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    Identifying call characteristics to detect fraudulent call activity and take corrective action without using recording, transcription or caller ID
    1.
    发明授权
    Identifying call characteristics to detect fraudulent call activity and take corrective action without using recording, transcription or caller ID 有权

    公开(公告)号：US10111102B2

    公开(公告)日：2018-10-23

    申请号：US14550089

    申请日：2014-11-21

    Applicant: Marchex, Inc.

    Inventor： Jason Flaks ,  Ziad Ismail

    IPC: H04W12/12 ,  H04M15/00 ,  H04M3/436 ,  H04L29/06 ,  H04W12/08

    Abstract: A system and method for monitoring telephone calls to detect fraudulent activity and take corrective action is described. The system receives a group of telephone calls having associated call characteristics and analyzes the group of telephone calls to identify and store a first set of distributions of call characteristics that are indicative of normal activity, fraudulent activity, or indeterminate activity. The system receives one or more subsequent telephone calls to be analyzed. The system analyzes the received one or more telephone calls to identify a second set of distributions of call characteristics associated with the received telephone call. The system then compares the second set of distributions of call characteristics to the stored first set of distributions of call characteristics to assess a probability that the one or more received telephone calls represents normal, fraudulent, or indeterminate activity. If the assessed probability of fraudulent activity exceeds a threshold, the system takes appropriate corrective action, such a flagging the fraudulent call or withholding a financial incentive associated with the fraudulent call.

    System and method for analyzing and classifying calls without transcription via keyword spotting
    2.
    发明授权
    System and method for analyzing and classifying calls without transcription via keyword spotting 有权

    公开(公告)号：US10049661B2

    公开(公告)日：2018-08-14

    申请号：US15339619

    申请日：2016-10-31

    Applicant: Marchex, Inc.

    Inventor： Jason Flaks ,  Ziad Ismail ,  Chris Kolbegger

    IPC: G10L15/00 ,  G10L15/193 ,  G10L15/18 ,  G10L15/14 ,  G10L15/16 ,  H04M3/51 ,  G10L15/08

    Abstract: A facility and method for analyzing and classifying calls without transcription via keyword spotting is disclosed. The facility uses a group of calls having known outcomes to generate one or more domain- or entity-specific grammars containing keywords and related information that are indicative of particular outcome. The facility monitors telephone calls by determining the domain or entity associated with the call, loading the appropriate grammar or grammars associated with the determined domain or entity, and tracking keywords contained in the loaded grammar or grammars that are spoken during the monitored call, along with additional information. The facility performs a statistical analysis on the tracked keywords and additional information to determine a classification for the monitored telephone call.

    IDENTIFYING CALL FEATURES AND ASSOCIATIONS TO DETECT CALL TRAFFIC PUMPING AND TAKE CORRECTIVE ACTION
    3.
    发明申请
    IDENTIFYING CALL FEATURES AND ASSOCIATIONS TO DETECT CALL TRAFFIC PUMPING AND TAKE CORRECTIVE ACTION 审中-公开

    公开(公告)号：US20170149984A1

    公开(公告)日：2017-05-25

    申请号：US15339682

    申请日：2016-10-31

    Applicant: Marchex, Inc.

    Inventor： Jason Flaks ,  Iroro Orife ,  Bryan Rieger ,  Ryan O Rourke ,  Shane Walker

    IPC: H04M15/00 ,  H04M3/22

    CPC classification number: H04M15/47 ,  H04M3/2263 ,  H04M3/2281 ,  H04M3/367 ,  H04M7/0078 ,  H04M15/50 ,  H04M15/58 ,  H04M2203/6027 ,  H04M2215/0148

    Abstract: 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.

    ANALYZING VOICE CHARACTERISTICS TO DETECT FRAUDULENT CALL ACTIVITY AND TAKE CORRECTIVE ACTION WITHOUT USING RECORDING, TRANSCRIPTION OR CALLER ID
    5.
    发明申请
    ANALYZING VOICE CHARACTERISTICS TO DETECT FRAUDULENT CALL ACTIVITY AND TAKE CORRECTIVE ACTION WITHOUT USING RECORDING, TRANSCRIPTION OR CALLER ID 审中-公开
    分析语音特征以检测欺骗性呼叫活动并采取正确的行为，不使用录音，转录或电话号码

    公开(公告)号：US20160150092A1

    公开(公告)日：2016-05-26

    申请号：US14987565

    申请日：2016-01-04

    Applicant: Marchex, Inc.

    Inventor： Jason Flaks ,  Ziad Ismail

    IPC: H04M15/00 ,  H04M3/38

    CPC classification number: H04M15/47 ,  G10L15/00 ,  G10L17/26 ,  H04L63/1425 ,  H04M3/2281 ,  H04M3/38 ,  H04M2203/6027

    Abstract: A system and method for monitoring telephone calls to detect fraudulent activity and take corrective action is described. The system receives a first group of telephone calls having associated voice characteristics and analyzes the first group of telephone calls to identify and store a first set of distributions of voice characteristics that are indicative of normal activity, fraudulent activity, or indeterminate activity. The system receives a second group of telephone calls to be analyzed. The system analyzes the second group of telephone calls to identify a second set of distributions of voice characteristics associated with the second group of telephone calls. The system then compares the second set of distributions of voice characteristics to the stored first set of distributions of voice characteristics to assess a probability that one or more telephone calls in the second group of telephone calls represents normal, fraudulent, or indeterminate activity. If the assessed probability of fraudulent activity exceeds a threshold, the system takes appropriate corrective action, such a flagging the fraudulent call or withholding a financial incentive associated with the fraudulent call.

    Abstract translation: 描述用于监视电话呼叫以检测欺诈活动并采取纠正措施的系统和方法。 该系统接收具有相关语音特征的第一组电话呼叫，并分析第一组电话呼叫以识别和存储指示正常活动，欺诈活动或不确定活动的语音特征分布的第一组。 该系统接收要分析的第二组电话呼叫。 系统分析第二组电话呼叫，以识别与第二组电话呼叫相关联的语音特征的第二组分布。 然后，系统将语音特征的第二组分布与存储的第一组语音特征分布进行比较，以评估第二组电话中的一个或多个电话呼叫代表正常，欺诈或不确定的活动的概率。 如果欺诈活动的评估概率超过阈值，则系统采取适当的纠正措施，例如标示欺诈性呼叫或扣除与欺诈性呼叫相关的经济激励。

    Automatic speech recognition (ASR) model training
    7.
    发明授权
    Automatic speech recognition (ASR) model training 有权

    公开(公告)号：US10810995B2

    公开(公告)日：2020-10-20

    申请号：US15965690

    申请日：2018-04-27

    Applicant: Marchex, Inc.

    Inventor： Jason Flaks ,  Shane Walker ,  Iroro Orife ,  Morten Pedersen

    IPC: G10L15/06 ,  G10L15/26

    Abstract: The disclosed system continuously refines a model used by an Automatic Speech Recognition (ASR) system to enable fast and accurate transcriptions of detected speech activity. The ASR system analyzes speech activity to generate text transcriptions and associated metrics (such as minimum Bayes risk and/or perplexity) that correspond to the quality of or confidence in each generated transcription. The system employs a filtering process to select certain text transcriptions based in part on one or more associated quality metrics. In addition, the system corrects for known systemic errors within the ASR system and provides a mechanism for manual review and correction of transcriptions. The system selects a subset of transcriptions based on factors including confidence score, and uses the selected subset of transcriptions to re-train the ASR model. By continuously retraining the ASR model, the system is able to provide ever faster and more accurate text transcriptions of detected speech activity.

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