Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of vehicular health monitoring systems and insurance policy generation systems. More particularly, the present disclosure relates to artificial intelligence and Internet of Things (IoT) based system and method for prediction of condition and risk profile of various parts of a vehicle, and correspondingly generating an insurance policy.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Insurance ensures peace of mind of people or customer to some extent during an event of loss or theft of articles, accidents, poor medical conditions, or loss of life. When a person or customer insures his/her health, life, home or other property, and vehicle, the insurance company provides an insurance amount for the insured article or the person. Health and life insurance are not mandatory but are subject to a person’s requirement. However, vehicle insurance is mandatory and recommended for every vehicle owner as per government norms, and also protecting the owner from monetary loss after an accident.
[0004] Customers are required to pay a certain amount of premium for the vehicle annually, and they receive the insurance benefits when needed. Aside from the sharp turn, the owner of the vehicle can imagine one’s valuable passion in encountering a road accident when the vehicle and/or the owner encounter injuries and damage. The owner may be reimbursed for any cost of repairs as the insurance provider promises to compensate for the loss. But owners are required to pay for the vehicle insurance in advance to claim for the insurance when required.
[0005] In India, vehicle insurance policy types are of two types, namely, third-party liability insurance and comprehensive insurance. Third-party liability insurance offers a basic cover; it protects against damages caused to the third-party bodily injuries and damages to their vehicle. A comprehensive two-wheeler insurance policy covers damage caused to third-party as well as own damage. Own damage incidents could be natural calamities, burglary, theft, riots or terrorist activity, or damage caused during travel. The comprehensive vehicle policy does not offer protection if there is a loss of personal belongings, accidental loss, or damage to the vehicle if the person is under the influence of alcohol or drugs.
[0006] In a traditional vehicle insurance reimbursement claim, customers or owners have to initially accept the repair costs of the vehicle from their pocket and then register for the insurance claim. Then, the insurance company inspects the original repair bill, receipt, policy document, RC, and other supporting documents, and finally reimburses the paid amount to the beneficiary. However, for instances when there is an accident, and there is no money in the pocket of the owner of the vehicle, the owner might be subjected to extreme stress.
[0007] Therefore, there is a need in the art to implement the above concepts in an efficient, cost-effective, and feasible way, by providing a platform that can be implemented in a vehicle, to allow users as well as an insurance company to remotely determine of condition and risk profile of various parts of the vehicle, so that the insurance company can correspondingly generate an insurance policy, and can reimburse the insurance amount to the users when required.

OBJECTS OF THE PRESENT DISCLOSURE
[0008] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0009] It is an object of the present disclosure to provide artificial intelligence and Internet of Things (IoT) based system and method for prediction of condition and risk profile of various parts of a vehicle, and correspondingly generating an insurance policy.
[00010] It is an object of the present disclosure to provide a system that can be implemented on a vehicle, which allows a user of the vehicle as well as the insurance company to remotely monitor the condition and risk profile of various parts of the vehicle.
[00011] It is an object of the present disclosure to provide a system that allows a user of the vehicle to capture images of the parts of the vehicle, and also remotely monitors attributes of the vehicle using sensors fitted in the vehicle.
[00012] It is an object of the present disclosure to provide a system that can allow the insurance company to generate an insurance policy for a vehicle based on remotely monitored condition and risk profile of various parts of the vehicle, and further reimburse the insurance amount when required or requested by the user.
[00013] It is an object of the present disclosure to provide a system that can be implemented on a vehicle, which alerts a user of the vehicle when the monitored condition and risk profile of any part of the vehicle exceeds a safety limit.
[00014] It is an object of the present disclosure to provide a system and method that allows user as well as the insurance company to remotely determine the condition and risk profile of various parts of the vehicle, so that the insurance company can correspondingly generate an insurance policy, and can reimburse the insurance amount to the users when required

SUMMARY
[00015] The present disclosure relates to artificial intelligence and IoT-based system and method for prediction of the condition, and risk profile of various parts of a vehicle, and correspondingly generating an insurance policy.
[00016] According to an aspect, the present disclosure discloses a system to predict the condition of components of a vehicle and generate an insurance policy. The system may comprise a set of sensors configured with one or more components of a host vehicle. The set of sensors may be configured to monitor health attributes of the respective components of the host vehicle, and correspondingly generate a set of first signals. The system may further comprise one or more mobile devices associated with a user of the host vehicle. The one or more mobile devices may be configured to allow the user to capture a set of images of the one or more components of the host vehicle. Further, a server may be in communication with the set of sensors, and the one or more mobile devices. The server may be associated with an insurance company and comprising one or more processors configured with an artificial intelligence unit operatively coupled to a memory storing instructions executable by the one or more processors, and configured to: receive the set of first signals from the set of sensors, and the captured set of images from the one or more mobile devices; extract the health attributes associated with the one or more components. The server may further determine any or a combination of risk profile, health status, and insurance premium policy for the one or more components of the host vehicle based on the extracted health attributes and the received set of images of the one or more components, and correspondingly generates a set of second signals.
[00017] In an aspect, the server may be configured to compare and match any or a combination of the extracted health attributes with a set of predetermined health attributes, and the captured set of images with pre-stored images of components of one or more vehicles. The set of predefined health attributes and the pre-stored images, and corresponding predefined risk profile, predefined health status, and predefined insurance premium policies may be stored in a dataset associated with the server. Further, upon a positive matching, the server may map any or a combination of the matched health attributes and the matched images with the corresponding predefined risk profile, predefined health status, and predefined insurance premium policies to determine the risk profile, the health status, and the corresponding insurance premium policy for the one or more components of the host vehicle.
[00018] In an aspect, the set of sensors may comprise any or a combination of HVAC actuator sensor, headlamp leveling, seat position and belt switch, mirror memory sensor, pedal sensor, mass airflow sensor, engine speed sensor, oxygen sensor, spark knock sensor, coolant sensor, chassis-level sensor, brake wheel sensor, passive wheel speed sensor, accelerator pedal angle sensor, brake position sensor, headlight range sensor, transmission sensor, throttle position sensor, steering torque sensor, steering angle sensor, and suspension sensor.
[00019] In an aspect, the set of second signals indicative of the determined risk profile, health status, and insurance premium policy, may be transmitted to the one or more mobile devices associated with the user.
[00020] In an aspect, the one or more mobile devices may comprise an image capturing unit comprising a camera to capture the set of images of the one or more components of the host vehicle.
[00021] In an aspect, the server may comprise a communication unit operatively coupled to the one or more processors to connect the one or more mobile devices, and the set of sensors to the server through a secured communication channel.
[00022] In an aspect, the host vehicle may be a two-wheeler vehicle, four-wheeler vehicle, and commercial vehicle.
[00023] In an aspect, the server may be configured to transmit a set of alert signals to the one or more mobile devices of the user, when the determined health status and the risk profile of at least one of the one or more components of the vehicle exceed beyond a predefined safety limit.
[00024] According to another aspect, the present disclosure discloses a method for predicting the condition of components of a vehicle and generating an insurance policy. The method may comprise a step of monitoring, by a set of sensors configured with one or more components of a host vehicle, health attributes associated with the respective components of the host vehicle. The method may further comprise a step of capturing, by one or more mobile devices associated with a user of the host vehicle, a set of images of the one or more components of the host vehicle. The method may further comprise a step of receiving, by a server configured with an artificial intelligence unit, the monitored health attributes from the set of sensors and the captured set of images from the one or more mobile devices. The method may further comprise a step of determining, by the server, any or a combination of risk profile, health status, and insurance premium policy for the one or more components of the host vehicle based on the extracted health attributes and the received set of images of the one or more components.
[00025] In an aspect, the method of determining risk profile, health status, and insurance premium policy for the one or more components of the host vehicle may comprise a step of comparing and matching, by the server, any or a combination of the received health attributes with a set of predetermined health attributes, and the received set of images with pre-stored images of components of one or more vehicles. The set of predefined health attributes and the pre-stored images, and corresponding predefined risk profile, predefined health status, and predefined insurance premium policy may be stored in a dataset associated with the server. The method may further comprise a step of mapping, by the server, upon a positive matching, any or a combination of the matched health attributes and the pre-stored images with the corresponding predefined risk profile, predefined health status, predefined insurance premium policy to determine the risk profile, the health status, and the insurance premium for the one or more components of the host vehicle.
[00026] Various objects, features, aspects, and advantages of the present disclosure will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like features.
[00027] Within the scope of this application it is expressly envisaged that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments, unless such features are incompatible.

BRIEF DESCRIPTION OF DRAWINGS
[00028] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.
[00029] In the figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
[00030] FIG. 1 illustrates an exemplary network diagram of the proposed system, in accordance with an embodiment of the present disclosure.
[00031] FIG. 2 illustrates an exemplary block diagram of the proposed system, in accordance with an embodiment of the present disclosure
[00032] FIG. 3 illustrates an exemplary architecture of the server of the proposed system, in accordance with an embodiment of the present disclosure.
[00033] FIGs. 4A and 4B illustrate exemplary views of different vehicles and associated sensors of the proposed system, in accordance with an embodiment of the present disclosure.
[00034] FIGs. 5A to 5E illustrate exemplary views of various sensors involved in the proposed system, in accordance with an embodiment of the present disclosure.
[00035] FIG. 6 illustrates an exemplary flow diagram of the proposed method, in accordance with an embodiment of the present disclosure.
[00036] FIG. 7 illustrates an exemplary view of artificial neural network implemented in the server of the proposed system, in accordance with an embodiment of the present disclosure
[00037] FIG. 8 illustrates an exemplary view depicting monitoring of various parts of vehicles using mobile devices, in accordance with an embodiment of the present disclosure

DETAILED DESCRIPTION
[00038] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[00039] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details
[00040] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[00041] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[00042] The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Further, the use of terms “first”, “second”, and “third”, and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.
[00043] The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[00044] Embodiments of the present disclosure relate to artificial intelligence and IoT-based system and method for prediction of condition, and risk profile of various parts of a vehicle, and correspondingly generating an insurance policy.
[00045] According to an aspect, the present disclosure elaborates upon a system to predict the condition of components of a vehicle and generate an insurance policy. The system can include a set of sensors configured with one or more components of a host vehicle. The set of sensors can be configured to monitor health attributes of the respective components of the host vehicle, and correspondingly generate a set of first signals. The system can further include one or more mobile devices associated with a user of the host vehicle. The one or more mobile devices can be configured to allow the user to capture a set of images of the one or more components of the host vehicle. Further, a server can be in communication with the set of sensors, and the one or more mobile devices. The server can be associated with an insurance company and comprising one or more processors configured with an artificial intelligence unit operatively coupled to a memory storing instructions executable by the one or more processors, and configured to: receive the set of first signals from the set of sensors, and the captured set of images from the one or more mobile devices; extract the health attributes associated with the one or more components. The server can further determine any or a combination of risk profile, health status, and insurance premium policy for the one or more components of the host vehicle based on the extracted health attributes and the received set of images of the one or more components, and correspondingly generates a set of second signals.
[00046] In an embodiment, the server can be configured to compare and match any or a combination of the extracted health attributes with a set of predetermined health attributes, and the captured set of images with pre-stored images of components of one or more vehicles. The set of predefined health attributes and the pre-stored images, and corresponding predefined risk profile, predefined health status, and predefined insurance premium policies can be stored in a dataset associated with the server. Further, upon a positive matching, the server can map any or a combination of the matched health attributes and the matched images with the corresponding predefined risk profile, predefined health status, and predefined insurance premium policies to determine the risk profile, the health status, and the corresponding insurance premium policy for the one or more components of the host vehicle.
[00047] In an embodiment, the set of sensors can include any or a combination of HVAC actuator sensor, headlamp leveling, seat position and belt switch, mirror memory sensor, pedal sensor, mass airflow sensor, engine speed sensor, oxygen sensor, spark knock sensor, coolant sensor, chassis-level sensor, brake wheel sensor, passive wheel speed sensor, accelerator pedal angle sensor, brake position sensor, headlight range sensor, transmission sensor, throttle position sensor, steering torque sensor, steering angle sensor, and suspension sensor.
[00048] In an embodiment, the set of second signals indicative of the determined risk profile, health status, and insurance premium policy, can be transmitted to the one or more mobile devices associated with the user.
[00049] In an embodiment, the one or more mobile devices can include an image capturing unit including a camera to capture the set of images of the one or more components of the host vehicle.
[00050] In an embodiment, the server can include a communication unit operatively coupled to the one or more processors to connect the one or more mobile devices, and the set of sensors to the server through a secured communication channel.
[00051] In an embodiment, the host vehicle can be a two-wheeler vehicle, four-wheeler vehicle, and commercial vehicle.
[00052] In an embodiment, the server can be configured to transmit a set of alert signals to the one or more mobile devices of the user, when the determined health status and the risk profile of at least one of the one or more components of the vehicle exceed beyond a predefined safety limit.
[00053] According to another aspect, the present disclosure elaborates upon a method for predicting the condition of components of a vehicle and generating an insurance policy. The method can include a step of monitoring, by a set of sensors configured with one or more components of a host vehicle, health attributes associated with the respective components of the host vehicle. The method can further include a step of capturing, by one or more mobile devices associated with a user of the host vehicle, a set of images of the one or more components of the host vehicle. The method can further include a step of receiving, by a server configured with an artificial intelligence unit, the monitored health attributes from the set of sensors and the captured set of images from the one or more mobile devices. The method can further include a step of determining, by the server, any or a combination of risk profile, health status, and insurance premium policy for the one or more components of the host vehicle based on the extracted the health attributes and the received set of images of the one or more components.
[00054] In an embodiment, the method of determining risk profile, health status, and insurance premium policy for the one or more components of the host vehicle can include a step of comparing and matching, by the server, any or a combination of the received health attributes with a set of predetermined health attributes, and the received set of images with pre-stored images of components of one or more vehicles. The set of predefined health attributes and the pre-stored images, and corresponding predefined risk profile, predefined health status, and predefined insurance premium policy can be stored in a dataset associated with the server. The method can further include a step of mapping, by the server, upon a positive matching, any or a combination of the matched health attributes and the pre-stored images with the corresponding predefined risk profile, predefined health status, predefined insurance premium policy to determine the risk profile, the health status, and the insurance premium for the one or more components of the host vehicle.
[00055] Referring to FIG. 1 and 2, in an aspect, the proposed system 100 can facilitate one or more host vehicles 102-1 to 102-N (collectively referred to as host vehicles or vehicles 102, herein) and one or more users 106-1 to 106-N (collectively referred to as users 106, herein) of the vehicles 102 to connect to a server 108 associated with the system 100 through a network 110. The users 106 can connect to the server 108 through one or more mobile devices 104-1 to 104-N (collectively referred to as mobile devices 104, herein) associated with them. Server 108 can be at an insurance unit 204 or insurance provider end, which may provide the users 106 with the insurance service for their vehicles 102. The vehicle 102 can be a two-wheeler vehicle, four-wheeler vehicle, and commercial vehicle, but not limited to the likes.
[00056] In an embodiment, the system 100 can include a set of sensors 202 being configured with components or parts of the vehicles 102 associated with each user 106. The sensors 202 can be in communication with server 108 through network 110. The sensors 202 can be configured to monitor the health attributes of the respective components of vehicle 102, and correspondingly generate and transmit a set of first signals to server 108 through the network 110. Further, the mobile device 104 can be configured to allow the users 106 to capture a set of images of the components of the vehicle 102, which can then be transmitted to server 108 through the network 110. Server 108 can then extract the health attributes associated with the components and the captured images of the components of the vehicle 102. The server 108 can then determine any or a combination of risk profile, health status, and insurance premium policy for the components of the vehicle based on the extracted health attributes and the set of images of the components, and can correspondingly transmit a set of second signals to the insurance unit 204 and the mobile devices 104 associated with the respective users 106. The second set of signals can be indicative of the risk profile, health status, and insurance premium policy for vehicle 102.
[00057] Referring to FIGs. 4A to 4B, and 5A to 5E, in an embodiment, the set of sensors 202 can be any or a combination of HVAC actuator sensor, headlamp leveling, seat position and belt switch, mirror memory sensor, pedal sensor, mass airflow sensor, engine speed sensor, oxygen sensor, spark knock sensor, coolant sensor, chassis-level sensor, brake wheel sensor, passive wheel speed sensor, accelerator pedal angle sensor, brake position sensor, headlight range sensor, transmission sensor, throttle position sensor, steering torque sensor, steering angle sensor, and suspension sensor, but not limited to the likes. FIG. 5A illustrates a mass airflow sensor. FIG. 5B illustrates an engine speed sensor. FIG. 5C illustrates an oxygen sensor. FIG. 5D illustrates a spark knock sensor. FIG. 5E illustrates a coolant sensor.
[00058] In an embodiment, the mobile device 104 can include an image capturing unit comprising any or a combination of a camera and image sensors to capture the set of images of the components of the vehicle 102 as shown in FIG. 8. Based on the requirement, user 106 may be asked by an insurance service provider 204 at the server end 108 to capture images of the required components of the vehicle 102.
[00059] In an exemplary embodiment, the mobile devices 104 can be any or a combination of smartphone, laptop, computer, and hand-held computing devices, but not limited to the likes. In an embodiment, the mobile devices 104 can include a communication unit selected from any or a combination of GSM module, WIFI Module, LTE/VoLTE chipset, and the likes to communicatively couple the mobile devices 104 with the server 108 of the proposed system 100. The mobile device 104 can also include a display unit and input means to provide an interface for facilitating users to select already stored images of the components and facilitate users 106 to view and input necessary and required details of the users 106 into the system 100.
[00060] In an embodiment, the proposed system 100 can be implemented using any or a combination of hardware components and software components such as a cloud, a server, a computing system, a computing device, a network device, and the like (collectively designated as server, herein). Further, system 100 and server 108 can interact with the users 106 through a mobile application that can reside in the mobile devices 104 of the users 106. In an implementation, the system 100 can be accessed by an application that can be configured with any operating system, including but not limited to, AndroidTM, iOSTM, and the like.
[00061] Further, network 110 can be a wireless network, a wired network or a combination thereof that can be implemented as one of the different types of networks, such as Intranet, Local Area Network (LAN), Wide Area Network (WAN), Internet, and the like. Further, network 110 can either be a dedicated network or a shared network. The shared network 110 can represent an association of the different types of networks that can use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like.
[00062] Referring to FIG. 3, an exemplary architecture of server 108 of the proposed system 100 is disclosed. As illustrated, server 108 of the proposed system 100 can include one or more processor(s) 302. The one or more processor(s) 302 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, one or more processor(s) 302 are configured to fetch and execute computer-readable instructions stored in a memory 304 of the server 108. The memory 304 can store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 304 can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[00063] In an embodiment, server 108 can also include an interface(s) 306. The interface(s) 306 can include a variety of interfaces, for example, interfaces for data input and output devices referred to as I/O devices, storage devices, and the like. The interface(s) 306 can facilitate communication of the server with various devices coupled to server 108. The interface(s) 306 can also provide a communication pathway for one or more components of the server. Examples of such components include, but are not limited to, processing engine(s) 312 and database 310.
[00064] In an embodiment, server 108 can include a communication unit 308 operatively coupled to one or more processor(s) 302. The communication unit 308 can be configured to communicatively couple the server 108 to the mobile devices 104 of the users 106. In an exemplary embodiment, the communication unit 308 can include any or a combination of Bluetooth module, NFS Module, WIFI module, transceiver, and wired media, but not limited to the likes.
[00065] In an embodiment, the processing engine(s) 312 can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 312. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 312 can be processor-executable instructions stored on a non-transitory machine-readable storage medium, and the hardware for the processing engine(s) 312 can include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 312. In such examples, the server can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the server and the processing resource. In other examples, the processing engine(s) 312 can be implemented by electronic circuitry. Database 310 can include data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 312.
[00066] In an embodiment, the processing engine(s) 312 can include a data extraction unit 314, an artificial intelligence unit 316, a condition and risk factor unit 318, and an insurance policy unit 320, and other unit (s). but not limited to the likes. The other unit(s) can implement functionalities that supplement applications or functions performed by the server or the processing engine(s) 312.
[00067] In an embodiment, the data extraction unit 314 can enable the processor 302 to allow the sensors 202 to monitor health attributes of the components of the vehicle 102, and receive the monitored health attributes from the sensors 202. Further, the data extraction unit 314 can allow the users 106 to capture images of respective components of the vehicles 102, and receive the captured images from the mobile devices 104. The communication unit 308 can enable server 108 to communicate with the mobile devices 104 and the sensors 202, and allow transfer of data through the network 110.
[00068] In an embodiment, the artificial intelligence unit 316 can enable the condition and risk factor unit 318, and the insurance policy unit 320 to use the extracted health attributes and images of the components of the vehicle 102 to determine any or a combination of risk profile, health status, and insurance premium policy for the components or the vehicle 102. An exemplary view of an artificial neural network (ANN) of the artificial intelligence unit 316 is shown in FIG. 7. The ANN can have an input layer, an output layer, and a number of hidden layers, which may process the input data and correspondingly generate an output.
[00069] In an exemplary embodiment, the artificial intelligence unit 316 can enable the processor 302 to compare and match any or a combination of the extracted health attributes with a set of predetermined health attributes, and the captured set of images with pre-stored images of components of the vehicle 102. The set of predefined health attributes and the pre-stored images, and corresponding predefined risk profile, predefined health status, and predefined insurance premium policies can be stored in a dataset or database 310 associated with server 108. Further, upon a positive matching, the condition and risk factor unit 318, and the insurance policy unit 320 can enable the processor 302 to map any or a combination of the matched health attributes and the matched images with the corresponding predefined risk profile, predefined health status, and predefined insurance premium policies to determine the risk profile, the health status, and the corresponding insurance premium policy for the components of the vehicle 102.
[00070] In an embodiment, server 108 can be configured to transmit a set of alert signals to the mobile devices 104 of the users 106, when the determined health status and the risk profile of at least one of the components of the vehicle 102 exceeds beyond a predefined safety limit. Accordingly, the proposed system 100 can save user 106 from any accidents due to failure of components of the vehicle 102, and also allow the user 106 to replace the faulty components before complete damage or failure of the components of the vehicle 102.
[00071] FIG. 6 illustrates an exemplary flow diagram of the proposed method 600 for predicting the condition of components of a vehicle and generating an insurance policy is disclosed. Method 600 can include step 602 of monitoring, by a set of sensors 202 configured with components of the vehicle 102, health attributes associated with the respective components of the vehicle 102. Method 600 can further include step 604 of capturing, by mobile devices 104 associated with a user 106 of the vehicle 102, a set of images of the components of the vehicle 102. Method 600 can further include step 606 of receiving, by the server 108 configured with an artificial intelligence unit 316, the monitored health attributes from the set of sensors 202 and the captured set of images from the mobile devices 104. Method 600 can further include step 608 of determining, by server 108, any or a combination of risk profile, health status, and insurance premium policy for the components of the vehicle 102 based on the extracted health attributes and the received set of images of the components.
[00072] In an exemplary embodiment, step 608 of determining risk profile, health status, and insurance premium policy for the components of the vehicle 102 can include the step of comparing and matching, by the server 108, any or a combination of the received health attributes with a set of predetermined health attributes, and the received set of images with pre-stored images of components of vehicles 102, wherein the set of predefined health attributes and the pre-stored images, and corresponding predefined risk profile, predefined health status, and predefined insurance premium policy are stored in a dataset 310 associated with the server 108. Further, step 608 can include mapping, by the server 108, upon a positive matching, any or a combination of the matched health attributes and the pre-stored images with the corresponding predefined risk profile, predefined health status, predefined insurance premium policy to determine the risk profile, the health status, and the insurance premium for the components of the vehicle 102.
[00073] While some embodiments of the present disclosure have been illustrated and described, those are completely exemplary in nature. The disclosure is not limited to the embodiments as elaborated herein only and it would be apparent to those skilled in the art that numerous modifications besides those already described are possible without departing from the inventive concepts herein. All such modifications, changes, variations, substitutions, and equivalents are completely within the scope of the present disclosure. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims.

ADVANTAGES OF THE PRESENT INVENTION
[00074] The present invention provides artificial intelligence and Internet of Things (IoT) based system and method for prediction of condition and risk profile of various parts of a vehicle, and correspondingly generating an insurance policy.
[00075] The present invention provides a system that can be implemented on a vehicle, which allows a user of the vehicle as well as the insurance company to remotely monitor the condition and risk profile of various parts of the vehicle.
[00076] The present invention provides a system that allows a user of the vehicle to capture images of the parts of the vehicle, and also remotely monitors attributes of the vehicle using sensors fitted in the vehicle.
[00077] The present invention provides a system that can allow the insurance company to generate an insurance policy for a vehicle based on remotely monitored condition and risk profile of various parts of the vehicle, and further reimburse the insurance amount when required or requested by the user.
[00078] The present invention provides a system that can be implemented on a vehicle, which alerts a user of the vehicle when the monitored condition and risk profile of any part of the vehicle exceeds a safety limit.
[00079] The present invention provides a system and method that allows users as well as the insurance company to remotely determine the condition and risk profile of various parts of the vehicle, so that the insurance company can correspondingly generate an insurance policy, and can reimburse the insurance amount to the users when required.

We Claims:

1. A system to predict condition of components of a vehicle, which correspondingly generates an insurance policy, the system comprising:
a set of sensors configured with one or more components of a host vehicle, the set of sensors configured to monitor health attributes of the respective components of the host vehicle, and correspondingly generate a set of first signals;
one or more mobile devices associated with a user of the host vehicle, the one or more mobile devices configured to allow the user to capture a set of images of the one or more components of the host vehicle; and
a server in communication with the set of sensors, and the one or more mobile devices, the server is associated with an insurance company and comprising one or more processors configured with an artificial intelligence unit operatively coupled to a memory storing instructions executable by the one or more processors, and configured to:
receive the set of first signals from the set of sensors, and the captured set of images from the one or more mobile devices;
extract the health attributes associated with the one or more components; and
determine any or a combination of risk profile, health status, and insurance premium policy for the one or more components of the host vehicle based on the extracted health attributes and the received set of images of the one or more components, and correspondingly generates a set of second signals.
2. The system as claimed in claim 1, wherein the server is configured to:
compare and match any or a combination of the extracted health attributes with a set of predetermined health attributes, and the captured set of images with pre-stored images of components of one or more vehicles, wherein the set of predefined health attributes and the pre-stored images, and corresponding predefined risk profile, predefined health status, and predefined insurance premium policies are stored in a dataset associated with the server, and
wherein upon a positive matching, the server maps any or a combination of the matched health attributes and the matched images with the corresponding predefined risk profile, predefined health status, and predefined insurance premium policies to determine the risk profile, the health status, and the corresponding insurance premium policy for the one or more components of the host vehicle.
3. The system as claimed in claim 1, wherein the set of sensors comprises any or a combination of HVAC actuator sensor, headlamp leveling, seat position and belt switch, mirror memory sensor, pedal sensor, mass airflow sensor, engine speed sensor, oxygen sensor, spark knock sensor, coolant sensor, chassis-level sensor, brake wheel sensor, passive wheel speed sensor, accelerator pedal angle sensor, brake position sensor, headlight range sensor, transmission sensor, throttle position sensor, steering torque sensor, steering angle sensor, and suspension sensor.
4. The system as claimed in claim 1, wherein the set of second signals indicative of the determined risk profile, health status, and insurance premium policy, is transmitted to the one or more mobile devices associated with the user.
5. The system as claimed in claim 1, wherein the one or more mobile devices comprises an image capturing unit comprising a camera to capture the set of images of the one or more components of the host vehicle.
6. The system as claimed in claim 1, wherein the server comprises a communication unit operatively coupled to the one or more processors to connect the one or more mobile devices, and the set of sensors to the server through a secured communication channel.
7. The system as claimed in claim 1, wherein the host vehicle comprises a two-wheeler vehicle, four-wheeler vehicle, and commercial vehicles.
8. The system as claimed in claim 1, wherein the server is configured to transmit a set of alert signals to the one or more mobile devices of the user, when the determined health status and the risk profile of at least one of the one or more components of the host vehicle exceed beyond a predefined safety limit.
9. A method for predicting condition of components of a vehicle and generation of insurance policy, the method comprising the steps of:
monitoring, by a set of sensors configured with one or more components of a host vehicle, health attributes associated with the respective components of the host vehicle;
capturing, by one or more mobile devices associated with a user of the host vehicle, a set of images of the one or more components of the host vehicle;
receiving, by a server configured with an artificial intelligence unit, the monitored health attributes from the set of sensors and the captured set of images from the one or more mobile devices; and
determining, by the server, any or a combination of risk profile, health status, and insurance premium policy for the one or more components of the host vehicle based on the extracted the health attributes and the received set of images of the one or more components.
10. The method as claimed in claim 9, wherein the method of determining risk profile, health status, and insurance premium policy for the one or more components of the host vehicle comprises the step of:
comparing and matching, by the server, any or a combination of the received health attributes with a set of predetermined health attributes, and the received set of images with pre-stored images of components of one or more vehicles, wherein the set of predefined health attributes and the pre-stored images, and corresponding predefined risk profile, predefined health status, and predefined insurance premium policy are stored in a dataset associated with the server; and
mapping, by the server, upon a positive matching, any or a combination of the matched health attributes and the pre-stored images with the corresponding predefined risk profile, predefined health status, predefined insurance premium policy to determine the risk profile, the health status, and the insurance premium for the one or more components of the host vehicle.