Navigation device and method for predicting the destination of a trip
First Claim
1. A navigation device for predicting a destination of a trip, the navigation device comprising:
- a position determination unit for determining the current position of the navigation device;
a clock unit for determining the current time and date;
a computer-readable non-transitory data storage for storing a trip history, the trip history comprising a set of trip data objects, each trip data object representing a past trip and comprising starting parameters and an actual destination of the represented trip, the actual destination being the destination chosen and reached by a user of the navigation device, the starting parameters comprising at least;
a starting time and date of the trip, anda starting point of the trip;
a learning module for generating a destination prediction algorithm, the destination prediction algorithm being generated by using information of the trip history;
a destination prediction module for predicting the destination of the trip, thereby using the destination prediction algorithm, the destination prediction algorithm being operable to predict the destination of the trip by using the starting parameters of the trip;
wherein the destination prediction algorithm is to calculate prediction scores for corresponding predicted destinations;
wherein the navigation device is to use a value of the highest score among the prediction scores to select a navigation mode among a plurality of navigation modes;
a processor for executing read/write operations on the data storage and for executing instructions of the learning and prediction modules;
a notification device for indicating to a user of the navigation device the route to a destination of a trip.
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Accused Products
Abstract
A navigation device and computer implemented method for predicting the destination of a trip, the method being executed by a navigation device, the method comprising the steps of: determining starting parameters, the starting parameters comprising at least the starting point, starting time and date of the trip, executing a destination prediction algorithm, the destination prediction algorithm taking the starting parameters as input and predicting a destination, wherein the destination prediction algorithm is generated by using information of a trip history; determining, upon arrival at the predicted or another destination, the actual destination.
81 Citations
19 Claims
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1. A navigation device for predicting a destination of a trip, the navigation device comprising:
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a position determination unit for determining the current position of the navigation device; a clock unit for determining the current time and date; a computer-readable non-transitory data storage for storing a trip history, the trip history comprising a set of trip data objects, each trip data object representing a past trip and comprising starting parameters and an actual destination of the represented trip, the actual destination being the destination chosen and reached by a user of the navigation device, the starting parameters comprising at least; a starting time and date of the trip, and a starting point of the trip; a learning module for generating a destination prediction algorithm, the destination prediction algorithm being generated by using information of the trip history; a destination prediction module for predicting the destination of the trip, thereby using the destination prediction algorithm, the destination prediction algorithm being operable to predict the destination of the trip by using the starting parameters of the trip; wherein the destination prediction algorithm is to calculate prediction scores for corresponding predicted destinations;
wherein the navigation device is to use a value of the highest score among the prediction scores to select a navigation mode among a plurality of navigation modes;a processor for executing read/write operations on the data storage and for executing instructions of the learning and prediction modules; a notification device for indicating to a user of the navigation device the route to a destination of a trip. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A navigation device for predicting a destination of a trip, the navigation device comprising:
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a position determination unit for determining the current position of the navigation device; a clock unit for determining the current time and date; a computer-readable non-transitory data storage for storing a trip history, the trip history comprising a set of trip data objects, each trip data object representing a past trip and comprising starting parameters and an actual destination of the represented trip, the actual destination being the destination chosen and reached by a user of the navigation device, the starting parameters comprising at least; a starting time and date of the trip, and a starting point of the trip; a learning module for generating a destination prediction algorithm, the destination prediction algorithm being generated by using information of the trip history; a destination prediction module for predicting the destination of the trip, thereby using the destination prediction algorithm, the destination prediction algorithm being operable to predict the destination of the trip by using the starting parameters of the trip; a processor for executing read/write operations on the data storage and for executing instructions of the learning and prediction modules; a notification device for indicating to a user of the navigation device the route to a destination of a trip; wherein the destination prediction algorithm is implemented as a neural network, wherein the starting parameters and destinations of all trip data objects of the trip history are input data for training the neural network, wherein the neural network as a result of training predicts the destination of a trip by selecting one particular destination from a set of known destinations, the set of known destinations being selected from the group consisting of destinations having been explicitly entered by the user of the navigation device into the navigation device, destinations the user of the navigation device chose without entering this data explicitly into the navigation device, implicit destinations, wherein implicit destinations are locations the navigation device has determined automatically during a trip along a route, and application derived destinations, an application derived destination being an address derived from an application installed on the navigation device; wherein the neural network is implemented as a “
feed-forward back-propagation network,”
wherein weights are assigned to each starting parameter in dependence on its type and wherein the weights of the starting parameters are adapted in each layer of the network by the back-propagation algorithm to minimize a mean squared error value, the mean squared error value being indicative of the prediction accuracy of the destination prediction algorithm.
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11. A computer implemented method for predicting the destination of a trip, the method being executed by a navigation device, the method comprising the steps of:
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determining, by the position determination unit, the current position of the navigation device as the starting point of the trip, the starting point to be used as a starting parameter; determining, by the clock unit, the current time and date as the starting time and date of the trip, the starting time and date to be used as starting parameters; executing a destination prediction algorithm, the destination prediction algorithm taking the starting parameters as input and predicting at least one destination, each predicted destination having assigned a prediction score, the prediction score being derived from the accuracy of the destination prediction algorithm and the probability value of the predicted destination, wherein the destination prediction algorithm is generated by using information of a trip history, the trip history comprising a set of trip data objects, each trip data object in the trip history representing a past trip, each trip data object comprising at least the starting parameters and the actual destination of the past trip, the actual destination being the destination chosen and reached by a user of the navigation device during the past trip represented by said trip data object; switching, by the navigation device, the mode of operation in dependence on the prediction score value of the predicted destination having assigned the highest prediction score value; determining an actual destination of the trip; and storing the actual destination in association with all determined starting parameters in the form of a trip data object in the trip history. - View Dependent Claims (12, 13, 14, 15, 16)
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17. A computer implemented method for predicting the destination of a trip, the method being executed by a navigation device, the method comprising the steps of:
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determining, by the position determination unit, the current position of the navigation device as the starting point of the trip, the starting point to be used as a starting parameter; determining, by the clock unit, the current time and date as the starting time and date of the trip, the starting time and date to be used as starting parameters; executing a destination prediction algorithm, the destination prediction algorithm taking the starting parameters as input and predicting at least one destination, each predicted destination having assigned a prediction score, the prediction score being derived from the accuracy of the destination prediction algorithm and the probability value of the predicted destination, wherein the destination prediction algorithm is generated by using information of a trip history, the trip history comprising a set of trip data objects, each trip data object in the trip history representing a past trip, each trip data object comprising at least the starting parameters and the actual destination of the past trip, the actual destination being the destination chosen and reached by a user of the navigation device during the past trip represented by said trip data object; determining an actual destination of the trip; and storing the actual destination in association with all determined starting parameters in the form of a trip data object in the trip history; wherein the destination prediction algorithm is implemented as a neural network, wherein each starting parameter comprises a weight, wherein the weighted starting parameters of all trip data objects of the trip history are used as input for training the neural network, wherein the neural network, as a result of training, predicts the destination of a trip by selecting one particular destination from a set of known destinations, the set of known destinations being selected from the group consisting of the set of all destinations stored in the trip history, comprising destinations explicitly entered into the navigation system by the user and destinations chosen by the user without explicitly entering them into the navigation device, and the set of all destinations having been explicitly entered by the user of the navigation device into the navigation device; wherein the neural network is implemented as a “
feed-forward back-propagation network,” and
wherein the weights of the starting parameters are adapted in each layer of the network by the back-propagation algorithm to minimize a mean squared error value, the mean squared error value being indicative of the prediction accuracy of the destination prediction algorithm.
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18. A data processing system comprising a server and at least a first navigation device, the server hosting a trip sharing service, the first navigation device including
a position determination unit for determining the current position of the navigation device; -
a clock unit for determining the current time and date; a computer-readable non-transitory data storage for storing a trip history, the trip history comprising a set of trip data objects, each trip data object representing a past trip and comprising starting parameters and an actual destination of the represented trip, the actual destination being the destination chosen and reached by a user of the navigation device, the starting parameters comprising at least; a starting time and date of the trip, and a starting point of the trip; a learning module for generating a destination prediction algorithm, the destination prediction algorithm being generated by using information of the trip history; a destination prediction module for predicting the destination of the trip, thereby using the destination prediction algorithm, the destination prediction algorithm being operable to predict the destination of the trip by using the starting parameters of the trip; a processor for executing read/write operations on the data storage and for executing instructions of the learning and prediction modules; and a notification device for indicating to a user of the navigation device the route to a destination of a trip, the server including; a processor for executing computer-interpretable instructions; a network interface for connecting the server to a network; a web service interface for providing remote access to the program logic of the trip sharing service to the at least one navigation device; a computer-readable non-transitory storage medium comprising instructions which, when executed by the processor, result in the execution of the trip sharing service, the trip sharing service in operation allocating users with similar user profiles and trip plans as trip accompanies; wherein the exchanged data is selected from the group consisting of a request being submitted from the navigation device to the trip sharing service, the request indicating other participants of the trip sharing service, the starting time, starting place and destination of a trip, the destination of the trip being predicted by the destination prediction algorithm, and a result being returned by the trip sharing service to the navigation device, the result comprising at least contact information of a second user having been assigned to the user of the navigation device by the trip sharing service as trip accompany. - View Dependent Claims (19)
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Specification