Patents by Inventor Michelle Newman
Michelle Newman 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: 11122343Abstract: Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, selecting a video based on a learning objective, determining types of video interjections using an analytics engine that compares a learner model and the learning objective, determining a location for the video interjections using the analytics engine, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.Type: GrantFiled: November 5, 2019Date of Patent: September 14, 2021Assignees: International Business Machines Corporation, Sesame WorkshopInventors: Ravindranath Kokku, Tamer E. Abuelsaad, Prasenjit Dey, Jodi M. Cutler, Allison C. Allain, Aditya Sinha, Satyanarayana V. Nitta, Miles Ludwig, Emily Reardon, Nick Bartzokas, James Gray, Michelle Newman-Kaplan
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Publication number: 20200068271Abstract: Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, selecting a video based on a learning objective, determining types of video interjections using an analytics engine that compares a learner model and the learning objective, determining a location for the video interjections using the analytics engine, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.Type: ApplicationFiled: November 5, 2019Publication date: February 27, 2020Inventors: Ravindranath Kokku, Tamer E. Abuelsaad, Prasenjit Dey, Jodi M. Cutler, Allison C. Allain, Aditya Sinha, Satyanarayana V. Nitta, Miles Ludwig, Emily Reardon, Nick Bartzokas, James Gray, Michelle Newman-Kaplan
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Patent number: 10506303Abstract: Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, generating and storing educational content metadata on a timeline for the videos, selecting a video based on a user specific learning objective, determining types of video interjections using an analytics engine that compares a user learner model, the user learning objective and the metadata, determining a location for the video interjections using the analytics engine that compares the user learner model, the learning objective, the metadata and timeline, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.Type: GrantFiled: July 19, 2018Date of Patent: December 10, 2019Assignee: International Business Machines CorporationInventors: Ravindranath Kokku, Tamer E. Abuelsaad, Prasenjit Dey, Jodi M. Cutler, Allison C. Allain, Aditya Sinha, Satyanarayana V. Nitta, Miles Ludwig, Emily Reardon, Nick Bartzokas, James Gray, Michelle Newman-Kaplan
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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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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