Patents by Inventor Poonam Suryanarayan
Poonam Suryanarayan 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: 11657318Abstract: The disclosure relates to training models for identification of events likely to cause discomfort to passengers of autonomous vehicles and for assessment of overall ride quality of autonomous vehicle rides. For instance, ride data may be associated with a ride quality value indicative of a level of discomfort and/or a first overall ride quality value indicating an overall ride quality provided by the passenger for the first ride. This ride data may be used to train a model such that the model is configured to, in response to receiving ride data for a second ride as input, output a list of events likely to cause discomfort to a passenger during the second ride and/or such that the model is configured to, in response to receiving second ride data for a second ride as input, output a second overall ride quality value for the second ride.Type: GrantFiled: December 10, 2018Date of Patent: May 23, 2023Assignee: Waymo LLCInventors: Ioan-Alexandru Sucan, Fang Da, Poonam Suryanarayan, Nathaniel Fairfield, Yutaka Leon Suematsu, Omer Baror, Jian Leong, Michael Epstein
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Publication number: 20200125989Abstract: The disclosure relates to training models for identification of events likely to cause discomfort to passengers of autonomous vehicles and for assessment of overall ride quality of autonomous vehicle rides. For instance, ride data may be associated with a ride quality value indicative of a level of discomfort and/or a first overall ride quality value indicating an overall ride quality provided by the passenger for the first ride. This ride data may be used to train a model such that the model is configured to, in response to receiving ride data for a second ride as input, output a list of events likely to cause discomfort to a passenger during the second ride and/or such that the model is configured to, in response to receiving second ride data for a second ride as input, output a second overall ride quality value for the second ride.Type: ApplicationFiled: December 10, 2018Publication date: April 23, 2020Inventors: Ioan-Alexandru Sucan, Fang Da, Poonam Suryanarayan, Nathaniel Fairfield, Yutaka Leon Suematsu, Omer Baror, Jian Leong, Michael Epstein
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Patent number: 10043099Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.Type: GrantFiled: January 11, 2018Date of Patent: August 7, 2018Assignee: The Penn State Research FoundationInventors: James Z. Wang, Xin Lu, Poonam Suryanarayan, Reginald B. Adams, Jia Li, Michelle Newman
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Publication number: 20180150719Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.Type: ApplicationFiled: January 11, 2018Publication date: May 31, 2018Inventors: James Z. Wang, Xin Lu, Poonam Suryanarayan, Reginald B. Adams, Jia Li, Michelle Newman
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Patent number: 9904869Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.Type: GrantFiled: December 30, 2016Date of Patent: February 27, 2018Assignee: The Penn State Research FoundationInventors: James Z. Wang, Xin Lu, Poonam Suryanarayan, Reginald B. Adams, Jr., Jia Li, Michelle Newman
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Publication number: 20170109603Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.Type: ApplicationFiled: December 30, 2016Publication date: April 20, 2017Inventors: James Z. Wang, Xin Lu, Poonam Suryanarayan, Reginald B. Adams, Jr., Jia Li, Michelle Newman
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Patent number: 9558425Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.Type: GrantFiled: July 27, 2015Date of Patent: January 31, 2017Assignee: The Penn State Research FoundationInventors: James Z. Wang, Xin Lu, Poonam Suryanarayan, Reginald B. Adams, Jr., Jia Li, Michelle Newman
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Publication number: 20150332118Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.Type: ApplicationFiled: July 27, 2015Publication date: November 19, 2015Inventors: James Z. Wang, Xin Lu, Poonam Suryanarayan, Reginald B. Adams, JR., Jia Li, Michelle Newman
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Patent number: 8781175Abstract: A comprehensive system to enhance the aesthetic quality of the photographs captured by mobile consumers provides on-site composition and aesthetics feedback through retrieved examples. Composition feedback is qualitative in nature and responds by retrieving highly aesthetic exemplar images from the corpus which are similar in content and composition to the snapshot. Color combination feedback provides confidence on the snapshot to contain good color combinations. Overall aesthetics feedback predicts the aesthetic ratings for both color and monochromatic images. An algorithm is used to provide ratings for color images, while new features and a new model are developed to treat monochromatic images. This system was designed keeping the next generation photography needs in mind and is the first of its kind. The feedback rendered is guiding and intuitive in nature. It is computed in situ while requiring minimal input from the user.Type: GrantFiled: June 11, 2012Date of Patent: July 15, 2014Assignee: The Penn State Research FoundationInventors: James Z. Wang, Jia Li, Lei Yao, Poonam Suryanarayan, Mu Qiao
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Patent number: 8768048Abstract: A computing device segments an image into a plurality of segments, wherein each segment of the plurality of segments comprises a set of pixels that share visual characteristics. The computing device then determines expected contexts for the segments, wherein an expected context for a segment comprises at least one of additional segments or features expected to occur in the image together with the segment. The computing device then identifies a probable object based on the expected contexts.Type: GrantFiled: November 18, 2011Date of Patent: July 1, 2014Assignee: Google Inc.Inventors: Vivek Kwatra, Jay Yagnik, Alexander T. Toshev, Poonam Suryanarayan
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Patent number: 8660342Abstract: A method that includes classifying photographs into categories; generating computational models of image aesthetics, each computational model of image aesthetics associated to one different category of the categories; extracting a plurality of features from a photograph, the plurality of features including simplicity features, global features computed in the whole image and/or low-level features in contrasting regions of an image of the photograph, the contrasting regions being partitions of the image obtained by applying an image segmentation algorithm based on feature contrast to the photograph; and applying a computational model of image aesthetics to at least part of the plurality of features extracted from the photograph, the computational model applied according to the category of the photograph, in order to assess aesthetic quality of the photograph.Type: GrantFiled: January 24, 2012Date of Patent: February 25, 2014Assignee: Telefonica, S.A.Inventors: Pere Obrador, Michele Saad, Poonam Suryanarayan, Nuria Oliver
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Publication number: 20140049546Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.Type: ApplicationFiled: August 9, 2013Publication date: February 20, 2014Applicant: THE PENN STATE RESEARCH FOUNDATIONInventors: James Z. Wang, Xin Lu, Poonam Suryanarayan, Reginald B. Adams, JR., Jia Li, Michelle Newman
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Publication number: 20130188866Abstract: A method to assess aesthetic quality of photographs, including classifying photographs into categories; generating computational models of image aesthetics, each computational model of image aesthetics associated to one different category of the categories; extracting a plurality of features from a photograph, the plurality of features being simplicity features, global features computed in the whole image and/or low-level features in contrasting regions of an image of said photograph. The contrasting regions are partitions of the image obtained by applying an image segmentation algorithm based on feature contrast to the photograph. The method also including applying a computational model of image aesthetics to at least part of the plurality of features extracted from said photograph, the computational model applied according to the category of the photograph, in order to assess aesthetic quality of the photograph.Type: ApplicationFiled: January 24, 2012Publication date: July 25, 2013Applicant: Telefonica, S.A.Inventors: Pere OBRADOR, Michele SAAD, Poonam SURYANARAYAN, Nuria OLIVER
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Publication number: 20120268612Abstract: A comprehensive system to enhance the aesthetic quality of the photographs captured by mobile consumers provides on-site composition and aesthetics feedback through retrieved examples. Composition feedback is qualitative in nature and responds by retrieving highly aesthetic exemplar images from the corpus which are similar in content and composition to the snapshot. Color combination feedback provides confidence on the snapshot to contain good color combinations. Overall aesthetics feedback predicts the aesthetic ratings for both color and monochromatic images. An algorithm is used to provide ratings for color images, while new features and a new model are developed to treat monochromatic images. This system was designed keeping the next generation photography needs in mind and is the first of its kind. The feedback rendered is guiding and intuitive in nature. It is computed in situ while requiring minimal input from the user.Type: ApplicationFiled: June 11, 2012Publication date: October 25, 2012Applicant: The Penn State Research FoundationInventors: James Z. Wang, Jia Li, Lei Yao, Poonam Suryanarayan, Mu Qiao