DESC:FORM 2

THE PATENTS ACT 1970
[39 OF 1970]
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION

[See section 10; rule 13]

TITLE: “METHOD AND SYSTEM FOR RECOMMENDING A PREFERRED ROUTE FOR NAVIGATION”

Name and address of the Applicant:
TATA MOTORS LIMITED, an Indian company having its registered office at Bombay house, 24 Homi Mody Street, Hutatma Chowk, Mumbai 400 001, Maharashtra, INDIA.

Nationality: INDIA

The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD

The present disclosure generally relates to field of vehicle navigation system. Particularly, but not exclusively, the present disclosure relates to a method and a system for recommending preferred routes for navigation.

BACKGROUND

Generally, vehicle users travelling from a source point to a destination point may prefer to use a navigation application to obtain various route options. Currently, the navigation applications may provide various route options from the source point to the destination point indicating total distance and an estimated time to reach the destination point. Some of the existing techniques may further indicate dynamic traffic along selected route option. As an example, consider the vehicle user selected the route option that indicated least distance and least estimated time to reach the destination point, among other route options. However, while travelling along the selected route option, the vehicle user may realize that the road condition is uneven and that the vehicle may have to navigate along a 5 km stretch of forest area. This condition is not good for vehicle health and may not be safe for the vehicle user. Such scenarios may lead to delay in reaching the destination, accidents, ill health of the vehicle users, and the like, which can be avoided if the vehicle users were provided with more useful information about the route options along with the distance, estimated time and traffic conditions.

The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the disclosure and should not be taken as an acknowledgement or any form of suggestion that this information forms prior art already known to a person skilled in the art.

SUMMARY

One or more shortcomings of the prior art may be overcome, and additional advantages may be provided through the present disclosure. Additional features and advantages may be realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.

Disclosed herein is a method of recommending a preferred route for navigation. The method includes, receiving, by a route recommending system, a source point, a destination point and one or more travel priorities of a user from a user device associated with the user. Further, the method includes determining one or more preferred routes among the plurality of routes between the source point and the destination point based on the one or more travel priorities of the user and interpreted data, in real-time. The interpreted data is generated by analysing input data related to plurality of routes, to classify the interpreted data into at least one of plurality of predefined categories. The input data is received from one or more data sources. Upon determining the one or more preferred routes, the method includes assigning a customized rating to each of the one or more preferred routes based on the one or more travel priorities of the user, the interpreted data and predefined rating rules. Finally, the method includes providing the one or more preferred routes along with the customized rating and one or more recommendations related to the one or more preferred routes to the user. The one or more recommendations are provided based on the interpreted data related to the one or more preferred routes.

Further, the present disclosure includes a route recommending system for recommending a preferred route for navigation. The route recommending system comprises a processor and a memory communicatively coupled to the processor. The memory stores the processor-executable instructions, which, on execution, causes the processor to receive a source point, a destination point and one or more travel priorities of a user from a user device associated with the user. Further, the processor determines one or more preferred routes among the plurality of routes between the source point and the destination point based on the one or more travel priorities of the user and interpreted data, in real-time. The interpreted data is generated by analysing input data related to plurality of routes, to classify the interpreted data into at least one of plurality of predefined categories. The input data is received from one or more data sources. Upon determining the one or more preferred routes, the processor assigns a customized rating to each of the one or more preferred routes based on the one or more travel priorities of the user, the interpreted data and predefined rating rules. Finally, the processor provides the one or more preferred routes along with the customized rating and one or more recommendations related to the one or more preferred routes to the user. The one or more recommendations are provided based on the interpreted data related to the one or more preferred routes.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DIAGRAMS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. 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 figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:

FIG.1A shows an exemplary architecture for recommending a preferred route for navigation in accordance with some embodiments of the present disclosure;

FIG.1B shows an exemplary embodiment illustrating a method of recommending a preferred route for navigation in accordance with some embodiments of the present disclosure;

FIG.2A shows a detailed block diagram of a route recommending system for recommending a preferred route for navigation in accordance with some embodiments of the present disclosure;

FIG.2B shows exemplary predefined rating rules for assigning customized rating in accordance with some embodiments of the present disclosure;

FIG.2C shows exemplary selection of one or more travel priorities in accordance with some embodiments of the present disclosure;

FIG.2D shows a graph illustrating route analysis score of one or more preferred routes in accordance with some embodiments of the present disclosure;

FIG.2E shows an exemplary representation of customized rating in accordance with some embodiments of the present disclosure;

FIG.3 shows a flowchart illustrating a method of recommending a preferred route for navigation in accordance with some embodiments of the present disclosure; and

FIG.4 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily be construed as preferred or advantageous over other embodiments.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.
The terms “comprises”, “comprising”, “includes” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.
Disclosed herein are a method and a system for recommending a preferred route for navigation.
A route recommending system disclosed in the present disclosure may be interfaced with existing navigation applications. The route recommending system may analyse input data received from one or more data sources to generate interpreted data. The interpreted data may be classified into at least one of plurality of predefined categories. Further, the route recommending system may determine one or more preferred routes between a source point and a destination point received from a user device associated with the route recommending system. For each of the one or more preferred routes, the route recommending system may assign a customized rating based on one or more travel priorities of the user received from the user device. Upon assigning the customized rating, the route recommending system may provide the one or more preferred route along with the customized rating and one or more recommendations related to the one or more preferred routes to the user. In some embodiments, the one or more recommendations are provided based on the interpreted data related to the one or more preferred routes.

In the present disclosure, the route recommending system provides preferred route to the user not only based on the distance and time but based on at least one of road condition, crime statistics, tourist attraction, commuter route preference, commuter reviews, roadmaps, traffic details and vehicle part failure analysis with predictive life of parts. Further, the customized rating and the one or more recommendations provided to the user along with the one or more preferred routes allow the user to make an informed decision while selecting the preferred route to commute. Therefore, the present disclosure improves user experience related to navigation by allowing the user to plan a comfortable trip according to his preference. Further, the present disclosure also improves overall life of the vehicle and reduces maintenance cost of the vehicle. In some embodiments, the present disclosure may assist authorities to take necessary measures in maintaining roads across various locations, due to the real-time interpreted data analysed by the route recommending system.

FIG.1A shows an exemplary architecture for recommending a preferred route for navigation in accordance with some embodiments of the present disclosure.

In some embodiments, the architecture 100 comprises one or more data sources, data source 1011 to data source 101n (collectively referred as one or more data sources 101), a route database 103, a user device 105 and a route recommending system 107. As an example, the one or more data sources 101 may include, but not limited to, a telematics system, Intelligent Travel System (ITS), online and offline repositories, tourist reports, social media, news updates, reports from local police stations, road signages and the like. In some embodiments, the one or more data sources 101 may be associated with the route recommending system 107 via a communication network (not shown in the FIG.1A). The communication network may be at least one of wired communication network and a wireless communication network. In some embodiments, the route recommending system 107 may be integrated with an existing navigation application that may be present in the user device 105. In an alternative embodiment, the route recommending system 107 may be implemented as a separate application in the user device 105. As an example, the user device 105 may be a mobile, a tablet, a laptop and the like. Further, the route database 103 may be externally associated with the route recommending system 107 as shown in the FIG.1A. In some embodiments, the route database 103 may also be configured in the route recommending system 107.

In some embodiments, the route recommending system 107 may include, but not limited to, a processor 109, an Input/Output (I/O) interface 111 and a memory 113 as shown in the FIG.1A. The I/O interface 111 may be configured to receive input data from the one or more data sources 101 dynamically. As an example, the input data may include, but not limited to, data related to at least one of images of roads, traffic, telematics of vehicles, accidents, criminal activities, current status of roads, tourist spots, reviews of travellers, tourists and dwellers, statistics of vehicle breakdown and weather conditions. As an example, data related to telematics of the vehicles may include, but not limited to, brake pedal actuation pattern, clutch pedal actuation pattern, gear shift pattern, speed pattern and the like, of vehicles navigating along various routes. The processor 109 may store the input data received from the one or more data sources 101 in the memory 113. In some embodiments, the input data may be mainly related to routes across various locations.

Further, the processor 109 may generate interpreted data by analysing the input data. In some embodiments, the processor 109 may analyse the input data using pre-trained machine learning techniques. Upon generating the interpreted data, the processor 109 may classify the interpreted data into plurality of predefined categories. As an example, the plurality of predefined categories may include, but not limited to, road condition, crime statistics, tourist attractions, commuter route preferences, commuter reviews, roadmaps, traffic status and vehicle part failure. In some embodiments, the processor 109 may store the interpreted data under plurality of predefined categories in the route database 103. In some embodiments, the processor 109 may dynamically update the interpreted data stored under each of the plurality of predefined categories based on the input data received in real-time.

In some embodiments, the processor 109 may receive a source point, a destination point and one or more travel priorities of the user from the user device 105 associated with the user. In some embodiments, the one or more travel priorities may be selected by the user based on a predefined list of travel priorities displayed to the user. Further, the processor 109 may determine one or more preferred routes among the plurality of routes between the source point and the destination point based on the one or more travel priorities of the user and interpreted data, in real-time. Upon determining the preferred routes, the processor 109 may assign a customized rating to each of the one or more preferred routes based on the one or more travel priorities of the user, the interpreted data and predefined rating rules. Further, the processor 109 may provide the one or more preferred routes along with the customized rating and one or more recommendations related to the one or more preferred routes to the user. In some embodiments, the one or more recommendations may be provided based on the interpreted data related to the one or more preferred routes. In an alternative embodiment, the processor 109 may provide relevant interpreted data corresponding to each of the one or more preferred routes and one or more recommendations, to the user, in a predefined format.

Further, FIG.1B shows an exemplary embodiment illustrating a method of recommending a preferred route for navigation in accordance with some embodiments of the present disclosure. FIG.1B indicates one or more data sources 101, the route database 103 exhibiting one or more predefined categories, a user device 105 and a route recommending system 107. As shown in the FIG.1B, exemplary data sources 101 may be a telematics system, mobile phones, Intelligent Travel System (ITS), internet, tourism reports, social media, news updates, reports from local police stations, road signages and the like. Using the input data received from the exemplary data sources 101, the route recommending system 107 may generate interpreted data that may be classified into at least one of the plurality of predefined categories. As shown in the FIG.1B, exemplary predefined categories may be road condition, criminal activity statistics, tourist attractions, commuter route preferences, commuter reviews, roadmaps, traffic status, vehicle part failure and the like. Further, the route recommending system 107 may receive a source point, a destination point and one or more travel priorities from the user device 105. As an example, the user device 105 may be a mobile phone as shown in the FIG.1B. Further, the route recommending system 107 may determine the one or more preferred routes between the source point and the destination point based on the one or more travel priorities of the user and the interpreted data. Upon determining the one or more preferred routes, the route recommending system 107 may provide an output including the one or more preferred routes arranged in accordance with their preference, customized rating corresponding to each of the preferred routes, and one or more recommendations related to the one or more preferred routes.

FIG.2A shows a detailed block diagram of a route recommending system for recommending a preferred route for navigation in accordance with some embodiments of the present disclosure.

In some implementations, the route recommending system 107 may include data 203 and modules 205. As an example, the data 203 is stored in a memory 113 configured in the route recommending system 107 as shown in the FIG.2A. In one embodiment, the data 203 may include input data 207, interpreted data 209, travel priority data 211, rating data 213, result data 215 and other data 217. In the illustrated FIG.2A, modules 205 are described herein in detail.

In some embodiments, the data 203 may be stored in the memory 113 in form of various data structures. Additionally, the data 203 can be organized using data models, such as relational or hierarchical data models. The other data 217 may store data, including temporary data and temporary files, generated by the modules 205 for performing the various functions of the route recommending system 107.

In some embodiments, the input data 207 may be received from one or more data sources 101. As an example, the one or more data sources 101 may include, but not limited to, a telematics system, Intelligent Travel System (ITS), online and offline repositories, tourist reports, social media, news updates, reports from local police stations, road signages and the like. The input data 207 may be data utilized for generating interpreted data related to plurality of routes. As an example, the input data 207 may include, but not limited to, data related to at least one of images of roads, traffic, telematics of vehicles, accidents, criminal activities, current status of roads, tourist spots, reviews of travellers, tourists and dwellers, statistics of vehicle breakdown and weather conditions. In some embodiments, the input data 207 may be received from the one or more data sources 101 in real-time. In some other embodiments, the input data 207 may be pre-stored in a repository, which is periodically updated.

Further, in some embodiments, the interpreted data 209 may be data interpreted based on analysis of the input data 207. Further, the interpreted data 209 may be related to the plurality of routes. In some embodiments, the interpreted data 209 may be stored in a route database 103 associated with the route recommending system 107. The processor 109 may retrieve the interpreted data 209 as per requirement from the route database 103. In some other embodiments, the route database 103 may be present in the route recommending system 107. memory 113 of the route recommending system 107. Further, the interpreted data 209 may be stored under plurality of predefined categories. As an example, the plurality of predefined categories may include, but not limited to, road condition, crime statistics, tourist attractions, commuter route preferences, commuter reviews, roadmaps, traffic status and vehicle part failure. In some embodiments, the predefined category “road condition” may include information related to condition of the road. As an example, information such as “Road 1 has uneven surface for a stretch of 5kms”, “Road 2 passes through a jungle”, “Road 3 is a six lane highway”, “Road 4 has speed breakers at a distance of every 1 km” and the like may be stored under the predefined category “Road condition”. In some embodiments, the predefined category “crime statistics” may include information related to crimes that occurred in local area proximal to a route. As an example, the information such as type of crime, number of crimes that occurred, time at which maximum number of crimes have occurred and the like may be stored under the predefined category “crime statistics”. In some embodiments, the predefined category “tourist attractions” may include information related various tourist spots that may be located proximal to a route.

In some embodiments, the predefined category “commuter route preferences” may include information related to one or more routes which different types of commuters have previously preferred based on their requirement and inferences related to such route preferences. As an example, when the commuter is a family having kids, the preferred route may be route comprising maximum number of tourist spots. As an example, when the commuter is a lady, the preferred route may be a route which is safe. Further, in some embodiments, the predefined category “commuter reviews” may include reviews or feedback of commuters who have travelled or travelling along a certain route. In some embodiments, the predefined category “roadmaps” may include information related to roads that are part of the one or more preferred routes. In some embodiments, the predefined category “traffic status” may include information related to real-time traffic along the plurality of routes and general inferences based on traffic patterns along the plurality of routes. In some embodiments, the predefined category “vehicle part failure” may include information related to occurrences of failure of vehicle parts while travelling along a certain route or after travelling along the certain route. As an example, the information such as number of occurrences of tyre punctures, brake failure, vehicle breakdown and the like and reasons of such failures may be stored in the predefined category “vehicle part failure”.

In some embodiments, the travel priority data 211 may include a predefined list of travel priorities. In some embodiments, the predefined list of travel priorities may be in-line with the plurality of predefined categories. As an example, the predefined list of travel priorities may include, but not limited to, road condition, road safety, tourist attraction, commuter route preferences, risk of vehicle part failure/maintenance, distance and time. Further, in some embodiments, the travel priority data 211 may also include default values assigned to each of the one or more travel priorities based on type of commuters. As an example, the type of commuters may be a family/group, a child, a female, a male, a senior citizen, a medical patient and the like. Below Table 1 shows exemplary default values for the one or more travel priorities for different types of commuters.

Travel priorities
Family/Group Child Female Senior Citizen Medical Patient

Road Condition
Significant

Moderate
Moderate
Significant
Significant

Road Safety Minor Moderate Significant Significant Moderate
Tourist Attraction Moderate Significant Significant Moderate Minor
Commuter Route Preferences Moderate Moderate Significant Significant Moderate
Risk of Vehicle Part Failure/maintenance Moderate Minor Minor Minor Minor
Distance Moderate Minor Moderate Moderate Significant
Time Moderate Minor Moderate Minor Significant

Table 1
Further, the travel priority data 211 may include one or more travel priorities and corresponding values received from the user in real-time for travelling from a source point to a destination point.

In some embodiments, the rating data 213 may include predefined rating rules. In some embodiments, the predefined rating rules define a rating scale based on which the processor 109 may assign a customized rating to each of the one or more preferred routes. The table in FIG.2B shows exemplary predefined rating rules for assigning customized rating to each of the one or more preferred routes. In some embodiments, the table in FIG.2B shows predefined rating parameters which may be same as the predefined list of one or more travel priorities, an predefined rating conditions for each of the predefined rating parameters, and predefined rating between a range of +5 to -5 assigned to each of the predefined rating parameters based on the predefined rating condition. As an example, in the table of FIG.2B, for a rating parameter “road safety”, when the rating condition is “number of incidents that occurred on the road are 4”, then the rating assigned is “+1”. Similarly, in the table of FIG.2B, for a rating parameter “road condition”, when the rating condition is “road is 96% to 98% better”, then the rating is “+4”. In some embodiments, the predefined rating parameters may be scaled or reduced. Further, in some embodiments, the predefined rating conditions and the predefined rating corresponding to the predefined rating conditions may be modified.

In some embodiments, the result data 215 may include one or more preferred routes determined for the user, a customized rating assigned to each of the one or more preferred routes and one or more recommendations provided to the user for the one or more preferred routes. In some embodiments, the result data 215 may also include one or more predictions related to the plurality of routes.

In some embodiments, the data 203 stored in the memory 113 may be processed by the modules 205 of the route recommending system 107. The modules 205 may be stored within the memory 113. In an example, the modules 205 communicatively coupled to the processor 109 configured in the route recommending system 107, may also be present outside the memory 113 as shown in FIG.2A and implemented as hardware. As used herein, the term modules 205 may refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.

In some embodiments, the modules 205 may include, for example, a receiving module 221, a route determining module 223, a rating assigning module 225, a route recommending module 227, a predicting module 229 and other modules 231. The other modules 231 may be used to perform various miscellaneous functionalities of the route recommending system 107. It will be appreciated that such aforementioned modules 205 may be represented as a single module or a combination of different modules.
In some embodiments, the receiving module 221 may receive a source point, a destination point and the one or more travel priorities of a user from a user device 105 associated with the user. As an example, the user device 105 may be a mobile, a tablet, a laptop and the like. In some embodiments, the user may select the one or more travel priorities from the predefined list of travel priorities in the form of check boxes as shown in the FIG.2C. In yet other embodiments, the user may also provide a value to each of the one or more travel priorities selected by the user. As an example, the values assigned to the one or more travel priorities may be may be one of significant, moderate and minor. In some embodiments, other term such as high, medium and low, or excellent, good and bad, and the like may also be used to assign values to the one or more travel priorities. In some embodiments, values may be in any other form such as numerals, symbols, alphabets and the like. As an example, the one or more travel priorities selected by the user and corresponding values provided by the user are as shown below:

Road condition: Significant
Road safety: Minor
Tourist attractions: Moderate

Further, in an alternative embodiment, the user may select the default values assigned to each of the one or more travel priorities based on the type of the commuters. Additionally, the user may also modify the default values of the one or more travel priorities according to his requirement.

Further, the route determining module 223 may determine one or more preferred routes among the plurality of routes between the source point and the destination point based on the one or more travel priorities of the user and the interpreted data, in real-time. In some embodiments, the processor 109 may generate the interpreted data 209 by analysing the input data 207 using pre-trained machine learning techniques. As an example, consider the input data 207 received from is related to telematics data such as brake pedal actuation pattern of a vehicle navigating along a route. The processor 109 may correlate the telematics data with traffic condition and one or more images related to the route along which the vehicle is currently navigating. As an example, consider the processor 109 inferred, based on the traffic condition and the one or more images that the route is “traffic free”. Though the route is “traffic free”, the telematics data indicates that the vehicle is subjected to frequent brakes. Based on the pre-trained machine learning techniques, the processor 109 may interpret that, in scenarios where the route is “traffic free” but the telematics data of the vehicle indicates frequent application of brakes by the vehicle in this route, the road condition along a stretch of the route is uneven and unsafe for the vehicle. The processor 109 may therefore store the interpreted data thus generated under the predefined category “road condition”.

Referring back to the determination of the one or more preferred routes, consider source point is “A” and destination point is “B”. Further, consider the type of commuter is a child, and the one or more travel priorities selected by the user are:

Road condition: Moderate
Road safety: Significant
Tourist attractions: Significant
Commuter route preferences: Moderate
Risk of vehicle part failure: Minor
Distance to be covered: Minor
Time required to cover the distance: Minor

In some embodiments, the route determining module 223 may review the interpreted data classified into the predefined category “commuter route preferences” to determine the one or more routes between the source point “A” and the destination point “B” which are preferred by majority of commuters who have travelled earlier, having similar travel priorities as mentioned above. In some other embodiments, the route determining module 223 may review the interpreted data classified into the predefined category “tourist attractions” to determine the one or more routes between the source point “A” and the destination point “B”, having majority of tourist attractions. Further, the route determining module 223 may also check road safety of the one or more routes having majority of tourist attractions by reviewing the interpreted data 209 classified into the predefined category “Road safety”. Similarly, the route determining module 223 may review the interpreted data 209 classified under plurality of predefined categories in order to meet the requirement of the one or more travel priorities of the user. The one or more routes thus determined may be the one or more preferred routes for the user between the source point “A” and the destination point “B”. As an example, consider the 5 preferred routes, Route A, Route B, Route C, Route D and Route E, are determined between the source point “A” and the destination point “B”.

In some embodiments, upon generating the one or more preferred routes, the rating assigning module 225 may assign a customized rating to each of the one or more preferred routes based on the one or more travel priorities of the user, the interpreted data and predefined rating rules. The customized rating corresponding to each of the one or more preferred routes may indicate quality of each of the one or more preferred routes, in view of the one or more travel priorities of the user. In some embodiments, the method of assigning the customized rating for each of the one or more preferred routes is explained below with an example.

Considering the above mentioned example, the rating assigning module 225 may initially identify the rating condition associated with the rating parameters for all the 5 preferred routes. As an example, for the above example, consider the rating conditions and the rating parameters as given in the below Table 2.

Rating parameters
Route A Route B Route C Route D Route E
Road condition (%) 95% 99% 75% 95% 78%
Road Safety (Number of incidents) 2 1 3 6 18
Tourist attractions 16 8 17 12 6
Commuter Route Preference (%) 8% 45% 55% 42% 47%
Risk of vehicle part failure No risk No risk Moderate risk Moderate risk High risk
Distance Long Average Shortest Long Second Shortest
Time taken for Journey Average shortest Average Average Second shortest time

Table 2
In the above Table 2, the rating condition of the rating parameter “road condition” for route A is 95%. The rating assigning module 225 may compare the rating condition with the predefined rating condition of the rating scale. Based on the comparison, the rating assigning module 225 may assign a customized rating as per the rating scale defined in the predefined rating rules. Similarly, for the rating conditions of each of the rating parameters for each of the routes, the rating assigning module 225 may assign a rating as per the predefined rating rules. In some embodiments, the rating assigning module 225 may analyse the rating conditions with respect to the one or more travel priorities selected by the user and may modify the rating, if required. An exemplary rating assigned, as per the predefined rating rules, to each of the rating parameters mentioned in the above Table 2, is shown in the below Table 3.

Rating Parameters Route A Route B Route C Route D Route E
Road Condition +3 +5 -2 +3 -2
Road Safety +3 +4 +2 -1 -3
Tourist Attraction +3 +1 +3 +2 +1
Commuter Route Preferences -5 -1 +1 -1 -1
Risk of Vehicle Part Failure/maintenance +5 +5 -3 -3 -5
Distance -3 +1 +5 -3 +3
Time +1 +5 +1 +1 +3

Table 3
As shown in the above Table 3, since the rating condition of the rating parameter “road condition” for route A is 95%, the rating assigned to the rating parameter in accordance with the rating scale is “+3”. Similarly, since the rating condition of the rating parameter “Tourist attractions” for route A is 17, the rating assigned to the rating parameter is “+3”. Similarly, the rating is assigned to each of the one or more rating parameters for each of the one or more preferred routes.

Further, the rating assigning module 225 may analyse the rating assigned to each of the rating parameters of each of the one or more preferred routes, to determine whether one or more preferred routes can be eliminated due to a large gap between the one or more travel priorities of the user and the rating. In some embodiments, the large gap may indicate that the routes are completely non-compliant with the one or more travel priorities of the user. Further, the rating assigning module 225 may determine a route analysis score for each of the one or more preferred routes based on the one or more travel priorities of the user and the rating assigned to each of the rating parameters for each of the one or more preferred routes using one or more predefined techniques. As an example, the route analysis score for each of the 3 routes, Route A, Route B and Route C of the above mentioned example, is shown in the below Table 4. Route D and Route E are eliminated by the rating assigning module 225 due to gap observed between the rating assigned to the route D and route E with respect to the one or more travel priorities of the user.

Rating parameters Route A Route B Route C
Road Condition 27.0 45.0 -18.0
Road Safety 27.0 36.0 18.0
Tourist Attraction 42.0 15.0 45.0
Commuter Route Preferences -45.0 -9.0 9.0
Risk of Vehicle Part Failure/maintenance 15.0 15.0 -9.0
Distance -9.0 3.0 15.0
Time 3.0 15.0 3.0
TOTAL SCORE 60.0 120.0 63.0

Table 4
In the above Table 4, the rating assigning module 225 has determined the route analysis score for each of the rating parameters for routes A, B and C. In some embodiments, the rating assigning module 225 may represent the route analysis score in a graphical format as shown in the FIG.2D. Based on the route analysis score, the rating assigning module 225 may infer that Route B is a best route for the user to travel from source point “A” to destination point “B” in view of the one or more travel priorities of the user. Further, based on the route analysis score, the rating assigning module 225 may provide the customized rating to each of the one or more preferred routes, Route A, Route B and Route C. As an example, the customized rating may be represented in the form of stars as shown in the FIG.2E. As an example, 4.5 solid stars or 5 solid stars may represent the best route that satisfies each of the one or more travel priorities of the user. As an example, 4 solid stars may represent a second best route that satisfies one or more travel priorities of the user. As an example, 3 solid stars may represent a third best route that satisfies one or more travel priorities of the user. Similarly, the customized rating may be represented for each of the one or more preferred routes based on the route analysis score.

Further, the route recommending module 227 may provide the one or more preferred routes along with the customized rating and one or more recommendations related to the one or more preferred routes to the user. In some embodiments, the one or more preferred routes may be arranged in order of preference. As an example, the preferred route having the highest customized rating may be arranged first. In some embodiments, the one or more recommendations may be provided based on the interpreted data 209 related to the one or more preferred routes. In some other embodiments, the one or more recommendations may be provided by correlating the interpreted data 209, the route analysis score and the one or more travel priorities of the user. Considering the above example, the route recommending module 227 may provide the one or more preferred routes, Route A, Route B and Route C along with the customized rating. Further, the one or more recommendations may be provided regarding each of the one or more preferred routes. As an example, the one or more recommendations may be:
Route B is the best route, however, Route B was not preferred by few former commuters.
Route C is second best route, which was most preferred by the former commuters and also has highest number of tourist attractions compared to Route B. However, Route C is less safe compared to Route B due to robberies that have been recorded along Route C. Also, travelling along Route C carries a risk of vehicle part failure.
Route A is least preferred by the former commuters and the distance to be covered is higher when compared to Route B and Route C. However, Route A is safer compared to Route C and also has good number of tourist attractions.

Further, the predicting module 229 may provide one or more predictions based on the interpreted data in real-time. In some embodiments, the predicting module 229 may correlate the interpreted data 209 classified into each of the plurality of predefined categories to provide predictions that may affect navigation of the vehicle along a route. As an example, the predicting module 229 may correlate weather conditions and road conditions of a selected route to predict occurrence of an eventual calamity. In another example, the predicting module 229 may correlate various news updates and reports of local police stations to predict routes that may be unsafe for travelling at night, due to criminal activities.

Therefore, the route recommending system 107 may allow the user to make an informed decision regarding selection of the route for travelling from the one or more preferred routes, based on the customized rating, the one or more recommendations and the one or more predictions rather than just selecting the route that indicates shortest distance and least estimated time to reach the destination point.
FIG.3 shows a flowchart illustrating a method of recommending a preferred route for navigation in accordance with some embodiments of the present disclosure.

As illustrated in FIG.3, the method 300 includes one or more blocks illustrating a method of recommending a preferred route for navigation. The method 300 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, and functions, which perform functions or implement abstract data types.

The order in which the method 300 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 300. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 300 can be implemented in any suitable hardware, software, firmware, or combination thereof.

At block 301, the method 300 may include receiving, by a processor 109 of the route recommending system 107, a source point, a destination point and one or more travel priorities of a user from a user device 105 associated with the user. In some embodiments, the one or more travel priorities may indicate aspects of significance to the user while travelling. The one or more travel priorities may be selected from a predefined list of travel priorities displayed to the user.

At block 303, the method 300 may include determining, by the processor 109, one or more preferred routes among plurality of routes between the source point and the destination point based on the one or more travel priorities of the user and interpreted data 209, in real-time. In some embodiments, the interpreted data 209 may be generated by analysing input data 207 related to plurality of routes, received from one or more data sources 101. The interpreted data 209 may be classified into at least one of plurality of predefined categories. In some embodiments, the interpreted data 209 is continuously updated based on the input data 207 received in real-time.

At block 305, the method 300 may include, assigning, by the processor 109, a customized rating to each of the one or more preferred routes based on the one or more travel priorities of the user, the interpreted data 209 and predefined rating rules. In some embodiments, the predefined rating rules may provide a rating scale based on which the customized rating is assigned to each of the one or more preferred routes.

At block 307, the method 300 may include, providing, by the processor 109, the one or more preferred routes along with the customized rating and one or more recommendations related to the one or more preferred routes to the user. In some embodiments, the one or more recommendations may be provided based on the interpreted data 209 related to the one or more preferred routes. In some other embodiments, the one or more recommendations may be provided by correlating the interpreted data 209, route analysis score and the one or more travel priorities of the user. Further, the processor 109 may also provide one or more predictions based on the interpreted data 209 in real-time.

FIG.4 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.

In some embodiments, FIG.4 illustrates a block diagram of an exemplary computer system 400 for implementing embodiments consistent with the present invention. In some embodiments, the computer system 400 can be route recommending system 107 that is used for recommending a preferred route for navigation. The computer system 400 may include a central processing unit (“CPU” or “processor”) 402. The processor 402 may include at least one data processor for executing program components for executing user or system-generated business processes. A user may include a person, a person using a device such as those included in this invention, or such a device itself. The processor 402 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

The processor 402 may be disposed in communication with input devices 411 and output devices 412 via I/O interface 401. The I/O interface 401 may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE), WiMax, or the like), etc.
Using the I/O interface 401, computer system 400 may communicate with input devices 411 and output devices 412.

In some embodiments, the processor 402 may be disposed in communication with a communication network 409 via a network interface 403. The network interface 403 may communicate with the communication network 409. The network interface 403 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. Using the network interface 403 and the communication network 409, the computer system 400 may communicate with one or more data sources 101 (1011 up to 101n), a user device 105 and a route database 103. The one or more data sources 101 may include, but not limited to, a telematics system, Intelligent Travel System (ITS), online and offline repositories, tourist reports, social media, news updates, reports from local police stations, road signages and the like. Further, the user device 105 may be a mobile, a tablet, a laptop and the like. The communication network 409 can be implemented as one of the different types of networks, such as intranet or Local Area Network (LAN), Closed Area Network (CAN) and such. The communication network 409 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), CAN Protocol, Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network 409 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc. In some embodiments, the processor 402 may be disposed in communication with a memory 405 (e.g., RAM, ROM, etc. not shown in FIG.4) via a storage interface 404. The storage interface 404 may connect to memory 405 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fibre channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

The memory 405 may store a collection of program or database components, including, without limitation, a user interface 406, an operating system 407, a web browser 408 etc. In some embodiments, the computer system 400 may store user/application data, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.

The operating system 407 may facilitate resource management and operation of the computer system 400. Examples of operating systems include, without limitation, APPLE® MACINTOSH® OS X®, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION® (BSD), FREEBSD®, NETBSD®, OPENBSD, etc.), LINUX® DISTRIBUTIONS (E.G., RED HAT®, UBUNTU®, KUBUNTU®, etc.), IBM®OS/2®, MICROSOFT® WINDOWS® (XP®, VISTA®/7/8, 10 etc.), APPLE® IOS®, GOOGLETM ANDROIDTM, BLACKBERRY® OS, or the like. The User interface 406 may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system 400, such as cursors, icons, checkboxes, menus, scrollers, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, Apple® Macintosh® operating systems’ Aqua®, IBM® OS/2®, Microsoft® Windows® (e.g., Aero, Metro, etc.), web interface libraries (e.g., ActiveX®, Java®, Javascript®, AJAX, HTML, Adobe® Flash®, etc.), or the like.
In some embodiments, the computer system 400 may implement the web browser 408 stored program components. The web browser 408 may be a hypertext viewing application, such as MICROSOFT® INTERNET EXPLORER®, GOOGLETM CHROMETM, MOZILLA® FIREFOX®, APPLE® SAFARI®, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers 408 may utilize facilities such as AJAX, DHTML, ADOBE® FLASH®, JAVASCRIPT®, JAVA®, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system 400 may implement a mail server stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as Active Server Pages (ASP), ACTIVEX®, ANSI® C++/C#, MICROSOFT®, .NET, CGI SCRIPTS, JAVA®, JAVASCRIPT®, PERL®, PHP, PYTHON®, WEBOBJECTS®, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system 400 may implement a mail client stored program component. The mail client may be a mail viewing application, such as APPLE® MAIL, MICROSOFT® ENTOURAGE®, MICROSOFT® OUTLOOK®, MOZILLA® THUNDERBIRD®, etc.

Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.

The present disclosure provides a method and a system for determining preferred routes for recommending a preferred route for navigation.

The present disclosure provides a feature wherein the user is provided with one or more recommendations and the one or more predictions related to the one or more preferred routes. The customized rating, the one or more recommendations and predictions are not just based on distance and time required to travel through a route, but also based on various other factors such as road condition, crime statistics, tourist attractions, commuter route preferences, commuter reviews, roadmaps, traffic status and vehicle part failure details. Therefore, the one or more recommendations and predictions provided to the user by analysing the abovementioned factors may provide comprehensive information related to each of the one or more preferred routes to the user. This comprehensive information may enable the user to make an informed decision thus eliminating scenarios such as travelling along a route having an uneven road condition, damaging the vehicle parts suspension can, tyres, rims, wheels and the like due to the road condition, travelling through an unsafe road and the like. Therefore, the present disclosure helps in improving user experience related to navigation by allowing the user to plan a comfortable trip according to his preference. Further, the present disclosure also helps in improving overall life of the vehicle and reduces maintenance cost of the vehicle.

The present disclosure provides a feature wherein the user is provided with a list of preferred routes, arranged in an order of preference, along with a customized rating for each preferred route based on one or more travel priorities of the user.

The present disclosure continuously integrates real-time interpreted data with existing interpreted data, which ensures that customized rating of the preferred routes is based on the latest developments.

Further, the present disclosure may assist authorities to take necessary measures in maintaining roads across various locations, due to the real-time interpreted data analysed by the route recommending system.

A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

The specification has described a method and a system for recommending a preferred route for navigation. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that on-going technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words "comprising," "having," "containing," and "including," and other similar forms 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.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

Referral Numerals:

Reference Number Description
100 Architecture
101 One or more data sources
103 Route database
105 User device
107 Route rating system
109 Processor
111 Input/Output (I/O) interface
113 Memory
203 Data
205 Modules
207 Input data
209 Interpreted data
211 Travel priority data
213 Rating data
215 Result data
217 Other data
221 Receiving module
223 Route determining module
225 Rating assigning module
227 Route recommending module
229 Predicting module
231 Other modules
400 Exemplary computer system
401 I/O Interface of the exemplary computer system
402 Processor of the exemplary computer system
403 Network interface
404 Storage interface
405 Memory of the exemplary computer system
406 User interface
407 Operating system
408 Web browser
409 Communication network
411 Input devices
412 Output devices

,CLAIMS:We claim:

1. A method of recommending a preferred route for navigation, the method comprising:
receiving, by the route recommending system (107), a source point, a destination point and one or more travel priorities of a user from a user device (105) associated with the user;
determining, by the route recommending system (107), one or more preferred routes among plurality of routes between the source point and the destination point based on the one or more travel priorities of the user and interpreted data (209), in real-time, wherein the interpreted data (209) is generated by analysing input data (207) related to plurality of routes, to classify the interpreted data (209) into at least one of plurality of predefined categories, wherein the input data (207) is received from one or more data sources (101);
assigning, by the route recommending system (107), a customized rating to each of the one or more preferred routes based on the one or more travel priorities of the user, the interpreted data (209) and predefined rating rules; and
providing, by the route recommending system (107), the one or more preferred routes along with the customized rating and one or more recommendations related to the one or more preferred routes to the user, wherein the one or more recommendations are provided based on the interpreted data (209) related to the one or more preferred routes.

2. The method as claimed in claim 1, wherein the input data (207) is received from the one or more data sources (101) in real-time.

3. The method as claimed in claim 1, wherein the input data (207) comprises data related to at least one of images of roads, traffic, telematics of vehicles, accidents, criminal activities, current status of roads, tourist spots, reviews of travelers, tourists and dwellers, statistics of vehicle breakdown and weather conditions.

4. The method as claimed in claim 1, wherein the plurality of predefined categories are road condition, crime statistics, tourist attractions, commuter route preferences, commuter reviews, roadmaps, traffic status and vehicle part failure.

5. The method as claimed in claim 1, wherein the one or more travel priorities are selected by the user based on a predefined list of travel priorities displayed to the user.

6. The method as claimed in claim 1, wherein the interpreted data (209) is continuously updated based on the input data (207) received in real-time.

7. The method as claimed in claim 1, wherein the input data (207) is analyzed using pre-trained machine learning techniques.

8. The method as claimed in claim 1 further comprises providing, by the route recommending system (107), one or more predictions based on the interpreted data (209) in real-time.

9. A route recommending system (107) for recommending a preferred route for navigation, the route recommending system (107) comprising:
a processor (109); and
a memory (113) communicatively coupled to the processor (109), wherein the memory (113) stores the processor-executable instructions, which, on execution, causes the processor (109) to:
receive a source point, a destination point and one or more travel priorities of a user from a user device (105) associated with the user;
determine one or more preferred routes among plurality of routes between the source point and the destination point based on the one or more travel priorities of the user and interpreted data (209), in real-time, wherein the interpreted data (209) is generated by analysing input data (207) related to plurality of routes, to classify the interpreted data (209) into at least one of plurality of predefined categories, wherein the input data (207) is received from one or more data sources (101);
assign a customized rating to each of the one or more preferred routes based on the one or more travel priorities of the user, the interpreted data (209) and predefined rating rules; and
provide the one or more preferred routes along with the customized rating and one or more recommendations related to the one or more preferred routes to the user, wherein the one or more recommendations are provided based on the interpreted data (209) related to the one or more preferred routes.

10. The route recommending system (107) as claimed in claim 9, wherein the input data (207) is received from the one or more data sources (101) in real-time.

11. The route recommending system (107) as claimed in claim 9, wherein the input data (207) comprises data related to at least one of images of roads, traffic, telematics of vehicles, accidents, criminal activities, current status of roads, tourist spots, reviews of travelers, tourists and dwellers, statistics of vehicle breakdown and weather conditions.

12. The route recommending system (107) as claimed in claim 9, wherein the plurality of predefined categories are road condition, crime statistics, tourist attractions, commuter route preferences, commuter reviews, roadmaps, traffic status and vehicle part failure.

13. The route recommending system (107) as claimed in claim 9, wherein the one or more travel priorities are selected by the user based on a predefined list of travel priorities displayed to the user.

14. The route recommending system (107) as claimed in claim 9, wherein the interpreted data (209) is continuously updated based on the input data (207) received in real-time.

15. The route recommending system (107) as claimed in claim 9, wherein the processor (109) analyses the input data (207) using pre-trained machine learning techniques.

16. The route recommending system (107) as claimed in claim 9, wherein the processor (109) is further configured to provide one or more predictions based on the interpreted data (209) in real-time.

Dated this 28th day of March 2019

R. RAMYA RAO
IN/PA-1607
K&S PARTNERS
AGENT FOR THE APPLICANT