Source: https://insight.rpxcorp.com/pat/US8392116B2
Timestamp: 2019-10-22 06:38:09
Document Index: 114509164

Matched Legal Cases: ['art[8', 'art0', 'art0', 'art402', 'art700', 'art701']

Patent US 8,392,116 B2
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;
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;
US 20120310534A1
US 8,775,080 B2
Method and apparatus for vehicular travel assistance
US 10,006,778 B2
US 10,068,186 B2
US 20110153629A1
an interface for receiving external data, the external data being selected from a group consisting of;
weather data, traffic data, traffic jam data, diversion routes, data of restaurants, public buildings or shops lying within a specified distance from the current position of the navigation device, data of hostels, hotels or leisure facilities lying within a specified distance from the current position of the navigation device, data of gas stations or car repair shops lying within a specified distance from the current position of the navigation device, trip sharing requests of participants of a trip sharing service lying within a specified distance from the current position of the navigation device, and the destination and user profile data of participants of a trip sharing service having been determined by said trip sharing service as potential trip accompanies, wherein the received external data is used as one or multiple additional starting parameters by the destination prediction algorithm.
an interface for receiving vehicle data, the vehicle data being selected from the group consisting of the number of occupied seats in the vehicle, the position of the occupied seats in the vehicle, the filling level of the gas tank, the vehicles oil level, error messages generated by any of the vehicle's components, and status messages generated by any of the vehicle's components, wherein the received vehicle data is used as one or multiple additional starting parameter by the destination prediction algorithm.
5. The navigation device according to claim 1, the navigation device further comprising an interface for receiving application data, the application data being provided by a software application running on the navigation device, the application data being selected from the group consisting of calendar event data being received from a calendar application, data on goods to be sold or bought being received from CRM systems, and project specific data being received from project management software programs, wherein the received application data is used as one or multiple additional starting parameters by the destination prediction algorithm.
6. The navigation device according to claim 1, 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 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;
13. The computer implemented method according to claim 11, 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.
14. The computer implemented method according to claim 11, wherein each starting parameter comprises a weight depending on its type, wherein for each type of starting parameter and for each known destination a probability value is calculated, the probability value indicating the probability value that the known destination will be the actual destination given a set of starting parameters, wherein each parameter type is weighted, and wherein an overall probability is calculated for all known destinations by summing up the weighted probabilities for each known destination given the current starting parameters values, wherein the set of known destinations is 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 system, 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 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;
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 notification device for indicating to a user of the navigation device the route to a destination of a trip, the server including;
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.
Probability to choose a particular destinationgiven a particular starting parameter value
StartingStarting Pa-Destina-Destina-Destina-Parameter Typerameter Valuetion Ation Btion CSeat Occupation2 out of 4%90%6%4 seatsSeat Occupation1 out of10%95%5%4 seatsWeather at startsunny80%15%5%Time of start[8.00 a.m.-81% 4%15% 8.30 a.m.]Place ofHome80%17%3%departure
Starting Parameter TypeStarting Parameter Type WeightSeat Occupation0.10Weather at start0.05Time of start0.45Place of departure0.40
Weighted probabilities of a user of a navigation device to choose aparticular destination given a particular starting parameter value;(Normalized) sum of weighted probabilities for each destination.
StartingStarting Param-Destina-Destina-Destina-Parameter Valueeter Type Weighttion Ation Btion CSeat occ. = 2/40.10 0.4%9.0%0.6%Weather at0.05 4.0%0.8%0.3%start = sunnyTime of0.4536.5%1.8%6.8%start: = 8.15 a.m.Place of0.4032.0%6.8%1.2%Depart. = HomeSum of weighted72.9%18.4% 8.8%probabilitiesNormalized sum72.8%18.4% 8.8%of weightedprobabilities
GPS Global Positioning SystemLPS Local Positioning SystemSVMSupport Vector MachineNNNeural NetworkLDAPLightweight Directory Access ProtocolPOI Point Of Interest
101smart phone102notebook, netbook103mobile phone104navigation device200navigation device201trip data object202storage medium203trip history204starting parameter205actual destination of a trip represented by trip data object 201206learning module207prediction algorithm208destination prediction algorithm209position determination unit210clock for determining current time and date211notification device for indicating navigation instructions212processor213interface for external data214interface for application data215interface for vehicle data216other application running on navigation device300-309steps310decision311-313steps314decision315arrow316set of steps317method400vehicle with navigation device401start402route403route404route405route is chosen in case of sunny weather406route is chosen in case of bad weather407tennis place409gas station410destination411-417implicit destinations418route419route500-505steps506method600bar chart700start701implicit destination702sub-route from start to implicit destination701 having a probability to be chosen of 96%703other destination with 4% probability to bechosen given current starting parameters704implicit destination705implicit destination706destination/office E707destination/office F708destination/gas station G800server (hosting trip sharing service)801computer-readable, non-transitory storagemedium802processor803network interface804trip sharing service805web service interface of trip sharing service806service request807interface for external data808profile service809matching service810other service811feedback service813trip sharing database814network900data processing system
Lehmann, Jens, Sommer, David
701408-430, 701/450, 701/451, 701/461, 701/468, 701/481, 706/21, 706/25, 706/52, 707/758, 707/736, 707/769, 707/748, 340/994, 3409951-99525
701/524