Mapping gathered location information to short form place names using correlations and confidence measures that pertain to lengths of overlaps of location data and calendar data sets
First Claim
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1. A device comprising:
- at least one non-transitory computer readable storage medium with instructions executable by at least one processor to configure the processor for;
receiving geographic location information from at least one source of location information;
receiving calendar information from at least one data storage;
correlating at least one calendar data set from the calendar information with at least first and second location data sets derived from the location information based at least in part on the first and second data sets temporally overlapping with the calendar data set to render first and second correlations;
associating the first and second correlations with respective first and second confidence measures, each confidence measure being based at least in part on a length of temporal overlap between the respective location data set and the calendar data set;
selecting one of the correlations based at least in part on a relationship between respective confidence measures; and
outputting human-perceptible user information associated with the calendar data set and the location data set associated with the correlation selected based at least in part on the confidence measures.
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Abstract
Using the short form information people tend to use in their calendar locations (not full address or GPS location), machine learning techniques are used to map gathered location information to these short form names.
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Citations
8 Claims
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1. A device comprising:
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at least one non-transitory computer readable storage medium with instructions executable by at least one processor to configure the processor for; receiving geographic location information from at least one source of location information; receiving calendar information from at least one data storage; correlating at least one calendar data set from the calendar information with at least first and second location data sets derived from the location information based at least in part on the first and second data sets temporally overlapping with the calendar data set to render first and second correlations; associating the first and second correlations with respective first and second confidence measures, each confidence measure being based at least in part on a length of temporal overlap between the respective location data set and the calendar data set; selecting one of the correlations based at least in part on a relationship between respective confidence measures; and outputting human-perceptible user information associated with the calendar data set and the location data set associated with the correlation selected based at least in part on the confidence measures. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification