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: 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.

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: System and methods for running a location-based information campaign (campaign) select one or more first geo-blocks to form a first geo-fence from a plurality of geo-blocks each corresponding to a geographical region having at least one border defined by a real-world object and overlapping substantially with a targeted region associated with the location-based information campaign, and process information requests with respect to the first geo-fence. Each of the one or more first geo-blocks is associated with a respective performance score above a first threshold. The system and methods further monitor a pacing status associated with the campaign, and in response to a pacing goal associated with the campaign not being met, define a second geo-fence for the campaign, the second geo-fence including the one or more first geo-blocks and one or more second geo-blocks each associated with a respective performance score above a second threshold that is below the first threshold.

Abstract: A method performed by one or more computer systems coupled to a packet-based network to determine the location of a mobile device comprises receiving a set of input signals associated with the mobile device from the packet-based network. The set of input signals include a plurality of location components and context information. The method further comprises determining whether the set of input signals include a pair of latitude/longitude coordinates, and determining whether the pair of latitude/longitude coordinates are valid using a set of criteria. The method further comprises defining at least some of the set of criteria using historical data associated with a plurality of mobile devices communicating with the packet-based network. The method further comprises deriving the location of the mobile device using one or more other location components of the plurality of location components and the context information, in response to the pair of latitude/longitude coordinates being marked as invalid.

Abstract: 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.

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: The disclosed technology includes system and method for discovering key places for a mobile device based on historical location data associated with the mobile device. The key places are discovered by mapping the historical location data into predefined land areas in a geographical region, aggregating the historical location data into time interval sessions, selecting a first set of the predefined areas based on density of location data in each of the predefined land areas, and filtering the first set of the predefined areas to obtain the key places. The key places are used to estimate one or more travel routes routinely taken by a user of the mobile device, and to build an association database, where one or more billboards and/or points of interests near the travel routes are associated with the mobile device or its user. Information documents for delivering to the mobile device are selected based at least on data in the association database.

Abstract: Location data of a large number of mobile devices and map data are used to derive a large number of commute routes in a geographical area, which are used to build a relational database including associations between a plurality of road segments and a plurality of information documents. Each of the plurality of information documents corresponds to a billboard in the geographical area and includes information displayed by the billboard. A respective road segment in the relational database has one or more associations with one or more information documents, each of the one or more information documents being associated with the respective road segment by a corresponding probability value indicating a degree of likelihood of a traveler on the respective road segment being exposed to information displayed on a billboard corresponding to the each of the one or more information documents.

Abstract: A method performed by one or more computer systems coupled to a packet-based network to determine the location of a mobile device comprises receiving a set of input signals associated with the mobile device from the packet-based network. The set of input signals include a plurality of location components and context information. The method further comprises determining whether the set of input signals include a pair of latitude/longitude coordinates, and determining whether the pair of latitude/longitude coordinates are valid using a set of criteria. The method further comprises defining at least some of the set of criteria using historical data associated with a plurality of mobile devices communicating with the packet-based network. The method further comprises deriving the location of the mobile device using one or more other location components of the plurality of location components and the context information, in response to the pair of latitude/longitude coordinates being marked as invalid.

Abstract: A method of delivering location-based information comprises receiving a request including a location indicator from a packet-based network and translating the location indicator into a probabilistic representation of one or more possible locations of a mobile user based on historical data collected from mobile devices. By generating the probabilistic user location representation, the method can be used to deal with the inherent uncertainty in user location data, particularly in a mobile world. In some embodiments, this is done by using validation, context analysis, creation of consistent groups, and probability allocation procedures. Location-based document retrieval can be enabled using the probabilistic user location representation.

Abstract: The present disclosure provides method and apparatus for measuring effect of information delivered to mobile devices. In certain embodiments, a method performed by one or more computer systems coupled to a packet-based network comprises receiving a first plurality of request data packets via the packet-based network, receiving panel data packets via the packet-based network, and selecting a set of calibration mobile devices from the first plurality of mobile devices, each of the set of calibration mobile device having transmitted at least one of the panel data packets. The calibration mobile devices are used to derive a calibration factor.

Abstract: System and methods for running a location-based information campaign (campaign) select one or more first geo-blocks to form a first geo-fence from a plurality of geo-blocks each corresponding to a geographical region having at least one border defined by a real-world object and overlapping substantially with a targeted region associated with the location-based information campaign, and process information requests with respect to the first geo-fence. Each of the one or more first geo-blocks is associated with a respective performance score above a first threshold. The system and methods further monitor a pacing status associated with the campaign, and in response to a pacing goal associated with the campaign not being met, define a second geo-fence for the campaign, the second geo-fence including the one or more first geo-blocks and one or more second geo-blocks each associated with a respective performance score above a second threshold that is below the first threshold.

Abstract: A method of delivering location-based information comprises receiving a request including a location indicator from a packet-based network and translating the location indicator into a probabilistic representation of one or more possible locations of a mobile user based on historical data collected from mobile devices. By generating the probabilistic user location representation, the method can be used to deal with the inherent uncertainty in user location data, particularly in a mobile world. In some embodiments, this is done by using validation, context analysis, creation of consistent groups, and probability allocation procedures. Location-based document retrieval can be enabled using the probabilistic user location representation.

Abstract: 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.

Abstract: Systems and methods of detecting and correcting redeye in an image are described. In one aspect, pixels of the input image are segmented based on projections of color values of the pixels onto two-dimensional thresholding planes. Candidate redeye pixel areas are identified in the input image based on the segmented pixels of the input image.

Abstract: The present invention provides a method and a system for providing targeted advertisements to a user. The method includes determining a set of signals corresponding to at least one online activity associated with the user and then analyzing the set of signals to identify one or more user interests. The set of signals includes at least one of a share signal, a view signal, a search signal, and a click signal. Further, the method includes tagging the user with at least one ad exchange cookie based on the identified user interests and an available advertisement pool. Lastly, the method includes displaying, by an advertisement network, an advertisement to the user based on the ad exchange cookie.