Patents by Inventor Bryan Rieger
Bryan Rieger 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: 10348896Abstract: A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialog patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialog patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.Type: GrantFiled: March 31, 2017Date of Patent: July 9, 2019Assignee: Marchex, Inc.Inventors: Chris Kolbegger, Jason Flaks, Tim Graber, Bryan Rieger, Ziad Ismail, Govindaraj Ramanathan, Darren Spehr, Matthew Berk
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Identifying call features and associations to detect call traffic pumping and take corrective action
Patent number: 10230851Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.Type: GrantFiled: October 31, 2016Date of Patent: March 12, 2019Assignee: Marchex, Inc.Inventors: Jason Flaks, Iroro Orife, Bryan Rieger, Ryan O Rourke, Shane Walker -
Publication number: 20170366668Abstract: A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialogue patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialogue patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.Type: ApplicationFiled: March 31, 2017Publication date: December 21, 2017Inventors: Chris Kolbegger, Jason Flaks, Tim Graber, Bryan Rieger, Ziad Ismail, Govindaraj Ramanathan, Darren Spehr, Matthew Berk
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IDENTIFYING CALL FEATURES AND ASSOCIATIONS TO DETECT CALL TRAFFIC PUMPING AND TAKE CORRECTIVE ACTION
Publication number: 20170149984Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.Type: ApplicationFiled: October 31, 2016Publication date: May 25, 2017Inventors: Jason Flaks, Iroro Orife, Bryan Rieger, Ryan O Rourke, Shane Walker -
Patent number: 9614962Abstract: A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialog patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialog patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.Type: GrantFiled: August 24, 2015Date of Patent: April 4, 2017Assignee: Marchex, Inc.Inventors: Chris Kolbegger, Jason Flaks, Tim Graber, Bryan Rieger, Ziad Ismail, Govindaraj Ramanathan, Darren Spehr, Matthew Berk
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IDENTIFYING CALL FEATURES AND ASSOCIATIONS TO DETECT CALL TRAFFIC PUMPING AND TAKE CORRECTIVE ACTION
Publication number: 20160330317Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.Type: ApplicationFiled: May 26, 2015Publication date: November 10, 2016Inventors: Jason Flaks, Iroro Orife, Bryan Rieger, Ryan O'Rourke, Shane Walker -
Identifying call features and associations to detect call traffic pumping and take corrective action
Patent number: 9485354Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.Type: GrantFiled: May 26, 2015Date of Patent: November 1, 2016Assignee: Marchex, Inc.Inventors: Jason Flaks, Iroro Orife, Bryan Rieger, Ryan O'Rourke, Shane Walker -
Publication number: 20150365530Abstract: A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialogue patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialogue patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.Type: ApplicationFiled: August 24, 2015Publication date: December 17, 2015Inventors: Chris Kolbegger, Jason Flaks, Tim Graber, Bryan Rieger, Ziad Ismail, Govindaraj Ramanathan, Darren Spehr, Matthew Berk
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Patent number: 9118751Abstract: A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialogue patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialogue patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.Type: GrantFiled: March 15, 2013Date of Patent: August 25, 2015Assignee: Marchex, Inc.Inventors: Chris Kolbegger, Jason Flaks, Tim Graber, Bryan Rieger, Ziad Ismail, Govindaraj Ramanathan, Darren Spehr, Matthew Berk
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Publication number: 20140270114Abstract: A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialogue patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialogue patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.Type: ApplicationFiled: March 15, 2013Publication date: September 18, 2014Applicant: MARCHEX, INC.Inventors: Chris Kolbegger, Jason Flaks, Tim Graber, Bryan Rieger, Ziad Ismail, Govindaraj Ramanathan, Darren Spehr, Matthew Berk