Bryan S Rieger, Age 65Seattle, WA

2017014, May 25, 2017

Oct 31, 2016

15/339682

- Seattle WA, US
Iroro Orife - Seattle WA, US
Bryan Rieger - Seattle WA, US
Ryan O Rourke - Seattle WA, US
Shane Walker - Kent WA, US

H04M 15/00
H04M 3/22

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. If the assessed probability of call traffic pumping activity exceeds a threshold, the system takes appropriate corrective action, such as terminating or flagging the monitored call.

2016033, Nov 10, 2016

May 26, 2015

14/721912

- Seattle WA, US
Iroro Orife - Seattle WA, US
Bryan Rieger - Seattle WA, US
Ryan O'Rourke - Seattle WA, US
Shane Walker - Kent WA, US

H04M 3/36
H04M 3/22
H04M 15/00

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. If the assessed probability of call traffic pumping activity exceeds a threshold, the system takes appropriate corrective action, such as terminating or flagging the monitored call.

2015036, Dec 17, 2015

Aug 24, 2015

14/834196

- Seattle WA, US
Jason Flaks - Redmond WA, US
Tim Graber - Issaquah WA, US
Bryan Rieger - Mill Creek WA, US
Ziad Ismail - Seattle WA, US
Govindaraj Ramanathan - Redmond WA, US
Darren Spehr - Seattle WA, US
Matthew Berk - Seattle WA, US

H04M 3/51
H04M 3/42
G10L 25/93
H04M 3/22

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.

2014027, Sep 18, 2014

Mar 15, 2013

13/842769

- Seattle WA, US
Jason Flaks - Redmond WA, US
Tim Graber - Issaquah WA, US
Bryan Rieger - Mill Creek WA, US
Ziad Ismail - Seattle WA, US
Govindaraj Ramanathan - Redmond WA, US
Darren Spehr - Seattle WA, US
Matthew Berk - Seattle WA, US

MARCHEX, INC. - Seattle WA

G10L 15/04

379 8808

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.