Claims:We claim:

1. A computing device (104) comprising:

one or more processors (106); and

a memory (108) coupled to the one or more processors (106), the memory (108) for storing instructions which, when executed by the one or more processors (106), cause the one or more processors (106) to perform a method for determining context based route for a user (102), the method comprising:

receiving one or more context based inputs from the user (102), wherein the one or more context based inputs are associated to at least one aspect of a vehicle and the user (102);

obtaining a first set of vehicular data, wherein the first set of vehicular data comprises data received from a plurality of vehicle sensors;

collecting a second set of contextual data, wherein the second set of contextual data comprises data received from a plurality of sources;

determining a route for the user (102) based on the one or more context based inputs, the first set of vehicular data and the second set of contextual data; and

rendering the route to the user (102).

2. The computing device (104) as claimed in claim 1, wherein the determining of the route comprises:

correlating the one or more context based inputs with the first set of vehicular data and the second set of contextual data; and

finding an accurate match of the one or more context based inputs from the first set of vehicular data and the second set of contextual data for determining the route for the user (102).

3. The computing device (104) as claimed in claim 1, wherein the one or more context based inputs are received from the user (102) using one or more techniques, wherein the one or more techniques comprises at least one of:

entering the one or more context based inputs manually;

selecting the one or more context based inputs from a pre-defined list of contextual inputs, wherein the pre-defined list of contextual inputs is updated dynamically based on the one or more context based inputs entered manually by the user (102); and

selecting the one or more context based inputs from auto-complete suggestions.

4. The computing device (104) as claimed in claim 1, wherein the one or more context-based inputs is entered in one or more formats, wherein the one or more formats is at least one of text format, audio format, gesture format, single click format and video format.

5. The computing device (104) as claimed in claim 1, wherein the first set of vehicular data comprises at least one of type of vehicle, one or more safety features associated with the vehicle, weight of the vehicle, one or more dimensions of the vehicle, age of the vehicle, torque of the vehicle, radius of wheels of the vehicle, wheelbase of the vehicle, engine of the vehicle, turning radius of the vehicle, power of the vehicle, and ground clearance of the vehicle.

6. The computing device (104) as claimed in claim 1, wherein the determining of the route for the user (102) comprises:

communicating with a server (110) on a network (116);

fetching a probable determined route; and

refining the probable determined route to be rendered.

7. The computing device (104) as claimed in claim 1, wherein the one or more context based inputs is provided as a combination of at least two different attributes related to at least one of the user (102) and the vehicle for determining the route.

8. The computing device (104) as claimed in claim1, wherein the one or more context based inputs is associated with one or more categories of contextual input and one or more sub-categories of contextual input, wherein the one or more categories of contextual input and the one or more sub-categories of contextual input is determined by an administrator.

9. The computing device (104) as claimed in claim 1, wherein the one or more sources comprises at least one of Global Positioning System, road sensors, third party databases and transportation departments.

, Description:SYSTEM FOR DETERMINING CONTEXT BASED ROUTE FOR A USER
TECHNICAL FIELD
[0001] The present disclosure relates to the field of route determination. More particularly, the present disclosure relates to a system for context based route determination for rendering best route to a user.
BACKGROUND
[0002] Over the last few years, navigation industry is focusing on providing accurate route information to improve travel experience for its customers. Typically, navigation industry uses systems that collect route information input from a user. The route information input includes starting point and end point of route, number of co-passengers travelling with the user, type of vehicle and so on. The presently available systems aim at solving problems such as minimizing distance travelled, reducing travel time of the user, and many more. Additionally, the present systems perform selection of optimal routes to deliver goods to various places. However, the present systems collect input data manually from the user. The present systems focus on providing only shortest route along with travel information to the user.
OBJECT OF THE DISCLOSURE
[0003] A primary object of the present disclosure is to provide a system for determining context based route for a user.
[0004] Another object of the present disclosure is to provide a method for determining the context based route by refining the context based route in real time.
SUMMARY
[0005] In an aspect, the present disclosure provides a computing device. The computing device includes one or more processors, a signal generator circuitry embedded inside the computing device for generating a signal, and a memory. The memory is coupled to the one or more processors. The memory stores instructions. The instructions are executed by the one or more processors. The execution of instructions causes the one or more processors to perform a method for determining a context based route for a user. The method includes a first step of receiving one or more context based inputs from the user. In addition, the method includes another step of obtaining a first set of vehicular data. Further, the method includes yet another step of collecting a second set of contextual data. Furthermore, the method includes yet another step of determining a route for the user based on the one or more context based inputs, the first set of vehicular data and the second set of contextual data. Moreover, the method includes yet another step of rendering the route to the user. The one or more context based inputs are associated to at least one aspect of a vehicle and the user. The first set of vehicular data corresponds to data received from a plurality of vehicle sensors. The second set of contextual data includes data received from a plurality of sources.
[0006] In an embodiment of the present disclosure, the determining of the route includes a first step of correlating the one or more context based inputs with the first set of vehicular data and the second set of contextual data. In addition, the determining of the route includes a second step of finding an accurate match of the one or more context based inputs from the first set of vehicular data and the second set of contextual data to determine the route for the user.
[0007] In an embodiment of the present disclosure, the one or more context based inputs are received from the user using one or more techniques. The one or more techniques include entering the one or more context based inputs manually. In addition, the one or more techniques include selecting the one or more context based inputs from a pre-defined list of contextual inputs. The pre-defined list of contextual inputs is updated dynamically based on the one or more context based inputs entered manually by the user. Further, the one or more techniques include selecting the one or more context based inputs from auto-complete suggestions.
[0008] In an embodiment of the present disclosure, the one or more context-based inputs are entered in one or more formats. The one or more formats include at least one of text format, audio format, gesture format, single click format and video format.
[0009] In an embodiment of the present disclosure, the first set of vehicular data includes at least one of type of vehicle, one or more safety features associated with the vehicle, weight of the vehicle, one or more dimensions of the vehicle, age of the vehicle, torque of the vehicle, radius of wheels of the vehicle, wheelbase of the vehicle, engine of the vehicle, turning radius of the vehicle, power of the vehicle, and ground clearance of the vehicle.
[0010] In an embodiment of the present disclosure, the determining of the route for the user includes communicating with a server on a network. In addition, the determining of the route includes fetching a probable determined route. Further, the determining of the route includes refining the probable determined route to be rendered to the user.
[0011] In an embodiment of the present disclosure, the one or more context based inputs is provided as a combination of at least two different attributes related to at least one of the user and the vehicle to determine the route.
STATEMENT OF THE DISCLOSURE
[0012] The present disclosure relates to a computing device. The computing device includes one or more processors, a signal generator circuitry embedded inside the computing device for generating a signal, and a memory. The memory is coupled to the one or more processors. The memory stores instructions. The instructions are executed by the one or more processors. The execution of instructions causes the one or more processors to perform a method for determining a context based route for a user. The method includes a first step of receiving one or more context based inputs from the user. The method includes another step of obtaining a first set of vehicular data. The method includes yet another step of collecting a second set of contextual data. The method includes yet another step of determining a route for the user based on the one or more context based inputs, the first set of vehicular data and the second set of contextual data. The method includes yet another step of rendering the route to the user. The one or more context based inputs are associated to at least one aspect of a vehicle and the user. The first set of vehicular data corresponds to data received from a plurality of vehicle sensors. The second set of contextual data includes data received from a plurality of sources.
BRIEF DESCRIPTION OF FIGURES
[0013] Having thus described the invention in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:
[0014] FIG. 1 illustrates an interactive computing environment to determine a context based route for a user, in accordance with various embodiments of the present disclosure;
[0015] FIG. 2 illustrates a flowchart depicting a method to determine the context based route for the user, in accordance with various embodiments of the present disclosure; and
[0016] FIG. 3 illustrates a block diagram of a hardware framework of a computing device, in accordance with various embodiments of the present disclosure.
[0017] It should be noted that the accompanying figures are intended to present illustrations of various embodiments of the present disclosure. These figures are not intended to limit the scope of the present disclosure. It should also be noted that accompanying figures are not necessarily drawn to scale.
DETAILED DESCRIPTION
[0018] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present technology. It will be apparent, however, to one skilled in the art that the present technology can be practiced without these specific details. In other instances, structures and devices are shown in block diagram form only in order to avoid obscuring the present technology.
[0019] Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present technology. The appearance of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not other embodiments.
[0020] Reference will now be made in detail to selected embodiments of the present disclosure in conjunction with accompanying figures. The embodiments described herein are not intended to limit the scope of the disclosure, and the present disclosure should not be construed as limited to the embodiments described. This disclosure may be embodied in different forms without departing from the scope and spirit of the disclosure. It should be understood that the accompanying figures are intended and provided to illustrate embodiments of the disclosure described below and are not necessarily drawn to scale. In the drawings, like numbers refer to like elements throughout, and thicknesses and dimensions of some components may be exaggerated for providing better clarity and ease of understanding.
[0021] It should be noted that the terms "first", "second", and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Further, the terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item.
[0022] FIG. 1 illustrates an interactive computing environment 100 to determine context based route for a user 102, in accordance with various embodiments of the present disclosure. The interactive computing environment 100 includes a computing device 104, a server 110, a network 116 and a database 118. The components of the interactive computing environment 100 work in conjunction with each other to enable determination of context based route for the user 102 to travel efficiently based on a real time context.
[0023] The computing device 104 is configured to determine context based route for the user 102. In an example, the user 102 is an owner of a vehicle. In another example, the user 102 is a driver of the vehicle. In yet another example, the user 102 is a passenger in the vehicle. The user 102 enters data associated with start and end location of a route in the computing device 104. In general, computing devices are electronic devices that take inputs, process the inputs and give outputs. The computing device 104 includes but may not be limited to laptop, palmtop, tablet, smartphone, and personal digital assistance (PDA). The computing device 104 determines a plurality of routes for the user 102 based on the data associated with start and end point of the route. In an example, a user A has entered “X metro station” as a start location and “Y bus stop” as end location. The computing device 104 determines all possible routes from X metro station to Y bus stop. The computing device 104 determines the plurality of routes using Global Positioning System (GPS, hereinafter). In general, GPS is a satellite navigation system that provides users with positioning, navigation, and timing services.
[0024] The computing device 104 receives one or more context based inputs from the user 102. The one or more context based inputs are associated to at least one aspect of the vehicle and the user 102. In an embodiment of the present disclosure, the one or more context based inputs associated with the vehicle correspond to attributes of a plurality of goods present inside the vehicle. The attributes of the plurality of goods include at least one of bulk goods, refrigerated goods, liquid goods, dangerous goods, breakable goods, unbreakable goods, and the like. In an example, bulk goods include but may not be limited to sand, gravel, and animal feed. In addition, refrigerated goods include frozen food, ice, ice cream and the like. Further, liquid goods include milk, liquid chocolate, oil, fuels, and the like. Furthermore, dangerous goods include but may not be limited to flammable chemicals, gases, corrosive substances, and radioactive objects. Moreover, breakable goods include but may not be limited to mirrors, eggs, and liquor bottles. Also, unbreakable goods include unbreakable crockery and the like. In another embodiment of the present disclosure, the one or more context based inputs associated with the user 102 correspond to real-time situation of the user 102. In an example, the one or more context based inputs associated with the user 102 includes but may not be limited to health condition, pregnancy, and feeling of drowsiness, nausea, and dizziness. In another example, the one or more context based inputs associated with the user 102 includes safest route, shortest route, cost-effective route, route with less aberrations, and the like.
[0025] The computing device 104 receives the one or more context based inputs in one or more formats. The one or more formats include at least one of text format, audio format, gesture format, single click format, video format, and the like. In an example, the user 102 provides the one or more context based inputs in text format. In another example, the user 102 provides the one or more context based inputs in audio format. In yet another example, the user 102 provides the one or more context based inputs in gesture format. In yet another example, the user 102 provides the one or more context based inputs in single click format. In yet another example, the user 102 provides the one or more context based inputs in video format. In yet another example, the user 102 provides the one or more context based inputs in any of the suitable format of the like.
[0026] The one or more context based inputs are received from the user 102 using one or more techniques. In an example, the one or more techniques include entering the one or more context based inputs manually. The user 102 enters the one or more context based inputs manually by typing through a keyboard associated with the computing device 104. In another example, the one or more techniques include selecting the one or more context based inputs from a pre-defined list of contextual inputs. The user 102 selects the one or more context based inputs from the pre-defined list of contextual inputs. The computing device 104 creates the pre-defined list of contextual inputs based on learning from the one or more context based inputs entered manually by the user 102. In addition, the computing device 104 learns from the one or more context based inputs entered manually by the user 102 using one or more machine learning algorithms. The pre-defined list of contextual inputs is updated dynamically based on the one or more context based inputs entered manually by the user 102.
[0027] In yet another example, the one or more techniques include selecting the one or more context based inputs from auto-complete suggestions. The user 102 utilizes auto-complete suggestions from the pre-defined list of contextual inputs to select the one or more context based inputs. The computing device 104 displays auto-complete suggestions from the pre-defined list of contextual inputs to the user 102 based on one or more characters entered by the user 102. The user 102 enters the one or more characters as the context based input in any unorganized format. In addition, the computing device 104 interprets appropriate context of the context based input entered by the user 102. Further, the computing device 104 shows auto-complete suggestions to the user 102 based on interpretation of appropriate context. In an example, the user 102 provides the context based input as “hospital”. The computing device 104 shows a plurality of auto-complete suggestions based on the context based input “hospital”. The plurality of auto-complete suggestions includes but may not be limited to “reaching the hospital for a regular health checkup”, and “rushing to the hospital due to an emergency”. In another example, the user 102 enters the characters “I am” as the context based input. The computing device 104 shows a plurality of suggestions starting from the characters “I am”. The plurality of suggestions includes but may not be limited to “I am pregnant”, “I am feeling nauseous”, “I am going for an official meeting”, and “I am in need of medicine”.
[0028] The computing device 104 obtains a first set of vehicular data. The first set of vehicular data corresponds to data received from a plurality of vehicle sensors. Each of the plurality of vehicle sensors is installed inside the vehicle. The first set of vehicular data includes type of vehicle, one or more safety features associated with the vehicle, weight of the vehicle, one or more dimensions of the vehicle, and the like. In addition, the first set of vehicular data includes age of the vehicle, torque of the vehicle, radius of wheels of the vehicle, wheelbase of the vehicle, engine of the vehicle, turning radius of the vehicle, power of the vehicle, ground clearance of the vehicle and the like. In an example, type of vehicle includes but may not be limited to car, truck, bus, and van. In another example, type of vehicle includes but may not be limited to brand of the car, brand of the truck, brand of the bus, and brand of the van. In addition, the one or more safety features associated with the vehicle includes airbags, anti-lock brakes, and the like. Further, the one or more dimensions of the vehicle include but may not be limited to length of the vehicle, and width of the vehicle. Furthermore, wheelbase of the vehicle corresponds to distance between centers of front and rear wheels of the vehicle. In general, the vehicle with long wheelbase provides comfortable ride and makes driver of the vehicle feel safe and stabilized even during high speeds. Moreover, ground clearance corresponds to minimum amount of distance between bottom of the vehicle body and the ground.
[0029] The computing device 104 collects a second set of contextual data. The second set of contextual data corresponds to data associated with each of the plurality of routes. The second set of contextual data is data received at the sever 110 from one or more sources. The computing device 104 collects the second set of contextual data from the server 110. The one or more sources include but may not be limited to Global Positioning System, road sensors, third party databases and transportation departments. The second set of contextual data includes but may not be limited to speed limit of each of the plurality of routes, accident prone areas of the plurality of routes, current weather conditions, expected weather conditions, public events on the plurality of routes, number of tolls on the plurality of routes, live traffic information of the plurality of routes, and pot holes on the plurality of routes.
[0030] In an example, the computing device 104 analyses the second set of contextual data using one or more artificial intelligence based algorithms. In addition, the computing device 104 analyses the second set of contextual data to determine one or more types of each of the plurality of routes. The one or more types of the plurality of routes include but not be limited to shortest route, longest route, cost-effective route, most used route, and most secure route, accident prone route, least adopted route, route with maximum number of tolls, and route with minimum number of tolls. Each of the plurality of routes belongs to at least one of the one or more types of the plurality of routes. In an example, the longest route may be the route with minimum number of tolls. Also, the longest route may be the least adopted route. In another example, the shortest route may be the most used route and the cost-effective route.
[0031] The computing device 104 includes a memory 108. The memory 108 includes computer-storage media in the form of volatile and/or nonvolatile memory. The memory 108 may be removable, non-removable, or a combination thereof. In an example, hardware devices include solid-state memory, hard drives, optical-disc drives, and the like. The memory 108 stores instructions. In an example, the memory 108 is utilized to store the one or more contextual inputs, the first set of vehicular data and the second set of contextual data. The computing device 104 includes one or more processors 106. The one or more processors 106 read data from various entities such as memory or I/O components. The one or more presentation components present data indications to an administrator. In an example, presentation components include a display device, speaker, printing component, vibrating component, and the like. The one or more I/O ports allow the computing device 104 to be logically coupled to other devices including the one or more I/O components, some of which may be built in. Illustrative components include a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc. In addition, the one or more processors 106 execute the instructions to determine a context based route for the user 102.
[0032] The computing device 104 is associated with the server 110. In general, server is a computer program or device that provides functionality for other programs or devices. The server 110 provides various functionalities, such as sharing data or resources among multiple users, or performing computation for the user 102. Further, the server 110 includes a memory 114 and one or more processors 112. In an example, the memory 114 is a random access memory connected to a non-volatile storage system. The random access memory is connected to the non-volatile storage system through a bus. In addition, the random access memory allows the server 110 to store data associated with the computing device 104. The memory 114 stores instructions. The instructions are executed by the one or more processors 112 to determine the context based route for the user 102. The memory 114 of the server 110 stores the one or more context based inputs received from the user 102. In addition, the memory 114 of the server 110 stores the first set of vehicular data received from the plurality of vehicle sensors. Further, the server 110 receives the second set contextual data from the plurality of sources. The memory 114 of the server 110 stores the second set of contextual data.
[0033] The server 110 is associated with an administrator. In general, administrator manages the different components of the computing device 104. The administrator coordinates the activities of the computing device 104. The administrator is any person or individual who monitors working of the computing device 104 and the server 110 in real-time. The administrator monitors working of the computing device 104 and the server 110 through a communication device. The communication device includes laptop, desktop computer, tablet, personal digital assistant and the like. The computing device 104 is associated with the server 110 using the network 116. The network 116 provides a medium for the computing device 104 to connect with the server 110. In an embodiment of the present disclosure, the network 116 is an internet connection. In another embodiment of the present disclosure, the network 116 is a wireless mobile network. The network 116 includes a set of channels. Each channel of the set of channels supports a finite bandwidth. Moreover, the finite bandwidth of each channel of the set of channels is based on capacity of the network 116. The network 116 connects the computing device 104 to the server 110 using a plurality of methods. The plurality of methods used to provide network connectivity to the computing device 104 includes 2G, 3G, 4G, 5G, Wifi and the like.
[0034] The server 110 is associated with the database 118 using the network 116. The database 118 provides storage location for all the data associated with the computing device 104 and the server 110. The database 118 is used to hold general information and specialized data associated with the user 102, the first set of vehicular data and the second set of contextual data. The database 118 organizes the data using models such as relational models or hierarchical models to perform one or more tasks.
[0035] The computing device 104 determines a route for the user 102 based on the one or more context based inputs, the first set of vehicular data and the second set of contextual data. The route corresponds to the context based route. In addition, the computing device 104 renders the route to the user 102. The determination of the route includes a first step and a second step. The first step includes correlating the one or more context based inputs with the first set of vehicular data and the second set of contextual data. In addition, the second step includes finding an accurate match of the one or more context based inputs from the first set of vehicular data and the second set of contextual data to determine the route for the user 102. In an embodiment of the present disclosure, the computing device 104 performs the determination of the route. In another embodiment of the present disclosure, the server 110 performs the determination of the route in association with the computing device 104. The server 110 performs the determination of the route for the user 102 based on the one or more context based inputs, the first set of vehicular data, and the second set of contextual data.
[0036] In an example, a user X enters source and destination address. In addition, the user X enters a contextual input associated with attributes of goods present inside the vehicle. The contextual input entered by the user X is “The vehicle carries breakable goods-eggs”. The computing device 104 finds all possible routes (1st route, and 2nd route) based on source address and destination address. In addition, the computing device 104 collects the second set of contextual data associated with each of the two possible routes. The computing device 104 analyzes the second set of contextual data to determine condition of each of the two possible routes. Further, the computing device 104 determines that the 1st route is the shortest route but has a lot of pot holes throughout the entire route. Furthermore, the computing device 104 determines that the 2nd route is the longest route without a single pot hole. The computing device 104 correlates the contextual input with the second set of contextual data. Moreover, the computing device 104 finds an accurate match of the contextual input from the second set of contextual data. The computing device 104 determines the 2nd route as the best context based route and renders the 2nd route to the user X.
[0037] In another example, a user Y enters source address and destination address. In addition, the user Y enters a contextual input associated with attributes of goods present inside the vehicle. The contextual input entered by the user Y is “The vehicle carries refrigerated goods”. The computing device 104 finds all possible routes (1st route, and 2nd route) based on source address and destination address. In addition, the computing device 104 collects the second set of contextual data associated with each of the two possible routes. The computing device 104 analyzes the second set of contextual data to determine condition of each of the two possible routes. Further, the computing device 104 determines that the 1st route is the shortest route but has a few pot holes. Furthermore, the computing device 104 determines that the 2nd route is the longest route without a single pot hole. The computing device 104 correlates the contextual input with the second set of contextual data. Moreover, the computing device 104 finds an accurate match of the contextual input from the second set of contextual data. The computing device 104 determines the 1st route as the best context based route and renders the 1st route to the user Y. The shortest route is determined as the best context based route and the longest route is avoided because refrigerated goods cannot be kept in the vehicle for longer durations.
[0038] In yet another example, a user XX enters a contextual input as “I am going for an official meeting and want to reach destination on time” along with source address and destination address. In addition, the computing device 104 obtains the vehicular data and identifies that the vehicle is a luxury car, Rolls Royce that has all safety features such as passenger airbag, driver airbag, anti-lock braking system, power steering and the like. The computing device 104 finds all possible routes (1st route, 2nd route, and 3rd route) based on source address and destination address. Further, the computing device 104 collects the second set of contextual data associated with each of the three possible routes. The computing device 104 analyzes the second set of contextual data and determines that the 1st route is the shortest route but has a lot of pot holes, the 2nd route is the longest but the safest route without a single pot hole, and the 3rd route is under construction. The computing device 104 correlates the contextual input with the vehicular data and the second set of contextual data. Furthermore, the computing device 104 finds an accurate match of the contextual input from the vehicular data and the second set of contextual data. The computing device 104 determines the 1st route as the best context based route and renders the 1st route to the user XX. The computing device 104 determines the 1st route as the best context based route because the user XX wants to reach the destination on time and the vehicle is the luxury car that has all safety features.
[0039] The computing device 104 determines the route for the user 102 by communicating with the server 110 through the network 116. The server 110 performs the determination of the route for the user 102. The computing device 104 fetches the probable determined route from the server 110. In addition, the computing device 104 refines the probable determined route to be rendered to the user 102 in real time. In an example, a user YY enters a context based input as “need a stress free ride and need to reach destination in short time”. In addition, the computing device 104 obtains the vehicular data and identifies that the vehicle is a luxury car, Mercedes Benz that has all safety features such as passenger airbag, driver airbag, anti-lock braking system, power steering and the like. The computing device 104 finds 4 possible routes (1st route, 2nd route, 3rd route, and 4th route) based on source address and destination address. Further, the computing device 104 collects the second set of contextual data associated with each of the 4 possible routes. The computing device 104 analyzes the second set of contextual data and determines that the 1st route is the shortest route but has a few pot holes, the 2nd route is the longest but the safest route without a single pot hole, the 3rd route is under construction, and the 4th route is a short route but has 5 traffic lights. The computing device 104 is associated with the server 110. The computing device 104 correlates the contextual input with the vehicular data and the second set of contextual data. Furthermore, the computing device 104 finds an accurate match of the contextual input from the vehicular data and the second set of contextual data. The computing device 104 communicates with the server 110 to determine a probable route for the user YY. The server 110 determines the 1st route as the best probable route. The server 110 determines the 1st route as the best probable route because the user YY needs a stress free ride and needs to reach the destination in short time. The 1st route is the shortest route but has a few pot holes. The vehicle is luxury car that has all safety features, therefore route with pot holes is determined as the best probable route. The computing device 104 fetches the best probable route from the server 110. The computing device 104 receives real time traffic information from the one or more sources. The real time traffic information states that the 1st route is congested due to a sudden accident. The computing device 104 refines the best probable route (1st route) and determines the 4th route as the best probable route. The computing device 104 refines the best probable route in real time. The computing device 104 renders the best probable route (the 4th route) to the user YY.
[0040] In an embodiment of the present disclosure, the user 102 provides the one or more context based inputs as a combination of at least two different attributes related to at least one of the user 102 and the vehicle to determine the route. In an example, a user AA enters a context based input as combination of user context and vehicle context. The user AA enters the user context as “short and safe route desired” and the vehicle context as “headlights of the vehicle not working properly” along with source and destination address. Time of travelling is night time. The computing device 104 finds three possible routes based on source address and destination address. In addition, the computing device 104 collects the vehicular data and identifies that the vehicle is an economy class car, Ford Figo that does not have safety features such as passenger airbag, driver airbag, and the like. Further, the computing device 104 collects the second set of contextual data associated with each of the three possible routes. The computing device 104 analyzes the second set of contextual data and determines that the 1st route is the shortest route but there are no street lights throughout the 1st route, the 2nd route is the longest with a plurality of street lights but has a lot of pot holes, and the 3rd route is short route with the plurality of street lights and no pot holes. The computing device 104 correlates the user context and the vehicle context with the vehicular data and the second set of contextual data. Furthermore, the computing device 104 finds an accurate match of the user context and the vehicle context from the vehicular data and the second set of contextual data. The computing device 104 determines the 3rd route as the best context based route and renders the 3rd route to the user AA. The computing device 104 determines the 3rd route as the best context based route because the 3rd route is short, safe and has the plurality of street lights. In addition, the user AA wants to reach the destination in short time and headlights of the vehicle are not working properly. In addition, the vehicle is the economy car that does not have safety features, therefore, the 2nd route is not determined the best context based route.
[0041] In another example, a user B enters a context based input as combination of user context and vehicle context along with source and destination address. The user B enters the user context as “I am pregnant” and the vehicle context as “petrol of the vehicle has reached reserve level. The computing device 104 finds 3 possible routes based on the source and destination address. In addition, the computing device 104 obtains the vehicular data and identifies that the vehicle is a luxury car that have safety features such as passenger airbag, driver airbag, and the like. Further, the computing device 104 collects the second set of contextual data associated with each of the 3 possible routes. The computing device 104 analyzes the second set of contextual data and determines that the 1st route is the long route having petrol pump at distance of 5 km from the source address, and the 2nd route is the shortest route having petrol pump at distance of 2 km from the source address. In addition, the computing device 104 determines that the 2nd route is an accident prone route. Further, the computing device 104 determines that the 3rd route is short and safe route having petrol pump at distance of 3 km from the source address. The computing device 104 correlates the user context and the vehicle context with the vehicular data and the second set of contextual data. Furthermore, the computing device 104 finds an accurate match of the user context and the vehicle context from the vehicular data and the second set of contextual data. The computing device 104 determines the 3rd route as the best context based route and renders the 3rd route to the user B. The computing device 104 determines the 3rd route as the best context based route because the 3rd route is short, safe and has petrol pump near the source address. The 2nd route is the shortest route and the vehicle is a luxury car but still the 2nd route is not determined as the best context based route because the user B is pregnant and the 2nd route is the accident prone route.
[0042] The one or more context based inputs is associated with one or more categories of contextual input and one or more sub-categories of contextual input. The administrator determines the one or more categories of contextual input and the one or more sub-categories of contextual input. In an example, the one or more categories of contextual input is “going to the hospital”. In addition, the one or more sub categories of contextual input is “reason for going to the hospital”. In another example, the one or more categories of contextual input is “going to office”. In addition, the one or more sub categories of contextual input is “urgent official meeting at office”. The computing device 104 determines the route to be rendered to the user 102 by analyzing the one or more categories and the one or more sub categories. In addition, the computing device 104 correlates the one or more context based inputs with the first set of vehicular data and the second set of contextual data. Further, the computing device 104 determines the route by finding an accurate match of the one or more categories and the one or more sub categories from the first set of vehicular data and the second set of contextual data.
[0043] FIG. 2 illustrates a flowchart 200 depicting a method to determine the context based route for the user 102, in accordance with various embodiment of the present disclosure. The flowchart 200 initiates at step 202. Following step 202, at step 204, the computing device 104 receives the one or more context based inputs from the user 102. The one or more context based inputs are associated to at least one aspect of the vehicle and the user 102. At step 206, the computing device 104 obtains the first set of vehicular data. The first set of vehicular data includes data received from the plurality of vehicle sensors. At step 208, the computing device 104 collects the second set of contextual data. The second set of contextual data includes data received from the plurality of sources. The computing device 104 collects the second set of contextual data from the server 110.
[0044] In addition, at step 210, the computing device 104 determines the route for the user 102 based on the one or more context based inputs, the first set of vehicular data, and the second set of contextual data. In an embodiment of the present disclosure, the computing device 104 performs determining of the route. In an example, the determining of the route includes the first step of correlating the one or more context based inputs with the first set of vehicular data and the second set of contextual data. In addition, the determining of the route includes the second step of finding the accurate match of the one or more context based inputs from the first set of vehicular data and the second set of contextual data. In another embodiment of the present disclosure, the determining of the route is performed at the server 110 in association with the computing device 104. In an example, the one or more context based inputs, the first set of vehicular data, and the second set of contextual data is stored in the memory 114 of the server 110. The server 110 determines a probable route for the user 102 based on the one or more context based inputs, the first set of vehicular data, and the second set of contextual data. Further, the computing device 104 communicates with the server 110 on the network 116. The computing device 104 fetches the probable determined route from the server 110. Furthermore, the computing device 104 refines the probable determined route to be rendered to the user 102 in real time.
[0045] At step 212, the computing device 104 renders the route to the user 102 on the computing device 104. The computing device 104 renders the refined route to the user 102. The flowchart 200 terminates at step 214.
[0046] FIG. 3 illustrates a block diagram of a hardware framework 300 of the computing device 104, in accordance with various embodiments of the present disclosure. The hardware framework 300 of the computing device 104 includes a bus 302 that directly or indirectly couples the following devices: a memory 304, one or more processors 306, one or more presentation components 308, one or more input/output (I/O) ports 310, one or more input/output components 312, and an illustrative power supply 314. The bus 302 represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Although the various blocks of FIG. 3 are shown with lines for the sake of clarity, in reality, delineating various components is not so clear, and metaphorically, the lines would more accurately be grey and fuzzy. For example, one may consider a presentation component such as a display device to be an I/O component. Also, processors have memory. The inventors recognize that such is the nature of the art, and reiterate that the diagram of FIG. 3 is merely illustrative of an example of the computing device 104 that can be used in connection with one or more embodiments of the present invention. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “hand-held device,” etc., as all are contemplated within the scope of FIG. 3 and reference to “computing device.”
[0047] The computing device 104 typically includes a variety of computer-readable media. The computer-readable media can be any available media that can be accessed by the computing device 104 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer storage media and communication media. The computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data.
[0048] The computer storage media includes but may not be limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing device 104. The communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.
[0049] The memory 304 includes computer-storage media in the form of volatile and/or nonvolatile memory. The memory 304 may be removable, non-removable, or a combination thereof.
[0050] The present invention has been described with reference to particular embodiments and examples. It is to be understood that these embodiments and examples are merely illustrative of the principles and applications of the present invention. It is, therefore, to be understood that the present invention is not limited to the above examples and embodiments.
[0051] The foregoing descriptions of specific embodiments of the present technology have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present technology to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, to thereby enable others skilled in the art to best utilize the present technology and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present technology.
[0052] While several possible embodiments of the invention have been described above and illustrated in some cases, it should be interpreted and understood as to have been presented only by way of illustration and example, but not by limitation. Thus, the breadth and scope of a preferred embodiment should not be limited by any of the above-described embodiments.