TECHNICAL FIELD
[001] The present subj ect matter described herein, in general, relates to a navigation system and method, and more particularly to a system and a method for navigating with improved user experience.
BACKGROUND
[002] Navigation applications such as Google maps™ are generally used for viewing routes to reach destinations. With usage of such navigation applications, finding route to any destination is no more a hurdle. Such navigation applications provide multiple routes to reach a destination. Not only for four-wheel vehicles, the navigation applications also provide routes to travel via two-wheel vehicles and routes for walking by pedestrians.
[003] Multiple routes are provided to a user along with other important factors, based on which the user could select one route. Such factors include length of routes, travel time through each route, and congestion of traffic at the routes. However, such factors are many times not sufficient for a user to select a route because of other inconveniences that a user has to face while travelling through a route. Such inconveniences include bad scenic views along the route, bad air quality along the route, sound pollution, and poor quality of roads. There is no way by which a user could get aware of such factors and make his decision to select a route based on these factors. Thus, a navigation system and method that could improve the travel experience of users is much desired.
SUMMARY
[004] Before the present systems and methods for suggesting routes to a user, are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular implementations or versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce aspects related to a system and a method for suggesting routes to a user. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.

[005] In one implementation, a system for suggesting routes to a user is disclosed. In one aspect, the system comprises a memory and a processor coupled to the memory. Further, the processor may be capable of executing instructions in the memory to perform one or more steps. In the aspect, the system may receive ratings for routes used by users to reach their destinations. The ratings may depend on experiences of the users travelling through the routes. The ratings greater than a predefined threshold may correspond to good ratings and the ratings lesser than the predefined threshold may correspond to bad ratings. The system may partition the routes into segments. Further, the system may identify segments associated with the bad ratings by discarding, using a mutual exclusion technique, from the routes having the bad ratings, common segments present in both the routes having the bad ratings and the routes having the good ratings, and remaining segments may be identified as the segments associated with the good ratings. Further, the system may assign individual ratings to the segments associated with the bad ratings and the good ratings. To the user, the system may suggest routes for reaching the destination along with ratings associated with the routes. The ratings associated with the routes may be determined based on weightage of the individual ratings of the segments present in the routes.
[006] In one implementation, a method for suggesting routes to a user is disclosed. In one aspect, the method may comprise receiving ratings for routes used by users to reach their destinations. The ratings may depend on experiences of the users travelling through the routes. The ratings greater than a predefined threshold may correspond to good ratings and the ratings lesser than the predefined threshold may correspond to bad ratings. The routes may be partitioned into segments. Thereupon, segments associated with the bad ratings may be identified by discarding, using a mutual exclusion technique, from the routes having the bad ratings, common segments present in both the routes having the bad ratings and the routes having the good ratings. Remaining segments may be identified as segments associated with the good ratings. Individual ratings may be assigned to the segments associated with the bad ratings and the good ratings. To the user, routes for reaching the destination may be suggested along with ratings associated with the routes. The ratings associated with the routes may be determined based on weightage of the individual ratings of the segments present in the routes.
[007] In yet another implementation, non-transitory computer readable medium embodying a program executable in a computing device for suggesting routes to a user is disclosed. In one aspect, the program may comprise a program code for receiving ratings for routes used by users

to reach their destinations. The ratings may depend on experiences of the users travelling through the routes. The ratings greater than a predefined threshold may correspond to good ratings and the ratings lesser than the predefined threshold may correspond to bad ratings. The program may further comprise a program code for partitioning the routes into segments. The program may further comprise a program code for identifying segments associated with the bad ratings by discarding, using a mutual exclusion technique, from the routes having the bad ratings, common segments present in both the routes having the bad ratings and the route having the good ratings. Remaining segments may be identified as segments associated with the good ratings. The program may further comprise a program code for assigning individual ratings to the segments associated with the bad ratings and the good ratings. The program may further comprise a program code for suggesting, to the user, routes for reaching the destination along with ratings associated with the routes. The ratings associated with the routes may be determined based on weightage of the individual ratings of the segments present in the routes.
BRIEF DESCRIPTION OF THE DRAWINGS
[008] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating of the present subject matter, an example of construction of the present subject matter is provided as figures; however, the invention is not limited to the specific method and system disclosed in the document and the figures.
[009] The present subject matter is described in detail with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer various features of the present subject matter.
[010] FIG. 1 illustrates a network implementation diagram of a system 100 for suggesting routes to a user, to reach a destination, in accordance with an embodiment of the present subject matter.
[011] FIG. 2 illustrates a map of a route comprising multiple road segments, in accordance with an embodiment of the present subject matter.
[012] FIG. 3 illustrates a navigating interface providing possible routes to reach a destination along with ratings of users, in accordance with an embodiment of the present subject matter.

[013] FIG. 4 illustrates a method 400 for suggesting routes to a user, to reach a destination, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
[014] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods for suggesting routes to a user, to reach a destination, similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods for suggesting routes to a user, to reach a destination are now described. The disclosed embodiments for suggesting routes to a user, to reach a destination are merely examples of the disclosure, which may be embodied in various forms.
[015] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments for suggesting routes to a user along with user ratings. However, one of ordinary skill in the art will readily recognize that the present disclosure for suggesting routes to a user along with user ratings is not intended to be limited to the embodiments described, but is to be accorded the widest scope consistent with the principles and features described herein.
[016] In an implementation, a system and method for suggesting routes to a user, to reach a destination are described. The system and the method allows users to provide user ratings for the routes used by them to reach their destinations. The user ratings may be provided based on experiences that the users had while travelling through the routes. The routes may be partitioned into segments.
[017] Thereafter, segments associated with the bad ratings may be identified by discarding, from the routes having the bad ratings, common segments present in both the routes having the bad ratings and the routes having the good ratings. Remaining segments may be identified as segments associated with the good ratings. Individual ratings may be assigned to the segments associated with the bad ratings and the good ratings. Routes for reaching a destination may be

suggested to the user along with ratings associated with the routes. The ratings associated with the routes may be determined based on weightage of the individual ratings of the segments present in the routes.
[018] Referring now to FIG. 1, a network implementation diagram 100 of a system 102 for suggesting routes to a user, to reach a destination, in accordance with an embodiment of the present subject matter may be described. In one example the system 102 may be implemented as a standalone system 102 connected to a communication network 104.
[019] It may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a cloud-based computing environment, or a mobile and the like. It may also be understood that the system 102 supports a plurality of browsers and all viewports. Examples of the plurality of browsers may include, but not limited to, Chrome™, Mozilla™, Internet Explorer™, Safari™, and Opera™. It will also be understood that the system 102 may be accessed by multiple users through their devices 106-1 through 106-N. In an example, the devices operated by the users may include smartphones, tablets, phablets, laptops, smart watches, and the like. Furthermore, the system 102 may be communicatively coupled to a database for storing data. In one example, the database may be any of the relationship database and the like.
[020] In one implementation, the communication network 104 may be a wireless network, a wired network, or a combination thereof. The communication network 104 can be implemented as one of the different types of networks, such as intranet, Local Area Network (LAN), Wireless Personal Area Network (WPAN), Wireless Local Area Network (WLAN), wide area network (WAN), the internet, and the like. The communication network 104 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, MQ Telemetry Transport (MQTT), Extensible Messaging and Presence Protocol (XMPP), Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the communication network 104 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

[021] In one embodiment, the system 102 may include an input/output (I/O) interface 108, at least one processor 110, and a memory 112. The I/O interface 108 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 108 may allow the system 102 to interact with the user directly or through the devices 106 operated by the users. Further, the I/O interface 108 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 108 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 108 may include one or more ports for connecting a number of devices to one another or to another server.
[022] The at least one processor 110 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 110 may be configured to fetch and execute computer-readable instructions stored in the memory 112.
[023] The memory 112 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as Static Random Access Memory (SRAM) and Dynamic Random Access Memory (DRAM), and/or non-volatile memory, such as Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable and Programmable ROM (EEPROM), flash memories, hard disks, optical disks, and magnetic tapes. The memory may be used for storing ratings provided by the users, and other intermediate data generated during processing by the system 102.
[024] In one implementation, at first, ratings may be received from several users after they complete their journeys. The users may be prompted to provide the ratings on the navigation application used by them during the journeys. The ratings greater than a predefined threshold may correspond to good ratings and the ratings lesser than the predefined threshold may correspond to bad ratings. In one case, the rating may be a score ranging from 1 to 5. Therefore, in one case, ratings greater than the predefined threshold of 3 may be identified as the good ratings, and the ratings lesser than the predefined threshold of 3 may be identified as the bad ratings. The ratings may be shared based on the experiences that the users had while travelling through the routes. A few factors that could define the experiences may include road quality,

number of lanes in the road, hygienic conditions around the road, air quality, and noise. Based on such factors, the users may share their rating for each of the routes that they travel through.
[025] Alternatively or additionally, the ratings may be obtained using sensors installed in a vehicle driven by the users. For example, air quality sensors may be used to determine quality of air around the routes used by the users. Air quality may be indicated in terms of dust, smoke, and other air pollutants present in the air. Further, cameras may be used to capture images of views around the routes used by the users. These images may be processed to determine hygienic conditions around the routes that may be identified as the cleanliness and scenic views around the routes. Further, noise may be captured using a sound sensor, such as a microphone.
[026] Post receiving the ratings, the routes may be partitioned into segments. All the routes would have received bad ratings or good ratings from the users, and the segments present in all the routes may be identified. The segments associated with the routes may be identified based on several parameters such as road crossings, traffic signals, road category, and road name. For example, a particular route to a destination may have a traffic signal. Such route may be identified to include two segments i.e. a first segment present before the traffic signal and a second segment present after the traffic signal. As shown in FIG. 2, a route from home to work location of a person includes three road segments. These three road segments are identified based on the different names of the roads present within the route.
[027] Upon identifying all the segments of the routes, segments associated with the bad ratings may be identified. The segments associated with the bad ratings may be identified using a mutual exclusion technique. Using a mutual exclusion technique may involve discarding from the routes having the bad ratings, the common segments present in both the routes having the bad ratings and the routes having the good ratings. Further, remaining segments may be identified as segments associated with the good ratings.
[028] Working of the mutual exclusion technique is now provided with reference to an example. Route A may have one segment RS2, and a good rating may be provided to Route A. Route B may include segments RS2, RS5, RS6, and RS7, and a bad rating may be provided to Route B. Now, since RS2 is already marked good based on the rating of the Route A, RS2 may be excluded here from bad rating. This would mean that one or more of RS5, RS6, and RS7 will have bad rating. Route C may comprise segments RS9, RS6, RS10, and RSI 1, and a good rating may be provided to Route C. This would mean that segments RS9, RS6, RS10, and

RSI 1, all have good ratings. Hence, segment RS6 from Route B may also be excluded, and the segments RS5 and RS7 may be identified as the segments associated with a bad rating. The mutual exclusion technique would work continuously to update the results as more ratings are received from other users.
[029] Once the segments having the good ratings and the segments having the bad ratings are identified, individual ratings may be assigned to each of the segments. In one case, the individual ratings may be assigned using the several ratings provided by different users and the mutual exclusion technique used for identifying the segments having good or bad ratings.
[030] In an example, Route X having segments RS20, RS22, and RS24 may receive a good rating of 4. In such case, while none of the segments RS20, RS22, and RS24 are identified to be associated with bad ratings, the good rating of 4 may be assigned to each of the segments RS20, RS22, and RS24.
[031] In one embodiment, using data analytics and machine learning, the data i.e. the segments and the ratings, may be processed continuously, by the system 102. Similarly, a data model may be created, over the time, for each user by learning about specific routes or types of routes preferred by the user. For example, a user may give more preference to good user experience over travelling distance and travel time associated with the route. Then, the navigation application may provide, by default, routes having good user experience. However, the user could also be allowed to select other routes that may depend on his preference at the moment. For example, while the user is in a hurry, he may select a route that involves least travel time instead of the route having good user experience. Apart from the default preference of the user, other preferences may also be captured. For example, after good user experience, the user may select least travel time as his second preference and least travelling distance as his third preference. In this manner, personalized navigation may be provided to each user.
[032] Once a data model is prepared for a user, the user may query for routes to reach a destination, using a navigation application. Towards the user's query, all possible routes may be presented before the user along with ratings associated with the routes. In one case, the ratings associated with the routes may be determined based on weightage of the individual ratings of the segments present in the routes. Further, the ratings associated with each route may be provided in addition to length of the routes, and expected travel time to reach destination through the routes.

[033] The process of determining ratings associated with the routes, using the individual ratings, is now explained using examples. In one case, if all segments of a route have got a rating of 4, an overall rating of the route may be set as 4. In another case, while 50% segments of a route have a rating of 4 and other 50% segments of the route have a rating of 3, an overall rating of the route may be set as 3.5. In yet another case, if 70% segments of a route have a rating of 2, and remaining 30% segments of the route have a rating of 5, an overall rating of the route may be set as 2.5.
[034] FIG. 3 illustrates a navigating interface providing possible routes to reach a destination along with ratings of users. The ratings are shown as star marks in FIG. 3. A user may be interested to travel from location 'A' to a location 'B.' The navigation application may provide three alternate routes to the user. Route Rl is shown to take 11 minutes of time for travelling a 1.3 miles distance, and a bad rating of 1. Alternate route R2 is shown to take 13 minutes of time for travelling a 1.2 miles distance, and a bad rating of 2. Yet another route R3 is shown to take 13 minutes of time for travelling a 1.4 miles distance, and a good rating of 4. In such case, the user could choose to travel through route R3 to have a good experience of the journey.
[035] Exemplary embodiments for suggesting routes to a user, to reach a destination, as discussed above, may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[036] Some embodiments of the system and the method allow the users to capture their experience as ratings to travel across routes.
[037] Some embodiments of the system and the method enable the users to select a route that allows the users to experience a comfortable and pleasant journey.
[038] Referring now to FIG. 4, a method 400 for suggesting routes to a user, to reach a destination is described, in accordance with an embodiment of the present subject matter. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types.
[039] The order in which the method 400 for suggesting routes to a user, to reach a destination is described is not intended to be construed as a limitation, and any number of the described

method blocks can be combined in any order to implement the method 400 or alternate methods. Additionally, individual blocks may be deleted from the method 400 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 400 may be considered to be implemented in the above described system 102.
[040] At block 402, ratings may be received for routes used by users to reach their destinations. The ratings may depend on experiences of the users travelling through the routes. The ratings greater than a predefined threshold may correspond to good ratings and the ratings lesser than the predefined threshold may correspond to bad ratings.
[041] At block 404, the routes may be partitioned into segments. The routes may be partitioned based on parameters comprising road crossings, traffic signals, road category, and road name.
[042] At block 406, segments associated with the bad ratings and the good ratings may be identified. Such segments may be identified using a mutual exclusion technique. Usage of the mutual exclusion technique may involve discarding from the routes having the bad ratings, common segments present in both the routes having the bad ratings and the routes having the good ratings. Remaining segments may be identified as segments associated with the good ratings.
[043] At block 408, individual ratings may be assigned to all segments. Individual ratings may be assigned to the segments associated with the bad ratings and the segments associated with the good ratings.
[044] At block 410, routes for reaching a destination may be suggested to a user along with ratings for the routes. The ratings associated with the routes may be determined based on weightage of the individual ratings of the segments present in the routes.
[045] Although implementations for methods and systems for suggesting routes to a user, to reach a destination have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for suggesting routes to a user, to reach a destination.

WE CLAIM:
1. A method of suggesting routes to a user, to reach a destination, the method comprising:
receiving, by a processor, ratings for routes used by users to reach their destinations, wherein the ratings are provided on a navigation application used by the users, and the ratings depend on experiences of the users travelling through the routes, wherein the ratings greater than a predefined threshold correspond to good ratings and the ratings lesser than the predefined threshold correspond to bad ratings;
partitioning, by the processor, the routes into segments;
identifying, by the processor, segments associated with the bad ratings by discarding, using a mutual exclusion technique, from the routes having the bad ratings, common segments present in both the routes having the bad ratings and the routes having the good ratings, and identifying remaining segments as segments associated with the good ratings;
assigning, by the processor, individual ratings to the segments associated with the bad ratings and the good ratings; and
suggesting, by the processor, to the user, routes for reaching the destination along with ratings associated with the routes, wherein the ratings associated with the routes are determined based on weightage of the individual ratings of the segments present in the routes suggested to the user.
2. The method of claim 1, wherein the ratings are shared based on factors selected from a group consisting of road quality, number of lanes in the road, hygienic conditions around the road, air quality, and noise.
3. The method of claim 1, wherein the ratings range from 1 to 5.
4. The method of claim 3, wherein the ratings greater than the predefined threshold of 3 are identified as the good ratings, and the ratings lesser than the predefined threshold of 3 are identified as the bad ratings.
5. The method of claim 1, wherein the routes are partitioned into segments based on parameters comprising road crossings, traffic signals, road category, and road name.

6. The method of claim 1, further comprising obtaining the ratings using sensors installed in a vehicle.
7. The method of claim 6, wherein the sensors comprise air quality sensors to determine quality of air around the routes used by the users.
8. The method of claim 6, wherein the sensors comprise at least one camera to capture images of views around the routes used by the users, and wherein the images are processed to determine hygienic conditions around the road.
9. The method of claim 1, further comprising developing a data model using the ratings provided by the user.
10. The method of claim 1, further comprising suggesting default routes based on the user's preferences.
11. A system for suggesting routes to a user, to reach a destination, the system comprising:
a memory; and
a processor coupled to the memory, wherein the processor is capable of executing instructions to perform steps of:
receiving ratings for routes used by users to reach their destinations, wherein the ratings are provided on a navigation application used by the users, and the ratings depend on experiences of the users travelling through the routes, wherein the ratings greater than a predefined threshold correspond to good ratings and the ratings lesser than the predefined threshold correspond to bad ratings;
partitioning the routes into segments;
identifying segments associated with the bad ratings by discarding, using a mutual exclusion technique, from the routes having the bad ratings, common segments present in both the routes having the bad ratings and the route having the good ratings, and identifying remaining segments as segments associated with the good ratings;
assigning individual ratings to the segments associated with the bad ratings and the good ratings; and

suggesting to the user, routes for reaching the destination along with ratings associated with the routes, wherein the ratings associated with the routes are determined based on weightage of the individual ratings of the segments present in the routes.
12. The system of claim 11, wherein the ratings are shared based on factors selected from a group consisting of road quality, number of lanes in the road, hygienic conditions around the road, air quality, and noise.
13. The system of claim 11, wherein the ratings range from 1 to 5.
14. The system of claim 13, wherein the ratings greater than the predefined threshold of 3 are identified as the good ratings, and the ratings lesser than the predefined threshold of 3 are identified as the bad ratings.
15. The system of claim 11, wherein the routes are partitioned into segments based on parameters comprising road crossings, traffic signals, road category, and road name.
16. The system of claim 11, further comprising obtaining the ratings using sensors installed in a vehicle.
17. The system of claim 16, wherein the sensors comprise air quality sensors to determine quality of air around the routes used by the users.
18. The system of claim 16, wherein the sensors comprise at least one camera to capture images of views around the routes used by the users, and wherein the images are processed to determine hygienic conditions around the road.
19. The system of claim 11, further comprising developing a data model using the ratings provided by the user.
20. The system of claim 11, further comprising suggesting default routes based on the user's preferences.

21. A non-transitory computer program product having embodied thereon a computer program for suggesting routes to a user, to reach a destination, the computer program product storing instructions for:
receiving ratings for routes used by users to reach their destinations, wherein the ratings are provided on a navigation application used by the users, and the ratings depend on experiences of the users travelling through the routes, wherein the ratings greater than a predefined threshold correspond to good ratings and the ratings lesser than the predefined threshold correspond to bad ratings;
partitioning the routes into segments;
identifying segments associated with the bad ratings by discarding, using a mutual exclusion technique, from the routes having the bad ratings, the common segments present in both the routes having the bad ratings and the routes having the good ratings, and identifying remaining segments as segments associates with the good ratings;
assigning individual ratings to the segments associated with the bad ratings and the good ratings; and
suggesting to the user, routes for reaching the destination along with ratings associated with the routes, wherein the ratings associated with the routes are determined based on weightage of the individual ratings of the segments present in the routes.