Xad, Inc.

Can Liang

Pravesh Katyal

Huitao Luo

Yilin Chen

Shashi Seth

Hari Venkatesan

Saravana Ravindran

Yi Jia

Jingqi Huang

Shun Jiang

Amit Goswami

Sunil Kumar

Guoxin Li

Vimpy Batra

Richard Chiou

Crystal Shi

Fan Yang

Prakash Muttineni

Srihari Venkatesan

Dipanshu Sharma

Jonathan I Schwartz

Shanshan Tuo

Nishant Khatri

Weiqing Yu

Stephen Anderson

George Rekouts

David Chock

Dipanshu Shama

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.

Systems and methods for pacing information delivery to mobile devices

A system for processing information requests associated with mobile devices comprises an evaluation module configured to determine at least one performance measure for each of a plurality of information documents using at least data in one or both of a requests database and events database. The at least one performance measure includes at least one of an impression-based performance measure, a click/call-based performance measure, and an off-line site-visit-based performance measure. The system further comprises an information server configured to select a first information document for transmitting to a first mobile device to fulfill a first request. The information server includes a volume control unit configured to derive an off-line site visit projection in response to the first document being selected based at least in part on an off-line site-visit-based performance measure and having been impressed on the first mobile device, and to adjust a budget associated with the first document using the off-line site visit projection.

Using on-line and off-line projections to control information delivery to mobile devices

A system coupled to a packet-based network is configured to generate geo-blocks for location-based information services. Each of the geo-blocks corresponds to a geographical region within a geographical area and has at least one border defined by a real-world object. The system is configured to obtain geo data related to real-world objects including transportation routes and natural boundaries in the geographical area, partition the geographical area into initial geo-blocks using the geo data, process a plurality of information requests with respect to at least some of the initial geo-blocks and with respect to initial geo-fences associated with a plurality of points of interests (POIs) to generate entries in one or more databases, and machine train a mobile device location prediction model based on the entries in the one or more databases.

Systems and methods for mobile device location prediction

A system coupled to a packet-based network is configured to generate geo-blocks for location-based information services. Each of the geo-blocks corresponds to a geographical region within a geographical area and has at least one border defined by a real-world object. The system is configured to obtain geo data related to real-world objects including transportation routes and natural boundaries in the geographical area, partition the geographical area into initial geo-blocks using the geo data, process a plurality of information requests with respect to at least some of the initial geo-blocks and with respect to initial geo-fences associated with a plurality of points of interests (POIs) to generate entries in one or more databases, and enrich at least some of the initial geo-block with block-level meta data derived from the entries in the one or more databases to form enriched geo-blocks that are highly indicative of location dependent attributes such as intention and demographics.

Systems and methods for predicting mobile device locations using processed mobile device signals

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.

Systems and methods for predicting targeted location events

A system includes a machine learning module configured to train a location prediction model for an information campaign, a front-end server configured to receive and process information requests, and a prediction unit. During the information campaign, the prediction unit is configured to use the location prediction model to predict a conversion probability for any particular mobile device associated with a qualified information request received during any respective time unit. The conversion probability corresponds to a predicted probability of the particular mobile device having at least one location event at any of one or more POIs during a particular time frame. The front-end server is further configured to determine a respective target number of conversions to be achieved by the information campaign during the respective time unit, and to determine a response to the particular information request based at least in part on the conversion probability and on the respective target number of conversions.

Systems and methods for pacing information campaigns based on predicted and observed location events

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.

Systems and methods for calibrated location prediction

The present disclosure provides a mobile advertising platform in which mobile user locations and other information are translated into indications of mobile user intent to approach certain businesses, and advertisers can fill mobile advertising requests or choose to price their bids for mobile supplies based on such indications. In certain embodiments, pre-defined places associated with business/brand names are created, and mobile advertising requests are processed to determine if the associated with mobile devices have triggered any of these pre-defined places. If a mobile advertising request is determined to have triggered one or more of the pre-defined places, it is annotated with the triggered place(s), and advertisements are selected based on the triggered places and other factors. The annotated requests with the triggered places can also be commodities in a location market place, which are auctioned to the mobile advertisers, who can place their bids on the triggered places.

System and method for marketing mobile advertising supplies

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.

System and method for real-time prediction of mobile device locations

A system coupled to a packet-based network is configured to generate geo-blocks for location-based information services. Each of the geo-blocks corresponds to a geographical region within a geographical area and has at least one border defined by a real-world object. The system is configured to obtain geo data related to real-world objects including transportation routes and natural boundaries in the geographical area, partition the geographical area into initial geo-blocks using the geo data, process a plurality of information requests with respect to at least some of the initial geo-blocks and with respect to initial geo-fences associated with a plurality of points of interests (POIs) to generate entries in one or more databases, and enrich at least some of the initial geo-block with block-level meta data extracted from the entries in the one or more databases to form enriched geo-blocks that are highly indicative of location dependent attributes such as intention and demographics.

Systems and methods for creating and using geo-blocks for location-based information service

A system for processing information requests associated with mobile devices comprises an information server configured to build a search query for an information request based on the location features and other data therein and to search an information database for matching information documents. The matching information documents including information documents having different types of performance measure, including a first document using an impression-based performance measure, a second document using a click/call-based performance measure and a third document using an off-line site-visit-based performance measure. The information server is further configured rank the matching documents based on their respective performance measures and to select a matching document based on their respective rankings. The information server is further configured to generate a projected probability of an off-line site visit in response to the second document being selected to fulfill the each request and impressed on an associated mobile device, and to adjust a budget of the second document based on the projected probability.

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.

Systems and methods for predicting lookalike mobile devices

A system coupled to a packet-based network is configured to generate geo-blocks based on geo data related to real-world objects including transportation routes and natural boundaries in a geographical area such that each of the geo-blocks corresponds to a geographical region within the geographical area and has at least one border defined by a real-world object. The system is further configured to determine a performance measure with respect to a point of interest (POI) for each of a set of geo-blocks, and to select one or more geo-blocks each having a performance measure above a threshold from the set of geo-blocks to form a geo-fence for the POI. The threshold is adjustable based on a pacing status of an information campaign associated with the POI.

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Sunnyvale, CA, United States of America

Can Liang