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: 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 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 method of forecasting comprises, for each respective time period of a plurality of time periods, transforming a plurality of observations from the respective time period into observation sketches. The method then proceeds to extract a set of targeted attributes and constraints from a forecast request, and construct a set expression using the targeted attributes and the associated constraints. The method further comprises selecting one or more sample periods from the plurality of time periods, and for each sample period of the one or more selected sample periods: mapping the set of targeted attributes to observation sketches in the sample period to identify a subset of sketches, and performing a set operation on the subset of sketches using the set expression to determine a cardinality corresponding to the sample period; and generating a forecast result using one or more cardinalities corresponding to the one or more sample periods.

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 impressing publicly displayed information on mobile devices. The method comprises determining at least a first place and a second place for a mobile device based on locations associated with the mobile device. Each of the first place and the second place being a place where the mobile device is regularly located as indicated by densities of the locations mapped over a geographical region and the corresponding time stamps. The first place and second place are used to determine one or more travel routes routinely taken by a user of the mobile device, and to build an association database, where the mobile device is associated with one or more information documents, each of the one or more information documents including information publicly displayed along the one or more routes.

Abstract: The present disclosure provides method and system to facilitate definition, tuning and visualization of a geo-fence at a computer system. The method comprises: receiving input parameters for a geo-fence, the input parameters including one or more parameters specifying a geographical region; sampling historical mobile signals based on one or more of the input parameters; dividing the geographical region into a plurality of areas; determining a weight for each respective area of the plurality of areas based at least on density of sampled mobile signals associated with geographical locations in the respective area; selecting a subset of the plurality of areas based on respective weights of the plurality of areas; and forming the geo-fence using the subset of the plurality of areas, the geo-fence including one or more contiguously closed regions each formed by a cluster of adjacent areas among the subset of the plurality of areas.

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 mobile device running a software application is configured to provide initial location data to a server when it is first turned on, or reaches a predetermined movement status. The server determines, based on the initial location data, geo data including a set of geo-fences near the mobile device and a region including areas defined by the set of geo-fences, and provides the geo data to the mobile device. The mobile device is further configured to collect its locations when the mobile device is within any of the set of geo-fences and to provide the collected locations to the server. The mobile device is further configured to track its movement status after it has been outside the region and, in response to having reached a predetermined movement status, obtain updated geo data including a new region and a new set of geo-fences from the server. The software application enables efficient tracking of mobile device locations and reduces power consumption.

Abstract: 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.

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: 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: The present disclosure provides method and system to facilitate definition, tuning and visualization of a geo-fence at a computer system. The method comprises receiving an application program interface (API) call from the computer system, and in response to the API call, enabling the computer system to display a user interface (UI) on a display screen of the second computer system. The UI includes a first region and a second region, the first region including a plurality of screen areas for receiving input parameters for defining the geo-fence via a user input device of the computer system, and a second region is configured to display the geo-fence over a map of a geographical region around a location specified in the input parameters. The method further comprises, in response to receiving user inputs in the second region, generating the geo-fence based on the input parameters and mobile device events data stored in an electronic storage.

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: 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 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: 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 mobile device running a software application is configured to provide initial location data to a server when it is first turned on, or achieves a predetermined movement status. The server determines, based on the initial location data, geo data including a set of areas of interest (AOIs) near the mobile device and a region including the set of AOIs, and provides the geo data to the mobile device. The mobile device is further configured to track its locations when the mobile device is within any of the set of AOIs and to provide the tracked locations to the server. The mobile device is further configured to track its movement status after it has been outside the region, and, in response to a predetermined movement status being detected for a predetermined amount of time, obtain updated geo data including a new region and a new set of AOIs from the server. The software application enables efficient tracking of mobile device locations and reduces power consumption.