Patents by Inventor Hojjat Seyed Mousavi
Hojjat Seyed Mousavi 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: 11954288Abstract: In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.Type: GrantFiled: August 19, 2021Date of Patent: April 9, 2024Assignee: Apple Inc.Inventors: Lichen Wang, Behrooz Shahsavari, Hojjat Seyed Mousavi, Nima Ferdosi, Baboo V. Gowreesunker
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Publication number: 20240103633Abstract: Embodiments are disclosed for hold gesture recognition using machine learning (ML). In an embodiment, a method comprises: receiving sensor signals indicative of a hand gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn by the user; generating a first embedding of first features extracted from the sensor signals; predicting a first part of a hold gesture based on a first ML gesture classifier and the first embedding; generating a second embedding of second features extracted from the sensor signals; predicting a second part of the hold gesture based on a second ML gesture classifier and the second embedding; predicting a hold gesture based at least in part on outputs of the first and second ML gesture classifiers and a prediction policy; and performing an action on the wearable device or other device based on the predicted hold gesture.Type: ApplicationFiled: September 20, 2023Publication date: March 28, 2024Inventors: Bongsoo Suh, Behrooz Shahsavari, Charles Maalouf, Hojjat Seyed Mousavi, Laurence Lindsey, Shivam Kumar Gupta
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Patent number: 11907475Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface. In some examples, the electronic device can use machine learning techniques to estimate the (x, y) position and/or tilt and/or azimuth angles of the stylus tip at the touch sensitive surface based on a subset of stylus data.Type: GrantFiled: September 24, 2021Date of Patent: February 20, 2024Assignee: Apple Inc.Inventors: Hojjat Seyed Mousavi, Behrooz Shahsavari, Bongsoo Suh, Utkarsh Gaur, Nima Ferdosi, Baboo V. Gowreesunker
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Patent number: 11899881Abstract: In some examples, touch data can include noise. The noise can be generated by a component of an electronic device that includes a touch screen. For example, one or more signals transmitted to the display circuitry of an electronic device can become capacitively coupled to the touch circuitry of the device and cause noise in the touch data. Machine learning techniques, such as gated recurrent units and/or convolutional neural networks can estimate and reduce or remove noise from touch data when provided data or information about the displayed image as input. In some examples, the algorithm includes one or more of a gated recurrent unit stage and a convolutional neural network stage. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network.Type: GrantFiled: December 15, 2020Date of Patent: February 13, 2024Assignee: Apple Inc.Inventors: Hojjat Seyed Mousavi, Nima Ferdosi, Baboo V. Gowreesunker, Behrooz Shahsavari
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Patent number: 11847311Abstract: An electronic device includes a pressure sensor and a processor. The pressure sensor is disposed within an interior volume of the electronic device and configured to generate a time-dependent sequence of measurements related to a force applied to the electronic device. The processor is configured to characterize, using at least the time-dependent sequence of measurements, a venting state of the interior volume. In some embodiments, the electronic device may also include a capacitive force sensor disposed to detect distortion of the interior volume. A second time-dependent sequence of measurements related to the force may be generated by the capacitive force sensor, and used by the processor to characterize the venting state of the interior volume.Type: GrantFiled: April 13, 2020Date of Patent: December 19, 2023Assignee: Apple Inc.Inventors: Hojjat Seyed Mousavi, Yonathan Morin
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Publication number: 20230325719Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.Type: ApplicationFiled: May 26, 2023Publication date: October 12, 2023Inventors: Charles MAALOUF, Shawn R. SCULLY, Christopher B. FLEIZACH, Tu K. NGUYEN, Lilian H. LIANG, Warren J. SETO, Julian QUINTANA, Michael J. BEYHS, Hojjat SEYED MOUSAVI, Behrooz SHAHSAVARI
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Patent number: 11699104Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.Type: GrantFiled: July 20, 2022Date of Patent: July 11, 2023Assignee: Apple Inc.Inventors: Charles Maalouf, Shawn R. Scully, Christopher B. Fleizach, Tu K. Nguyen, Lilian H. Liang, Warren J. Seto, Julian Quintana, Michael J. Beyhs, Hojjat Seyed Mousavi, Behrooz Shahsavari
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Publication number: 20230195237Abstract: The present disclosure generally relates to navigating user interfaces using hand gestures.Type: ApplicationFiled: February 14, 2023Publication date: June 22, 2023Inventors: Tu K. NGUYEN, James N. CARTWRIGHT, Elizabeth C. CRANFILL, Christopher B. FLEIZACH, Joshua R. FORD, Jeremiah R. JOHNSON, Charles MAALOUF, Heriberto NIETO, Jennifer D. PATTON, Hojjat SEYED MOUSAVI, Shawn R. SCULLY, Ibrahim G. YUSUF
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Publication number: 20230095810Abstract: A method for authenticating a user is disclosed. The method includes collecting, by a processor of an electronic device and while the electronic device is worn by a user, measurement data from a set of sensors of the electronic device. The method also includes providing, by the processor and to a machine-learning model, the collected measurement data from the set of sensors and previously collected sets of measurement data for a known user. The method also includes obtaining, by the processor, an indication of whether an extracted feature set is similar to one of a number of classified feature sets. At least one of the classified feature sets is classified as belonging to the known user and generated based on the previously collected sets of measurement data for the known user. The method also includes determining, by the processor, whether the user is the known user based on the obtained indication.Type: ApplicationFiled: June 2, 2022Publication date: March 30, 2023Inventors: Hojjat Seyed Mousavi, Behrooz Shahsavari
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Patent number: 11599223Abstract: In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.Type: GrantFiled: March 12, 2021Date of Patent: March 7, 2023Assignee: Apple Inc.Inventors: Baboo V. Gowreesunker, Behrooz Shahsavari, Hojjat Seyed Mousavi, Nima Ferdosi, Lichen Wang, Nariman Farsad
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Publication number: 20220391697Abstract: Embodiments are disclosed for a machine learning (ML) gesture recognition with a framework for adding user-customized gestures. In an embodiment, a method comprises: receiving sensor data indicative of a gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn on a limb of the user; generating a current encoding of features extracted from the sensor data using a machine learning model with the features as input; generating similarity metrics between the current encoding and each encoding in a set of previously generated encodings for gestures; generating similarity scores based on the similarity metrics; predicting the gesture made by the user based on the similarity scores; and performing an action on the wearable device or other device based on the predicted gesture.Type: ApplicationFiled: May 9, 2022Publication date: December 8, 2022Inventors: Hojjat Seyed Mousavi, Behrooz Shahsavari, Nima Ferdosi, Charles Maalouf, Xuhai Xu
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Publication number: 20220374085Abstract: The present disclosure generally relates to navigating user interfaces using hand gestures.Type: ApplicationFiled: May 18, 2022Publication date: November 24, 2022Inventors: Tu K. NGUYEN, James N. CARTWRIGHT, Elizabeth C. CRANFILL, Christopher B. FLEIZACH, Joshua R. FORD, Jeremiah R. JOHNSON, Charles MAALOUF, Heriberto NIETO, Jennifer D. PATTON, Hojjat SEYED MOUSAVI, Shawn R. SCULLY, Ibrahim G. YUSUF
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Publication number: 20220351086Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.Type: ApplicationFiled: July 20, 2022Publication date: November 3, 2022Inventors: Charles MAALOUF, Shawn R. SCULLY, Christopher B. FLEIZACH, Tu K. NGUYEN, Lilian H. LIANG, Warren J. SETO, Julian QUINTANA, Michael J. BEYHS, Hojjat SEYED MOUSAVI, Behrooz SHAHSAVARI
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Patent number: 11449802Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.Type: GrantFiled: July 23, 2020Date of Patent: September 20, 2022Assignee: Apple Inc.Inventors: Charles Maalouf, Shawn R. Scully, Christopher B. Fleizach, Tu K. Nguyen, Lilian H. Liang, Warren J. Seto, Julian Quintana, Michael J. Beyhs, Hojjat Seyed Mousavi, Behrooz Shahsavari
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Patent number: 11301099Abstract: Finger detection and separation techniques on a multi-touch touch sensor panel can be improved using machine learning models (particularly for touch sensor panels with relatively low signal-to-noise ratio). In some examples, a machine learning model can be used to process an input patch to disambiguate whether the input patch corresponds to one contact or two contacts. In some examples, the machine learning model can be implemented using a neural network. The neural network can receive a sub-image including an input patch as an input, and can output a number of contacts. In some examples, the neural network can output one or more sub-image masks representing the one or more contacts.Type: GrantFiled: March 27, 2020Date of Patent: April 12, 2022Assignee: Apple Inc.Inventors: Behrooz Shahsavari, Hojjat Seyed Mousavi, Nima Ferdosi, Baboo V. Gowreesunker
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Publication number: 20220100341Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface. In some examples, the electronic device can use machine learning techniques to estimate the (x, y) position and/or tilt and/or azimuth angles of the stylus tip at the touch sensitive surface based on a subset of stylus data.Type: ApplicationFiled: September 24, 2021Publication date: March 31, 2022Inventors: Hojjat SEYED MOUSAVI, Behrooz SHAHSAVARI, Bongsoo SUH, Utkarsh GAUR, Nima FERDOSI, Baboo V. GOWREESUNKER
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Publication number: 20220019311Abstract: In some examples, touch data can include noise. The noise can be generated by a component of an electronic device that includes a touch screen. For example, one or more signals transmitted to the display circuitry of an electronic device can become capacitively coupled to the touch circuitry of the device and cause noise in the touch data. Machine learning techniques, such as gated recurrent units and/or convolutional neural networks can estimate and reduce or remove noise from touch data when provided data or information about the displayed image as input. In some examples, the algorithm includes one or more of a gated recurrent unit stage and a convolutional neural network stage. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network.Type: ApplicationFiled: December 15, 2020Publication date: January 20, 2022Inventors: Hojjat SEYED MOUSAVI, Nima FERDOSI, Baboo V. GOWREESUNKER, Behrooz SHAHSAVARI
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Publication number: 20210142214Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.Type: ApplicationFiled: July 23, 2020Publication date: May 13, 2021Inventors: Charles MAALOUF, Shawn R. SCULLY, Christopher B. FLEIZACH, Tu K. NGUYEN, Lilian H. LIANG, Warren J. SETO, Julian QUINTANA, Michael J. BEYHS, Hojjat SEYED MOUSAVI, Behrooz SHAHSAVARI
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Patent number: 10866683Abstract: A device includes a housing defining part of an interior volume and an opening to the interior volume; a cover mounted to the housing to cover the opening and further define the interior volume; a display mounted within the interior volume and viewable through the cover; and a system in package (SiP) mounted within the interior volume. The SiP includes a self-capacitance sense pad adjacent a first surface of the SiP; a set of solder structures attached to a second surface of the SiP, the second surface opposite the first surface; and an IC coupled to the self-capacitance sense pad and configured to output, at one or more solder structures in the set of solder structures, a digital value related to a measured capacitance of the self-capacitance sense pad. The SiP is mounted within the interior volume with the first surface positioned closer to the cover than the second surface.Type: GrantFiled: August 23, 2019Date of Patent: December 15, 2020Assignee: Apple Inc.Inventors: Pavan O. Gupta, Andrew W. Joyce, Benedict Drevniok, Mo Li, David S. Graff, Albert Lin, Julian K. Shutzberg, Hojjat Seyed Mousavi
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Publication number: 20200371657Abstract: An electronic device includes a pressure sensor and a processor. The pressure sensor is disposed within an interior volume of the electronic device and configured to generate a time-dependent sequence of measurements related to a force applied to the electronic device. The processor is configured to characterize, using at least the time-dependent sequence of measurements, a venting state of the interior volume. In some embodiments, the electronic device may also include a capacitive force sensor disposed to detect distortion of the interior volume. A second time-dependent sequence of measurements related to the force may be generated by the capacitive force sensor, and used by the processor to characterize the venting state of the interior volume.Type: ApplicationFiled: April 13, 2020Publication date: November 26, 2020Inventors: Hojjat Seyed Mousavi, Yonathan Morin