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公开(公告)号：US10715962B2
公开(公告)日：2020-07-14
申请号：US16660686
申请日：2019-10-22
申请人： XAD INC.
发明人： Can Liang , Pravesh Katyal , Yilin Chen , Crystal Shi , Huitao Luo
摘要： The present disclosure provides methods and systems that utilize mobile device location events and machine learning and generate predicative classification/regression model for lookalike prediction. Location related features, together with other user level information, are extracted, transformed and used as model feature input, and a client specified list of mobile devices or their associated users are used as prediction target. This system makes efficient use of different types of location events and thus offers improved scale and performance. It also enjoys many benefits offered by a machine learning platform, such as automatic adaptation to different lists of seed lists, addition of new features and changes in data statistical properties.
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公开(公告)号：US10349208B1
公开(公告)日：2019-07-09
申请号：US15999331
申请日：2018-08-17
申请人： XAD, INC.
发明人： Can Liang , Pravesh Katyal , Guoxin Li , Yilin Chen
摘要： A system coupled to a packet-based network is configured to predict the locations of one or more mobile devices communicating with the packet-based network. The system comprises a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The system further comprises an off-line prediction subsystem configured to train a plurality of off-line prediction models and an on-line prediction model using various historical location events. The off-line prediction subsystem is further configured to generate off-line prediction results corresponding to the off-line prediction models. The system further comprises an on-line prediction subsystem configured to construct a feature vector using the off-line prediction results and recently detected location events in response to a request received in real-time, and to generate on-line prediction results by applying the on-line prediction model to the feature vector.
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3.发明授权公开(公告)号：US10455363B2
公开(公告)日：2019-10-22
申请号：US16157010
申请日：2018-10-10
申请人： xAd, Inc.
发明人： Can Liang , Pravesh Katyal , Yilin Chen , Crystal Shi , Huitao Luo
摘要： The present disclosure provides methods and systems that utilize mobile device location events and machine learning and generate predicative classification/regression model for lookalike prediction. Location related features, together with other user level information, are extracted, transformed and used as model feature input, and a client specified list of mobile devices or their associated users are used as prediction target. This system makes efficient use of different types of location events and thus offers improved scale and performance. It also enjoys many benefits offered by a machine learning platform, such as automatic adaptation to different lists of seed lists, addition of new features and changes in data statistical properties.
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4.发明申请公开(公告)号：US20190007793A1
公开(公告)日：2019-01-03
申请号：US15999330
申请日：2018-08-17
申请人： Xad, Inc.
发明人： Can Liang , Pravesh Katyal , Guoxin Li , Yilin Chen
摘要： A system coupled to a packet-based network is configured to predict the locations of mobile devices that have communicated with the packet-based network. The system includes a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The geo-places include geo-blocks and geo-fences. The system further comprises a location prediction subsystem configured to construct first feature space using first location events and second feature space using second location events, and to extract a set of labels from third location events. The location prediction subsystem is further configured to train a prediction model using the first feature space and the set of labels, and to apply the prediction model to the second feature space to obtain prediction results.
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公开(公告)号：US11134359B2
公开(公告)日：2021-09-28
申请号：US16726056
申请日：2019-12-23
申请人： xAd, Inc.
发明人： Can Liang , Yilin Chen , Jingqi Huang , Shun Jiang , Amit Goswami
摘要： A system includes a machine learning module configured to train a location prediction model using features constructed from mobile device data with time stamps in a training time period, and labels extracted from mobile device data with time stamps in a training time frame. The system further includes a prediction module configured apply the prediction model to a feature set constructed using mobile device data associated with a mobile device with time stamps in a prediction time period to obtain a prediction result corresponding to the mobile device. The system further includes a calibration module configured to obtain a calibration model corresponding to an information campaign, and a calibrated prediction module configured to apply the calibration model to the prediction result to obtain a calibrated probability for the mobile device to have at least one location event at any of one or more locations associated with the information campaign during a prediction time frame.
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公开(公告)号：US20200053515A1
公开(公告)日：2020-02-13
申请号：US16660686
申请日：2019-10-22
发明人： Can Liang , Pravesh Katyal , Yilin Chen , Crystal Shi , Huitao Luo
摘要： The present disclosure provides methods and systems that utilize mobile device location events and machine learning and generate predicative classification/regression model for lookalike prediction. Location related features, together with other user level information, are extracted, transformed and used as model feature input, and a client specified list of mobile devices or their associated users are used as prediction target. This system makes efficient use of different types of location events and thus offers improved scale and performance. It also enjoys many benefits offered by a machine learning platform, such as automatic adaptation to different lists of seed lists, addition of new features and changes in data statistical properties.
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公开(公告)号：US11743679B2
公开(公告)日：2023-08-29
申请号：US17517650
申请日：2021-11-02
申请人： xAd, Inc.
发明人： Can Liang , Yilin Chen , Jingqi Huang , Shun Jiang , Amit Goswami
摘要： Described herein are system and method for pacing information delivery to mobile devices. The method comprises, for each respective request of a first plurality of requests received during a time unit that qualifies for information delivery, predicting a respective conversion probability corresponding to a predicted probability of a mobile device associated with the respective request having at least one location event at any of one or more POIs during a time frame corresponding to the time unit. The method further comprises placing a bid for fulfilling the respective request based on the respective conversion probability and a bidding model, determining a set of predicted numbers of conversions corresponding, respectively, to a set of ranges of predicted conversion probabilities for a first number of fulfilled requests corresponding to the time unit, and adjusting the bidding model based at least on the predicted number of conversions.
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公开(公告)号：US20220060847A1
公开(公告)日：2022-02-24
申请号：US17517650
申请日：2021-11-02
申请人： xAd, Inc.
发明人： Can Liang , Yilin Chen , Jingqi Huang , Shun Jiang , Amit Goswami
摘要： Described herein are system and method for pacing information delivery to mobile devices. The method comprises, for each respective request of a first plurality of requests received during a time unit that qualifies for information delivery, predicting a respective conversion probability corresponding to a predicted probability of a mobile device associated with the respective request having at least one location event at any of one or more POIs during a time frame corresponding to the time unit. The method further comprises placing a bid for fulfilling the respective request based on the respective conversion probability and a bidding model, determining a set of predicted numbers of conversions corresponding, respectively, to a set of ranges of predicted conversion probabilities for a first number of fulfilled requests corresponding to the time unit, and adjusting the bidding model based at least on the predicted number of conversions.
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公开(公告)号：US11172324B2
公开(公告)日：2021-11-09
申请号：US16780802
申请日：2020-02-03
申请人： xAd, Inc.
发明人： Can Liang , Yilin Chen , Weiqing Yu , Fan Yang
摘要： A system for predicting a conversion rate relating to targeted location events for a test campaign includes one or more campaign databases configured to store campaign parameters of a set of historical campaigns. The system further includes a feature engineering module configured to construct a training feature space corresponding to the set of historical campaigns, and to determine a set of labels including a respective conversion rate for each respective historical campaign of the plurality of historical campaigns. The system further includes a model training module configured to machine train a conversion rate prediction model using the training feature space and the set of labels. The feature engineering module is further configured to construct a set of test features from campaign parameters of the test campaign, and the system further includes a prediction module configured to apply the conversion rate prediction model to the set of test features to obtain a predicted conversion rate for the test campaign.
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公开(公告)号：US20200059753A1
公开(公告)日：2020-02-20
申请号：US16506940
申请日：2019-07-09
申请人： XAD, INC.
发明人： Can Liang , Pravesh Katyal , Guoxin Li , Yilin Chen
摘要： A system coupled to a packet-based network is configured to predict the locations of one or more mobile devices communicating with the packet-based network. The system comprises a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The system further comprises an off-line prediction subsystem configured to train a plurality of off-line prediction models and an on-line prediction model using various historical location events. The off-line prediction subsystem is further configured to generate off-line prediction results corresponding to the off-line prediction models. The system further comprises an on-line prediction subsystem configured to construct a feature vector using the off-line prediction results and recently detected location events in response to a request received in real-time, and to generate on-line prediction results by applying the on-line prediction model to the feature vector.
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