Patents by Inventor Heng-Tze Cheng
Heng-Tze Cheng 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).
-
Publication number: 20200372359Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.Type: ApplicationFiled: August 12, 2020Publication date: November 26, 2020Inventors: Tal Shaked, Rohan Anil, Hrishikesh Balkrishna Aradhye, Mustafa Ispir, Glen Anderson, Wei Chai, Mehmet Levent Koc, Jeremiah Joseph Harmsen, Xiaobing Liu, Gregory Sean Corrado, Tushar Deepak Chandra, Heng-Tze Cheng
-
Patent number: 10762422Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.Type: GrantFiled: December 29, 2016Date of Patent: September 1, 2020Assignee: Google LLCInventors: Tal Shaked, Rohan Anil, Hrishikesh Balkrishna Aradhye, Mustafa Ispir, Glen Anderson, Wei Chai, Mehmet Levent Koc, Jeremiah Harmsen, Xiaobing Liu, Gregory Sean Corrado, Tushar Deepak Chandra, Heng-Tze Cheng
-
Patent number: 10102482Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.Type: GrantFiled: August 7, 2015Date of Patent: October 16, 2018Assignee: Google LLCInventors: Heng-Tze Cheng, Jeremiah Harmsen, Alexandre Tachard Passos, David Edgar Lluncor, Shahar Jamshy, Tal Shaked, Tushar Deepak Chandra
-
Publication number: 20170300814Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.Type: ApplicationFiled: December 29, 2016Publication date: October 19, 2017Inventors: Tal Shaked, Rohan Anil, Hrishikesh Balkrishna Aradhye, Mustafa Ispir, Glen Anderson, Wei Chai, Mehmet Levent Koc, Jeremiah Harmsen, Xiaobing Liu, Gregory Sean Corrado, Tushar Deepak Chandra, Heng-Tze Cheng
-
Publication number: 20170039483Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.Type: ApplicationFiled: August 7, 2015Publication date: February 9, 2017Inventors: Heng-Tze Cheng, Jeremiah Harmsen, Alexandre Tachard Passos, David Edgar Lluncor, Shahar Jamshy, Tal Shaked, Tushar Deepak Chandra
-
Patent number: 9278255Abstract: A method for automatic recognition of human activity is provided and includes the steps of decomposing human activity into a plurality of fundamental component attributes needed to perform an activity and defining ontologies of fundamental component attributes from the plurality of the fundamental component attributes identified during the decomposing step for each of a plurality of different targeted activities. The method also includes the steps of converting a data stream captured during a performance of an activity performed by a human into a sequence of fundamental component attributes and classifying the performed activity as one of the plurality of different targeted activities based on a closest match of the sequence of fundamental component attributes obtained during the converting step to at least a part of one of the ontologies of fundamental component attributes defined during the defining step. A system for performing the method is also disclosed.Type: GrantFiled: December 20, 2012Date of Patent: March 8, 2016Assignees: ARRIS Enterprises, Inc., Carnegie Mellon UniversityInventors: Heng-Tze Cheng, Paul C. Davis, Jianguo Li, Di You
-
Publication number: 20140161322Abstract: A method for automatic recognition of human activity is provided and includes the steps of decomposing human activity into a plurality of fundamental component attributes needed to perform an activity and defining ontologies of fundamental component attributes from the plurality of the fundamental component attributes identified during the decomposing step for each of a plurality of different targeted activities. The method also includes the steps of converting a data stream captured during a performance of an activity performed by a human into a sequence of fundamental component attributes and classifying the performed activity as one of the plurality of different targeted activities based on a closest match of the sequence of fundamental component attributes obtained during the converting step to at least a part of one of the ontologies of fundamental component attributes defined during the defining step. A system for performing the method is also disclosed.Type: ApplicationFiled: December 20, 2012Publication date: June 12, 2014Applicants: CARNEGIE MELLON UNIVERSITY, GENERAL INSTRUMENT CORPORATIONInventors: Heng-Tze Cheng, Paul C. Davis, Jianguo Li, Di You
-
Publication number: 20110310005Abstract: Systems and methods are described for performing contactless gesture recognition for a computing device, such as a mobile computing device. An example technique for managing a gesture-based input mechanism for a computing device described herein includes identifying parameters of the computing device relating to accuracy of gesture classification performed by the gesture-based input mechanism and managing a power consumption level of at least an infrared (IR) light emitting diode (LED) or an IR proximity sensor of the gesture-based input mechanism based on the parameters of the computing device.Type: ApplicationFiled: June 16, 2011Publication date: December 22, 2011Applicant: QUALCOMM IncorporatedInventors: An M. Chen, Heng-Tze Cheng, Ashu Razdan, Elliot B. Buller