Claims:WE CLAIM:
1. A method of recommending an optimal route to a user, the method comprising:
receiving, by a navigation system, a potential travel request from the user, wherein the potential travel request comprises a start location, an end location, and a time of departure from the start location;
generating, by the navigation system, regional context data by analyzing the potential travel request;
generating, by the navigation system, cost context data based on the regional context data, wherein the cost context data comprises at least one potential route from the start location to the end location, and wherein each of the at least one potential route is associated with a travel time and a travel cost value;
correlating, by the navigation system, the potential travel request, the regional context data and the cost context data to obtain travel context data;
determining, by the navigation system, the optimal route amongst the at least one potential route by applying one or more rules to a user profile and the travel context data; and
dynamically updating, by the navigation system, the optimal route, to be recommended to the user, based on at least one of presence of one or more passengers in a vehicle and High Occupancy Vehicle (HOV) lane data.

2. The method of claim 1, wherein receiving the potential travel request further comprises:
receiving, by the navigation system, the user profile indicating user preferences, past travel requests, social profiles, a to-do list, calendar invites, and a mode of travel; and
determining, by the navigation system, the potential travel request by analyzing the user profile.

3. The method of claim 1, wherein generating regional context data further comprises:
extracting, by the navigation system, at least one region to be covered during the travel based on the potential travel request;
determining, by the navigation system, traffic conditions, number of tolls, modes of transport, the High Occupancy Vehicle (HOV) lane data, public transport details, and fuel price in the at least one region to generate the regional context data.

4. The method of claim 1, wherein the cost context data is determined by analyzing number of halts, average speed, fuel price, and driving pattern of the user.

5. The method of claim 1, wherein dynamically updating the optimal route further comprises:
determining, by the navigation system, presence of the one or more passengers in the vehicle;
extracting, by the navigation system, a passenger profile for each of the one or more passengers from at least one data source, wherein the passenger profile comprises preferences, social media profile, and a travel itinerary; and
updating, by the navigation system, the optimal route to be recommended to the user based on the passenger profile.

6. The method of claim 1, wherein dynamically updating the optimal route further comprises:
determining, by the navigation system, a count of the one or more passengers in the vehicle using at least one sensor; and
updating, by the navigation system, the optimal route to be recommended to the user based on the count and the HOV lane data.

7. A navigation system for recommending an optimal route to a user, the system comprising:
at least one processor; and
a computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
receiving a potential travel request from the user, wherein the potential travel request comprises a start location, an end location, and a time of departure from the start location;
generating regional context data by analyzing the potential travel request;
generating cost context data based on the regional context data, wherein the cost context data comprises at least one potential route from the start location to the end location, and wherein each of the at least one potential route is associated with a travel time and a travel cost value;
correlating the potential travel request, the regional context data and the cost context data to obtain travel context data;
determining the optimal route amongst the at least one potential route by applying one or more rules to a user profile and the travel context data; and
dynamically updating the optimal route, to be recommended to the user, based on at least one of presence of one or more passengers in a vehicle and High Occupancy Vehicle (HOV) lane data.

8. The system of claim 7, wherein receiving the potential travel request further comprises:
receiving the user profile indicating user preferences, past travel requests, social profiles, a to-do list, calendar invites, and a mode of travel; and
determining the potential travel request by analyzing the user profile.

9. The system of claim 7, wherein generating regional context data further comprises:
extracting at least one region to be covered during the travel based on the potential travel request;
determining traffic conditions, number of tolls, modes of transport, the High Occupancy Vehicle (HOV) lane data, public transport details, and fuel price in the at least one region to generate the regional context data.

10. The system of claim 7, wherein the cost context data is determined by analyzing number of halts, average speed, fuel price, and driving pattern of the user.

11. The system of claim 7, wherein dynamically updating the optimal route further comprises:
determining presence of the one or more passengers in the vehicle;
extracting a passenger profile for each of the one or more passengers from at least one data source, wherein the passenger profile comprises preferences, social media profile, and a travel itinerary; and
updating, the optimal route to be recommended to the user based on the passenger profile.

12. The system of claim 7, wherein dynamically updating the optimal route further comprises:
determining a count of the one or more passengers in the vehicle using at least one sensor; and
updating the optimal route to be recommended to the user based on the count and the HOV lane data.

13. A non-transitory computer-readable medium storing computer-executable instructions for performing operations comprising:
receiving a potential travel request from a user, wherein the potential travel request comprises a start location, an end location, and a time of departure from the start location;
generating regional context data by analyzing the potential travel request;
generating cost context data based on the regional context data, wherein the cost context data comprises at least one potential route from the start location to the end location, and wherein each of the at least one potential route is associated with a travel time and a travel cost value;
correlating the potential travel request, the regional context data and the cost context data to obtain travel context data;
determining an optimal route amongst the at least one potential route by applying one or more rules to a user profile and the travel context data; and
dynamically updating the optimal route, to be recommended to the user, based on at least one of presence of one or more passengers in a vehicle and High Occupancy Vehicle (HOV) lane data.

14. The medium of claim 13, wherein receiving the potential travel request further comprises:
receiving the user profile indicating user preferences, past travel requests, social profiles, a to-do list, calendar invites, and a mode of travel; and
determining the potential travel request by analyzing the user profile.

15. The medium of claim 13, wherein generating regional context data further comprises:
extracting at least one region to be covered during the travel based on the potential travel request;
determining traffic conditions, number of tolls, modes of transport, the High Occupancy Vehicle (HOV) lane data, public transport details, and fuel price in the at least one region to generate the regional context data.

16. The medium of claim 13, wherein the cost context data is determined by analyzing number of halts, average speed, fuel price, and driving pattern of the user.

17. The medium of claim 13, wherein dynamically updating the optimal route further comprises:
determining presence of the one or more passengers in the vehicle;
extracting a passenger profile for each of the one or more passengers from at least one data source, wherein the passenger profile comprises preferences, social media profile, and a travel itinerary; and
updating, the optimal route to be recommended to the user based on the passenger profile.

18. The medium of claim 13, wherein dynamically updating the optimal route further comprises:
determining a count of the one or more passengers in the vehicle using at least one sensor; and
updating the optimal route to be recommended to the user based on the count and the HOV lane data.

Dated this 8th day of August, 2016

R Ramya Rao
Of K&S Partners
Agent for the Applicant
, Description:TECHNICAL FIELD
This disclosure relates generally to a navigation device and more particularly to a system and a method for recommending an optimal route to a user.