hwan Ko
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Publication number: 20220365599Abstract: A computer device is provided that includes a display device, and a sensor system configured to be mounted adjacent to a user's head and to measure an electrical potential near one or more electrodes of the sensor system. The computer device further includes a processor configured to present a periodic motion-based visual stimulus having a changing motion that is frequency-modulated for a target frequency or code-modulated for a target code, detect changes in the electrical potential via the one or more electrodes, identify a corresponding visual evoked potential feature in the detected changes in electrical potential that corresponds to the periodic motion-based visual stimulus, and recognize a user input to the computing device based on identifying the corresponding visual evoked potential feature.Type: ApplicationFiled: July 27, 2022Publication date: November 17, 2022Applicant: Microsoft Technology Licensing, LLCInventors: Andrew D. WILSON, Hakim SI MOHAMMED, Christian HOLZ, Adrian Kuo Ching LEE, Ivan Jelev TASHEV, Hannes GAMPER, Edward Bryan CUTRELL, David Emerson JOHNSTON, Dimitra EMMANOUILIDOU, Mihai R. JALOBEANU
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Patent number: 11409361Abstract: A computer device is provided that includes a display device, and a sensor system configured to be mounted adjacent to a user's head and to measure an electrical potential near one or more electrodes of the sensor system. The computer device further includes a processor configured to present a periodic motion-based visual stimulus having a changing motion that is frequency-modulated for a target frequency or code-modulated for a target code, detect changes in the electrical potential via the one or more electrodes, identify a corresponding visual evoked potential feature in the detected changes in electrical potential that corresponds to the periodic motion-based visual stimulus, and recognize a user input to the computing device based on identifying the corresponding visual evoked potential feature.Type: GrantFiled: February 3, 2020Date of Patent: August 9, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Andrew D. Wilson, Hakim Si Mohammed, Christian Holz, Adrian Kuo Ching Lee, Ivan Jelev Tashev, Hannes Gamper, Edward Bryan Cutrell, David Emerson Johnston, Dimitra Emmanouilidou, Mihai R. Jalobeanu
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Patent number: 11205443Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved audio feature discovery using a neural network. One method including: receiving a trained neural network model, the trained neural network configured to output an audio feature classification of audio data; deconstructing the trained neural network model to generate at least one saliency map, the at least one saliency map providing a successful classification of the audio feature; and extracting at least one visualization of the audio feature the trained neural network model relies on for classification based on the at least one saliency map.Type: GrantFiled: July 27, 2018Date of Patent: December 21, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Etienne Thuillier, Hannes Gamper, Ivan Jelev Tashev
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Publication number: 20210240264Abstract: A computer device is provided is includes a display device, and a sensor system configured to be mounted adjacent to a user's head and to measure an electrical potential near one or more electrodes of the sensor system. The computer device further includes a processor configured to present a periodic motion-based visual stimulus having a changing motion that is frequency-modulated for a target frequency or code-modulated for a target code, detect changes in the electrical potential via the one or more electrodes, identify a corresponding visual evoked potential feature in the detected changes in electrical potential that corresponds to the periodic motion-based visual stimulus, and recognize a user input to the computing device based on identifying the corresponding visual evoked potential feature.Type: ApplicationFiled: February 3, 2020Publication date: August 5, 2021Applicant: Microsoft Technology Licensing, LLCInventors: Andrew D. WILSON, Hakim SI MOHAMMED, Christian HOLZ, Adrian Kuo Ching LEE, Ivan Jelev TASHEV, Hannes GAMPER, Edward Bryan CUTRELL, David Emerson JOHNSTON, Dimitra EMMANOUILIDOU, Mihai R. JALOBEANU
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Patent number: 11012802Abstract: A computing system that facilitates decoding a spherical harmonics (SH) representation of a three-dimensional sound signal to a binaural sound signal is described herein. The computing system generates a binaural sound signal based upon the SH representation, a tapering window function that is selected based on an SH encoding order of the SH representation, and a coloration compensation filter that incorporates the tapering window function. The computing system causes the binaural sound signal to be played over at least two speakers.Type: GrantFiled: July 2, 2019Date of Patent: May 18, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Christoph Felix Hold, Hannes Gamper, Ville Topias Pulkki, Nikunj Raghuvanshi, Ivan Jelev Tashev
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Patent number: 10984315Abstract: A facility for processing output from a network of mechanical sensors is described. The facility accesses time-series data outputted by the network of sensors. The facility applies to the accessed time-series data a trained autoencoder to obtain a version of the accessed time-series data in which noise present in the accessed time-series data is at least partially suppressed. The facility stores the obtained version of the accessed time-series data, such as in order to perform human activity recognition against the obtained version of the accessed time-series data.Type: GrantFiled: April 28, 2017Date of Patent: April 20, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Shuayb M Zarar, Ivan Jelev Tashev
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Publication number: 20210006923Abstract: A computing system that facilitates decoding a spherical harmonics (SH) representation of a three-dimensional sound signal to a binaural sound signal is described herein. The computing system generates a binaural sound signal based upon the SH representation, a tapering window function that is selected based on an SH encoding order of the SH representation, and a coloration compensation filter that incorporates the tapering window function. The computing system causes the binaural sound signal to be played over at least two speakers.Type: ApplicationFiled: July 2, 2019Publication date: January 7, 2021Inventors: Christoph Felix HOLD, Hannes GAMPER, Ville Topias PULKKI, Nikunj RAGHUVANSHI, Ivan Jelev TASHEV
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Patent number: 10672414Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved real-time audio processing. One method including: receiving audio data including a plurality of frames having a plurality of frequency bins; calculating, for each frequency bin, an approximate speech signal estimation based on the plurality of frames; calculating, for each approximate speech signal estimation, a clean speech estimation and at least one additional target including an ideal ratio mask using a trained neural network model; and calculating, for each frequency bin, a final clean speech estimation using the calculated at least one additional target including the calculated ideal ratio mask and the calculated clean speech estimation.Type: GrantFiled: April 13, 2018Date of Patent: June 2, 2020Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Ivan Jelev Tashev, Shuayb M Zarar, Yan-Hui Tu, Chin-Hui Lee, Han Zhao
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Publication number: 20200035259Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved audio feature discovery using a neural network. One method including: receiving a trained neural network model, the trained neural network configured to output an audio feature classification of audio data; deconstructing the trained neural network model to generate at least one saliency map, the at least one saliency map providing a successful classification of the audio feature; and extracting at least one visualization of the audio feature the trained neural network model relies on for classification based on the at least one saliency map.Type: ApplicationFiled: July 27, 2018Publication date: January 30, 2020Applicant: Microsoft Technology Licensing, LLCInventors: Etienne THUILLIER, Hannes GAMPER, Ivan Jelev TASHEV
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Patent number: 10528147Abstract: An ultrasonic gesture recognition system is provided that recognizes gestures based on analysis of return signals of an ultrasonic pulse that is reflected from a gesture. The system transmits an ultrasonic chirp and samples a microphone array at sample intervals to collect a return signal for each microphone. The system then applies a beamforming technique to frequency domain representations of the return signals to generate an acoustic image with a beamformed return signal for multiple directions. The system then generates a feature image from the acoustic images to identify, for example, distance or depth from the microphone array to the gesture for each direction. The system then submits the feature image to a deep learning system to classify the gesture.Type: GrantFiled: June 30, 2017Date of Patent: January 7, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Ivan Jelev Tashev, Shuayb Zarar, Amit Das
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Publication number: 20190318237Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved real-time audio processing. One method including: constructing a deep neural network model, including a plurality of at least one-bit neurons, configured to output a predicted label of audio data, the plurality of at least one-bit neurons arranged in a plurality of layers, including at least one hidden layer, and being connected by a plurality of connections, each connection having at least a one-bit weight, wherein one or both of the plurality of at least one-bit neurons and the plurality of connections have a reduced bit precision; receiving a training data set, the training data set including audio data; training the deep neural network model using the training data set; and outputting a trained deep neural network model configured to output a predicted label of real-time audio data.Type: ApplicationFiled: April 13, 2018Publication date: October 17, 2019Applicant: Microsoft Technology Licensing, LLCInventors: Ivan Jelev TASHEV, Shuayb M ZARAR, Matthai PHILIPOSE, Jong HWAN KO
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Publication number: 20190318755Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved real-time audio processing. One method including: receiving audio data including a plurality of frames having a plurality of frequency bins; calculating, for each frequency bin, an approximate speech signal estimation based on the plurality of frames; calculating, for each approximate speech signal estimation, a clean speech estimation and at least one additional target including an ideal ratio mask using a trained neural network model; and calculating, for each frequency bin, a final clean speech estimation using the calculated at least one additional target including the calculated ideal ratio mask and the calculated clean speech estimation.Type: ApplicationFiled: April 13, 2018Publication date: October 17, 2019Applicant: Microsoft Technology Licensing, LLCInventors: Ivan Jelev TASHEV, Shuayb M ZARAR, Yan-Hui TU, Chin-Hui LEE, Han ZHAO