Journey learning system
First Claim
1. A computer-implemented method comprising:
- converting, by one or more computing devices, a set of driver history data to a set of learning parameters;
analyzing, by the one or more computing devices, the set of learning parameters and current journey data describing a current journey to generate estimated journey data describing one or more potential journeys;
retrieving, by the one or more computing devices, a set of current status data;
determining, by the one or more computing devices, a destination for each of the one or more potential journeys, a direction for each of the one or more potential journeys, a time of day for each of the one or more potential journeys, and day of the week for each of the one or more potential journeys;
calculating, by the one or more computing devices, a joint conditional probability metric for each potential journey of the one or more potential journeys based on the destination, the direction, the time of day, and the day of the week associated with the potential journey;
determining by the one or more computing devices, a quality score for each potential journey of the one or more potential journeys based on the joint conditional probability metric associated with the potential journey;
outputting, by the one or more computing devices, display data for depicting the one or more potential journeys and the quality score associated with each of the one or more potential journeys on a display; and
storing, by the one or more computing devices, the current journey data as additional driver history data.
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Accused Products
Abstract
A system and method for estimating journey destinations is disclosed. The system comprises a conversion module, a frequency module, a metric module, a quality module and a summary module. The conversion module converts a set of driver history data to a set of learning parameters. The frequency module analyzes the set of learning parameters and current journey data to generate estimated journey data describing one or more potential journeys. The metric module analyzes the estimated journey data and the set of current status data to determine one or more metrics associated with the estimated journey data. The quality module determines one or more quality scores associated with the estimated journey data. The summary module determines one or more status summaries and one or more estimate summaries. The summary module associates the one or more status summaries and the one or more estimate summaries with the estimated journey data.
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Citations
27 Claims
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1. A computer-implemented method comprising:
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converting, by one or more computing devices, a set of driver history data to a set of learning parameters; analyzing, by the one or more computing devices, the set of learning parameters and current journey data describing a current journey to generate estimated journey data describing one or more potential journeys; retrieving, by the one or more computing devices, a set of current status data; determining, by the one or more computing devices, a destination for each of the one or more potential journeys, a direction for each of the one or more potential journeys, a time of day for each of the one or more potential journeys, and day of the week for each of the one or more potential journeys; calculating, by the one or more computing devices, a joint conditional probability metric for each potential journey of the one or more potential journeys based on the destination, the direction, the time of day, and the day of the week associated with the potential journey; determining by the one or more computing devices, a quality score for each potential journey of the one or more potential journeys based on the joint conditional probability metric associated with the potential journey; outputting, by the one or more computing devices, display data for depicting the one or more potential journeys and the quality score associated with each of the one or more potential journeys on a display; and storing, by the one or more computing devices, the current journey data as additional driver history data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer program product comprising a non-transitory computer readable medium encoding instructions that, in response to execution by a computing device, cause the computing device to perform operations comprising:
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converting a set of driver history data to a set of learning parameters; analyzing the set of learning parameters and current journey data describing a current journey to generate estimated journey data describing one or more potential journeys; retrieving a set of current status data; determining a destination for each of the one or more potential journeys, a direction for each of the one or more potential journeys, a time of day for each of the one or more potential journeys, and day of the week for each of the one or more potential journeys; calculating a joint conditional probability metric for each potential journey of the one or more potential journeys based on the destination, the direction, the time of day, and the day of the week associated with the potential journey; determining a quality score for each potential journey of the one or more potential journeys based on the joint conditional probability metric associated with the potential journey; outputting display data for depicting the one or more potential journeys and the quality score associated with each of the one or more potential journeys on a display; and storing the current journey data as additional driver history data. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A system comprising:
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one or more computing devices; a conversion module executable on the one or more computing devices to convert a set of driver history data to a set of learning parameters; a frequency module communicatively coupled to the conversion module, the frequency module executable on the one or more computing devices to analyze the set of learning parameters and current journey data describing a current journey to generate estimated journey data describing one or more potential journeys, the frequency module storing the current journey data as additional driver history data; a metric module communicatively coupled to the frequency module, the metric module executable on the one or more computing devices to determine a destination for each of the one or more potential journeys, a direction for each of the one or more potential journeys, a time of day for each of the one or more potential journeys, and day of the week for each of the one or more potential journeys, and to calculate one or more joint conditional probability metrics for each potential journey of the one or more potential journeys based on the destination, the direction, the time of day, and the day of the week associated with the potential journey; a quality module communicatively coupled to the metric module, the quality module executable on the one or more computing devices to determine a quality score for each potential journey of the one or more potential journeys data based on the joint conditional probability metric associated with the potential journey; and an output module communicatively coupled to the frequency module, the quality module and the summary module, the output module executable on the one or more computing devices to output display data for depicting the one or more potential journeys and the quality score associated with each of the one or more potential journeys on a display. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26, 27)
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Specification