Modeling location histories
First Claim
1. One or more processor-accessible media comprising processor-executable instructions that, when executed, direct a device to convert a location history to a stochastic model of the location history.
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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
39 Claims
- 1. One or more processor-accessible media comprising processor-executable instructions that, when executed, direct a device to convert a location history to a stochastic model of the location history.
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20. An arrangement for modeling location histories, the arrangement comprising:
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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 a recurring time period. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27)
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28. A method comprising:
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adding object locations to a training matrix for intervals of a designated cycle from a location history;
normalizing the intervals with regard to a number of cycles addressed in the adding to determine relative probabilities per location for each interval; and
building a stochastic model by recording respective location probabilities determined in the normalizing for corresponding intervals in a location history model matrix. - View Dependent Claims (29, 30, 31, 32)
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33. One or more processor-accessible media comprising processor-executable instructions that include a location history model data structure, the location history model data structure comprising a two-dimensional matrix having a plurality of probabilities;
- a first dimension comprising multiple intervals of a cycle, and a second dimension comprising multiple object locations;
each probability of the plurality of probabilities associated with an object location of the multiple object locations and corresponding to an interval of the multiple intervals. - View Dependent Claims (34, 35)
- a first dimension comprising multiple intervals of a cycle, and a second dimension comprising multiple object locations;
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36. A device comprising:
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at least one processor; and
one or more media including processor-executable instructions that are capable of being executed by the at least one processor, the processor-executable instructions including;
a location history model data structure, the location history model data structure 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. - View Dependent Claims (37, 38, 39)
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Specification