Abstract: Various arrangements of hybrid cloud cellular network systems are presented herein. A cellular radio access network (RAN) that includes multiple base stations (BSs) can be in communication with a cloud computing platform. Multiple cloud-implemented national data center (NDC) instantiations can be present and executed on the cloud computing platform. Network functions (NFs) are executed within each cloud-implemented NDC instantiation on the cloud computing platform. The system further includes instances of a cellular network database function that updates each other instance of the cellular network database function across the NDC instantiations.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system, method, and computer program product for presenting items within a list of items based on an individual user profile, a social network of friends of the user or a mood of the user. The system generates the individual profile of the user, determines passive friends of the user from the social network of the user and also determines the mood of the user. The user has a choice to receive an ordered list of items based on the individual user profile, the social network or the mood of the user.

Abstract: The present system relates to a method for indexing a document comprising the acts of retrieving in the content of the document true landmarks associated with said content, selecting one true landmark as representing the document, indexing the document to include a reference to the selected true landmark, wherein the retrieving act further comprises the act of identifying landmarks through the comparison of the content of the document to a landmark database, and, retaining only true landmarks among the identified landmark using a natural language approach, and wherein the selecting act further comprising the acts of assigning a weight to each true landmark based on at least one criterion, selecting the true landmark that represents the document based on the assigned weights.

Abstract: A system, method, and computer program product for presenting items within a list of items based on an individual user profile, a social network of friends of the user or a mood of the user. The system generates the individual profile of the user, determines passive friends of the user from the social network of the user and also determines the mood of the user. The user has a choice to receive an ordered list of items based on the individual user profile, the social network or the mood of the user.