Abstract: The present disclosure provides novel techniques to segment mobile entities based on signals from mobile devices associated with these mobile entities. In certain embodiments, a data file is built for each mobile entity to transform originally extremely high dimensional raw data derived from mobile devices communicating with a network into structured data characterizing associated mobile entities. The data files are structured for filtering and searching with respect to features associated with the signals from mobile devices, including location and non-location features. The data files are used to determine feature gains corresponding respectively to features related to a mobile segment. The features include first features of a first type and second features of a second type. The feature gains are used, among other things, to rank mobile entities for the mobile segment, with feature gains corresponding to the first features given a larger weight than feature gains corresponding to the second features.

Abstract: 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.

Abstract: The present disclosure provides novel techniques to derive characteristic of mobile entities based signals from mobile devices associated with these mobile entities. In certain embodiments, a data file is built for each mobile entity to transform originally extremely high dimensional raw data derived from mobile devices communicating with a network into structured data characterizing associated mobile entities. The data file are structured for filtering and searching with respect to location and non-location features. A plurality of filters for a behavioral segment are applied to the data files of a large number of mobile entities. The plurality of filters including a preset hard constraint (HC) filter, a preset soft constraint (SC) filter, and an extended constraint (EC) filter built using location features in data files that have passed through the HC filter and/or the SC filter.

Abstract: 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.

Abstract: The present disclosure provides novel techniques to derive characteristic of mobile entities based signals from mobile devices associated with these mobile entities. In certain embodiments, a data file is built for each mobile entity to transform originally extremely high dimensional raw data derived from mobile devices communicating with a network into structured data characterizing associated mobile entities. The data file are structured for filtering and searching with respect to location and non-location features. A plurality of filters for a behavioral segment are applied to the data files of a large number of mobile entities. The plurality of filters including a preset hard constraint (HC) filter, a preset soft constraint (SC) filter, and an extended constraint (EC) filter built using location features in data files that have passed through the HC filter and/or the SC filter.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure provides novel techniques to derive characteristic of mobile entities based signals from mobile devices associated with these mobile entities. In certain embodiments, a data file is built for each mobile entity to transform originally extremely high dimensional raw data derived from mobile devices communicating with a network into structured data characterizing associated mobile entities. The data file are structured for filtering and searching with respect to location and non-location features. A plurality of filters for a behavioral segment are applied to the data files of a large number of mobile entities. The plurality of filters including a preset hard constraint (HC) filter, a preset soft constraint (SC) filter, and an extended constraint (EC) filter built using location features in data files that have passed through the HC filter and/or the SC filter.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure provides novel techniques to derive characteristic of mobile entities based signals from mobile devices associated with these mobile entities. In certain embodiments, a data file is built for each mobile entity to transform originally extremely high dimensional raw data derived from mobile devices communicating with a network into structured data characterizing associated mobile entities. The data file are structured for filtering and searching with respect to location and non-location features. A plurality of filters for a behavioral segment are applied to the data files of a large number of mobile entities. The plurality of filters including a preset hard constraint (HC) filter, a preset soft constraint (SC) filter, and an extended constraint (EC) filter built using location features in data files that have passed through the HC filter and/or the SC filter.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.