Grouping ambient-location updates
First Claim
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1. A method comprising:
- by a computing device, receiving location data from a mobile computing device associated with a user, the location data comprising one or more location readings being sent automatically and without manual input from the user;
by the computing device, representing the location data as one or more geo-location data points based at least in part on a distance between the location readings and the geo-location data points; and
by the computing device, grouping, through a k-means spatial-clustering algorithm, one or more of the geo-location data points into one or more geo-location clusters based at least in part on a distance between each geo-location data point and a geo-location centroid of each geo-location cluster, wherein each geo-location data points is placed with a particular one of the geo-location clusters based at least in part on a distance between the geo-location data point and the geo-location centroid of the particular one of the geo-location clusters being less than a pre-determined threshold value.
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Abstract
In one embodiment, a method includes receiving location data from a mobile device associated with a user. The location data includes one or more location readings sent automatically and without manual input from the user. The method also includes representing the location data as one or more geo-location data points based at least in part on a distance between the location readings and the geo-location data points; and grouping one or more of the geo-location data points into one or more geo-location clusters based at least in part on a distance between each geo-location data point and a geo-location centroid of each geo-location cluster.
29 Citations
21 Claims
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1. A method comprising:
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by a computing device, receiving location data from a mobile computing device associated with a user, the location data comprising one or more location readings being sent automatically and without manual input from the user; by the computing device, representing the location data as one or more geo-location data points based at least in part on a distance between the location readings and the geo-location data points; and by the computing device, grouping, through a k-means spatial-clustering algorithm, one or more of the geo-location data points into one or more geo-location clusters based at least in part on a distance between each geo-location data point and a geo-location centroid of each geo-location cluster, wherein each geo-location data points is placed with a particular one of the geo-location clusters based at least in part on a distance between the geo-location data point and the geo-location centroid of the particular one of the geo-location clusters being less than a pre-determined threshold value. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. One or more computer-readable non-transitory storage media embodying software configured when executed to:
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receive location data from a mobile computing device associated with a user, the location data comprising one or more location readings being sent automatically and without manual input from the user; represent the location data as one or more geo-location data points based at least in part on a distance between consecutive location readings; and group, through a k-means spatial-clustering algorithm, one or more of the geo-location data points into one or more geo-location clusters based at least in part on a distance between each geo-location data point and a geo-location centroid of each geo-location cluster, wherein each of the geo-location data points is placed with a particular one of the geo-location clusters based at least in part on a distance between the geo-location data point and the geo-location centroid of the particular one of the geo-location clusters being less than a pre-determined threshold value. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A device comprising:
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one or more processors; and one or more computer-readable non-transitory storage media coupled to the processors and embodying software configured when executed to; receive location data from a mobile computing device associated with a user, the location data comprising one or more location readings being sent automatically and without manual input from the user; represent the location data as one or more geo-location data points based at least in part on a distance between consecutive location readings; and group, through a k-means spatial-clustering algorithm, one or more of the geo-location data points into one or more geo-location clusters based at least in part on a distance between each geo-location data point and a geo-location centroid of each geo-location cluster, wherein each of the geo-location data points is placed with a particular one of the geo-location clusters based at least in part on a distance between the geo-location data point and the geo-location centroid of the particular one of the geo-location clusters being less than a pre-determined threshold value. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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Specification