Abstract: Methods, systems, and devices for predicting a state of a user (e.g., asleep or awake). In some embodiments, the location sharing system accesses historical activity data of the user and extracts historical sleep records from the historical activity data. The system clusters the historical sleep records into a plurality of clusters and extracts a sleep pattern from each one of the plurality of clusters. Then, when the location sharing system receives current activity data of the user, the system can predict whether the user is currently asleep based on the current activity of the user and at least one of the sleep patterns. Some embodiments additionally compute an estimated wake up time of the user. Some embodiments share the predicted physiological state of the user with the user's friends via the map GUI. Some embodiments additionally share the estimated wake up time of the user.

Abstract: Methods, systems, and devices for identifying the habitual places of a user, the habitual places of a user being the places where the user spends most of his/her time. In some embodiments, historical location information of a user is accessed, and a plurality of visit points are identified based on the historical location information. The plurality of visit points is clustered into a plurality of clusters. Each one of the clusters is then associated with a habitual place of the user. When the server receives, from a client device associated with the user, current location information of the user, the server identifies a current location of the user based on the current location information, and, if the current location of the user is in a neighborhood of one of the clusters, determine that the user is at the habitual place associated with the cluster.

Abstract: Methods, systems, and devices for identifying the habitual places of a user, the habitual places of a user being the places where the user spends most of his/her time. In some embodiments, historical location information of a user is accessed, and a plurality of visit points are identified based on the historical location information. The plurality of visit points is clustered into a plurality of clusters. Each one of the clusters is then associated with a habitual place of the user. When the server receives, from a client device associated with the user, current location information of the user, the server identifies a current location of the user based on the current location information, and, if the current location of the user is in a neighborhood of one of the clusters, determine that the user is at the habitual place associated with the cluster.

Abstract: Systems, and methods for predicting that a user is located at a labeled place corresponding to a point of interest. A server computer accesses historical data comprising location data, and wireless network data collected from a plurality of client devices of a plurality of users over a period of time. For one or more labeled places, the data points corresponding to one of the users being located at the labeled place are identified. A labeled dataset is generated by tagging the identified data points with a label corresponding to the corresponding labeled place. A machine learning model is trained on the labeled dataset, so that when current location data are receiving from a client device of a user, it is possible to determine, using the trained machine learning model, whether the user is located at one of the one or more labeled places.

Abstract: Methods, systems, and devices for identifying the habitual places of a user, the habitual places of a user being the places where the user spends most of his/her time. In some embodiments, historical location information of a user is accessed, and a plurality of visit points are identified based on the historical location information. The plurality of visit points is clustered into a plurality of clusters. Each one of the clusters is then associated with a habitual place of the user. When the server receives, from a client device associated with the user, current location information of the user, the server identifies a current location of the user based on the current location information, and, if the current location of the user is in a neighborhood of one of the clusters, determine that the user is at the habitual place associated with the cluster.

Abstract: Methods, systems, and devices for predicting a state of a user (e.g., asleep or awake). In some embodiments, the location sharing system accesses historical activity data of the user and extracts historical sleep records from the historical activity data. The system clusters the historical sleep records into a plurality of clusters and extracts a sleep pattern from each one of the plurality of clusters. Then, when the location sharing system receives current activity data of the user, the system can predict whether the user is currently asleep based on the current activity of the user and at least one of the sleep patterns. Some embodiments additionally compute an estimated wake up time of the user. Some embodiments share the predicted physiological state of the user with the user's friends via the map GUI. Some embodiments additionally share the estimated wake up time of the user.

Abstract: Methods, systems, and devices for identifying the habitual places of a user, the habitual places of a user being the places where the user spends most of his/her time. In some embodiments, historical location information of a user is accessed, and a plurality of visit points are identified based on the historical location information. The plurality of visit points is clustered into a plurality of clusters. Each one of the clusters is then associated with a habitual place of the user. When the server receives, from a client device associated with the user, current location information of the user, the server identifies a current location of the user based on the current location information, and, if the current location of the user is in a neighborhood of one of the clusters, determine that the user is at the habitual place associated with the cluster.

Abstract: Methods, systems, and devices for predicting a state of a user (e.g., asleep or awake). In some embodiments, the location sharing system accesses historical activity data of the user and extracts historical sleep records from the historical activity data. The system clusters the historical sleep records into a plurality of clusters and extracts a sleep pattern from each one of the plurality of clusters. Then, when the location sharing system receives current activity data of the user, the system can predict whether the user is currently asleep based on the current activity of the user and at least one of the sleep patterns. Some embodiments additionally compute an estimated wake up time of the user. Some embodiments share the predicted physiological state of the user with the user's friends via the map GUI. Some embodiments additionally share the estimated wake up time of the user.

Abstract: Systems, and methods for predicting that a user is located at a labeled place corresponding to a point of interest. A server computer accesses historical data comprising location data, and wireless network data collected from a plurality of client devices of a plurality of users over a period of time. For one or more labeled places, the data points corresponding to one of the users being located at the labeled place are identified. A labeled dataset is generated by tagging the identified data points with a label corresponding to the corresponding labeled place. A machine learning model is trained on the labeled dataset, so that when current location data are receiving from a client device of a user, it is possible to determine, using the trained machine learning model, whether the user is located at one of the one or more labeled places.

Abstract: Systems, and methods for predicting that a user is located at a labeled place corresponding to a point of interest. A server computer accesses historical data comprising location data, and wireless network data collected from a plurality of client devices of a plurality of users over a period of time. For one or more labeled places, the data points corresponding to one of the users being located at the labeled place are identified. A labeled dataset is generated by tagging the identified data points with a label corresponding to the corresponding labeled place. A machine learning model is trained on the labeled dataset, so that when current location data are receiving from a client device of a user, it is possible to determine, using the trained machine learning model, whether the user is located at one of the one or more labeled places.

Abstract: Methods, systems, and devices for predicting a state of a user (e.g., asleep or awake). In some embodiments, the location sharing system accesses historical activity data of the user and extracts historical sleep records from the historical activity data. The system clusters the historical sleep records into a plurality of clusters and extracts a sleep pattern from each one of the plurality of clusters. Then, when the location sharing system receives current activity data of the user, the system can predict whether the user is currently asleep based on the current activity of the user and at least one of the sleep patterns. Some embodiments additionally compute an estimated wake up time of the user. Some embodiments share the predicted physiological state of the user with the user's friends via the map GUI. Some embodiments additionally share the estimated wake up time of the user.

Abstract: Methods, systems, and devices for predicting a state of a user (e.g., asleep or awake). In some embodiments, the location sharing system accesses historical activity data of the user and extracts historical sleep records from the historical activity data. The system clusters the historical sleep records into a plurality of clusters and extracts a sleep pattern from each one of the plurality of clusters. Then, when the location sharing system receives current activity data of the user, the system can predict whether the user is currently asleep based on the current activity of the user and at least one of the sleep patterns. Some embodiments additionally compute an estimated wake up time of the user. Some embodiments share the predicted physiological state of the user with the user's friends via the map GUI. Some embodiments additionally share the estimated wake up time of the user.

Abstract: Systems, and methods for predicting that a user is located at a labeled place corresponding to a point of interest. A server computer accesses historical data comprising location data, and wireless network data collected from a plurality of client devices of a plurality of users over a period of time. For one or more labeled places, the data points corresponding to one of the users being located at the labeled place are identified. A labeled dataset is generated by tagging the identified data points with a label corresponding to the corresponding labeled place. A machine learning model is trained on the labeled dataset, so that when current location data are receiving from a client device of a user, it is possible to determine, using the trained machine learning model, whether the user is located at one of the one or more labeled places.

Abstract: Methods, systems, and devices for predicting a state of a user (e.g., asleep or awake). In some embodiments, the location sharing system accesses historical activity data of the user and extracts historical sleep records from the historical activity data. The system clusters the historical sleep records into a plurality of clusters and extracts a sleep pattern from each one of the plurality of clusters. Then, when the location sharing system receives current activity data of the user, the system can predict whether the user is currently asleep based on the current activity of the user and at least one of the sleep patterns. Some embodiments additionally compute an estimated wake up time of the user. Some embodiments share the predicted physiological state of the user with the user's friends via the map GUI. Some embodiments additionally share the estimated wake up time of the user.

Abstract: Systems, and methods for predicting that a user is located at a labeled place corresponding to a point of interest. A server computer accesses historical data comprising location data, and wireless network data collected from a plurality of client devices of a plurality of users over a period of time. For one or more labeled places, the data points corresponding to one of the users being located at the labeled place are identified. A labeled dataset is generated by tagging the identified data points with a label corresponding to the corresponding labeled place. A machine learning model is trained on the labeled dataset, so that when current location data are receiving from a client device of a user, it is possible to determine, using the trained machine learning model, whether the user is located at one of the one or more labeled places.

Abstract: A computer readable medium containing computer executable instructions is described for extracting a reference representation from a mixture representation that comprises the reference representation and a residual representation wherein the reference representation, the mixture representation, and the residual representation are representations of collections of acoustical waves stored on computer readable media.