Abstract: Examples of the present disclosure describe systems and methods for venue detection. In aspects, a mobile device comprising a set of sensors may collect and store sensor data from in response to a detected movement event or user interaction data. The sensor data may be used to generate a set of candidate venues corresponding to the location of the mobile device. The candidate venues may be provided to a venue detection system. The venue detection system may process the candidate venues to generate a set of features. The set of features may be applied to, and/or used to generate, one or more probabilistic models. The probabilistic models may generate confidence metrics for each of the candidate venues. In some aspects, the top ‘N’ venues may be selected from the set. The top ‘N’ venues may then be presented to the user and/or used to effect one or more actions.

Abstract: Examples of the present disclosure describe systems and methods for venue detection. In aspects, a mobile device comprising a set of sensors may collect and store sensor data from in response to a detected movement event or user interaction data. The sensor data may be used to generate a set of candidate venues corresponding to the location of the mobile device. The candidate venues may be provided to a venue detection system. The venue detection system may process the candidate venues to generate a set of features. The set of features may be applied to, and/or used to generate, one or more probabilistic models. The probabilistic models may generate confidence metrics for each of the candidate venues. In some aspects, the top ‘N’ venues may be selected from the set. The top ‘N’ venues may then be presented to the user and/or used to effect one or more actions.

Abstract: A technique is disclosed for ranking venues and for providing a user with one or more of the ranked venues. To do so, a data processing system trains a model during a first, training sequence, by using a training dataset of past check-ins that were made by users visiting venues. The trained model, representing a learned hypothesis, represents venues that are ranked according to their relevance to users who are also represented by the model. During a second, run-time sequence, the system receives real-time geolocation data that represents users and generates a set of candidate venues for a user by determining venues that are nearby the geolocation of the user. The system then ranks the generated candidate venues by applying the learned hypothesis. One or more of the ranked venues can be provided to a user's wireless terminal, for example, where they can be presented as venue recommendations.

Abstract: Examples of the present disclosure describe systems and methods for venue detection. In aspects, a mobile device comprising a set of sensors may collect and store sensor data from in response to a detected movement event or user interaction data. The sensor data may be used to generate a set of candidate venues corresponding to the location of the mobile device. The candidate venues may be provided to a venue detection system. The venue detection system may process the candidate venues to generate a set of features. The set of features may be applied to, and/or used to generate, one or more probabilistic models. The probabilistic models may generate confidence metrics for each of the candidate venues. In some aspects, the top ‘N’ venues may be selected from the set. The top ‘N’ venues may then be presented to the user and/or used to effect one or more actions.

Abstract: In non-limiting examples, wireless scan data collected from a processing device is directly mapped to venues. Wireless scan data is received from a processing device where the wireless scan data comprises at least a first identifier of a wireless connection. Venues associated with the first identifier are identified from a mapping of wireless identifiers to venues based on active validation data associated with particular venues including historical wireless scan data of the particular venues. The plurality of venues are ranked according to a likelihood that the processing device is located at a particular venue based on evaluating the active validation data including the historical wireless scan data from the mapping. A predicted venue that the processing device is located at is determined based on the ranking of the plurality of venues. Other examples are also described.

Abstract: A technique is disclosed for ranking venues and for providing a user with one or more of the ranked venues. To do so, a data processing system trains a model during a first, training sequence, by using a training dataset of past check-ins that were made by users visiting venues. The trained model, representing a learned hypothesis, represents venues that are ranked according to their relevance to users who are also represented by the model. During a second, run-time sequence, the system receives real-time geolocation data that represents users and generates a set of candidate venues for a user by determining venues that are nearby the geolocation of the user. The system then ranks the generated candidate venues by applying the learned hypothesis. One or more of the ranked venues can be provided to a user's wireless terminal, for example, where they can be presented as venue recommendations.

Abstract: A technique is disclosed for presenting possible cheek-in venues to a mobile device user, such as for the purpose of checking in with social networking services such as Foursquare. The disclosed technique operates by training a model during an initial training sequence. The trained model, representing a teamed hypothesis, describes venues that are ranked according to their relevance to users who are also described by the model. During a subsequent run-time sequence, the system receives real-time geolocation data that represents users, wherein the data includes user geolocation and the level of accuracy of the geolocation. The system generates a set of candidate venues, ranks the generated candidate venues by applying the learned hypothesis, which can be dependent on the level of accuracy, and presents the user with the ranked venues as possible cheek-in venues. The user can then select the venue actually being checked into from the presented check-in venues.

Abstract: In non-limiting examples, wireless scan data collected from a processing device is directly mapped to venues. Wireless scan data is received from a processing device where the wireless scan data comprises at least a first identifier of a wireless connection. Venues associated with the first identifier are identified from a mapping of wireless identifiers to venues based on active validation data associated with particular venues including historical wireless scan data of the particular venues. The plurality of venues are ranked according to a likelihood that the processing device is located at a particular venue based on evaluating the active validation data including the historical wireless scan data from the mapping. A predicted venue that the processing device is located at is determined based on the ranking of the plurality of venues. Other examples are also described.

Abstract: A technique is disclosed for presenting possible cheek-in venues to a mobile device user, such as for the purpose of checking in with social networking services such as Foursquare. The disclosed technique operates by training a model during an initial training sequence. The trained model, representing a teamed hypothesis, describes venues that are ranked according to their relevance to users who are also described by the model. During a subsequent run-time sequence, the system receives real-time geolocation data that represents users, wherein the data includes user geolocation and the level of accuracy of the geolocation. The system generates a set of candidate venues, ranks the generated candidate venues by applying the learned hypothesis, which can be dependent on the level of accuracy, and presents the user with the ranked venues as possible cheek-in venues. The user can then select the venue actually being checked into from the presented check-in venues.

Abstract: A technique is disclosed for presenting possible check-in venues to a mobile device user, such as for the purpose of checking in with social networking services such as Foursquare. The disclosed technique operates by training a model during an initial training sequence. The trained model, representing a learned hypothesis, describes venues that are ranked according to their relevance to users who are also described by the model. During a subsequent run-time sequence, the system receives real-time geolocation data that represents users, wherein the data includes user geolocation and the level of accuracy of the geolocation. The system generates a set of candidate venues, ranks the generated candidate venues by applying the learned hypothesis, which can be dependent on the level of accuracy, and presents the user with the ranked venues as possible check-in venues. The user can then select the venue actually being checked into from the presented check-in venues.

Abstract: A technique is disclosed for presenting possible check-in venues to a mobile device user, such as for the purpose of checking in with social networking services such as Foursquare. The disclosed technique operates by training a model during an initial training sequence. The trained model, representing a learned hypothesis, describes venues that are ranked according to their relevance to users who are also described by the model. During a subsequent run-time sequence, the system receives real-time geolocation data that represents users, wherein the data includes user geolocation and the level of accuracy of the geolocation. The system generates a set of candidate venues, ranks the generated candidate venues by applying the learned hypothesis, which can be dependent on the level of accuracy, and presents the user with the ranked venues as possible check-in venues. The user can then select the venue actually being checked into from the presented check-in venues.

Abstract: A technique is disclosed for ranking venues and for providing a user with one or more of the ranked venues. To do so, a data processing system trains a model during a first, training sequence, by using a training dataset of past check-ins that were made by users visiting venues. The trained model, representing a learned hypothesis, represents venues that are ranked according to their relevance to users who are also represented by the model. During a second, run-time sequence, the system receives real-time geolocation data that represents users and generates a set of candidate venues for a user by determining venues that are nearby the geolocation of the user. The system then ranks the generated candidate venues by applying the learned hypothesis. One or more of the ranked venues can be provided to a user's wireless terminal, for example, where they can be presented as venue recommendations.