Patents by Inventor T. Scott Saponas
T. Scott Saponas 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: 9244888Abstract: A “Placement Detector” enables handheld or mobile electronic devices such as phones, media players, tablets, etc., to infer their current position or placement. Placement inference is performed by evaluating one or more sensors associated with the device relative to one or more trained probabilistic models to infer device relative to a user. Example placement inferences include, but are not limited to, inferring whether the device is currently in a user's pocket, in a user's purse (or other carrying bag or backpack), in a closed area such as a drawer or box, in an open area such as on a table, indoors, outdoors, etc. These types of placement inferences facilitate a wide range of automated user-device interactions, including, but not limited to, placement-dependent notifications, placement-dependent responses to various inputs, prevention of inadvertent “pocket dialing,” prevention of inadvertent power cycling of devices, lost or misplaced device location assistance, etc.Type: GrantFiled: March 15, 2013Date of Patent: January 26, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Alice Jane Bernheim Brush, T. Scott Saponas, Jason Wiese
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Patent number: 9174084Abstract: A physical activity monitoring device includes a sensor array with one or more sensors configured to measure physical activity attributes of a user. A controller automatically determines time intervals where the user is actively engaged in a physical activity based on the physical activity attributes. The controller also automatically determines a type of physical activity the user in actively engaged in during the determined time intervals based on the physical activity attributes. A reporter outputs information regarding the type of physical activity to the user.Type: GrantFiled: March 5, 2013Date of Patent: November 3, 2015Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Daniel Morris, Ilya Kelner, Farah Shariff, Dennis Tom, T. Scott Saponas, Andrew Guillory
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Publication number: 20150228062Abstract: A “Food Logger” provides various approaches for learning or training one or more image-based models (referred to herein as “meal models”) of nutritional content of meals. This training is based on one or more datasets of images of meals in combination with “meal features” that describe various parameters of the meal. Examples of meal features include, but are not limited to, food type, meal contents, portion size, nutritional content (e.g., calories, vitamins, minerals, carbohydrates, protein, salt, etc.), food source (e.g., specific restaurants or restaurant chains, grocery stores, particular pre-packaged foods, school meals, meals prepared at home, etc.). Given the trained models, the Food Logger automatically provides estimates of nutritional information based on automated recognition of new images of meals provided by (or for) the user. This nutritional information is then used to enable a wide range of user-centric interactions relating to food consumed by individual users.Type: ApplicationFiled: February 12, 2014Publication date: August 13, 2015Applicant: Microsoft CorporationInventors: Neel Suresh Joshi, Siddharth Khullar, T Scott Saponas, Daniel Morris, Oscar Beijbom
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Patent number: 9037530Abstract: A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCI) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller.Type: GrantFiled: March 29, 2012Date of Patent: May 19, 2015Assignee: Microsoft Technology Licensing, LLCInventors: Desney Tan, T. Scott Saponas, Dan Morris, Jim Turner
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Patent number: 8951164Abstract: A physical activity monitoring device receives an indication of one or more physical activities to be performed as an extension of a game being played on a game system and measures physical activity attributes of a user wearing the physical activity monitoring device. The physical activity monitoring device determines the user's progress towards completion of the one or more physical activities based on the physical activity attributes and outputs to the game device an indication of the user's progress towards completion of the one or more physical activities.Type: GrantFiled: March 5, 2013Date of Patent: February 10, 2015Assignee: Microsoft CorporationInventors: Daniel Morris, Ilya Kelner, Farah Shariff, Dennis Tom, T. Scott Saponas, Andrew Guillory
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Patent number: 8951165Abstract: A physical activity monitoring device receives a workout regimen including a plurality of exercises. For each of the plurality of exercises, the physical activity monitoring device indicates that exercise to a user and measures physical activity attributes of the user. The physical activity monitoring device outputs information regarding the user's progress towards completion of that exercise based on the physical activity attributes.Type: GrantFiled: March 5, 2013Date of Patent: February 10, 2015Assignee: Microsoft CorporationInventors: Daniel Morris, Ilya Kelner, Farah Shariff, Dennis Tom, T. Scott Saponas, Andrew Guillory
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Patent number: 8892479Abstract: A machine learning model is trained by instructing a user to perform various predefined gestures, sampling signals from EMG sensors arranged arbitrarily on the user's forearm with respect to locations of muscles in the forearm, extracting feature samples from the sampled signals, labeling the feature samples according to the corresponding gestures instructed to be performed, and training the machine learning model with the labeled feature samples. Subsequently, gestures may be recognized using the trained machine learning model by sampling signals from the EMG sensors, extracting from the signals unlabeled feature samples of a same type as those extracted during the training, passing the unlabeled feature samples to the machine learning model, and outputting from the machine learning model indicia of a gesture classified by the machine learning model.Type: GrantFiled: April 20, 2013Date of Patent: November 18, 2014Assignee: Microsoft CorporationInventors: Desney Tan, Dan Morris, T. Scott Saponas, Ravin Balakrishnan
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Publication number: 20140274159Abstract: A “Placement Detector” enables handheld or mobile electronic devices such as phones, media players, tablets, etc., to infer their current position or placement. Placement inference is performed by evaluating one or more sensors associated with the device relative to one or more trained probabilistic models to infer device relative to a user. Example placement inferences include, but are not limited to, inferring whether the device is currently in a user's pocket, in a user's purse (or other carrying bag or backpack), in a closed area such as a drawer or box, in an open area such as on a table, indoors, outdoors, etc. These types of placement inferences facilitate a wide range of automated user-device interactions, including, but not limited to, placement-dependent notifications, placement-dependent responses to various inputs, prevention of inadvertent “pocket dialing,” prevention of inadvertent power cycling of devices, lost or misplaced device location assistance, etc.Type: ApplicationFiled: March 15, 2013Publication date: September 18, 2014Applicant: MICROSOFT CORPORATIONInventors: Alice Jane Bernheim Brush, T. Scott Saponas, Jason Wiese
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Publication number: 20140257533Abstract: A physical activity monitoring device includes a sensor array with one or more sensors configured to measure physical activity attributes of a user. A controller automatically determines time intervals where the user is actively engaged in a physical activity based on the physical activity attributes. The controller also automatically determines a type of physical activity the user in actively engaged in during the determined time intervals based on the physical activity attributes. A reporter outputs information regarding the type of physical activity to the user.Type: ApplicationFiled: March 5, 2013Publication date: September 11, 2014Applicant: Microsoft CorporationInventors: Daniel Morris, Ilya Kelner, Farah Shariff, Dennis Tom, T. Scott Saponas, Andrew Guillory
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Publication number: 20140257535Abstract: A physical activity monitoring device receives a workout regimen including a plurality of exercises. For each of the plurality of exercises, the physical activity monitoring device indicates that exercise to a user and measures physical activity attributes of the user. The physical activity monitoring device outputs information regarding the user's progress towards completion of that exercise based on the physical activity attributes.Type: ApplicationFiled: March 5, 2013Publication date: September 11, 2014Applicant: MICROSOFT CORPORATIONInventors: Daniel Morris, Ilya Kelner, Farah Shariff, Dennis Tom, T. Scott Saponas, Andrew Guillory
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Publication number: 20140257534Abstract: A physical activity monitoring device receives an indication of one or more physical activities to be performed as an extension of a game being played on a game system and measures physical activity attributes of a user wearing the physical activity monitoring device. The physical activity monitoring device determines the user's progress towards completion of the one or more physical activities based on the physical activity attributes and outputs to the game device an indication of the user's progress towards completion of the one or more physical activities.Type: ApplicationFiled: March 5, 2013Publication date: September 11, 2014Applicant: MICROSOFT CORPORATIONInventors: Daniel Morris, Ilya Kelner, Farah Shariff, Dennis Tom, T. Scott Saponas, Andrew Guillory
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Publication number: 20140249398Abstract: A system and method to determine pulse transit time using a handheld device. The method includes generating an electrocardiogram (EKG) for a user of the handheld device. Two portions of the user's body are in contact with two contact points of the handheld device. The method also includes de-noising the EKG to identify a start time when a blood pulse leaves a heart of the user. The method further includes de-noising a plurality of video images of the user to identify a pressure wave indicating an arterial site and a time when the pressure wave appears. Additionally, the method includes determining the PTT based on the de-noised EKG and the de-noised video images.Type: ApplicationFiled: March 4, 2013Publication date: September 4, 2014Applicant: MICROSOFT CORPORATIONInventors: Daniel Morris, T. Scott Saponas, Desney S. Tan, Morgan Dixon, Siddharth Khullar, Harshvardhan Vathsangam
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Publication number: 20130232095Abstract: A machine learning model is trained by instructing a user to perform various predefined gestures, sampling signals from EMG sensors arranged arbitrarily on the user's forearm with respect to locations of muscles in the forearm, extracting feature samples from the sampled signals, labeling the feature samples according to the corresponding gestures instructed to be performed, and training the machine learning model with the labeled feature samples. Subsequently, gestures may be recognized using the trained machine learning model by sampling signals from the EMG sensors, extracting from the signals unlabeled feature samples of a same type as those extracted during the training, passing the unlabeled feature samples to the machine learning model, and outputting from the machine learning model indicia of a gesture classified by the machine learning model.Type: ApplicationFiled: April 20, 2013Publication date: September 5, 2013Applicant: Microsoft CorporationInventors: Desney Tan, Dan Morris, T. Scott Saponas, Ravin Balakrishnan
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Publication number: 20120319959Abstract: There is provided an electronic device having a touch-sensing element configured for sensing touches on a surface thereof. A baseline sensitivity setting determines a sensitivity of the touch-sensing element. The touch-sensing element is configured to register a touch that meets or exceeds the baseline sensitivity setting, and to ignore a touch that does not meet the baseline sensitivity setting. The device further includes a sensor that senses an operating condition of the device. A memory of the device includes code executable by the device and configured to adjust the baseline sensitivity setting based upon the sensed operating condition.Type: ApplicationFiled: June 14, 2011Publication date: December 20, 2012Applicant: MICROSOFT CORPORATIONInventors: T. Scott Saponas, Christopher Harrison, Hrvoje Benko
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Publication number: 20120188158Abstract: A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCl) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller.Type: ApplicationFiled: March 29, 2012Publication date: July 26, 2012Applicant: MICROSOFT CORPORATIONInventors: Desney Tan, T. Scott Saponas, Dan Morris, Jim Turner
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Patent number: 8170656Abstract: A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCl) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller.Type: GrantFiled: March 13, 2009Date of Patent: May 1, 2012Assignee: Microsoft CorporationInventors: Desney Tan, T. Scott Saponas, Dan Morris, Jim Turner
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Publication number: 20090326406Abstract: A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCl) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller.Type: ApplicationFiled: March 13, 2009Publication date: December 31, 2009Applicant: MICROSOFT CORPORATIONInventors: Desney Tan, T. Scott Saponas, Dan Morris, Jim Turner