Modeling Significant Locations
First Claim
1. A method comprising:
- receiving, by a mobile device and from a location determination subsystem of the mobile device, a plurality of locations of the mobile device, each location being associated with a timestamp indicating a time the location was determined by the location determination subsystem, the plurality of locations being ordered sequentially based on timestamps of the locations;
determining, by the mobile device and based on a clustering condition, that two or more consecutive locations in the ordered plurality of locations form a location cluster, the location cluster indicating that the mobile device has stayed at a geographic location that is sufficiently significant to be represented in a state model for forecasting a movement of the mobile device;
determining, by the mobile device and based on the location cluster, the state model, including designating the significant location as a state in the state model and representing each movement of the mobile device from a first significant location to a second significant location as a transition from a first state representing the first significant location to a second state representing the second significant location, the transition being associated with a transition start time and a transition end time; and
providing the state model to a forecasting subsystem of the mobile device for generating a forecast that a future location of the mobile device at a given future time is one of the significant locations represented in the state model, wherein generating the forecast is based on a current time, the future time, a current location, and a probability density determined based on the states and transitions of the state model.
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Abstract
Techniques for modeling significant locations are described. A significant location can be a location that is significant to a user of a mobile device for a variety of reasons. The mobile device can determine that a place or region is a significant location upon determining that, with sufficient certainty, the mobile device has stayed at the place or region for a sufficient amount of time. The mobile device can construct a state model that is an abstraction of one or more significant locations. The state model can include states representing the significant locations, and transitions representing movement of the mobile device between the locations. The mobile device can use the state model to provide predictive user assistance.
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Citations
30 Claims
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1. A method comprising:
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receiving, by a mobile device and from a location determination subsystem of the mobile device, a plurality of locations of the mobile device, each location being associated with a timestamp indicating a time the location was determined by the location determination subsystem, the plurality of locations being ordered sequentially based on timestamps of the locations; determining, by the mobile device and based on a clustering condition, that two or more consecutive locations in the ordered plurality of locations form a location cluster, the location cluster indicating that the mobile device has stayed at a geographic location that is sufficiently significant to be represented in a state model for forecasting a movement of the mobile device; determining, by the mobile device and based on the location cluster, the state model, including designating the significant location as a state in the state model and representing each movement of the mobile device from a first significant location to a second significant location as a transition from a first state representing the first significant location to a second state representing the second significant location, the transition being associated with a transition start time and a transition end time; and providing the state model to a forecasting subsystem of the mobile device for generating a forecast that a future location of the mobile device at a given future time is one of the significant locations represented in the state model, wherein generating the forecast is based on a current time, the future time, a current location, and a probability density determined based on the states and transitions of the state model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A mobile device comprising:
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one or more processors; and a non-transitory computer-readable medium coupled to the one or more processors and storing instructions operable to cause the one or more processors to perform operations comprising; receiving, from a location determination subsystem of the mobile device, a plurality of locations of the mobile device, each location being associated with a timestamp indicating a time the location was determined by the location determination subsystem, the plurality of locations being ordered sequentially based on timestamps of the locations; determining, based on a clustering condition, that two or more consecutive locations in the ordered plurality of locations form a location cluster, the location cluster indicating that the mobile device has stayed at a geographic location that is sufficiently significant to be represented in a state model for forecasting a movement of the mobile device; determining, based on the location cluster, the state model, including designating the significant location as a state in the state model and representing each movement of the mobile device from a first significant location to a second significant location as a transition from a first state representing the first significant location to a second state representing the second significant location, the transition being associated with a transition start time and a transition end time; and providing the state model to a forecasting subsystem of the mobile device for generating a forecast that a future location of the mobile device at a given future time is one of the significant locations represented in the state model, wherein generating the forecast is based on a current time, the future time, a current location, and a probability density determined based on the states and transitions of the state model. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A non-transitory computer-readable medium coupled to one or more processors of a mobile device and storing instructions operable to cause the one or more processors to perform operations comprising:
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receiving, from a location determination subsystem of the mobile device, a plurality of locations of the mobile device, each location being associated with a timestamp indicating a time the location was determined by the location determination subsystem, the plurality of locations being ordered sequentially based on timestamps of the locations; determining, based on a clustering condition, that two or more consecutive locations in the ordered plurality of locations form a location cluster, the location cluster indicating that the mobile device has stayed at a geographic location that is sufficiently significant to be represented in a state model for forecasting a movement of the mobile device; determining, based on the location cluster, the state model, including designating the significant location as a state in the state model and representing each movement of the mobile device from a first significant location to a second significant location as a transition from a first state representing the first significant location to a second state representing the second significant location, the transition being associated with a transition start time and a transition end time; and providing the state model to a forecasting subsystem of the mobile device for generating a forecast that a future location of the mobile device at a given future time is one of the significant locations represented in the state model, wherein generating the forecast is based on a current time, the future time, a current location, and a probability density determined based on the states and transitions of the state model. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30)
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Specification