Modeling location histories
First Claim
1. An article of manufacture comprising:
- a storage medium; and
a plurality of executable instructions stored on the storage medium that, when executed, direct a device to convert a location history to a stochastic model of the location history by applying at least one recurring time period to the location history, the recurring time period being divided into a number of time intervals, the stochastic model comprising a multi-dimensional matrix that includes multiple entries each corresponding to a combination of one of the time intervals and one of multiple locations, each entry including at least one probability that an object is present at the corresponding one of the locations during the one of the time intervals, the probability being calculated by dividing a total number of times that the location history shows the object as being at the corresponding one of the multiple locations during the corresponding one of the time intervals by a total number of recurrences of the time period.
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Accused Products
Abstract
A location history is a collection of locations over time for an object. By applying a recurring time period to a location history, it can be converted into a stochastic model of the location history. For example, a location history can be reorganized based on intervals that subside a recurring cycle. In a described implementation, training a location history model involves traversing each interval of multiple cycles of a target location history. After each object location at each interval is entered into a training matrix, the intervals can be normalized to determine relative probabilities per location for each interval of a designated cycle. The training and resulting location history model can be Markovian or non-Markovian. Applications include probabilistic location estimation, fusion of location estimates, location-history simulation, optimal scheduling, transition analysis, clique analysis, and so forth.
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Citations
31 Claims
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1. An article of manufacture comprising:
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a storage medium; and a plurality of executable instructions stored on the storage medium that, when executed, direct a device to convert a location history to a stochastic model of the location history by applying at least one recurring time period to the location history, the recurring time period being divided into a number of time intervals, the stochastic model comprising a multi-dimensional matrix that includes multiple entries each corresponding to a combination of one of the time intervals and one of multiple locations, each entry including at least one probability that an object is present at the corresponding one of the locations during the one of the time intervals, the probability being calculated by dividing a total number of times that the location history shows the object as being at the corresponding one of the multiple locations during the corresponding one of the time intervals by a total number of recurrences of the time period. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A system for modeling location histories, the system comprising:
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a processor; memory coupled to the processor; architecture means for architecting a stochastic model of a location history; and training means for training the stochastic model from the location history by applying at least one recurring time period to the location history, the recurring time period being divided into a number of time intervals, the stochastic model comprising a multi-dimensional matrix that includes multiple entries each corresponding to a combination of one of the time intervals and one of multiple locations, each entry including at least one probability that an object is present at the corresponding one of the location during the one of the time intervals, the probability being calculated by dividing a total number of times that the location history shows the object as being at the corresponding one of the multiple locations during the corresponding one of the time intervals by a total number of recurrences of the time period. - View Dependent Claims (19, 20, 21, 22, 23, 24)
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25. A method comprising:
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adding, by a computing device, object locations to a training matrix for time intervals of a designated cycle from a location history; normalizing, by the computing device, the time intervals with regard to a number of the cycles addressed in the adding to determine relative probabilities an object is present per location for each interval, each probability being calculated by dividing a total number of times that the location history shows the object as being at one of the locations during one of the time intervals by a total number of recurrences of the cycle; building and storing, by the computing device, a stochastic model by recording the respective location probabilities determined in the normalizing for corresponding intervals in a location history model matrix; and implementing, by the computing device, an application using a stored location history model matrix, the application selected from a group comprising;
a probabilistic location estimation, a fusion of location estimates, a location-history simulation, an optimal scheduling, a transition analysis, and a clique analysis. - View Dependent Claims (26, 27, 28)
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29. A device comprising:
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at least one processor; and one or more computer program products embodied on computer readable storage media including processor-executable instructions, the processor-executable instructions including; a stochastic location history model comprising a three-dimensional matrix having a plurality of probabilities;
a first dimension comprising multiple intervals of a cycle, a second dimension comprising multiple previous object locations, and a third dimension comprising multiple current object locations;
each probability of the plurality of probabilities corresponding to an intersection of the second and third dimensions, each particular probability associated with a particular current object location given an intersecting previous object location, each probability comprising a probability that an object is present at the associated current object location during a corresponding interval given the intersecting previous object location, the probability being calculated by dividing a total number of times that the location history shows the object as being at the corresponding one of the multiple locations during the corresponding one of the time intervals by a total number of recurrences of the time period, wherein the processor-executable instructions, when executed, direct the device to predict an object'"'"'s location during a particular interval using at least one cumulative distributive function (CDF) that is derived from the stochastic location history model. - View Dependent Claims (30, 31)
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Specification